SIRIS-Lab/citation-parser-SPAN · Datasets at Hugging Face

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deflator and exchange rate have an important effect on results. Depending on the objective of the comparison, one or the other indicator may be more relevant. GDP at current prices offers insights into market value, GDP at constant prices into volume growth, while purchasing power adjustment allows a comparison from the consumer perspective. 12THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 1FIGURE 4 GDP per capita gap GDP per capita, 2023, constant PPP prices (EUR) Source: AMECO, 2024. At the same time, three external conditions – in trade, energy and defence – that supported growth in Europe after the end of the Cold War have been fading . First, even as domestic growth slowed, the EU benefitted significantly from burgeoning world trade under multilateral rules. Between 2000 and 2019, international trade as a share of GDP rose from 30% to 43% in the EU, whereas in the US it rose from 25% to 26%. Trade openness ensured that Europe could import freely goods and services it lacked, ranging from raw materials to advanced technologies, while exporting manufactured goods in which it specialised, particularly to the growing markets of Asia. However, the multilateral trading order is now in deep crisis and the era of rapid world trade growth looks to have passed: the IMF projects world trade to grow at 3.2% over the medium term, a pace well below its annual average from 2000-19 of 4.9%iv. Second, as relations normalised with Russia, Europe was able to satisfy its demand for imported energy by procuring ample pipeline gas, which accounted for around 45% of the EU’s natural gas imports in 2021. But this source of relatively cheap energy has now disappeared at huge cost to Europe. The EU has lost more than a year of GDP growth while having to re-direct massive fiscal resources to energy subsidies and building new infrastructure for importing liquefied natural gas. Third, the era of geopolitical stability under US hegemony allowed the EU largely to separate economic policy from security considerations, as well as to use the “peace dividend” from lower defence spending to support its domestic goals. The geopolitical environment is however now in flux owing to Russia’s unwar - ranted aggression against Ukraine, deteriorating US-China relations and rising instability in Africa, which is a source of many commodities that are critical to the world economy. Raising the EU’s competitiveness is necessary to reignite	[ "deflator", "and", "exchange", "rate", "have", "an", "important", "effect", "on", "results", ".", "Depending", "on", "the", "objective", "of", "the", "comparison", ",", "one", "\n", "or", "the", "other", "indicator", "may", "be", "more", "relevant", ".", "GDP", "at", "current", "prices", "offers", "insights", "into", "market", "value", ",", "GDP", "at", "constant", "prices", "\n", "into", "volume", "growth", ",", "while", "purchasing", "power", "adjustment", "allows", "a", "comparison", "from", "the", "consumer", "perspective", ".", "\n", "12THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "1FIGURE", "4", "\n", "GDP", "per", "capita", "gap", " \n", "GDP", "per", "capita", ",", "2023", ",", "constant", "PPP", "prices", "(", "EUR", ")", "\n", "Source", ":", "AMECO", ",", "2024", ".", "\n", "At", "the", "same", "time", ",", "three", "external", "conditions", "–", "in", "trade", ",", "energy", "and", "defence", "–", "that", "supported", "growth", "in", "\n", "Europe", "after", "the", "end", "of", "the", "Cold", "War", "have", "been", "fading", ".", "First", ",", "even", "as", "domestic", "growth", "slowed", ",", "the", "EU", "benefitted", "\n", "significantly", "from", "burgeoning", "world", "trade", "under", "multilateral", "rules", ".", "Between", "2000", "and", "2019", ",", "international", "trade", "as", "a", "\n", "share", "of", "GDP", "rose", "from", "30", "%", "to", "43", "%", "in", "the", "EU", ",", "whereas", "in", "the", "US", "it", "rose", "from", "25", "%", "to", "26", "%", ".", "Trade", "openness", "ensured", "\n", "that", "Europe", "could", "import", "freely", "goods", "and", "services", "it", "lacked", ",", "ranging", "from", "raw", "materials", "to", "advanced", "technologies", ",", "\n", "while", "exporting", "manufactured", "goods", "in", "which", "it", "specialised", ",", "particularly", "to", "the", "growing", "markets", "of", "Asia", ".", "However", ",", "\n", "the", "multilateral", "trading", "order", "is", "now", "in", "deep", "crisis", "and", "the", "era", "of", "rapid", "world", "trade", "growth", "looks", "to", "have", "passed", ":", "the", "\n", "IMF", "projects", "world", "trade", "to", "grow", "at", "3.2", "%", "over", "the", "medium", "term", ",", "a", "pace", "well", "below", "its", "annual", "average", "from", "2000", "-", "19", "\n", "of", "4.9%iv", ".", "Second", ",", "as", "relations", "normalised", "with", "Russia", ",", "Europe", "was", "able", "to", "satisfy", "its", "demand", "for", "imported", "energy", "\n", "by", "procuring", "ample", "pipeline", "gas", ",", "which", "accounted", "for", "around", "45", "%", "of", "the", "EU", "’s", "natural", "gas", "imports", "in", "2021", ".", "But", "this", "\n", "source", "of", "relatively", "cheap", "energy", "has", "now", "disappeared", "at", "huge", "cost", "to", "Europe", ".", "The", "EU", "has", "lost", "more", "than", "a", "year", "of", "\n", "GDP", "growth", "while", "having", "to", "re", "-", "direct", "massive", "fiscal", "resources", "to", "energy", "subsidies", "and", "building", "new", "infrastructure", "\n", "for", "importing", "liquefied", "natural", "gas", ".", "Third", ",", "the", "era", "of", "geopolitical", "stability", "under", "US", "hegemony", "allowed", "the", "EU", "largely", "\n", "to", "separate", "economic", "policy", "from", "security", "considerations", ",", "as", "well", "as", "to", "use", "the", "“", "peace", "dividend", "”", "from", "lower", "defence", "\n", "spending", "to", "support", "its", "domestic", "goals", ".", "The", "geopolitical", "environment", "is", "however", "now", "in", "flux", "owing", "to", "Russia", "’s", "unwar", "-", "\n", "ranted", "aggression", "against", "Ukraine", ",", "deteriorating", "US", "-", "China", "relations", "and", "rising", "instability", "in", "Africa", ",", "which", "is", "a", "source", "\n", "of", "many", "commodities", "that", "are", "critical", "to", "the", "world", "economy", ".", "\n", "Raising", "the", "EU", "’s", "competitiveness", "is", "necessary", "to", "reignite" ]	[]
have been commercially available since 1991, and in 2009, their production reached approximately 1 billion cells per year [ 16]. By 2022, the market was EUR 3 billion, and it is expected that by 2030 it will grow to EUR 4.1 billion [ 17]. Before the emergence of Li-ion batteries, NiMH batteries were the dominating technology in electric and hybrid vehicles, and they have received extensive investment from main manufacturers [ 18]. One important advantage of NiMH batteries is safety (in comparison with Li-ion batteries). To our knowledge, there have been no reports of fire accidents in the press mentioning NiMH batteries. Moreover, NiMH batteries are the preferred system for industrial and consumer applications for PBGUs due to their design flexibility and low maintenance and cost (as of 2018, 80 EUR/kWh to 250 EUR/kWh) [ 19], and it is expected that by 2040, NiMH batteries will still be used in hybrid and plug-in hybrid vehicles [ 20]. Due to the strong presence of NiMH batteries in the market, various IEC standards have been created to guarantee interoperability and compatibility between the many elements of a battery system (e.g., design, manufacturing [ 21], and testing of battery equipment [ 22]). Other considerations covered by standards are performance [5] and safety [23]. This research uses European and international standards for NiMH batteries, such as IEC 61951-2:2017+AMD1:2022 CSV [ 5,6], as references. These standards have been analyzed previously by our group at JRC [24–26]. The goal of this study is to present our research on analyzing the performance of NiMH batteries used as PBGUs. This is to create a base for setting minimum performance values listed in the batteries regulation Annex III [ 1]. The analysis is developed using the current standard IEC 61951-2 for portable NiMH batteries as a base for testing battery performance. This research is organized as follows: In Section 2, the materials and methods are presented. In Section 3, we show the capacity analysis. In Section 4, the charge (capacity) retention is presented. In Section 5, charge (capacity) recovery is shown. In Section 6, the endurance in cycles is analyzed. Results are discussed in Section 7. In Section 8, the analysis of NiMH batteries in application test is shown. And lastly, Section 9 presents the conclusions. 2. Materials and Methods This section presents the materials and methods used in this research. Two items are presented: the battery regulation link	[ "have", "been", "commercially", "available", "since", "1991", ",", "and", "in", "2009", ",", "their", "\n", "production", "reached", "approximately", "1", "billion", "cells", "per", "year", "[", "16", "]", ".", "By", "2022", ",", "the", "market", "was", "\n", "EUR", "3", "billion", ",", "and", "it", "is", "expected", "that", "by", "2030", "it", "will", "grow", "to", "EUR", "4.1", "billion", "[", "17", "]", ".", "Before", "\n", "the", "emergence", "of", "Li", "-", "ion", "batteries", ",", "NiMH", "batteries", "were", "the", "dominating", "technology", "in", "\n", "electric", "and", "hybrid", "vehicles", ",", "and", "they", "have", "received", "extensive", "investment", "from", "main", "\n", "manufacturers", "[", "18", "]", ".", "One", "important", "advantage", "of", "NiMH", "batteries", "is", "safety", "(", "in", "comparison", "\n", "with", "Li", "-", "ion", "batteries", ")", ".", "To", "our", "knowledge", ",", "there", "have", "been", "no", "reports", "of", "fire", "accidents", "in", "\n", "the", "press", "mentioning", "NiMH", "batteries", ".", "Moreover", ",", "NiMH", "batteries", "are", "the", "preferred", "system", "\n", "for", "industrial", "and", "consumer", "applications", "for", "PBGUs", "due", "to", "their", "design", "flexibility", "and", "low", "\n", "maintenance", "and", "cost", "(", "as", "of", "2018", ",", "80", "EUR", "/", "kWh", "to", "250", "EUR", "/", "kWh", ")", "[", "19", "]", ",", "and", "it", "is", "expected", "\n", "that", "by", "2040", ",", "NiMH", "batteries", "will", "still", "be", "used", "in", "hybrid", "and", "plug", "-", "in", "hybrid", "vehicles", "[", "20", "]", ".", "\n", "Due", "to", "the", "strong", "presence", "of", "NiMH", "batteries", "in", "the", "market", ",", "various", "IEC", "standards", "have", "\n", "been", "created", "to", "guarantee", "interoperability", "and", "compatibility", "between", "the", "many", "elements", "of", "\n", "a", "battery", "system", "(", "e.g.", ",", "design", ",", "manufacturing", "[", "21", "]", ",", "and", "testing", "of", "battery", "equipment", "[", "22", "]", ")", ".", "\n", "Other", "considerations", "covered", "by", "standards", "are", "performance", "[", "5", "]", "and", "safety", "[", "23", "]", ".", "\n", "This", "research", "uses", "European", "and", "international", "standards", "for", "NiMH", "batteries", ",", "such", "\n", "as", "IEC", "61951", "-", "2:2017+AMD1:2022", "CSV", "[", "5,6", "]", ",", "as", "references", ".", "These", "standards", "have", "been", "\n", "analyzed", "previously", "by", "our", "group", "at", "JRC", "[", "24–26", "]", ".", "\n", "The", "goal", "of", "this", "study", "is", "to", "present", "our", "research", "on", "analyzing", "the", "performance", "of", "NiMH", "\n", "batteries", "used", "as", "PBGUs", ".", "This", "is", "to", "create", "a", "base", "for", "setting", "minimum", "performance", "values", "\n", "listed", "in", "the", "batteries", "regulation", "Annex", "III", "[", "1", "]", ".", "The", "analysis", "is", "developed", "using", "the", "current", "\n", "standard", "IEC", "61951", "-", "2", "for", "portable", "NiMH", "batteries", "as", "a", "base", "for", "testing", "battery", "performance", ".", "\n", "This", "research", "is", "organized", "as", "follows", ":", "In", "Section", "2", ",", "the", "materials", "and", "methods", "are", "\n", "presented", ".", "In", "Section", "3", ",", "we", "show", "the", "capacity", "analysis", ".", "In", "Section", "4", ",", "the", "charge", "(", "capacity", ")", "\n", "retention", "is", "presented", ".", "In", "Section", "5", ",", "charge", "(", "capacity", ")", "recovery", "is", "shown", ".", "In", "Section", "6", ",", "\n", "the", "endurance", "in", "cycles", "is", "analyzed", ".", "Results", "are", "discussed", "in", "Section", "7", ".", "In", "Section", "8", ",", "the", "\n", "analysis", "of", "NiMH", "batteries", "in", "application", "test", "is", "shown", ".", "And", "lastly", ",", "Section", "9", "presents", "\n", "the", "conclusions", ".", "\n", "2", ".", "Materials", "and", "Methods", "\n", "This", "section", "presents", "the", "materials", "and", "methods", "used", "in", "this", "research", ".", "Two", "items", "are", "\n", "presented", ":", "the", "battery", "regulation", "link" ]	[]
Partnership countries - Potential for knowledge-based economic cooperation329 NACE sector IPC class NOT matching 20.6 Manufacture of man-made fibres [20.6] D01F 21Manufacture of basic pharmaceutical products and pharmaceutical preparations [21]A61K; C07D; C07H; C07J; C12P; C12Q; C07K; A61P; C12NA61K 8/* 22Manufacture of rubber and plastic products [22]B29C; B29D; B60C; B67D 22.1 Manufacture of rubber products [22.1] C08C 23Manufacture of other non-metallic mineral products [23]B32B 23.1Manufacture of glass and glass products [23.1]C03C; C03B 23.3Manufacture of clay building materials [23.3]B28B; B28C 23.4Manufacture of other porcelain and ceramic products [23.4]E03D 23.5Manufacture of cement, lime and plaster [23.5]C04B 24 Manufacture of basic metals [24]B21C; B22D; C21B; C22B; C22C; C22F; C21C; C25C; C21D; C25F 24.4Manufacture of basic precious and other non-ferrous metals [24.4]G21H 25.1Manufacture of structural metal products [25.1]A44B; A47H; B21G; F27D 25.2Manufacture of tanks, reservoirs and containers of metal [25.2]F16T; F22B; F22G; F24J 25.3Manufacture of steam generators, except central heating hot water boilers [25.3]G21B; G21C; G21D 25.4Manufacture of weapons and ammunition [25.4]B63G; F41A; F41B; F41G; F41H; F41J; F41C; F42C; F41F; G21J 25.5Forging, pressing, stamping and roll- forming of metal; powder metallurgy [25.5]B22F 25.6Treatment and coating of metals; machining [25.6]C23D; C25D 25.7Manufacture of cutlery, tools and general hardware [25.7]E05B; E05D; E05F; E06B 25.9Manufacture of other fabricated metal products [25.9]A01L; E05C; F16B 26.1Manufacture of electronic components and boards [26.1]B81B; B81C; B82B; G11C; H01C; H01F; H01L; H05K; B82Y; H01G; C30B; H01J 26.2Manufacture of computers and peripheral equipment [26.2]G02F; G06C; G06D; G06G; G06J; G06N; G06E; G06T; G06F; G09C 26.3Manufacture of communication equipment [26.3]G03H; G08B; H01Q; H01S; H03B; H03C; H03M; H04L; H04S; H03D; H04B; H04M; H04W; H03G; H04H; H04N; H03H; H04J; H04Q; H03J; H04K; H04R 330 Annexes NACE sector IPC class NOT matching 26.4Manufacture of consumer electronics [26.4]H03F; H03K; H03L 26.5Manufacture of instruments and appliances for measuring, testing and navigation; watches and clocks [26.5]F15C; G01B; G01C; G01D; G01F; G01H; G01J; G01K; G01L; G01M; G01N; G01Q; G01R; G01S; G01V; G01W; G04B; G04C; G04D; G04F; G04G; G04R; G05B; G05F; G08C; G12B 26.6Manufacture of irradiation, electromedical and electrotherapeutic equipment [26.6]A61N; G21K; H05G; H05H 26.7Manufacture of optical instruments and photographic equipment [26.7]G02B; G02C; G03B 26.8Manufacture of magnetic and optical media [26.8]G03C 27.1Manufacture of electric motors, generators, transformers and electricity distribution and control apparatus [27.1]H02B; H02S; H02J; H02K; H02N; H02P 27.2Manufacture of batteries and accumulators [27.2]H01M 27.3Manufacture of wiring and wiring devices [27.3]H01B; H01H; H01R; H02G 27.4Manufacture of electric lighting equipment [27.4]F21P; F21Q; H01K; 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"instruments", "and", "\n", "appliances", "for", "measuring", ",", "testing", "and", "\n", "navigation", ";", "watches", "and", "clocks", "[", "26.5]F15C", ";", "G01B", ";", "G01C", ";", "G01D", ";", "G01F", ";", "G01H", ";", "\n", "G01J", ";", "G01", "K", ";", "G01L", ";", "G01", "M", ";", "G01N", ";", "G01Q", ";", "\n", "G01R", ";", "G01S", ";", "G01V", ";", "G01W", ";", "G04B", ";", "G04C", ";", "\n", "G04D", ";", "G04F", ";", "G04", "G", ";", "G04R", ";", "G05B", ";", "G05F", ";", "\n", "G08C", ";", "G12B", "\n", "26.6Manufacture", "of", "irradiation", ",", "electromedical", "\n", "and", "electrotherapeutic", "equipment", "[", "26.6]A61N", ";", "G21", "K", ";", "H05", "G", ";", "H05H", "\n", "26.7Manufacture", "of", "optical", "instruments", "and", "\n", "photographic", "equipment", "[", "26.7]G02B", ";", "G02C", ";", "G03B", "\n", "26.8Manufacture", "of", "magnetic", "and", "optical", "\n", "media", "[", "26.8]G03C", "\n", "27.1Manufacture", "of", "electric", "motors", ",", "\n", "generators", ",", "transformers", "and", "electricity", "\n", "distribution", "and", "control", "apparatus", "[", "27.1]H02B", ";", "H02S", ";", "H02J", ";", "H02", "K", ";", "H02N", ";", "H02P", "\n", "27.2Manufacture", "of", "batteries", "and", "\n", "accumulators", "[", "27.2]H01", "M", "\n", "27.3Manufacture", "of", "wiring", "and", "wiring", "devices", "\n", "[", "27.3]H01B", ";", "H01H", ";", "H01R", ";", "H02", "G", "\n", "27.4Manufacture", "of", "electric", "lighting", "\n", "equipment", "[", "27.4]F21P", ";", "F21Q", ";", "H01", "K", ";", "F21H", ";", "F21S", ";", "F21", "K", ";", "\n", "F21V", ";", "F21L", ";", "F21W", ";", "F21", "M", ";", "F21Y", "\n", "27.5Manufacture", "of", "domestic" ]	[]
pharmaceutical preparations; 22.1 Manufacture of rubber products; 22.2 Manufacture of plastics products; 23.1 Manufacture of glass and glass products; 23.2 Manufacture of refractory products; 23.3 Manufacture of clay building materials; 23.4 Manufacture of other porcelain and ceramic products; 23.5 Manufacture of cement, lime and plaster; 23.6 Manufacture of articles of concrete, cement and plaster; 23.7 Cutting, shaping and finishing of stone; 23.9 Manufacture of abrasive products and non-metallic mineral products n.e.c.; 24.1 Manufacture of basic iron and steel and of ferro-alloys; 24.2 Manufacture of tubes, pipes, hollow profiles and related fittings, of steel; 24.3 Manufacture of other products of first processing of steel; 24.4 Manufacture of basic precious and other non-ferrous metals; 24.5 Casting of metals; 25.1 Manufacture of structural metal products; 25.2 Manufacture of tanks, reservoirs and containers of metal; 25.3 Manufacture of steam generators, except central heating hot water boilers; 25.6 Treatment and coating of metals; machining; 25.7 Manufacture of cutlery, tools and general hardware; 25.9 Manufacture of other fabricated metal products; 26.1 Manufacture of electronic components and boards; 26.2 Manufacture of computers and peripheral equipment; 26.3 Manufacture of communication equipment; 26.4 Manufacture of consumer electronics; 26.5 Manufacture of instruments and appliances for measuring, testing and navigation; watches and clocks; 26.6 Manufacture of irradiation, electromedical and electrotherapeutic equipment; 26.7 Manufacture of optical instruments and photographic equipment; 26.8 Manufacture of magnetic and optical media; 27.1 Manufacture of electric motors, generators, transformers and electricity distribution and control apparatus; 27.2 Manufacture of batteries and accumulators; 27.3 Manufacture of wiring and wiring devices; 27.4 Manufacture of electric lighting equipment; 27.5 Manufacture of domestic appliances; 27.9 Manufacture of other electrical equipment; 28.1 Manufacture of general-purpose machinery; 28.2 Manufacture of other general-purpose machinery; 28.4 Manufacture of metal forming machinery and machine tools; 28.9 Manufacture of other special-purpose machinery; 29.1 Manufacture of motor vehicles; 29.2 Manufacture of bodies (coachwork) for motor vehicles; manufacture of trailers and semi-trailers; 29.3 Manufacture of parts and accessories for motor vehicles; 30.1 Building of ships and boats; 30.2 Manufacture of railway locomotives and rolling stock; 30.9 Manufacture of transport equipment n.e.c.; 32.1 Manufacture of jewellery, bijouterie and related articles; 32.2 Manufacture of musical instruments; 32.3 Manufacture of sports goods; 32.4 Manufacture of games and toys; 32.5 Manufacture of medical and dental instruments and supplies; 32.9 Manufacturing n.e.c.; 33.1 Repair of fabricated metal products, machinery and equipment; 33.2 Installation of industrial machinery and equipment; 62 Computer programming, consultancy and related activities;	[ "pharmaceutical", "preparations", ";", "\n", "22.1", "Manufacture", "of", "rubber", "products", ";", "\n", "22.2", "Manufacture", "of", "plastics", "products", ";", "23.1", "Manufacture", "of", "glass", "and", "glass", "\n", "products", ";", "23.2", "Manufacture", "of", "refractory", "products", ";", "23.3", "Manufacture", "of", "clay", "\n", "building", "materials", ";", "23.4", "Manufacture", "of", "other", "porcelain", "and", "ceramic", "products", ";", "\n", "23.5", "Manufacture", "of", "cement", ",", "lime", "and", "plaster", ";", "23.6", "Manufacture", "of", "articles", "\n", "of", "concrete", ",", "cement", "and", "plaster", ";", "23.7", "Cutting", ",", "shaping", "and", "finishing", "of", "stone", ";", "\n", "23.9", "Manufacture", "of", "abrasive", "products", "and", "non", "-", "metallic", "mineral", "products", "\n", "n.e.c", ".", ";", "24.1", "Manufacture", "of", "basic", "iron", "and", "steel", "and", "of", "ferro", "-", "alloys", ";", "24.2", "\n", "Manufacture", "of", "tubes", ",", "pipes", ",", "hollow", "profiles", "and", "related", "fittings", ",", "of", "steel", ";", "24.3", "\n", "Manufacture", "of", "other", "products", "of", "first", "processing", "of", "steel", ";", "24.4", "Manufacture", "\n", "of", "basic", "precious", "and", "other", "non", "-", "ferrous", "metals", ";", "24.5", "Casting", "of", "metals", ";", "\n", "25.1", "Manufacture", "of", "structural", "metal", "products", ";", "25.2", "Manufacture", "of", "tanks", ",", "\n", "reservoirs", "and", "containers", "of", "metal", ";", "25.3", "Manufacture", "of", "steam", "generators", ",", "\n", "except", "central", "heating", "hot", "water", "boilers", ";", "25.6", "Treatment", "and", "coating", "of", "\n", "metals", ";", "machining", ";", "25.7", "Manufacture", "of", "cutlery", ",", "tools", "and", "general", "hardware", ";", "\n", "25.9", "Manufacture", "of", "other", "fabricated", "metal", "products", ";", "26.1", "Manufacture", "\n", "of", "electronic", "components", "and", "boards", ";", "26.2", "Manufacture", "of", "computers", "and", "\n", "peripheral", "equipment", ";", "26.3", "Manufacture", "of", "communication", "equipment", ";", "26.4", "\n", "Manufacture", "of", "consumer", "electronics", ";", "26.5", "Manufacture", "of", "instruments", "and", "\n", "appliances", "for", "measuring", ",", "testing", "and", "navigation", ";", "watches", "and", "clocks", ";", "26.6", "\n", "Manufacture", "of", "irradiation", ",", "electromedical", "and", "electrotherapeutic", "equipment", ";", "\n", "26.7", "Manufacture", "of", "optical", "instruments", "and", "photographic", "equipment", ";", "\n", "26.8", "Manufacture", "of", "magnetic", "and", "optical", "media", ";", "27.1", "Manufacture", "of", "\n", "electric", "motors", ",", "generators", ",", "transformers", "and", "electricity", "distribution", "and", "\n", "control", "apparatus", ";", "27.2", "Manufacture", "of", "batteries", "and", "accumulators", ";", "27.3", "\n", "Manufacture", "of", "wiring", "and", "wiring", "devices", ";", "\n", "27.4", "Manufacture", "of", "electric", "lighting", "equipment", ";", "27.5", "Manufacture", "of", "\n", "domestic", "appliances", ";", "27.9", "Manufacture", "of", "other", "electrical", "equipment", ";", "\n", "28.1", "Manufacture", "of", "general", "-", "purpose", "machinery", ";", "28.2", "Manufacture", "of", "\n", "other", "general", "-", "purpose", "machinery", ";", "28.4", "Manufacture", "of", "metal", "forming", "\n", "machinery", "and", "machine", "tools", ";", "28.9", "Manufacture", "of", "other", "special", "-", "purpose", "\n", "machinery", ";", "29.1", "Manufacture", "of", "motor", "vehicles", ";", "29.2", "Manufacture", "of", "bodies", "\n", "(", "coachwork", ")", "for", "motor", "vehicles", ";", "manufacture", "of", "trailers", "and", "semi", "-", "trailers", ";", "\n", "29.3", "Manufacture", "of", "parts", "and", "accessories", "for", "motor", "vehicles", ";", "30.1", "Building", "\n", "of", "ships", "and", "boats", ";", "30.2", "Manufacture", "of", "railway", "locomotives", "and", "rolling", "\n", "stock", ";", "30.9", "Manufacture", "of", "transport", "equipment", "n.e.c", ".", ";", "32.1", "Manufacture", "\n", "of", "jewellery", ",", "bijouterie", "and", "related", "articles", ";", "32.2", "Manufacture", "of", "musical", "\n", "instruments", ";", "32.3", "Manufacture", "of", "sports", "goods", ";", "32.4", "Manufacture", "of", "\n", "games", "and", "toys", ";", "32.5", "Manufacture", "of", "medical", "and", "dental", "instruments", "and", "\n", "supplies", ";", "32.9", "Manufacturing", "n.e.c", ".", ";", "33.1", "Repair", "of", "fabricated", "metal", "products", ",", "\n", "machinery", "and", "equipment", ";", "33.2", "Installation", "of", "industrial", "machinery", "and", "\n", "equipment", ";", "62", "Computer", "programming", ",", "consultancy", "and", "related", "activities", ";", "\n" ]	[]
Smart Specialisation in the Eastern Partnership countries Potential for knowledge-based economic cooperation SMART SPECIALISATION IN THE EASTERN PARTNERSHIPThis publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this publi-cation do not necessarily reflect the position or opinion of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Contact information Monika Matusiak, Team Leader, European Commission - Joint Research Centre, Seville, Spaine-mail: [email protected] EU Science Hub https://joint-research-centre.ec.europa.eu JRC128524 EUR 31234 EN PDF ISBN 978-92-76-57301-2 ISSN 1831-9424 doi:10.2760/520032 KJ-NA-31-234-EN-N Print ISBN 978-92-76-57302-9 ISSN 1018-5593 doi:10.2760/893904 KJ-NA-31-234-EN-C Luxembourg: Publications Office of the European Union, 2022 © European Union, 2022 The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 De- cember 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons. org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. All content © European Union, 2022 The European Union does not o wn the cop yright in relation to the follo wing elements: Cover page photos (from left to right), ©nordroden/ stock.adobe.com, ©NDABCREATIVITY/ stock.adobe.com, ©Balazs/ stock.adobe.com, ©puhhha/ stock.adobe.com. How to cite this report: Bigas, E., Bovenzi, N., Fuster Martí, E., Massucci, F.A., Hollanders, H., Matusiak, M. and Reimeris, R., Smart Specialisation in the Eastern Partnership countries, Fuster Martí, E., Matusiak, M. and Reimeris, R. editors, EUR 31234 EN, Publications Office of the European Union, Luxembourg, 2022, doi:10.2760/520032, JRC128524.2022Enric Fuster (SIRIS Academic), Monika Matusiak, Ramojus Reimeris (European Commission – Joint Research Centre) Eloi Bigas, Nicandro Bovenzi, Enric Fuster, Francesco	[ "Smart", "Specialisation", "in", "the", "\n", "Eastern", "Partnership", "countries", "\n", "Potential", "for", "knowledge", "-", "based", "\n", "economic", "cooperation", "\n", "SMART", "SPECIALISATION", "IN", "THE", "EASTERN", "PARTNERSHIPThis", "publication", "is", "a", "Technical", "report", "by", "the", "Joint", "Research", "Centre", "(", "JRC", ")", ",", "the", "European", "Commission", "’s", "science", "and", "knowledge", "\n", "service", ".", "It", "aims", "to", "provide", "evidence", "-", "based", "scientific", "support", "to", "the", "European", "policymaking", "process", ".", "The", "contents", "of", "this", "publi", "-", "cation", "do", "not", "necessarily", "reflect", "the", "position", "or", "opinion", "of", "the", "European", "Commission", ".", "Neither", "the", "European", "Commission", "nor", "any", "person", "acting", "on", "behalf", "of", "the", "Commission", "is", "responsible", "for", "the", "use", "that", "might", "be", "made", "of", "this", "publication", ".", "For", "information", "on", "the", "methodology", "and", "quality", "underlying", "the", "data", "used", "in", "this", "publication", "for", "which", "the", "source", "is", "neither", "Eurostat", "nor", "other", "Commission", "services", ",", "users", "should", "contact", "the", "referenced", "source", ".", "The", "designations", "employed", "and", "the", "presentation", "of", "material", "on", "the", "maps", "do", "not", "imply", "the", "expression", "of", "any", "opinion", "whatsoever", "on", "the", "part", "of", "the", "European", "Union", "concerning", "the", "legal", "status", "of", "any", "country", ",", "territory", ",", "city", "or", "area", "or", "of", "its", "authorities", ",", "or", "concerning", "the", "delimitation", "of", "its", "frontiers", "or", "boundaries", ".", "\n", "Contact", "information", "\n", "Monika", "Matusiak", ",", "Team", "Leader", ",", "European", "Commission", "-", "Joint", "Research", "Centre", ",", "Seville", ",", "Spaine", "-", "mail", ":", "[email protected]", "\n", "EU", "Science", "Hub", "\n", "https://joint-research-centre.ec.europa.eu", "\n", "JRC128524", "\n", "EUR", "31234", "EN", "\n", "PDF", "\n ", "ISBN", "978", "-", "92", "-", "76", "-", "57301", "-", "2", "\n ", "ISSN", "1831", "-", "9424", "\n ", "doi:10.2760/520032", "\n \n", "KJ", "-", "NA-31", "-", "234", "-", "EN", "-", "N", "\n", "Print", "\n ", "ISBN", "978", "-", "92", "-", "76", "-", "57302", "-", "9", "\n ", "ISSN", "1018", "-", "5593", 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"Publications", "Office", "of", "the", "European", "Union", ",", "Luxembourg", ",", "2022", ",", "doi:10.2760/520032", ",", "JRC128524.2022Enric", "Fuster", "(", "SIRIS", "Academic", ")", ",", " \n", "Monika", "Matusiak", ",", "Ramojus", "Reimeris", "(", "European", "\n", "Commission", "–", "Joint", "Research", "Centre", ")", "\n", "Eloi", "Bigas", ",", "Nicandro", "Bovenzi", ",", "Enric", "Fuster", ",", " \n", "Francesco" ]	[ { "end": 1455, "label": "CITATION-SPAN", "start": 1397 }, { "end": 1620, "label": "CITATION-SPAN", "start": 1483 }, { "end": 2770, "label": "CITATION-SPAN", "start": 2443 } ]
particular metabolic problems, with pathways such as glycolysis and the citric acid cycle producing their end products highly efficiently and in a minimal number of steps.[4][5] The first pathways of enzyme-based metabolism may have been parts of purine nucleotide metabolism, while previous metabolic pathways were a part of the ancient RNA world.[123] Many models have been proposed to describe the mechanisms by which novel metabolic pathways evolve. These include the sequential addition of novel enzymes to a short ancestral pathway, the duplication and then divergence of entire pathways as well as the recruitment of pre-existing enzymes and their assembly into a novel reaction pathway.[124] The relative importance of these mechanisms is unclear, but genomic studies have shown that enzymes in a pathway are likely to have a shared ancestry, suggesting that many pathways have evolved in a step-by-step fashion with novel functions created from pre-existing steps in the pathway.[125] An alternative model comes from studies that trace the evolution of proteins' structures in metabolic networks, this has suggested that enzymes are pervasively recruited, borrowing enzymes to perform similar functions in different metabolic pathways (evident in the MANET database)[126] These recruitment processes result in an evolutionary enzymatic mosaic.[127] A third possibility is that some parts of metabolism might exist as "modules" that can be reused in different pathways and perform similar functions on different molecules.[128] As well as the evolution of new metabolic pathways, evolution can also cause the loss of metabolic functions. For example, in some parasites metabolic processes that are not essential for survival are lost and preformed amino acids, nucleotides and carbohydrates may instead be scavenged from the host.[129] Similar reduced metabolic capabilities are seen in endosymbiotic organisms.[130] Investigation and manipulation Further information: Protein methods, Proteomics, Metabolomics, and Metabolic network modelling Metabolic network of the Arabidopsis thaliana citric acid cycle. Enzymes and metabolites are shown as red squares and the interactions between them as black lines. Classically, metabolism is studied by a reductionist approach that focuses on a single metabolic pathway. Particularly valuable is the use of radioactive tracers at the whole-organism, tissue and cellular levels, which define the paths from precursors to final products by identifying radioactively labelled intermediates and products.[131] The enzymes that catalyze these chemical reactions can then be purified and their kinetics and responses to inhibitors investigated. A parallel approach is to identify the small molecules in a cell or tissue; the complete set	[ "particular", "metabolic", "problems", ",", "with", "pathways", "such", "as", "glycolysis", "and", "the", "citric", "acid", "cycle", "producing", "their", "end", "products", "highly", "efficiently", "and", "in", "a", "minimal", "number", "of", "steps.[4][5", "]", "The", "first", "pathways", "of", "enzyme", "-", "based", "metabolism", "may", "have", "been", "parts", "of", "purine", "nucleotide", "metabolism", ",", "while", "previous", "metabolic", "pathways", "were", "a", "part", "of", "the", "ancient", "RNA", "world.[123", "]", "\n\n", "Many", "models", "have", "been", "proposed", "to", "describe", "the", "mechanisms", "by", "which", "novel", "metabolic", "pathways", "evolve", ".", "These", "include", "the", "sequential", "addition", "of", "novel", "enzymes", "to", "a", "short", "ancestral", "pathway", ",", "the", "duplication", "and", "then", "divergence", "of", "entire", "pathways", "as", "well", "as", "the", "recruitment", "of", "pre", "-", "existing", "enzymes", "and", "their", "assembly", "into", "a", "novel", "reaction", "pathway.[124", "]", "The", "relative", "importance", "of", "these", "mechanisms", "is", "unclear", ",", "but", "genomic", "studies", "have", "shown", "that", "enzymes", "in", "a", "pathway", "are", "likely", "to", "have", "a", "shared", "ancestry", ",", "suggesting", "that", "many", "pathways", "have", "evolved", "in", "a", "step", "-", "by", "-", "step", "fashion", "with", "novel", "functions", "created", "from", "pre", "-", "existing", "steps", "in", "the", "pathway.[125", "]", "An", "alternative", "model", "comes", "from", "studies", "that", "trace", "the", "evolution", "of", "proteins", "'", "structures", "in", "metabolic", "networks", ",", "this", "has", "suggested", "that", "enzymes", "are", "pervasively", "recruited", ",", "borrowing", "enzymes", "to", "perform", "similar", "functions", "in", "different", "metabolic", "pathways", "(", "evident", "in", "the", "MANET", "database)[126", "]", "These", "recruitment", "processes", "result", "in", "an", "evolutionary", "enzymatic", "mosaic.[127", "]", "A", "third", "possibility", "is", "that", "some", "parts", "of", "metabolism", "might", "exist", "as", "\"", "modules", "\"", "that", "can", "be", "reused", "in", "different", "pathways", "and", "perform", "similar", "functions", "on", "different", "molecules.[128", "]", "\n\n", "As", "well", "as", "the", "evolution", "of", "new", "metabolic", "pathways", ",", "evolution", "can", "also", "cause", "the", "loss", "of", "metabolic", "functions", ".", "For", "example", ",", "in", "some", "parasites", "metabolic", "processes", "that", "are", "not", "essential", "for", "survival", "are", "lost", "and", "preformed", "amino", "acids", ",", "nucleotides", "and", "carbohydrates", "may", "instead", "be", "scavenged", "from", "the", "host.[129", "]", "Similar", "reduced", "metabolic", "capabilities", "are", "seen", "in", "endosymbiotic", "organisms.[130", "]", "\n\n", "Investigation", "and", "manipulation", "\n", "Further", "information", ":", "Protein", "methods", ",", "Proteomics", ",", "Metabolomics", ",", "and", "Metabolic", "network", "modelling", "\n\n", "Metabolic", "network", "of", "the", "Arabidopsis", "thaliana", "citric", "acid", "cycle", ".", "Enzymes", "and", "metabolites", "are", "shown", "as", "red", "squares", "and", "the", "interactions", "between", "them", "as", "black", "lines", ".", "\n", "Classically", ",", "metabolism", "is", "studied", "by", "a", "reductionist", "approach", "that", "focuses", "on", "a", "single", "metabolic", "pathway", ".", "Particularly", "valuable", "is", "the", "use", "of", "radioactive", "tracers", "at", "the", "whole", "-", "organism", ",", "tissue", "and", "cellular", "levels", ",", "which", "define", "the", "paths", "from", "precursors", "to", "final", "products", "by", "identifying", "radioactively", "labelled", "intermediates", "and", "products.[131", "]", "The", "enzymes", "that", "catalyze", "these", "chemical", "reactions", "can", "then", "be", "purified", "and", "their", "kinetics", "and", "responses", "to", "inhibitors", "investigated", ".", "A", "parallel", "approach", "is", "to", "identify", "the", "small", "molecules", "in", "a", "cell", "or", "tissue", ";", "the", "complete", "set" ]	[]
service] G. Kontonatsios, A. J. Brockmeier, P. Przybyła, J. McNaught, T. Mu, J. Y. Goulermas, S. Ananiadou, “A semi-supervised approach using label propagation to support citation screening,” Journal of Biomedical Informatics, vol. 72, 2017.[bib][paper] P. Przybyła, A. J. Soto and S. Ananiadou, “Identifying Personalised Treatments and Clinical Trials for Precision Medicine using Semantic Search with Thalia,” in Proceedings of the Twenty-Fifth Text REtrieval Conference (TREC 2017), Gaithersburg, Maryland, USA, 2017.[bib][paper] P. Przybyła, M. Shardlow, S. Aubin, R. Bossy, R. Eckart de Castilho, S. Piperidis, J. McNaught, S. Ananiadou, “Text Mining Resources for the Life Sciences,” Database: The Journal of Biological Databases and Curation, vol. 2016, 2016.[bib][paper] NLP for Polish P. Rybak, P. Przybyła, M. Ogrodniczuk, “PolQA: Polish Question Answering Dataset,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy, 2024. [bib][paper][corpus] Ł. Kobyliński, M. Ogrodniczuk, P. Rybak, P. Przybyła, P. Pęzik, A. Mikołajczyk, W. Janowski, M. Marcińczuk, A. Smywiński-Pohl, “PolEval 2022/23 Challenge Tasks and Results,” in Proceedings of the 18th Conference on Computer Science and Intelligence Systems (FedCSIS 2023), Warsaw, Poland, 2023. [bib][paper] M. Ogrodniczuk, P. Przybyła, “PolEval 2021 Task 4: Question Answering Challenge,” in Proceedings of the PolEval 2021 Workshop, Online, 2021. [bib][paper][data] P. Przybyła, “How big is big enough? Unsupervised word sense disambiguation using a very large corpus,” Manuscript arXiv:1710.07960 [cs.CL], 2017.[bib][paper] P. Przybyła, “Boosting Question Answering by Deep Entity Recognition,” Manuscript arXiv:1605.08675 [cs.CL], 2016.[bib][paper][data][corpus] P. Przybyła, “Odpowiadanie na pytania w języku polskim z użyciem głębokiego rozpoznawania nazw,” (Question Answering in Polish using Deep Entity Recognition), PhD thesis in Institute of Computer Science, Polish Academy of Sciences in Warsaw, Poland, 2015.[bib][paper][data][corpus] P. Przybyła, “Gathering Knowledge for Question Answering Beyond Named Entities,” in Proceedings of the 20th International Conference on Applications of Natural Language to Information Systems (NLDB 2015), Passau, Germany, 2015.[bib][paper][data][corpus] P. Przybyła and P. Teisseyre, “Analysing Utterances in Polish Parliament to Predict Speaker’s Background,” Journal of Quantitative Linguistics, vol. 21, no. 4, pp. 350–376, 2014.[bib][paper] P. Przybyła, “Question Analysis for Polish Question Answering,” in 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop, Sofia, Bulgaria, 2013.[bib][paper] P. Przybyła, “Question Classification for Polish Question Answering,” in Proceedings of the 20th International Conference on Language Processing and Intelligent Information Systems (LP&IIS 2013), Warsaw, Poland, 2013.[bib][paper] P. Przybyła, “Issues of Polish Question Answering,” in Proceedings of the first conference “Information Technologies: Research and their Interdisciplinary Applications” (ITRIA 2012), Warsaw, Poland, 2012.[bib][paper]	[ " ", "service", "]", "\n", "G.", "Kontonatsios", ",", "A.", "J.", "Brockmeier", ",", "P.", "Przybyła", ",", "J.", "McNaught", ",", "T.", "Mu", ",", "J.", "Y.", "Goulermas", ",", "S.", "Ananiadou", ",", "“", "A", "semi", "-", "supervised", "approach", "using", "label", "propagation", "to", "support", "citation", "screening", ",", "”", "Journal", "of", "Biomedical", "Informatics", ",", "vol", ".", "72", ",", "2017.[bib][paper", "]", "\n", "P.", "Przybyła", ",", "A.", "J.", "Soto", "and", "S.", "Ananiadou", ",", "“", "Identifying", "Personalised", "Treatments", "and", "Clinical", "Trials", "for", "Precision", "Medicine", "using", "Semantic", "Search", "with", "Thalia", ",", "”", "in", "Proceedings", "of", "the", "Twenty", "-", "Fifth", "Text", "REtrieval", "Conference", "(", 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spliceosomes and ribosomes is similar to enzymes as it can catalyze chemical reactions. Individual nucleosides are made by attaching a nucleobase to a ribose sugar. These bases are heterocyclic rings containing nitrogen, classified as purines or pyrimidines. Nucleotides also act as coenzymes in metabolic-group-transfer reactions.[18] Coenzymes Structure of the coenzyme acetyl-CoA. The transferable acetyl group is bonded to the sulfur atom at the extreme left. Main article: Coenzyme Metabolism involves a vast array of chemical reactions, but most fall under a few basic types of reactions that involve the transfer of functional groups of atoms and their bonds within molecules.[19] This common chemistry allows cells to use a small set of metabolic intermediates to carry chemical groups between different reactions.[18] These group-transfer intermediates are called coenzymes. Each class of group-transfer reactions is carried out by a particular coenzyme, which is the substrate for a set of enzymes that produce it, and a set of enzymes that consume it. These coenzymes are therefore continuously made, consumed and then recycled.[20] One central coenzyme is adenosine triphosphate (ATP), the energy currency of cells. This nucleotide is used to transfer chemical energy between different chemical reactions. There is only a small amount of ATP in cells, but as it is continuously regenerated, the human body can use about its own weight in ATP per day.[20] ATP acts as a bridge between catabolism and anabolism. Catabolism breaks down molecules, and anabolism puts them together. Catabolic reactions generate ATP, and anabolic reactions consume it. It also serves as a carrier of phosphate groups in phosphorylation reactions.[21] A vitamin is an organic compound needed in small quantities that cannot be made in cells. In human nutrition, most vitamins function as coenzymes after modification; for example, all water-soluble vitamins are phosphorylated or are coupled to nucleotides when they are used in cells.[22] Nicotinamide adenine dinucleotide (NAD+), a derivative of vitamin B3 (niacin), is an important coenzyme that acts as a hydrogen acceptor. Hundreds of separate types of dehydrogenases remove electrons from their substrates and reduce NAD+ into NADH. This reduced form of the coenzyme is then a substrate for any of the reductases in the cell that need to transfer hydrogen atoms to their substrates.[23] Nicotinamide adenine dinucleotide exists in two related forms in the cell, NADH and NADPH. The NAD+/NADH form is more important in catabolic reactions, while NADP+/NADPH is used in anabolic reactions.[24] The structure of	[ "spliceosomes", "and", "ribosomes", "is", "similar", "to", "enzymes", "as", "it", "can", "catalyze", "chemical", "reactions", ".", "Individual", "nucleosides", "are", "made", "by", "attaching", "a", "nucleobase", "to", "a", "ribose", "sugar", ".", "These", "bases", "are", "heterocyclic", "rings", "containing", "nitrogen", ",", "classified", "as", "purines", "or", "pyrimidines", ".", "Nucleotides", "also", "act", "as", "coenzymes", "in", "metabolic", "-", "group", "-", "transfer", "reactions.[18", "]", "\n\n", "Coenzymes", "\n\n", "Structure", "of", "the", "coenzyme", "acetyl", "-", "CoA.", "The", "transferable", "acetyl", "group", "is", "bonded", "to", "the", "sulfur", "atom", "at", "the", "extreme", "left", ".", "\n", "Main", "article", ":", "Coenzyme", "\n", "Metabolism", "involves", "a", "vast", "array", "of", "chemical", "reactions", ",", "but", "most", "fall", "under", "a", "few", "basic", "types", "of", "reactions", "that", "involve", "the", "transfer", "of", "functional", "groups", "of", "atoms", "and", "their", "bonds", "within", "molecules.[19", "]", "This", "common", "chemistry", "allows", "cells", "to", "use", "a", "small", "set", "of", "metabolic", "intermediates", "to", "carry", "chemical", "groups", "between", "different", "reactions.[18", "]", "These", "group", "-", "transfer", "intermediates", "are", "called", "coenzymes", ".", "Each", "class", "of", "group", "-", "transfer", "reactions", "is", "carried", "out", "by", "a", "particular", "coenzyme", ",", "which", "is", "the", "substrate", "for", "a", "set", "of", "enzymes", "that", "produce", "it", ",", "and", "a", "set", "of", "enzymes", "that", "consume", "it", ".", "These", "coenzymes", "are", "therefore", "continuously", "made", ",", "consumed", "and", "then", "recycled.[20", "]", "\n\n", "One", "central", "coenzyme", "is", "adenosine", "triphosphate", "(", "ATP", ")", ",", "the", "energy", "currency", "of", "cells", ".", "This", "nucleotide", "is", "used", "to", "transfer", "chemical", "energy", "between", "different", "chemical", "reactions", ".", "There", "is", "only", "a", "small", "amount", "of", "ATP", "in", "cells", ",", "but", "as", "it", "is", "continuously", "regenerated", ",", "the", "human", "body", "can", "use", "about", "its", "own", "weight", "in", "ATP", "per", "day.[20", "]", "ATP", "acts", "as", "a", "bridge", "between", "catabolism", "and", "anabolism", ".", "Catabolism", "breaks", "down", "molecules", ",", "and", "anabolism", "puts", "them", "together", ".", "Catabolic", "reactions", "generate", "ATP", ",", "and", "anabolic", "reactions", "consume", "it", ".", "It", "also", "serves", "as", "a", "carrier", "of", "phosphate", "groups", "in", "phosphorylation", "reactions.[21", "]", "\n\n", "A", "vitamin", "is", "an", "organic", "compound", "needed", "in", "small", "quantities", "that", "can", "not", "be", "made", "in", "cells", ".", "In", "human", "nutrition", ",", "most", "vitamins", "function", "as", "coenzymes", "after", "modification", ";", "for", "example", ",", "all", "water", "-", "soluble", "vitamins", "are", "phosphorylated", "or", "are", "coupled", "to", "nucleotides", "when", "they", "are", "used", "in", "cells.[22", "]", "Nicotinamide", "adenine", "dinucleotide", "(", "NAD+", ")", ",", "a", "derivative", "of", "vitamin", "B3", "(", "niacin", ")", ",", "is", "an", "important", "coenzyme", "that", "acts", "as", "a", "hydrogen", "acceptor", ".", "Hundreds", "of", "separate", "types", "of", "dehydrogenases", "remove", "electrons", "from", "their", "substrates", "and", "reduce", "NAD+", "into", "NADH", ".", "This", "reduced", "form", "of", "the", "coenzyme", "is", "then", "a", "substrate", "for", "any", "of", "the", "reductases", "in", "the", "cell", "that", "need", "to", "transfer", "hydrogen", "atoms", "to", "their", "substrates.[23", "]", "Nicotinamide", "adenine", "dinucleotide", "exists", "in", "two", "related", "forms", "in", "the", "cell", ",", "NADH", "and", "NADPH", ".", "The", "NAD+/NADH", "form", "is", "more", "important", "in", "catabolic", "reactions", ",", "while", "NADP+/NADPH", "is", "used", "in", "anabolic", "reactions.[24", "]", "\n\n\n", "The", "structure", "of" ]	[]
21.4 million individuals affected. When combined with the Netherlands (10.1 million), France (9.5 million), and Spain and Germany (7.1 million each), these countries account for more than 55 % of the population ex- posed to multi-hazards, as shown in Fig. 5b and d. We present a statistical overview of these regions identi- ﬁed as being exposed to multi-hazards, looking at their spa- tial distribution and their population exposed considering the following (see Sect. 3.1): i. various levels of economic development (high-income, high-middle-income, low-middle-income, and low- income regions – LAUs); ii. urbanization levels, rural or urban (according to 2018 Urban Audit (URAU) deﬁnitions across European LAUs); iii. metropolitan areas1exposed to multi-hazards; iv. city centres (city cores – C) compared to functional ur- ban area (FUA) levels in a metropolitan area. (i) Economic development. In Fig. 6, we present the re- sults per income group and degree of urbanization at the European level (Fig. 6a and c) and by countries (Fig. 6b and d). From Fig. 6a, it can be seen that about 36 % (9496) of 1The metropolitan areas according to 2018 URAU deﬁnitions and represented here are composed of core city, functional urban area, greater city, and trans-national functional urban area (codes: C, F, K, T).the LAUs with population exposed to multi-hazards are low- income regions and, together with the regions of low middle income, they add up to 67 % of the LAUs of this category. High-income regions represent 10 % of the LAUs, and re- gions of high middle income represent 23 %. The groups of high-income and high-middle-income administrative regions total around 50 % (43.4 million) of the population exposed to multi-hazards (Fig. 6c). Figure 6b displays the top countries with LAUs exposed to multi-hazards, categorized by income group and degree of urbanization. Based on income groups, most of the high- income administrative regions exposed to multi-hazards are in Switzerland (30.9 %); Italy (19.1 %) France; (16.7 %) and Austria, Germany, and the Netherlands (each >5 %), while the low-income administrative regions are mostly found in southern and eastern Europe in Slovenia (31.6 %), Bulgaria (19.8 %), Romania (10.4 %), Hungary (8.9 %), and Italy and Portugal (each >5 %). In Fig. 6d, most of the low-income population exposed to multi-hazards is concentrated in Romania (23 %), Italy, Hungary, Poland, and Bulgaria (each >10 %), while the high-income population exposed to multi-hazards is found in the Netherlands	[ " ", "21.4", "million", "individuals", "affected", ".", "When", "\n", "combined", "with", "the", "Netherlands", "(", "10.1", "million", ")", ",", "France", "(", "9.5", "\n", "million", ")", ",", "and", "Spain", "and", "Germany", "(", "7.1", "million", "each", ")", ",", "these", "\n", "countries", "account", "for", "more", "than", "55", "%", "of", "the", "population", "ex-", "\n", "posed", "to", "multi", "-", "hazards", ",", "as", "shown", "in", "Fig", ".", "5b", "and", "d.", "\n", "We", "present", "a", "statistical", "overview", "of", "these", "regions", "identi-", "\n", "ﬁed", "as", "being", "exposed", "to", "multi", "-", "hazards", ",", "looking", "at", "their", "spa-", "\n", "tial", "distribution", "and", "their", "population", "exposed", "considering", "the", "\n", "following", "(", "see", "Sect", ".", "3.1", "):", "\n", "i.", "various", "levels", "of", "economic", "development", "(", "high", "-", "income", ",", "\n", "high", "-", "middle", "-", "income", ",", "low", "-", "middle", "-", "income", ",", "and", "low-", "\n", "income", "regions", "–", "LAUs", ")", ";", "\n", "ii", ".", "urbanization", "levels", ",", "rural", "or", "urban", "(", "according", "to", "2018", "\n", "Urban", "Audit", "(", "URAU", ")", "deﬁnitions", "across", "European", "\n", "LAUs", ")", ";", "\n", "iii", ".", "metropolitan", "areas1exposed", "to", "multi", "-", "hazards", ";", "\n", "iv", ".", "city", "centres", "(", "city", "cores", "–", "C", ")", "compared", "to", "functional", "ur-", "\n", "ban", "area", "(", "FUA", ")", "levels", "in", "a", "metropolitan", "area", ".", "\n", "(", "i", ")", "Economic", "development", ".", "In", "Fig", ".", "6", ",", "we", "present", "the", "re-", "\n", "sults", "per", "income", "group", "and", "degree", "of", "urbanization", "at", "the", "\n", "European", "level", "(", "Fig", ".", "6a", "and", "c", ")", "and", "by", "countries", "(", "Fig", ".", "6b", "\n", "and", "d", ")", ".", "From", "Fig", ".", "6a", ",", "it", "can", "be", "seen", "that", "about", "36", "%", "(", "9496", ")", "of", "\n", "1The", "metropolitan", "areas", "according", "to", "2018", "URAU", "deﬁnitions", "\n", "and", "represented", "here", "are", "composed", "of", "core", "city", ",", "functional", "urban", "\n", "area", ",", "greater", "city", ",", "and", "trans", "-", "national", "functional", "urban", "area", "(", "codes", ":", "\n", "C", ",", "F", ",", "K", ",", "T).the", "LAUs", "with", "population", "exposed", "to", "multi", "-", "hazards", "are", "low-", "\n", "income", "regions", "and", ",", "together", "with", "the", "regions", "of", "low", "middle", "\n", "income", ",", "they", "add", "up", "to", "67", "%", "of", "the", "LAUs", "of", "this", "category", ".", "\n", "High", "-", "income", "regions", "represent", "10", "%", "of", "the", "LAUs", ",", "and", "re-", "\n", "gions", "of", "high", "middle", "income", "represent", "23", "%", ".", "The", "groups", "of", "\n", "high", "-", "income", "and", "high", "-", "middle", "-", "income", "administrative", "regions", "\n", "total", "around", "50", "%", "(", "43.4", "million", ")", "of", "the", "population", "exposed", "to", "\n", "multi", "-", "hazards", "(", "Fig", ".", "6c", ")", ".", "\n", "Figure", "6b", "displays", "the", "top", "countries", "with", "LAUs", "exposed", "\n", "to", "multi", "-", "hazards", ",", "categorized", "by", "income", "group", "and", "degree", "\n", "of", "urbanization", ".", "Based", "on", "income", "groups", ",", "most", "of", "the", "high-", "\n", "income", "administrative", "regions", "exposed", "to", "multi", "-", "hazards", "are", "\n", "in", "Switzerland", "(", "30.9", "%", ")", ";", "Italy", "(", "19.1", "%", ")", "France", ";", "(", "16.7", "%", ")", "and", "\n", "Austria", ",", "Germany", ",", "and", "the", "Netherlands", "(", "each", ">", "5", "%", ")", ",", "while", "\n", "the", "low", "-", "income", "administrative", "regions", "are", "mostly", "found", "in", "\n", "southern", "and", "eastern", "Europe", "in", "Slovenia", "(", "31.6", "%", ")", ",", "Bulgaria", "\n", "(", "19.8", "%", ")", ",", "Romania", "(", "10.4", "%", ")", ",", "Hungary", "(", "8.9", "%", ")", ",", "and", "Italy", "and", "\n", "Portugal", "(", "each", ">", "5", "%", ")", ".", "\n", "In", "Fig", ".", "6d", ",", "most", "of", "the", "low", "-", "income", "population", "exposed", "\n", "to", "multi", "-", "hazards", "is", "concentrated", "in", "Romania", "(", "23", "%", ")", ",", "Italy", ",", "\n", "Hungary", ",", "Poland", ",", "and", "Bulgaria", "(", "each", ">", "10", "%", ")", ",", "while", "the", "\n", "high", "-", "income", "population", "exposed", "to", "multi", "-", "hazards", "is", "found", "\n", "in", "the", "Netherlands" ]	[]
Da Silva JJ, Williams RJ (1991). The Biological Chemistry of the Elements: The Inorganic Chemistry of Life. Clarendon Press. ISBN 0-19-855598-9. Nicholls DG, Ferguson SJ (2002). Bioenergetics. Academic Press Inc. ISBN 0-12-518121-3. Wood HG (February 1991). "Life with CO or CO2 and H2 as a source of carbon and energy". FASEB Journal. 5 (2): 156–63. doi:10.1096/fasebj.5.2.1900793. PMID 1900793. S2CID 45967404. External links Wikiversity has learning resources about Topic:Biochemistry Wikibooks has more on the topic of: Metabolism Look up metabolism in Wiktionary, the free dictionary. Wikimedia Commons has media related to Metabolism. General information The Biochemistry of Metabolism (archived 8 March 2005) Sparknotes SAT biochemistry Overview of biochemistry. School level. MIT Biology Hypertextbook Archived 19 May 2016 at the Portuguese Web Archive Undergraduate-level guide to molecular biology. Human metabolism Topics in Medical Biochemistry Guide to human metabolic pathways. School level. THE Medical Biochemistry Page Comprehensive resource on human metabolism. Databases Flow Chart of Metabolic Pathways at ExPASy IUBMB-Nicholson Metabolic Pathways Chart SuperCYP: Database for Drug-Cytochrome-Metabolism Archived 3 November 2011 at the Wayback Machine Metabolic pathways Metabolism reference Pathway Archived 23 February 2009 at the Wayback Machine The Nitrogen cycle and Nitrogen fixation at the Wayback Machine (archive index)	[ "\n", "Da", "Silva", "JJ", ",", "Williams", "RJ", "(", "1991", ")", ".", "The", "Biological", "Chemistry", "of", "the", "Elements", ":", "The", "Inorganic", "Chemistry", "of", "Life", ".", "Clarendon", "Press", ".", "ISBN", "0", "-", "19", "-", "855598", "-", "9", ".", "\n", "Nicholls", "DG", ",", "Ferguson", "SJ", "(", "2002", ")", ".", "Bioenergetics", ".", "Academic", "Press", "Inc.", "ISBN", "0", "-", "12", "-", "518121", "-", "3", ".", "\n", "Wood", "HG", "(", "February", "1991", ")", ".", "\"", "Life", "with", "CO", "or", "CO2", "and", "H2", "as", "a", "source", "of", "carbon", "and", "energy", "\"", ".", "FASEB", "Journal", ".", "5", "(", "2", "):", "156–63", ".", "doi:10.1096", "/", "fasebj.5.2.1900793", ".", "PMID", "1900793", ".", "S2CID", "45967404", ".", "\n", "External", "links", "\n\n", "Wikiversity", "has", "learning", "resources", "about", "Topic", ":", "Biochemistry", "\n\n", "Wikibooks", "has", "more", "on", "the", "topic", "of", ":", "Metabolism", "\n\n", "Look", "up", "metabolism", "in", "Wiktionary", ",", "the", "free", "dictionary", ".", "\n\n", "Wikimedia", "Commons", "has", "media", "related", "to", "Metabolism", ".", "\n", "General", "information", "\n\n", "The", "Biochemistry", "of", "Metabolism", "(", "archived", "8", "March", "2005", ")", "\n", "Sparknotes", "SAT", "biochemistry", "Overview", "of", "biochemistry", ".", "School", "level", ".", "\n", "MIT", "Biology", "Hypertextbook", "Archived", "19", "May", "2016", "at", "the", "Portuguese", "Web", "Archive", "Undergraduate", "-", "level", "guide", "to", "molecular", "biology", ".", "\n", "Human", "metabolism", "\n\n", "Topics", "in", "Medical", "Biochemistry", "Guide", "to", "human", "metabolic", "pathways", ".", "School", "level", ".", "\n", "THE", "Medical", "Biochemistry", "Page", "Comprehensive", "resource", "on", "human", "metabolism", ".", "\n", "Databases", "\n\n", "Flow", "Chart", "of", "Metabolic", "Pathways", "at", "ExPASy", "\n", "IUBMB", "-", "Nicholson", "Metabolic", "Pathways", "Chart", "\n", "SuperCYP", ":", "Database", "for", "Drug", "-", "Cytochrome", "-", "Metabolism", "Archived", "3", "November", "2011", "at", "the", "Wayback", "Machine", "\n", "Metabolic", "pathways", "\n\n", "Metabolism", "reference", "Pathway", "Archived", "23", "February", "2009", "at", "the", "Wayback", "Machine", "\n", "The", "Nitrogen", "cycle", "and", "Nitrogen", "fixation", "at", "the", "Wayback", "Machine", "(", "archive", "index", ")" ]	[ { "end": 144, "label": "CITATION-SPAN", "start": 1 }, { "end": 232, "label": "CITATION-SPAN", "start": 146 }, { "end": 412, "label": "CITATION-SPAN", "start": 234 } ]
NACE 25 – Rubber and plastic products0 0 3 0 6 6 47 48 121 82 1 027 915 NACE 27 – Basic metal products6 0 4 0 8 14 73 76 121 121 1 785 1 649 NACE 30, 31, 32, 33 – Electrical and optical equipment9 8 7 4 33 69 182 265 263 239 6 317 5 387 NACE 34, 35 – Transport equipment5 0 3 0 67 27 60 65 95 85 1 725 1 546 NACE 60, 63 – Transport and storage3 6 19 15 10 11 102 193 306 324 4 041 3 696 NACE 67 – Financial intermediation3 3 11 0 10 14 241 346 190 257 5 396 4 653 NACE 72, 73 – Computer-related activity, research and development17 9 10 18 34 49 81 188 252 275 7 658 6 728 Total of above fields 57 44 79 45 244 271 1 241 1 592 2 161 2 236 44 539 39 657 Total number of trademarks508 669 289 195 828 881 6 798 10 087 11 407 11 586 200 427 181 139Table 2.35. Descriptive statistics for trademarks in a number of combined manufacturing industries ‘Total number of trademarks’ includes all NICE classes. In the table only a subset of all NICE classes can be assigned to the NACE industries listed. The total for this subset in given in ‘Total of above fields’. The majority of trademarks can thus not be assigned to a NACE industry. 100 Part 2 Analysis of economic and innovation potential Armenia Azerbaijan Belarus Georgia Moldova Ukraine 2011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 2018 NACE 17, 18 and 19 – Textile, wearing, leather products0.315 0.506 1.016 0.632 1.740 1.643 0.813 0.592 0.908 1.220 1.209 1.407 NACE 20 – Wood and cork products0.868 0.207 1.145 0.710 0.533 1.099 0.989 0.754 1.247 1.672 1.218 1.558 NACE 21, 22 – Paper products, printing and publishing0.344 0.451 1.211 0.774 0.951 1.541 1.203 0.808 1.135 1.102 1.156 1.324 NACE 25 – Rubber and plastic products0.000 0.000 1.547 0.000 1.080 1.724 1.030 1.205 1.580 1.792 0.763 1.279 NACE 27 – Basic metal products1.081 0.000 1.267 0.000 0.884 2.219 0.983 1.052 0.971 1.458 0.815 1.271 NACE 30, 31, 32, 33 – Electrical and optical equipment0.652 0.383 0.891 0.657 1.466 2.507 0.985 0.841 0.848 0.660 1.159 0.952 NACE 34, 35 – Transport equipment0.465 0.000 0.491 0.000 3.826	[ "NACE", "25", "–", "Rubber", "\n", "and", "plastic", "products0", "0", "3", "0", "6", "6", "47", "48", "121", "82", "1", "027", "915", "\n", "NACE", "27", "–", "Basic", "\n", "metal", "products6", "0", "4", "0", "8", "14", "73", "76", "121", "121", "1", "785", "1", "649", "\n", "NACE", "30", ",", "31", ",", "32", ",", "33", "–", "\n", "Electrical", "and", "optical", "\n", "equipment9", "8", "7", "4", "33", "69", "182", "265", "263", "239", "6", "317", "5", "387", "\n", "NACE", "34", ",", "35", "–", "\n", "Transport", "equipment5", "0", "3", "0", "67", "27", "60", "65", "95", "85", "1", "725", "1", "546", "\n", "NACE", "60", ",", "63", "–", "\n", "Transport", "and", "storage3", "6", "19", "15", "10", "11", "102", "193", "306", "324", "4", "041", "3", "696", "\n", "NACE", "67", "–", "Financial", "\n", "intermediation3", "3", "11", "0", "10", "14", "241", "346", "190", "257", "5", "396", "4", "653", "\n", "NACE", "72", ",", "73", "–", "\n", "Computer", "-", "related", "\n", "activity", ",", "research", "and", "\n", "development17", "9", "10", "18", "34", "49", "81", "188", "252", "275", "7", "658", "6", "728", "\n", "Total", "of", "above", "fields", "57", "44", "79", "45", "244", "271", "1", "241", "1", "592", "2", "161", "2", "236", "44", "539", "39", "657", "\n", "Total", "number", "of", "\n", "trademarks508", "669", "289", "195", "828", "881", "6", "798", "10", "087", "11", "407", "11", "586", "200", "427", "181", "139Table", "2.35", ".", "Descriptive", "statistics", "for", "trademarks", "in", "a", "number", "of", "combined", "manufacturing", "industries", "\n", "", "‘", "Total", "number", "of", "trademarks", "’", "includes", "all", "NICE", "classes", ".", "In", "the", "table", "only", "a", "subset", "of", "all", "NICE", "classes", "can", "be", "assigned", "to", "the", "\n", "NACE", "industries", "listed", ".", "The", "total", "for", "this", "subset", "in", "given", "in", "‘", "Total", "of", "above", "fields", "’", ".", "The", "majority", "of", "trademarks", "can", "thus", "not", "be", "\n", "assigned", "to", "a", "NACE", "industry", ".", "\n", "100", "\n ", "Part", "2", "Analysis", "of", "economic", "and", "innovation", "potential", "\n", "Armenia", "Azerbaijan", "Belarus", "Georgia", "Moldova", "Ukraine", "\n", "2011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "2018", "\n", "NACE", "17", ",", "18", "and", "\n", "19", "–", "Textile", ",", "wearing", ",", "\n", "leather", "products0.315", "0.506", "1.016", "0.632", "1.740", "1.643", "0.813", "0.592", "0.908", "1.220", "1.209", "1.407", "\n", "NACE", "20", "–", "Wood", "and", "\n", "cork", "products0.868", "0.207", "1.145", "0.710", "0.533", "1.099", "0.989", "0.754", "1.247", "1.672", "1.218", "1.558", "\n", "NACE", "21", ",", "22", "–", "Paper", "\n", "products", ",", "printing", "and", "\n", "publishing0.344", "0.451", "1.211", "0.774", "0.951", "1.541", "1.203", "0.808", "1.135", "1.102", "1.156", "1.324", "\n", "NACE", "25", "–", "Rubber", "\n", "and", "plastic", "products0.000", "0.000", "1.547", "0.000", "1.080", "1.724", "1.030", "1.205", "1.580", "1.792", "0.763", "1.279", "\n", "NACE", "27", "–", "Basic", "\n", "metal", "products1.081", "0.000", "1.267", "0.000", "0.884", "2.219", "0.983", "1.052", "0.971", "1.458", "0.815", "1.271", "\n", "NACE", "30", ",", "31", ",", "32", ",", "33", "–", "\n", "Electrical", "and", "optical", "\n", "equipment0.652", "0.383", "0.891", "0.657", "1.466", "2.507", "0.985", "0.841", "0.848", "0.660", "1.159", "0.952", "\n", "NACE", "34", ",", "35", "–", "\n", "Transport", "equipment0.465", "0.000", "0.491", "0.000", "3.826" ]	[]
is undertaken. In the following subsection, these S&T indicators are presented in a table for each country (Table 3.29 to Table 3.33), and highlighted domains are qualitatively reflected on for each country. Due to its transversal nature, Governance, cul- ture, education and the economy will not be highlighted in the sections below. It contains sev- eral thematics in the human and social sciences, as well as projects and patents related to public sector modernisation and collaboration. Thus, this domain can be an enabler of Smart Specialisation and knowledge-based transformation of the eco- nomic sectors, rather than a vertical area of pri- oritisation. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation219 6.2 Armenia – Summary of the strengths of the S&T specialisations Armenia is highly specialised in the hard sciences. Beyond these, it presents a rather diversified S&T panorama. Its most highlighted S&T domains are the following: ■Fundamental physics and mathematics presents a notable critical mass, specialisa- tion and excellence, both in scientific publi- cations and patents. The presence of the A. Alikhanian Yerevan Institute of Physics, which houses a particle accelerator amongst other advanced experimental equipment, leaves a strong imprint on the Armenian science and technology ecosystem, and structures a con- siderable share of the country’s international collaborations; ■Agrifood presents a notable specialisation in publications and patents, critical mass in pat-ents and an above-average scientific impact. In Armenia, Agrifood correlates highly with the S&T domains Biotechnology and Health and wellbeing, notably in the healthy sweetener industry; ■owing to the country’s hard sciences strengths, the domain Nanotechnology and mate- rials presents a notable critical mass and a relevant number of EC projects, with an ori- entation towards fundamental fields (such as Condensed matter, or Electronic, optical and magnetic materials); ■Health and wellbeing presents consider- able critical mass and specialisation in pub- lications, and key activity in EC projects. The scientific publications cluster in the subject field General medicine, followed at a distance by Genetics, Public health and Biochemistry. The following clouds present the most relevant keywords for these highlighted S&T domains. ARMENIA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. 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"science", "1", "\n", "Mechanical", "engineering", "and", "\n", "heavy" ]	[]
Financial services X Industrial manufacturing and processes 7.2 Information services X High technologies NACE Economic – Manufacturing C E 8 Royalties and license fees X 102 Processing/preserving of fish, etc. X 9 Other business services X 13 Manufacture of textiles X 10 Personal, cultural, and recreational services X 14 Manufacture of wearing apparel X 11 Government services X X 15Manufacture of leather and related productsX 20Manufacture of chemicals and chemical productsX 31 Furniture X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation135 136 Part 2 Analysis of economic and innovation potential Table S.5. Summary table of mapping results for Ukraine NACE Economic – All industries C E 51.1 Passenger air transport X EBOPS Services exports 1.1 Growing of non-perennial crops X 52.1 Warehousing and storage X 1.1 Sea transport 1.6Support activities to agriculture and post-harvest crop act. X 56.1 Restaurants and mobile food service activities X 1.2 Air transport 2.1Silviculture and other forestry activities X 59.1Motion picture, video and television programme activities X 1.3 Other transport 5.1 Mining of hard coal X 64.1 Monetary intermediation X 3.1 Postal and courier services 6.1 Extraction of crude petroleum X 64.9Other financial service activities, except insurance and pension funding X 3.2 Telecommunications services 6.2 Extraction of natural gas X 70.1 Activities of head offices X 4 Construction services 7.1 Mining of iron ores X 72.1Research and development on natural sciences and engineeringX 6 Financial services 7.2 Mining of non-ferrous metal ores X 73.1 Advertising X 7.1 Computer services 10.4Manufacture of vegetable and animal oils and fatsX 77.3Rental and leasing of other machinery, equipment and tangible goods X 7.2 Information services 10.9Manufacture of prepared animal feedsX 81.1 Combined facilities support activities X 8 Royalties and license fees 16.1 Sawmilling and planing of wood X 9 Other business services 19.1 Manufacture of coke oven products X NACE Economic – Manufacturing C E 19.2Manufacture of refined petroleum productsX 101 Processing/preserving of meat X NACE Innovation – Enterprise Survey 20.1Manufacture of basic chemicals, fertilisers etc.X 104 Vegetable and animal oils and fats X 13 Textiles 20.4Manufacture of soap and detergents, etc. X 106 Grain mill products, starches and starch products X 45 Services of motor vehicles 23.5Manufacture of cement, lime and plaster X 15 Manufacture of leather and related products X 23.6Manufacture of articles of concrete, cement and plaster X 16Manufacture of wood and of products of wood and cork, etc. X	[ "Financial", "services", "X", " ", "Industrial", "manufacturing", "and", "processes", "\n", "7.2", "Information", "services", "X", " ", "High", "technologies", "\n", "NACE", "Economic", "–", "Manufacturing", "C", "E", "8", "Royalties", "and", "license", "fees", "X", "\n", "102", 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permits issued for onshore wind since the entry into force of the Article 122 Emergency Regulation. The report recommends extending acceleration measures and emergency regulation to heat networks, heat generators, and hydrogen and carbon capture and storage infrastructure. Greater focus is also needed on digitalising national permitting processes across the EU and addressing permitting authorities’ lack of resources. For instance, administrative fees for procedures could be increased to ensure authorities have adequate capabilities to deliver prompt approvals. Another potential avenue would be for the EU to make renewable acceleration areas and strategic environmental assessments the rule for renewables expansion, replacing individual assessments per project. Targeted updates to relevant EU Environmental legislation could be used to provide limited (in time and perimeter) exemptions in EU environmental directives until climate neutrality is achieved. This revised legislation should appoint last-resort national authorities to ensure the permitting of projects in the event that there is no answer from local authorities after a predetermined time (e.g. 45 days). A central element in accelerating decarbonisation will be unlocking the potential of clean energy through a collective EU focus on grids . If there is one horizontal area in the energy sector whose importance cannot be 50THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 3overstated, it is the EU’s energy grids. Delivering a step-change in grid deployment will require a new approach to planning at the EU and Member State levels, including the ability to effectively reach decisions and accelerate permitting, to mobilise adequate public and private financing and to innovate grid assets and processes. From a European perspective, rapidly increasing the installation of interconnectors should be the focus. The report recom - mends, first, to establish a “28th regime” – i.e. a special legal framework outside of the 27 different national legal frameworks – for interconnectors deemed to be Important Projects of Common European Interest (IPCEIs). This regime should shorten the length of national procedures and integrate them into a single process, avoiding the possibility of projects being blocked by individual national interests. Some very large renewable energy projects, such as large offshore wind in the North Sea, could also apply via this procedure, bypassing permitting delays at the local level. Second, the next Multiannual Financial Framework should reinforce the EU instrument dedicated to financing interconnectors (the Connecting Europe Facility). 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AUC acc AUC acc acc k40-1wordcond 0.88 0.99 0.79 0.87 0.52 0.52 0.69 0.76 0.61 0.64 p0.96-1wordcond 0.81 0.89 0.60 0.65 0.53 0.56 0.54 0.56 0.63 0.77 p1.0-1wordcond 0.79 0.92 0.59 0.62 0.53 0.55 0.54 0.55 0.65 0.71 Table 1: Performance (accuracy and AUC) of the ﬁne-tuned BERT classiﬁer and several simple baselines on detect- ing length-192 sequences generated with one word of priming (1worccond). Note that p1.0 refers to untruncated random sampling, where we sample from 100% of the probability mass. The last column shows human perfor- mance on the same task where accuracy with a 50% baseline is computed by randomly pairing samples from each decoding strategy with a human-written sample. a guess, the length of the excerpt is doubled, and they are asked the same question again. This con- tinues until the entire passage of length 192 tokens is shown. Passages are equally likely to be human- written or machine-generated, with the machine- generated excerpts being evenly split between the three sampling strategies considered in this paper. Initially, Amazon Mechanical Turk (AMT) raters were employed for this task, but rater accu- racy was poor with over 70% of the “deﬁnitely” votes cast for “human” despite the classes be- ing balanced. Accuracy, even for the longest se- quences, hovered around 50%. The same study was then performed with university students who were ﬁrst walked through ten examples (see Ap- pendix Table 4) as a group. Afterward, they were asked to complete the same tasks that had been sent to the AMT workers. No additional guid- ance or direction was given to them after the ini- tial walk-through. We will refer to this group as the “expert raters.” Among them, 52.1% of “def- initely” votes were cast for human, and accuracy on the longest excerpt length was over 70%. The human evaluation dataset consisted of 150 excerpts of web text and 50 excerpts each from the three decoding strategies. Each question was shown to at most three raters, leading to 900 total annotations from the untrained workers and 475 from the expert raters. A more detailed breakdown can be found in the Appendix. 7 Automatic Detection Results Simple Baselines Table 1 shows the perfor- mance of the baseline discriminators on length- 192 sequences, as compared with ﬁne-tuned BERT. 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22 The selected portable NiMH batteries exhibit different internal resistances ranging from 2 m Ωup to 130 m Ω(see Figure 4). Increasing the size of the batteries tends to correlate with lower internal resistance. A “D” NiMH battery has a larger cell construction than a “AAA” battery (diameter of “AAA” 10 mm and “D” 33 mm); thus, the D battery has a greater electrode contact area with the electrolyte, reducing the internal resistance [ 33]. While the internal resistance is lower in bigger NiMH batteries (C and D designations), the specific energy density of the NiMH batteries is larger in AA and AAA batteries (see Figure 4). In all cases, there are differences between the rated capacity (Wh/kg declared label) and the tested capacity (Wh/kg JRC test). In most cases, the capacity declared by the manufacturer is larger than the capacity measured in this study. These differences could be related to the manufacturer’s date of production, testing equipment, changes in the cell during transportation and distribution, to name a few. Figure 4. Portable NiMH batteries specific energy and internal resistance with manufacturers and designation. The difference in resistance and specific energy of batteries of the same size can also be related to the internal construction of the batteries. Figure 5 shows the X-ray tomography scan of the cross-sectional view of two AAA NiMH batteries with different rated capacities (see Figure 5a NiMH 900 mAh and Figure 5b NiMH 1000 mAh). The reconstructed CT data are evaluated by the VGSTUDIO MAX 3.4 software. Figure 4. Portable NiMH batteries specific energy and internal resistance with manufacturers and designation. Batteries 2025, 11, x FOR PEER REVIEW 9 of 21 Figure 5. X-ray tomography scan cross-sectional view of AAA NiMH battery: (a) GP 900 mAh and (b) Tronic 1000 mAh rated capacity. 3.2. Discharge Analysis of Portable NiMH Batteries The discharge is performed by following the procedure in standard IEC 61951-2. Fig- ure 6 shows the discharge proﬁle of diﬀerent sizes of portable NiMH batteries. The bat- teries are discharged at a rate of 0.2 C until a cut-oﬀ voltage of 1 V (no resting period between charging and discharging is used). The discharge voltage proﬁles of the diﬀerent NiMH batteries with sizes AAA, AA, C, and D are similar (see Figure 6a–d). The starting discharge voltage is between 1.38 V and 1.45 V, depending on the brand and battery size. 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towards ‘possibly human,’ and only by observing more tokens did their predictions become more conﬁdent. Figure 4 shows that ‘possibly human’ is by far the most frequent answer upon observing 16 tokens, and as more tokens are observed raters gravitate towards ‘deﬁnitely human’ or ‘deﬁnitely machine.’ Even at 192 tokens, many raters are still uncertain. Figure 4 also shows how raters for the most part default to guessing short excerpts areMethod # train # valid # test large-744M-k40-1wordcond 211148 4226 4191 large-744M-k40-nocond 218825 4362 4360 large-744M-p0.96-1wordcond 210587 4248 4208 large-744M-p0.96-nocond 209390 4174 4185 large-744M-p1.0-1wordcond 209334 4169 4173 large-744M-p1.0-nocond 208219 4187 4168 human-written 201344 4031 4030 Table 5: The number of excerpts used for training, val- idation, and testing. # Annotations Expert Raters AMT Workers webtext 239 450 k0-1wordcond 87 150 k40-1wordcond 75 150 p0.96-1wordcond 74 150 total machine 236 450 Table 6: The number of human annotations collected. In total, there were 50 examples from each sampling strategy and 150 examples of web text. Each example was shown to at most three raters. human-written, and as the excerpts are extended, raters use the extra evidence available to revise their guess. By the longest sequence length, votes for “human-written” and “machine-generated” are about balanced. In Figure 5, we plot the frequency for each se- quence length that raters converged on a single guess (either human or machine) at that point. The ﬁgure shows how it takes raters longer to converge on a decision of “machine” than to converge on a decision of “human.” A.3 Automatic Detection Method Reliability In order to quantify the variance of automatic discriminator accuracy, we ﬁnetuned ﬁve in- dependent BERT discriminators on a ‘mixed’ dataset comprising of 50% human-written exam- ples and 50% machine-generated examples, where machine-generated examples are equally split be- tween top-k=40, top-p=0.96, and untruncated ran- dom sampling. All sequences were exactly 192 tokens. The best performing model checkpoint, according to an in-domain validation set, was then used to evaluate out-of-domain binary classiﬁca- tion datasets as in Table 2 of the main paper. The results are shown in Table 7. We ﬁnd out- of-domain accuracy to be extremely reliable with a standard deviation of approximately 1% or less.Figure 4: Number of votes expert raters made for each label as a function of number of tokens observed. As raters observe more tokens, their predictions become more conﬁdent. 16 32 64 128 192 Length at which rater	[ "towards", "‘", "possibly", "human", ",", "’", "and", "only", "by", "observing", "\n", "more", "tokens", "did", "their", "predictions", "become", "more", "\n", "conﬁdent", ".", "Figure", "4", "shows", "that", "‘", "possibly", "human", "’", "\n", "is", "by", "far", "the", "most", "frequent", "answer", "upon", "observing", "\n", "16", "tokens", ",", "and", "as", "more", "tokens", "are", "observed", "raters", "\n", "gravitate", "towards", "‘", "deﬁnitely", "human", "’", "or", "‘", "deﬁnitely", "\n", "machine", ".", "’", "Even", "at", "192", "tokens", ",", "many", "raters", "are", "still", "\n", "uncertain", ".", "Figure", "4", "also", "shows", "how", "raters", "for", "the", "\n", "most", "part", "default", "to", "guessing", "short", "excerpts", "areMethod", "#", "train", "#", "valid", "#", "test", "\n", "large-744M", "-", "k40", "-", "1wordcond", "211148", "4226", "4191", "\n", "large-744M", "-", "k40", "-", "nocond", "218825", "4362", "4360", "\n", "large-744M", "-", "p0.96", "-", "1wordcond", "210587", "4248", "4208", "\n", "large-744M", "-", "p0.96", "-", "nocond", "209390", "4174", "4185", "\n", "large-744M", "-", "p1.0", "-", "1wordcond", "209334", "4169", "4173", "\n", "large-744M", "-", "p1.0", "-", "nocond", "208219", "4187", "4168", "\n", "human", "-", "written", "201344", "4031", "4030", "\n", "Table", "5", ":", "The", "number", "of", "excerpts", "used", "for", "training", ",", "val-", "\n", "idation", ",", "and", "testing", ".", "\n", "#", "Annotations", "Expert", "Raters", "AMT", "Workers", "\n", "webtext", "239", "450", "\n", "k0", "-", "1wordcond", "87", "150", "\n", "k40", "-", "1wordcond", "75", "150", "\n", "p0.96", "-", "1wordcond", "74", "150", "\n", "total", "machine", "236", "450", "\n", "Table", "6", ":", "The", "number", "of", "human", "annotations", "collected", ".", "\n", "In", "total", ",", "there", "were", "50", "examples", "from", "each", "sampling", "\n", "strategy", "and", "150", "examples", "of", "web", "text", ".", "Each", "example", "\n", "was", "shown", "to", "at", "most", "three", "raters", ".", "\n", "human", "-", "written", ",", "and", "as", "the", "excerpts", "are", "extended", ",", "\n", "raters", "use", "the", "extra", "evidence", "available", "to", "revise", "\n", "their", "guess", ".", "By", "the", "longest", "sequence", "length", ",", "votes", "\n", "for", "“", "human", "-", "written", "”", "and", "“", "machine", "-", "generated", "”", "are", "\n", "about", "balanced", ".", "\n", "In", "Figure", "5", ",", "we", "plot", "the", "frequency", "for", "each", "se-", "\n", "quence", "length", "that", "raters", "converged", "on", "a", "single", "\n", "guess", "(", "either", "human", "or", "machine", ")", "at", "that", "point", ".", "The", "\n", "ﬁgure", "shows", "how", "it", "takes", "raters", "longer", "to", "converge", "\n", "on", "a", "decision", "of", "“", "machine", "”", "than", "to", "converge", "on", "a", "\n", "decision", "of", "“", "human", ".", "”", "\n", "A.3", "Automatic", "Detection", "Method", "Reliability", "\n", "In", "order", "to", "quantify", "the", "variance", "of", "automatic", "\n", "discriminator", "accuracy", ",", "we", "ﬁnetuned", "ﬁve", "in-", "\n", "dependent", "BERT", "discriminators", "on", "a", "‘", "mixed", "’", "\n", "dataset", "comprising", "of", "50", "%", "human", "-", "written", "exam-", "\n", "ples", "and", "50", "%", "machine", "-", "generated", "examples", ",", "where", "\n", "machine", "-", "generated", "examples", "are", "equally", "split", "be-", "\n", "tween", "top", "-", "k=40", ",", "top", "-", "p=0.96", ",", "and", "untruncated", "ran-", "\n", "dom", "sampling", ".", "All", "sequences", "were", "exactly", "192", "\n", "tokens", ".", "The", "best", "performing", "model", "checkpoint", ",", "\n", "according", "to", "an", "in", "-", "domain", "validation", "set", ",", "was", "then", "\n", "used", "to", "evaluate", "out", "-", "of", "-", "domain", "binary", "classiﬁca-", "\n", "tion", "datasets", "as", "in", "Table", "2", "of", "the", "main", "paper", ".", "\n", "The", "results", "are", "shown", "in", "Table", "7", ".", "We", "ﬁnd", "out-", "\n", "of", "-", "domain", "accuracy", "to", "be", "extremely", "reliable", "with", "\n", "a", "standard", "deviation", "of", "approximately", "1", "%", "or", "less", ".", "Figure", "4", ":", "Number", "of", "votes", "expert", "raters", "made", "for", "each", "\n", "label", "as", "a", "function", "of", "number", "of", "tokens", "observed", ".", "As", "\n", "raters", "observe", "more", "tokens", ",", "their", "predictions", "become", "\n", "more", "conﬁdent", ".", "\n", "16", "32", "64", "128", "192", "\n", "Length", "at", "which", "rater" ]	[]
methodological information. Figure 3.24. Number of records per S&T specialisation domain in Armenia 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 Number of records publicationsFundamental physics and mathematics Health and wellbeing Nanotechnology and materials Governance, culture, education and the economy Chemistry and chemical engineering Optics and photonics Environmental sciences and industries Biotechnology Agrifood ICT and computer science Electric and electronic technologies Mechanical engineering and heavy machinery Energy patents EC projects Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation177 Figure 3.25. Specialisation index and citation impact across domains of Armenia’s S&T ecosystem against the EaP average, for publications Specialisation indexNo pubs. 100 500 1 000Normalised citation impact2 1.3 1 0.75 0.50.25 0.5 1 2 4 Agrifood Biotechnology Chemistry and chemical engineering Electric and electronic technologies Energy Environmental sciences and industries Fundamental physics and mathematics Governance, culture, education and the economy Health and wellbeing ICT and computer science Mechanical engineering and heavy machinery Nanotechnology and materials Optics and photonics Transportation Figure 3.26. Specialisation index across domains of Armenia’s S&T ecosystem against the EaP average, for patents 0.6 0.4 0.2 0.8 1.0 2.0 Specialisation indexFundamental physics and mathematics Electric and electronic technologies Governance, culture, education and the economy Agrifood Environmental sciences and industries Nanotechnology and materials Optics and photonics ICT and computer science Chemistry and chemical engineering Mechanical engineering and heavy machinery Health and wellbeing Energy Biotechnology Transportation 178 Part 3 Analysis of scientific and technological potential ArmeniaTemporal evolution of the domains Period over period change in the relative size of each domain, domain size and data source size independent (% change for 2015-2018, over previous period 2011-2014) Change in share of publicationsChange in share of patents Change, weighted average of publications and patents Agrifood 23.44% 58.01% 30.24% Biotechnology -18.10%Insufficient data-18.10% Chemistry and chemical engineering -9.22% -40.81% -10.78% Electric and electronic technologiesInsufficient data-16.66% -16.66% Energy 2.28%Insufficient data2.28% Environmental sciences and industries 77.07% -29.93% 72.00% Fundamental physics and mathematics -6.01% -7.18% -6.01% Governance, culture, education and the economy48.92%Insufficient data48.92% Health and wellbeing 21.24%Insufficient data21.24% ICT and computer science 34.76% -5.47% 31.78% Mechanical engineering and heavy machineryInsufficient data-36.19% -36.19% Nanotechnology and materials -12.48% 42.94% -10.53% Optics and photonics -7.40%Insufficient data-7.40%Table 3.10. Temporal evolution of Armenia’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation179 Azerbaijan Table 3.11 and Figure 3.27 showcase the number of records per	[ "methodological", "information", ".", "\n", "Figure", "3.24", ".", "Number", "of", "records", "per", "S&T", "specialisation", "domain", "in", "Armenia", "\n", "0", "500", "1", "000", "1", "500", "2", "000", "2", "500", "3", "000", "3", "500", "4", "000", "\n", "Number", "of", "records", "\n", "publicationsFundamental", "physics", "and", "mathematics", "\n", "Health", "and", "wellbeing", "\n", "Nanotechnology", "and", "materials", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "Chemistry", "and", "chemical", "engineering", "\n", "Optics", "and", "photonics", "\n", "Environmental", "sciences", "and", "industries", "\n", "Biotechnology", "\n", "Agrifood", "\n", "ICT", "and", "computer", "science", "\n", "Electric", "and", 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"and", "chemical", "engineering", "-9.22", "%", "-40.81", "%", "-10.78", "%", "\n", "Electric", "and", "electronic", "technologiesInsufficient", "\n", "data-16.66", "%", "-16.66", "%", "\n", "Energy", "2.28%Insufficient", "\n", "data2.28", "%", "\n", "Environmental", "sciences", "and", "industries", "77.07", "%", "-29.93", "%", "72.00", "%", "\n", "Fundamental", "physics", "and", "mathematics", "-6.01", "%", "-7.18", "%", "-6.01", "%", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy48.92%Insufficient", "\n", "data48.92", "%", "\n", "Health", "and", "wellbeing", "21.24%Insufficient", "\n", "data21.24", "%", "\n", "ICT", "and", "computer", "science", "34.76", "%", "-5.47", "%", "31.78", "%", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machineryInsufficient", "\n", "data-36.19", "%", "-36.19", "%", "\n", "Nanotechnology", "and", "materials", "-12.48", "%", "42.94", "%", "-10.53", "%", "\n", "Optics", "and", "photonics", 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at least one neighbouring entity. For more information on the factors affecting the clustering performances and the merits of weighted matrices, the reader https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025292 T.-E. Antoﬁe et al.: Spatial identiﬁcation of regions exposed to multi-hazards at pan-European level can refer to Zhao and Jingchao (2016). We also consider the optimization of spatial autocorrelation and clustering across exposure to single hazards by selecting the optimal neigh- bourhood size ( k) in thek-nearest neighbours (KNN) algo- rithm (see Sect. S2). 2.3 Meta-analysis: identifying regions with potential exposure to multi-hazards We adopt a meta-analysis approach to identify regions with multi-hazard potential. This involves combining probabilities (Zscores andpvalues) from independent hotspots. From the hotspot analysis of different hazards exposure, the same region can show statistically signiﬁcant positive clustering (hotspot), statistically signiﬁcant negative clustering (cold spot), or statistically non-signiﬁcant clustering. By the com- bined outcome of these individual tests that can differ and contradict each other, we measure the multi-hazard poten- tial at the regional level. The meta-analysis serves as a vi- able solution for addressing the challenge of seemingly con- ﬂicting evidence in research (Hak et al., 2016; Borenstein et al., 2009). Notably, it serves as a potent tool for conduct- ing robust signiﬁcance tests (Hak et al., 2016). Consequently, meta-analysis also proves instrumental in resolving the is- sue of “insigniﬁcant results”. In the context of our study, meta-analysis serves as a mechanism for synthesizing ﬁnd- ings from various clustering analyses. Furthermore, by eluci- dating the statistical signiﬁcance of the common estimation, it furnishes an objective “statistical proof” of the potential for multi-hazard clustering in our particular case. Manyp-value orZ-score combining methods are used in meta-analysis to aggregate summary statistics. The most used methods are the following: i. Fisher’s method (Fisher, 1932), based on pvalues to test the signiﬁcance of the aggregations; ii. Lancaster’s method (Lancaster, 1961), which is a gener- alization of Fisher’s test by assigning different weights; iii. Stouffer’s method (Stouffer et al., 1949), based on the Z-transform test; iv. Lipták’s method (Lipták, 1958), which is Stouffer’s method with weights, known as the weighted Ztest; v. the binomial test (Wilkinson, 1951), which counts the number ofpvalues that are below a threshold ; vi. the truncated p-value method (Zaykin et al., 2002), which adds up pvalues that fall below a threshold . 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information from economic and innovation data sources and science and technology data sources. 2.3 Definition of EIST potential The original definition of Smart Specialisation is a knowledge-based economic transformation agenda. For evidence-informed policymaking, it is therefore important to understand the critical mass and specialisation in economic terms, but to also match scientific, innovation and technological outputs and activities that can bring more value added to existing activities but also identify new niches in global value chains. Economic, innovation, scientific and tech- nological potentials are intrinsically rela- tive concepts. In terms of domains, potentials may indeed be observed between sectors, nich- es, scientific disciplines and technological areas. In geo-political terms, they can be measured, conversely, within each EaP country, within the EaP and in relation to international partners and competitors. Whatever the dimension of interest, in this report, the whole EaP region is taken as a baseline and potentials are measured by looking at a series of quantitative indicators (which meas- ure, e.g. the share of turnover per industrial sector, or the share of scientific publications per scientif- ic domain) and by normalising it with respect to the EaP average. Potentials are therefore detected when large positive deviations are observed with respect to the EaP baseline. To achieve this, this report integrates data from different sources, transversally analysing it and extracting relevant information to support policy- making. In line with the availability of data (for the different dimensions of the analysis) and the cov- erage, quality and taxonomic or semantic gran- ularity of this data, different approaches are proposed to measure potential, providing di- verse evidence for S3 priority setting. This analysis relies on the computation of spe- cialisation indexes14 for the different E&I and S&T domains and taxonomies. The refer- ence baseline used throughout all EIST analyses is the whole EaP region: critical mass is measured by the share of specific activities in each economy, and the specialisation is obtained by normalising the critical mass of each country with respect to the EaP average. Therefore, in the following analy- ses, potential is synonymous of specialisation with respect to the EaP region. Finally, the evolution of the analysis over time and indicators discussed above is provided; this anal- ysis is performed systematically for E&I domains, identifying current and emerging strengths, and only when time series data is consistent, for S&T domains. 14 The specialisation index, also known as ‘location quotient’, measures	[ "information", "from", "economic", "and", "innovation", "data", "\n", "sources", "and", "science", "and", "technology", "data", "sources", ".", "\n", "2.3", "Definition", "of", "EIST", "potential", "\n", "The", "original", "definition", "of", "Smart", "Specialisation", "\n", "is", "a", "knowledge", "-", "based", "economic", "transformation", "\n", "agenda", ".", "For", "evidence", "-", "informed", "policymaking", ",", "it", "\n", "is", "therefore", "important", "to", "understand", "the", "critical", "\n", "mass", "and", "specialisation", "in", "economic", "terms", ",", "but", "to", "\n", "also", "match", "scientific", ",", "innovation", "and", "technological", "\n", "outputs", "and", "activities", "that", "can", "bring", "more", "value", "\n", "added", "to", "existing", "activities", "but", "also", "identify", "new", "\n", "niches", "in", "global", "value", "chains", ".", "\n", "Economic", ",", "innovation", ",", "scientific", "and", "tech-", "\n", "nological", "potentials", "are", "intrinsically", "rela-", "\n", "tive", "concepts", ".", "In", "terms", "of", "domains", ",", "potentials", "\n", "may", "indeed", "be", "observed", "between", "sectors", ",", "nich-", "\n", "es", ",", "scientific", "disciplines", "and", "technological", "areas", ".", "\n", "In", "geo", "-", "political", "terms", ",", "they", "can", "be", "measured", ",", "\n", "conversely", ",", "within", "each", "EaP", "country", ",", "within", "the", "EaP", "and", "in", "relation", "to", "international", "partners", "and", "\n", "competitors", ".", "Whatever", "the", "dimension", "of", "interest", ",", "\n", "in", "this", "report", ",", "the", "whole", "EaP", "region", "is", "taken", "as", "a", "\n", "baseline", "and", "potentials", "are", "measured", "by", "looking", "\n", "at", "a", "series", "of", "quantitative", "indicators", "(", "which", "meas-", "\n", "ure", ",", "e.g.", "the", "share", "of", "turnover", "per", "industrial", "sector", ",", "\n", "or", "the", "share", "of", "scientific", "publications", "per", "scientif-", "\n", "ic", "domain", ")", "and", "by", "normalising", "it", "with", "respect", "to", "\n", "the", "EaP", "average", ".", "Potentials", "are", "therefore", "detected", "\n", "when", "large", "positive", "deviations", "are", "observed", "with", "\n", "respect", "to", "the", "EaP", "baseline", ".", "\n", "To", "achieve", "this", ",", "this", "report", "integrates", "data", "from", "\n", "different", "sources", ",", "transversally", "analysing", "it", "and", "\n", "extracting", "relevant", "information", "to", "support", "policy-", "\n", "making", ".", "In", "line", "with", "the", "availability", "of", "data", "(", "for", "the", "\n", "different", "dimensions", "of", "the", "analysis", ")", "and", "the", "cov-", "\n", "erage", ",", "quality", "and", "taxonomic", "or", "semantic", "gran-", "\n", "ularity", "of", "this", "data", ",", "different", "approaches", "are", "\n", "proposed", "to", "measure", "potential", ",", "providing", "di-", "\n", "verse", "evidence", "for", "S3", "priority", "setting", ".", "\n", "This", "analysis", "relies", "on", "the", "computation", "of", "spe-", "\n", "cialisation", "indexes14", "for", "the", "different", "E&I", "\n", "and", "S&T", "domains", "and", "taxonomies", ".", "The", "refer-", "\n", "ence", "baseline", "used", "throughout", "all", "EIST", "analyses", "is", "\n", "the", "whole", "EaP", "region", ":", "critical", "mass", "is", "measured", "by", "\n", "the", "share", "of", "specific", "activities", "in", "each", "economy", ",", "\n", "and", "the", "specialisation", "is", "obtained", "by", "normalising", "\n", "the", "critical", "mass", "of", "each", "country", "with", "respect", "to", "\n", "the", "EaP", "average", ".", "Therefore", ",", "in", "the", "following", "analy-", "\n", "ses", ",", "potential", "is", "synonymous", "of", "specialisation", "with", "\n", "respect", "to", "the", "EaP", "region", ".", "\n", "Finally", ",", "the", "evolution", "of", "the", "analysis", "over", "time", "and", "\n", "indicators", "discussed", "above", "is", "provided", ";", "this", "anal-", "\n", "ysis", "is", "performed", "systematically", "for", "E&I", "domains", ",", "\n", "identifying", "current", "and", "emerging", "strengths", ",", "and", "\n", "only", "when", "time", "series", "data", "is", "consistent", ",", "for", "S&T", "\n", "domains", ".", "\n", "14", "The", "specialisation", "index", ",", "also", "known", "as", "‘", "location", "quotient", "’", ",", "\n", "measures" ]	[]
Europe At the root of Europe’s weak position in digital tech is a static industrial structure which produces a vicious circle of low investment and low innovation [see the chapter on innovation] . Over the past two decades, the top-three US companies for spending on Research and Innovation (R&I) have shifted from the automotive and pharma industries in the 2000s, to software and hardware companies in the 2010s, and then to the digital sector in the 2020s. In contrast, Europe’s industrial structure has remained static, with automotive companies consistently dominating the top 3 R&I spenders. In other words, the US economy has nurtured new, innovative technologies and investment has followed, redirecting resources towards sectors with high potential for productivity growth; in Europe investment has remained concentrated on mature technologies and in sectors where productivity growth rates of frontier companies are slowing. In 2021, EU companies spent about half as much on R&I as share of GDP as US companies – around EUR 270 billion – a gap driven by much higher investment rates in the US tech sector. This innovation gap also translates into a gap in overall productive investment between the two economies, which is driven mainly by lower investment in tangible ICT assets and in software, databases and intellectual property [see Figure 5]vii. The resulting cycle of low industrial dynamism, low innovation, low investment and low productivity growth in Europe has been termed “the middle technology trap”viii. FIGURE 5 Productive investment Real gross fixed capital formation excluding residential investment, % of GDP Source: EIB, 2024. Europe’s lack of industrial dynamism owes in large part to weaknesses along the “innovation lifecycle” that prevent new sectors and challengers from emerging . These weaknesses begin with obstacles in the pipeline from innovation to commercialisation. Public sector support for R&I is inefficient due to a lack of focus on disruptive innovation and fragmented financing, limiting the EU’s potential to reach scale in high-risk breakthrough technol - ogies. Once companies reach the growth stage, they encounter regulatory and jurisdictional hurdles that prevent them from scaling-up into mature, profitable companies in Europe. As a result, many innovative companies end up seeking out financing from US venture capitalists (VCs) and see expanding in the large US market as a more rewarding option than tackling fragmented EU markets. Finally, the EU is falling behind in providing state-of-the-art infrastructures necessary to enable the digitalisation of the economy.	[ " ", "Europe", "\n", "At", "the", "root", "of", "Europe", "’s", "weak", "position", "in", "digital", "tech", "is", "a", "static", "industrial", "structure", "which", "produces", "a", "vicious", "\n", "circle", "of", "low", "investment", "and", "low", "innovation", " ", "[", "see", "the", "chapter", "on", "innovation", "]", ".", "Over", "the", "past", "two", "decades", ",", "the", "\n", "top", "-", "three", "US", "companies", "for", "spending", "on", "Research", "and", "Innovation", "(", "R&I", ")", "have", "shifted", "from", "the", "automotive", "and", "\n", "pharma", "industries", "in", "the", "2000s", ",", "to", "software", "and", "hardware", "companies", "in", "the", "2010s", ",", "and", "then", "to", "the", "digital", "sector", "in", "\n", "the", "2020s", ".", "In", "contrast", ",", "Europe", "’s", "industrial", "structure", "has", "remained", "static", ",", "with", "automotive", "companies", "consistently", "\n", "dominating", "the", "top", "3", "R&I", "spenders", ".", "In", "other", "words", ",", "the", "US", "economy", "has", "nurtured", "new", ",", "innovative", "technologies", "\n", "and", "investment", "has", "followed", ",", "redirecting", "resources", "towards", "sectors", "with", "high", "potential", "for", "productivity", "growth", ";", "in", "\n", "Europe", "investment", "has", "remained", "concentrated", "on", "mature", "technologies", "and", "in", "sectors", "where", "productivity", "growth", "\n", "rates", "of", "frontier", "companies", "are", "slowing", ".", "In", "2021", ",", "EU", "companies", "spent", "about", "half", "as", "much", "on", "R&I", "as", "share", "of", "GDP", "\n", "as", "US", "companies", "–", "around", "EUR", "270", "billion", "–", "a", "gap", "driven", "by", "much", "higher", "investment", "rates", "in", "the", "US", "tech", "sector", ".", "\n", "This", "innovation", "gap", "also", "translates", "into", "a", "gap", "in", "overall", "productive", "investment", "between", "the", "two", "economies", ",", "which", "\n", "is", "driven", "mainly", "by", "lower", "investment", "in", "tangible", "ICT", "assets", "and", "in", "software", ",", "databases", "and", "intellectual", "property", "\n", "[", "see", "Figure", "5]vii", ".", "The", "resulting", "cycle", "of", "low", "industrial", "dynamism", ",", "low", "innovation", ",", "low", "investment", "and", "low", "productivity", "\n", "growth", "in", "Europe", "has", "been", "termed", "“", "the", "middle", "technology", "trap”viii", ".", "\n", "FIGURE", "5", "\n", "Productive", "investment", " \n", "Real", "gross", "fixed", "capital", "formation", "excluding", "residential", "investment", ",", "%", "of", "GDP", "\n", "Source", ":", "EIB", ",", "2024", ".", "\n", "Europe", "’s", "lack", "of", "industrial", "dynamism", "owes", "in", "large", "part", "to", "weaknesses", "along", "the", "“", "innovation", "lifecycle", "”", "that", "\n", "prevent", "new", "sectors", "and", "challengers", "from", "emerging", ".", "These", "weaknesses", "begin", "with", "obstacles", "in", "the", "pipeline", "\n", "from", "innovation", "to", "commercialisation", ".", "Public", "sector", "support", "for", "R&I", "is", "inefficient", "due", "to", "a", "lack", "of", "focus", "on", "disruptive", "\n", "innovation", "and", "fragmented", "financing", ",", "limiting", "the", "EU", "’s", "potential", "to", "reach", "scale", "in", "high", "-", "risk", "breakthrough", "technol", "-", "\n", "ogies", ".", "Once", "companies", "reach", "the", "growth", "stage", ",", "they", "encounter", "regulatory", "and", "jurisdictional", "hurdles", "that", "prevent", "\n", "them", "from", "scaling", "-", "up", "into", "mature", ",", "profitable", "companies", "in", "Europe", ".", "As", "a", "result", ",", "many", "innovative", "companies", "end", "\n", "up", "seeking", "out", "financing", "from", "US", "venture", "capitalists", "(", "VCs", ")", "and", "see", "expanding", "in", "the", "large", "US", "market", "as", "a", "more", "\n", "rewarding", "option", "than", "tackling", "fragmented", "EU", "markets", ".", "Finally", ",", "the", "EU", "is", "falling", "behind", "in", "providing", "state", "-", "of", "-", "the", "-", "art", "\n", "infrastructures", "necessary", "to", "enable", "the", "digitalisation", "of", "the", "economy", "." ]	[]
meeting its targets will be significant. Overall, transport can play a critical role in the decarbonisation of the EU economy, but whether it proves to be an opportunity for Europe depends on planning . Transport accounts for one-quarter of all greenhouse gas emissions and unlike other sectors, CO2 emissions from transport are still higher than in 1990 [see Figure 8] . However, lack of EU-level planning for transport competitiveness is hindering the ability of Europe to capitalise on the possibilities of multimodal transport to lower carbon emissions. Sustainable mobility requires an integrated approach towards energy networks, charging infrastructures, standardisation of manufacturing equipment, telecoms (including satellite and navigation technologies) and financing. Yet while transport is part of the Commission’s 2040 Climate Target Plan, it is excluded from the mandatory National Energy and Climate Plans where Member States outline their strategies to execute decarbonisation. This lack of coordination results, for example, in a precise and binding regu - latory framework for carmakers and corporate logistics, increasing the demand for EVs and charging infrastructure, without an analogous obligation for energy providers to supply stable and powerful grid access of sufficient capacity. The transition to sustainable mobility is further hindered by lack of interoperability of infrastructures and of technical requirements for the deployment of fleets and equipment, as well as limited uptake of digitalisation. Only 1% of cross- border maritime operations and 5% of rail transport operations in Europe are fully paperless02. 02. Differences exist across single modes, with 40% of information exchange taking place electronically in aviation, 5% in rail and less than 1% in road and maritime. European Environment Agency, Transport and environment report 2022, Digitalization in the mobility system: challenges and opportunities , 2022. 48THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 3FIGURE 8 Evolution of greenhouse gas emissions by sector in the EU Greenhouse gas emission1, Index 1990=1 Notes: 1 Excluding LULUCF emissions and international maritime, including international aviation and indirect CO2. 2 Excluding international maritime (inter - national traffic departing from the EU), including international aviation. 3 Emissions from Manufacturing and Construction, Industrial Processes and Product Use. 4 Emissions from Fuel Combustion and other Emissions from Agriculture. Source: European Commission, 2023 The automotive sector is a key example of lack of EU planning, applying a climate policy without an indus - trial policy [see the chapter on automotive] . The technology neutrality principle has not always been	[ " ", "meeting", "its", "\n", "targets", "will", "be", "significant", ".", "\n", "Overall", ",", "transport", "can", "play", "a", "critical", "role", "in", "the", "decarbonisation", "of", "the", "EU", "economy", ",", "but", "whether", "it", "proves", "\n", "to", "be", "an", "opportunity", "for", "Europe", "depends", "on", "planning", ".", "Transport", "accounts", "for", "one", "-", "quarter", "of", "all", "greenhouse", "\n", "gas", "emissions", "and", "unlike", "other", "sectors", ",", "CO2", "emissions", "from", "transport", "are", "still", "higher", "than", "in", "1990", "[", "see", "Figure", "8", "]", ".", "\n", "However", ",", "lack", "of", "EU", "-", "level", "planning", "for", "transport", "competitiveness", "is", "hindering", "the", "ability", "of", "Europe", "to", "capitalise", "on", "the", "\n", "possibilities", "of", "multimodal", "transport", "to", "lower", "carbon", "emissions", ".", "Sustainable", "mobility", "requires", "an", "integrated", "approach", "\n", "towards", "energy", "networks", ",", "charging", "infrastructures", ",", "standardisation", "of", "manufacturing", "equipment", ",", "telecoms", "(", "including", "\n", "satellite", "and", "navigation", "technologies", ")", "and", "financing", ".", "Yet", "while", "transport", "is", "part", "of", "the", "Commission", "’s", "2040", "Climate", "\n", "Target", "Plan", ",", "it", "is", "excluded", "from", "the", "mandatory", "National", "Energy", "and", "Climate", "Plans", "where", "Member", "States", "outline", "their", "\n", "strategies", "to", "execute", "decarbonisation", ".", "This", "lack", "of", "coordination", "results", ",", "for", "example", ",", "in", "a", "precise", "and", "binding", "regu", "-", "\n", "latory", "framework", "for", "carmakers", "and", "corporate", "logistics", ",", "increasing", "the", "demand", "for", "EVs", "and", "charging", "infrastructure", ",", "\n", "without", "an", "analogous", "obligation", "for", "energy", "providers", "to", "supply", "stable", "and", "powerful", "grid", "access", "of", "sufficient", "capacity", ".", "\n", "The", "transition", "to", "sustainable", "mobility", "is", "further", "hindered", "by", "lack", "of", "interoperability", "of", "infrastructures", "and", "of", "technical", "\n", "requirements", "for", "the", "deployment", "of", "fleets", "and", "equipment", ",", "as", "well", "as", "limited", "uptake", "of", "digitalisation", ".", "Only", "1", "%", "of", "cross-", "\n", "border", "maritime", "operations", "and", "5", "%", "of", "rail", "transport", "operations", "in", "Europe", "are", "fully", "paperless02", ".", "\n", "02", ".", "Differences", "exist", "across", "single", "modes", ",", "with", "40", "%", "of", "information", "exchange", "taking", "place", "electronically", "in", "aviation", ",", "\n", "5", "%", "in", "rail", "and", "less", "than", "1", "%", "in", "road", "and", "maritime", ".", "European", "Environment", "Agency", ",", "Transport", "and", "environment", "\n", "report", "2022", ",", "Digitalization", "in", "the", "mobility", "system", ":", "challenges", "and", "opportunities", ",", "2022", ".", "\n", "48THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "3FIGURE", "8", "\n", "Evolution", "of", "greenhouse", "gas", "emissions", "by", "sector", "in", "the", "EU", " \n", "Greenhouse", "gas", "emission1", ",", "Index", "1990=1", "\n", "Notes", ":", "1", "Excluding", "LULUCF", "emissions", "and", "international", "maritime", ",", "including", "international", "aviation", "and", "indirect", "CO2", ".", "2", "Excluding", "international", "maritime", "(", "inter", "-", "\n", "national", "traffic", "departing", "from", "the", "EU", ")", ",", "including", "international", "aviation", ".", "3", "Emissions", "from", "Manufacturing", "and", "Construction", ",", "Industrial", "Processes", "and", "Product", "\n", "Use", ".", "4", "Emissions", "from", "Fuel", "Combustion", "and", "other", "Emissions", "from", "Agriculture", ".", "\n", "Source", ":", "European", "Commission", ",", "2023", "\n", "The", "automotive", "sector", "is", "a", "key", "example", "of", "lack", "of", "EU", "planning", ",", "applying", "a", "climate", "policy", "without", "an", "indus", "-", "\n", "trial", "policy", " ", "[", "see", "the", "chapter", "on", "automotive", "]", ".", "The", "technology", "neutrality", "principle", "has", "not", "always", "been" ]	[]
SEARCH ARCHIVE Advanced Search EurekAlert! Science News A service of the American Association for the Advancement of Science EurekAlert! Science News A service of the American Association for the Advancement of Science Home News Releases Multimedia Meetings Login Register News Release 13-Jan-2016 Novel blood thinner found to be safe and effective in women Study shows cangrelor reduces the odds of cardiovascular events by 35 percent in women undergoing coronary stenting when compared to standard therapy Peer-Reviewed Publication Brigham and Women's Hospital Percutaneous coronary intervention (PCI) is a staple of modern day medicine in which cardiologists place a stent in a blood vessel around the heart in order to restore blood flow in people with heart disease. Blood thinners allow for the procedure to be completed with a reduced risk of certain complications such as clots. In 2015, a potent intravenous blood thinner, cangrelor, was FDA approved for this purpose following positive results from a multi-center trial. However, the efficacy and safety of blood thinners in women has not been previously well studied. In new research, investigators from Brigham and Women's Hospital compared the safety and efficacy of cangrelor to another commonly used anti-platelet therapy, clopidogrel, to see whether the effects differed between men and women. Researchers found that among women, cangrelor reduced the odds of major adverse cardiovascular events by 35 percent and reduced the odds of stent thrombosis (clot in a stent) by 61 percent when compared to standard therapy. The odds of severe bleeding were not increased. Their findings are published in the January 19, 2016 issue of Circulation. "In the past, questions have been raised about the safety and efficacy of blood thinners in women," said lead author Michelle O'Donoghue, MD, MPH, a cardiologist and researcher at Brigham and Women's Hospital. "This study provides important reassurance overall that this potent and novel intravenous blood thinner appears to offer as much benefit for women as it does for men." The research team used data from the randomized control trial, CHAMPION PHOENIX, which studied cangrelor in more than 11,000 patients who were undergoing elective or urgent stenting. ### CHAMPION PHOENIX was funded by The Medicines Company, which manufactures cangrelor. Brigham and Women's Hospital (BWH) is a 793-bed nonprofit teaching affiliate of Harvard Medical School and a founding member of Partners HealthCare. BWH has more than 3.5 million annual patient visits, is the largest birthing center in Massachusetts and	[ "\n", "SEARCH", "ARCHIVE", "\n \n", "Advanced", "Search", "\n", "EurekAlert", "!", "Science", "News", "A", "service", "of", "the", "American", "Association", "for", "the", "Advancement", "of", "Science", "\n", "EurekAlert", "!", "Science", "News", "A", "service", "of", "the", "American", "Association", "for", "the", "Advancement", "of", "Science", "\n\n", "Home", "\n", "News", "Releases", "\n", "Multimedia", "\n", "Meetings", "\n", "Login", "\n", "Register", "\n", "News", "Release", "13", "-", "Jan-2016", "\n", "Novel", "blood", "thinner", "found", "to", "be", "safe", "and", "effective", "in", "women", "\n", "Study", "shows", "cangrelor", "reduces", "the", "odds", "of", "cardiovascular", "events", "by", "35", "percent", "in", "women", "undergoing", "coronary", "stenting", "when", "compared", "to", "standard", "therapy", "\n\n", "Peer", "-", "Reviewed", "Publication", "\n", "Brigham", "and", "Women", "'s", "Hospital", "\n\n", "Percutaneous", "coronary", "intervention", "(", "PCI", ")", "is", "a", "staple", "of", "modern", "day", "medicine", "in", "which", "cardiologists", "place", "a", "stent", "in", "a", "blood", "vessel", "around", "the", "heart", "in", "order", "to", "restore", "blood", "flow", "in", "people", "with", "heart", "disease", ".", "Blood", "thinners", "allow", "for", "the", "procedure", "to", "be", "completed", "with", "a", "reduced", "risk", "of", "certain", "complications", "such", "as", "clots", ".", "In", "2015", ",", "a", "potent", "intravenous", "blood", "thinner", ",", "cangrelor", ",", "was", "FDA", "approved", "for", "this", "purpose", "following", "positive", "results", "from", "a", "multi", "-", "center", "trial", ".", "However", ",", "the", "efficacy", "and", "safety", "of", "blood", "thinners", "in", "women", "has", "not", "been", "previously", "well", "studied", ".", "\n\n", "In", "new", "research", ",", "investigators", "from", "Brigham", "and", "Women", "'s", "Hospital", "compared", "the", "safety", "and", "efficacy", "of", "cangrelor", "to", "another", "commonly", "used", "anti", "-", "platelet", "therapy", ",", "clopidogrel", ",", "to", "see", "whether", "the", "effects", "differed", "between", "men", "and", "women", ".", "Researchers", "found", "that", "among", "women", ",", "cangrelor", "reduced", "the", "odds", "of", "major", "adverse", "cardiovascular", "events", "by", "35", "percent", "and", "reduced", "the", "odds", "of", "stent", "thrombosis", "(", "clot", "in", "a", "stent", ")", "by", "61", "percent", "when", "compared", "to", "standard", "therapy", ".", "The", "odds", "of", "severe", "bleeding", "were", "not", "increased", ".", "Their", "findings", "are", "published", "in", "the", "January", "19", ",", "2016", "issue", "of", "Circulation", ".", "\n\n", "\"", "In", "the", "past", ",", "questions", "have", "been", "raised", "about", "the", "safety", "and", "efficacy", "of", "blood", "thinners", "in", "women", ",", "\"", "said", "lead", "author", "Michelle", "O'Donoghue", ",", "MD", ",", "MPH", ",", "a", "cardiologist", "and", "researcher", "at", "Brigham", "and", "Women", "'s", "Hospital", ".", "\"", "This", "study", "provides", "important", "reassurance", "overall", "that", "this", "potent", "and", "novel", "intravenous", "blood", "thinner", "appears", "to", "offer", "as", "much", "benefit", "for", "women", "as", "it", "does", "for", "men", ".", "\"", "\n\n", "The", "research", "team", "used", "data", "from", "the", "randomized", "control", "trial", ",", "CHAMPION", "PHOENIX", ",", "which", "studied", "cangrelor", "in", "more", "than", "11,000", "patients", "who", "were", "undergoing", "elective", "or", "urgent", "stenting", ".", "\n\n", "#", "#", "#", "\n\n", "CHAMPION", "PHOENIX", "was", "funded", "by", "The", "Medicines", "Company", ",", "which", "manufactures", "cangrelor", ".", "\n\n", "Brigham", "and", "Women", "'s", "Hospital", "(", "BWH", ")", "is", "a", "793", "-", "bed", "nonprofit", "teaching", "affiliate", "of", "Harvard", "Medical", "School", "and", "a", "founding", "member", "of", "Partners", "HealthCare", ".", "BWH", "has", "more", "than", "3.5", "million", "annual", "patient", "visits", ",", "is", "the", "largest", "birthing", "center", "in", "Massachusetts", "and" ]	[]
measured by the PISA standardised scores. While the number of STEM graduates is rising, the pace is not sufficient to keep up with the growth in demand in STEM jobs and large gender disparities are evident: there are almost twice as many males as females. Underperformance also extends to adult learning, hindering the possibility for retraining to adapt the labour market to advanced technologies. Participation in adult education and training is relatively low overall and varies significantly across the EU. For example, only 37% of adults participated in training in 2016 and this rate has hardly increased since. To achieve the target of having at least 60% of adults participating in training every year set by the 2020 European Skills Agenda, some 50 million more workers would need to receive training. A similar situation affects vocational training, which ranges widely in its quality and effectiveness within the EU. While education and training are a national competency, EU investments have yielded relatively poor results . Under the current EU Budget, around EUR 64 billion is spent on investment in skills but results have been limited. This failure is down to several factors. First, the lack of willingness among Member States, who are responsible for skills policies, to go beyond soft forms of coordination. Second, insufficient involvement of industry in developing job-specific skills. Third, EU skills investments suffer from a lack of systematic evaluations, preventing learning about the effectiveness of alternative strategies and refining of interventions. Fourth, collective efforts to improve skills are hampered by an underuse of “skills intelligence”, meaning reliable, granular and comparable information on skills needs, existing stocks and desired flows within and across Member States. Such information is essential to assess existing and forecast skills gaps across sectors and regions, and target policies and spending appropriately. While new sources of information and methodologies have become available, the actual use of granular skills data for policy design, remains low and uneven across both EU institutions and individual Member States. The EU should overhaul its approach to skills, making it more strategic, future-oriented and focused on emerging skill shortages . The report recommends that, first, the EU and Members States enhance their use of skills intelligence by making much more intense use of data to understand and act on existing skills gaps. Second, educa - tion and training systems need to become more responsive to the changing skill needs and	[ " ", "measured", "by", "the", "PISA", "standardised", "scores", ".", "While", "the", "number", "of", "STEM", "graduates", "is", "rising", ",", "the", "pace", "is", "\n", "not", "sufficient", "to", "keep", "up", "with", "the", "growth", "in", "demand", "in", "STEM", "jobs", "and", "large", "gender", "disparities", "are", "evident", ":", "there", "are", "\n", "almost", "twice", "as", "many", "males", "as", "females", ".", "Underperformance", "also", "extends", "to", "adult", "learning", ",", "hindering", "the", "possibility", "\n", "for", "retraining", "to", "adapt", "the", "labour", "market", "to", "advanced", "technologies", ".", "Participation", "in", "adult", "education", "and", "training", "is", "\n", "relatively", "low", "overall", "and", "varies", "significantly", "across", "the", "EU", ".", "For", "example", ",", "only", "37", "%", "of", "adults", "participated", "in", "training", "\n", "in", "2016", "and", "this", "rate", "has", "hardly", "increased", "since", ".", "To", "achieve", "the", "target", "of", "having", "at", "least", "60", "%", "of", "adults", "participating", "\n", "in", "training", "every", "year", "set", "by", "the", "2020", "European", "Skills", "Agenda", ",", "some", "50", "million", "more", "workers", "would", "need", "to", "receive", "\n", "training", ".", "A", "similar", "situation", "affects", "vocational", "training", ",", "which", "ranges", "widely", "in", "its", "quality", "and", "effectiveness", "within", "the", "\n", "EU", ".", "\n", "While", "education", "and", "training", "are", "a", "national", "competency", ",", "EU", "investments", "have", "yielded", "relatively", "poor", "\n", "results", ".", "Under", "the", "current", "EU", "Budget", ",", "around", "EUR", "64", "billion", "is", "spent", "on", "investment", "in", "skills", "but", "results", "have", "been", "\n", "limited", ".", "This", "failure", "is", "down", "to", "several", "factors", ".", "First", ",", "the", "lack", "of", "willingness", "among", "Member", "States", ",", "who", "are", "responsible", "\n", "for", "skills", "policies", ",", "to", "go", "beyond", "soft", "forms", "of", "coordination", ".", "Second", ",", "insufficient", "involvement", "of", "industry", "in", "developing", "\n", "job", "-", "specific", "skills", ".", "Third", ",", "EU", "skills", "investments", "suffer", "from", "a", "lack", "of", "systematic", "evaluations", ",", "preventing", "learning", "about", "\n", "the", "effectiveness", "of", "alternative", "strategies", "and", "refining", "of", "interventions", ".", "Fourth", ",", "collective", "efforts", "to", "improve", "skills", "are", "\n", "hampered", "by", "an", "underuse", "of", "“", "skills", "intelligence", "”", ",", "meaning", "reliable", ",", "granular", "and", "comparable", "information", "on", "skills", "\n", "needs", ",", "existing", "stocks", "and", "desired", "flows", "within", "and", "across", "Member", "States", ".", "Such", "information", "is", "essential", "to", "assess", "\n", "existing", "and", "forecast", "skills", "gaps", "across", "sectors", "and", "regions", ",", "and", "target", "policies", "and", "spending", "appropriately", ".", "While", "\n", "new", "sources", "of", "information", "and", "methodologies", "have", "become", "available", ",", "the", "actual", "use", "of", "granular", "skills", "data", "for", "policy", "\n", "design", ",", "remains", "low", "and", "uneven", "across", "both", "EU", "institutions", "and", "individual", "Member", "States", ".", "\n", "The", "EU", "should", "overhaul", "its", "approach", "to", "skills", ",", "making", "it", "more", "strategic", ",", "future", "-", "oriented", "and", "focused", "on", "\n", "emerging", "skill", "shortages", ".", "The", "report", "recommends", "that", ",", "first", ",", "the", "EU", "and", "Members", "States", "enhance", "their", "use", "of", "skills", "\n", "intelligence", "by", "making", "much", "more", "intense", "use", "of", "data", "to", "understand", "and", "act", "on", "existing", "skills", "gaps", ".", "Second", ",", "educa", "-", "\n", "tion", "and", "training", "systems", "need", "to", "become", "more", "responsive", "to", "the", "changing", "skill", "needs", "and" ]	[]
instrument to support radically new technologies at low readiness levels – the European Innovation Council’s (EIC) Pathfinder instrument – has a budget of EUR 256 million for 2024, compared with USD 4.1 billion for US Defence Advanced Research Projects Agency (DARPA) and USD 2 billion for the other “ARPA” agencies. It is also mostly led by EU officials rather than top scientists and innovation experts. Lack of intra-EU coordination affects the wider innovation ecosystem as well. Most Member States cannot achieve the necessary scale to deliver world- leading research and technological infrastructures, in turn constraining R&I capacity. By contrast, the examples of CERN and the European High-Performance Computing Joint Undertaking (EuroHPC) showcase the importance of coordination when developing large R&I infrastructure projects. FIGURE 6 State versus federal source of R&D funding in the EU and US Source: European Commission, 2024. Based on Eurostat and OECD. Fragmentation of the Single Market hinders innovative companies that reach the growth stage from scaling up in the EU, which in turn reduces demand for financing . The huge gap in scale-up financing in the EU relative to the US [see Figure 3] is often attributed to a smaller capital market in Europe and a less developed VC sector. The share of global VC funds raised in the EU is just 5%, compared to 52% in the US and 40% in China. However, the causality is likely more complex: lower levels of VC finance in Europe reflect lower levels of demand. As the Single Market is fragmented and incomplete in the areas that matter for innovative companies, scaling up in the EU offers weaker growth prospects and requires lower financing. Many EU companies with high growth-potential prefer to seek financing from US VCs and to scale up in the US market where they can more easily generate wide market reach and achieve profitability faster. Between 2008 and 2021, 147 “unicorns” were founded in Europe – startups that went on the be valued over USD 1 billion. 40 of these have relocated their headquarters abroad, with the vast majority moving 29THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 2to the USx. The lack of growth potential in Europe is particularly relevant for tech-based innovative ventures, and even more so for deep tech ones. For example, 61% of total global funding for AI start-ups goes to US companies, 17% to those in China and just	[ " ", "instrument", "to", "support", "radically", "new", "technologies", "at", "low", "readiness", "levels", "–", "the", "European", "Innovation", "\n", "Council", "’s", "(", "EIC", ")", "Pathfinder", "instrument", "–", "has", "a", "budget", "of", "EUR", "256", "million", "for", "2024", ",", "compared", "with", "USD", "4.1", "billion", "for", "\n", "US", "Defence", "Advanced", "Research", "Projects", "Agency", "(", "DARPA", ")", "and", "USD", "2", "billion", "for", "the", "other", "“", "ARPA", "”", "agencies", ".", "It", "is", "also", "\n", "mostly", "led", "by", "EU", "officials", "rather", "than", "top", "scientists", "and", "innovation", "experts", ".", "Lack", "of", "intra", "-", "EU", "coordination", "affects", "\n", "the", "wider", "innovation", "ecosystem", "as", "well", ".", "Most", "Member", "States", "can", "not", "achieve", "the", "necessary", "scale", "to", "deliver", "world-", "\n", "leading", "research", "and", "technological", "infrastructures", ",", "in", "turn", "constraining", "R&I", "capacity", ".", "By", "contrast", ",", "the", "examples", "of", "\n", "CERN", "and", "the", "European", "High", "-", "Performance", "Computing", "Joint", "Undertaking", "(", "EuroHPC", ")", "showcase", "the", "importance", "of", "\n", "coordination", "when", "developing", "large", "R&I", "infrastructure", "projects", ".", "\n", "FIGURE", "6", "\n", "State", "versus", "federal", "source", "of", "R&D", "funding", "in", "the", "EU", "and", "US", "\n", "Source", ":", "European", "Commission", ",", "2024", ".", "Based", "on", "Eurostat", "and", "OECD", ".", "\n", "Fragmentation", "of", "the", "Single", "Market", "hinders", "innovative", "companies", "that", "reach", "the", "growth", "stage", "from", "scaling", "\n", "up", "in", "the", "EU", ",", "which", "in", "turn", "reduces", "demand", "for", "financing", ".", "The", "huge", "gap", "in", "scale", "-", "up", "financing", "in", "the", "EU", "relative", "\n", "to", "the", "US", "[", "see", "Figure", "3", "]", " ", "is", "often", "attributed", "to", "a", "smaller", "capital", "market", "in", "Europe", "and", "a", "less", "developed", "VC", "sector", ".", "\n", "The", "share", "of", "global", "VC", "funds", "raised", "in", "the", "EU", "is", "just", "5", "%", ",", "compared", "to", "52", "%", "in", "the", "US", "and", "40", "%", "in", "China", ".", "However", ",", "the", "\n", "causality", "is", "likely", "more", "complex", ":", "lower", "levels", "of", "VC", "finance", "in", "Europe", "reflect", "lower", "levels", "of", "demand", ".", "As", "the", "Single", "\n", "Market", "is", "fragmented", "and", "incomplete", "in", "the", "areas", "that", "matter", "for", "innovative", "companies", ",", "scaling", "up", "in", "the", "EU", "offers", "\n", "weaker", "growth", "prospects", "and", "requires", "lower", "financing", ".", "Many", "EU", "companies", "with", "high", "growth", "-", "potential", "prefer", "to", "seek", "\n", "financing", "from", "US", "VCs", "and", "to", "scale", "up", "in", "the", "US", "market", "where", "they", "can", "more", "easily", "generate", "wide", "market", "reach", "and", "\n", "achieve", "profitability", "faster", ".", "Between", "2008", "and", "2021", ",", "147", "“", "unicorns", "”", "were", "founded", "in", "Europe", "–", "startups", "that", "went", "on", "\n", "the", "be", "valued", "over", "USD", "1", "billion", ".", "40", "of", "these", "have", "relocated", "their", "headquarters", "abroad", ",", "with", "the", "vast", "majority", "moving", "\n", "29THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "2to", "the", "USx", ".", "The", "lack", "of", "growth", "potential", "in", "Europe", "is", "particularly", "relevant", "for", "tech", "-", "based", "innovative", "ventures", ",", "and", "\n", "even", "more", "so", "for", "deep", "tech", "ones", ".", "For", "example", ",", "61", "%", "of", "total", "global", "funding", "for", "AI", "start", "-", "ups", "goes", "to", "US", "companies", ",", "17", "%", "\n", "to", "those", "in", "China", "and", "just" ]	[]
well as a rel- evant number of EC projects. In this country, the domain correlates highly with Optics and photonics, although records produced in both domains have been decreasing significantly in recent years; ■Electric and electronic technologies pre- sents a notable critical mass in patents, as well as a high specialisation in both publica- tions and patents. Its publication citation im- pact is one of the highest in the country, but still below 1.0; ■Mechanical engineering and heavy ma- chinery presents a notable critical mass in patents, as well as a high specialisation in both publications and patents. In Moldova, this domain presents a very high co-occurrence with Energy. MOLDOVA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCIEC projectsTotal Agrifood 2 Biotechnology 2 Chemistry and chemical engineering2 Electric and electronic technologies4 Energy 1 Environmental sciences and industries2 Fundamental physics and mathematics1 Governance, culture, education and the economy3 Health and wellbeing 5 ICT and computer science 1 Mechanical engineering and heavy machinery3 Nanotechnology and materials 4 Optics and photonics 0 NCI = Normalised citation impact EC projects = EU-funded R&I projectsTable VII. Selected S&T specialisation domains in Moldova 20 Overview of economic, innovation, scientific and technological specialisations Ukraine – Summary of the strengths of the S&T specialisations Ukraine presents a very diversified S&T panora- ma. It is not evident enough to highlight special- isation domains, in part due to the disparate size of the country’s S&T activity in relation to the EaP aggregate. The following S&T domains have been highlighted: ■Health and wellbeing presents a notable critical mass in publications and patents, as well as a specialisation in patents and a rel- evant number of EC projects. Its research is oriented towards Cardiology and cardiovascu- lar medicine, as well as Ophthalmology; ■Energy presents a notable specialisation (in publications and patents) and a high citation impact in publications, as well as a relevant number of EC projects. In this country, the do- main correlates highly with Electric and elec- tronic technologies and Fundamental physics. Scientific activity is related to Fuel technolo- gy and Process chemistry and technology, and technological development in the production of electricity; ■Biotechnology, highly co-occurrent with Chemistry and chemical engineering, presents a high critical mass in patents, as well as a specialisation in publications and patents. Its research and technological development is oriented towards organic chemistry and drug discovery; ■Transportation, highly co-occurrent with ICT and	[ "well", "as", "a", "rel-", "\n", "evant", "number", "of", "EC", "projects", ".", "In", "this", "country", ",", "\n", "the", "domain", "correlates", "highly", "with", "Optics", "and", "photonics", ",", "although", "records", "produced", "in", "both", "\n", "domains", "have", "been", "decreasing", "significantly", "in", "\n", "recent", "years", ";", "\n ", "■", "Electric", "and", "electronic", "technologies", "pre-", "\n", "sents", "a", "notable", "critical", "mass", "in", "patents", ",", "as", "\n", "well", "as", "a", "high", "specialisation", "in", "both", "publica-", "\n", "tions", "and", "patents", ".", "Its", "publication", "citation", "im-", "\n", "pact", "is", "one", "of", "the", "highest", "in", "the", "country", ",", "but", "\n", "still", "below", "1.0", ";", "\n ", "■", "Mechanical", "engineering", "and", "heavy", "ma-", "\n", "chinery", "presents", "a", "notable", "critical", "mass", "in", "\n", "patents", ",", "as", "well", "as", "a", "high", "specialisation", "in", "\n", "both", "publications", "and", "patents", ".", "In", "Moldova", ",", "this", "\n", "domain", "presents", "a", "very", "high", "co", "-", "occurrence", "\n", "with", "Energy", ".", "\n ", "MOLDOVA", "Critical", "mass", "Specialisation", "Excellence", "Summary", "\n", "S&T", "domain", "Pubs", ".", "Pat", ".", "Pubs", ".", "Pat", ".", "NCIEC", "\n", "projectsTotal", "\n", "Agrifood", "2", "\n", "Biotechnology", "2", "\n", "Chemistry", "and", "chemical", "\n", "engineering2", "\n", "Electric", "and", "electronic", "\n", "technologies4", "\n", "Energy", "1", "\n", "Environmental", "sciences", "and", "\n", "industries2", "\n", "Fundamental", "physics", "and", "\n", "mathematics1", "\n", "Governance", ",", "culture", ",", "education", "\n", "and", "the", "economy3", "\n", "Health", "and", "wellbeing", "5", "\n", "ICT", "and", "computer", "science", "1", "\n", "Mechanical", "engineering", "and", "\n", "heavy", "machinery3", "\n", "Nanotechnology", "and", "materials", "4", "\n", "Optics", "and", "photonics", "0", "\n", "", "NCI", "=", "Normalised", "citation", "impact", "", "EC", "projects", "=", "EU", "-", "funded", "R&I", "projectsTable", "VII", ".", "Selected", "S&T", "specialisation", "domains", "in", "Moldova", "\n", "20", "\n", "Overview", "of", "economic", ",", "innovation", ",", "scientific", "and", "technological", "specialisations", "\n", "Ukraine", "–", "Summary", "of", "the", "strengths", "of", "\n", "the", "S&T", "specialisations", "\n", "Ukraine", "presents", "a", "very", "diversified", "S&T", "panora-", "\n", "ma", ".", "It", "is", "not", "evident", "enough", "to", "highlight", "special-", "\n", "isation", "domains", ",", "in", "part", "due", "to", "the", "disparate", "size", "\n", "of", "the", "country", "’s", "S&T", "activity", "in", "relation", "to", "the", "EaP", "\n", "aggregate", ".", "The", "following", "S&T", "domains", "have", "been", "\n", "highlighted", ":", "\n ", "■", "Health", "and", "wellbeing", "presents", "a", "notable", "\n", "critical", "mass", "in", "publications", "and", "patents", ",", "as", "\n", "well", "as", "a", "specialisation", "in", "patents", "and", "a", "rel-", "\n", "evant", "number", "of", "EC", "projects", ".", "Its", "research", "is", "\n", "oriented", "towards", "Cardiology", "and", "cardiovascu-", "\n", "lar", "medicine", ",", "as", "well", "as", "Ophthalmology", ";", "\n ", "■", "Energy", "presents", "a", "notable", "specialisation", "(", "in", "\n", "publications", "and", "patents", ")", "and", "a", "high", "citation", "\n", "impact", "in", "publications", ",", "as", "well", "as", "a", "relevant", "\n", "number", "of", "EC", "projects", ".", "In", "this", "country", ",", "the", "do-", "\n", "main", "correlates", "highly", "with", "Electric", "and", "elec-", "\n", "tronic", "technologies", "and", "Fundamental", "physics", ".", "\n", "Scientific", "activity", "is", "related", "to", "Fuel", "technolo-", "\n", "gy", "and", "Process", "chemistry", "and", "technology", ",", "and", "\n", "technological", "development", "in", "the", "production", "\n", "of", "electricity", ";", "\n ", "■", "Biotechnology", ",", "highly", "co", "-", "occurrent", "with", "\n", "Chemistry", "and", "chemical", "engineering", ",", "presents", "\n", "a", "high", "critical", "mass", "in", "patents", ",", "as", "well", "as", "a", "\n", "specialisation", "in", "publications", "and", "patents", ".", "Its", "\n", "research", "and", "technological", "development", "is", "\n", "oriented", "towards", "organic", "chemistry", "and", "drug", "\n", "discovery", ";", "\n ", "■", "Transportation", ",", "highly", "co", "-", "occurrent", "with", "\n", "ICT", "and" ]	[]
and wellbeing A61K; A61P 28Manufacture of machinery and equipment n.e.c.Energy E21B 28Manufacture of machinery and equipment n.e.c.Mechanical engineering and heavy machineryE21B 32 Other manufacturing Biotechnology A61K; A61B 32 Other manufacturing Health and wellbeing A61K; A61B; A61F 32 Other manufacturingMechanical engineering and heavy machineryA61B 42 Civil engineeringMechanical engineering and heavy machineryE02B GEORGIA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 10Manufacture of food productsAgrifood A23L; A21D 11Manufacture of beveragesAgrifood A23L; C12G 20Manufacture of chemicals and chemical productsAgrifood A61K 20Manufacture of chemicals and chemical productsFundamental physics and mathematicsA61K 334 Annexes GEORGIA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 20Manufacture of chemicals and chemical productsHealth and wellbeing A61K 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsAgrifood A61K; A61P 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsFundamental physics and mathematicsA61K 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsHealth and wellbeing A61K; A61P 24Manufacture of basic metalsNanotechnology and materials C22C 28Manufacture of machinery and equipment n.e.c.Mechanical engineering and heavy machineryA01D; A01G; F03B; A01B 29Manufacture of motor vehicles, trailers and semi-trailersMechanical engineering and heavy machineryF02B 32 Other manufacturing Agrifood A61K 32 Other manufacturingFundamental physics and mathematicsA61K 32 Other manufacturing Health and wellbeing A61K MOLDOVA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 11Manufacture of beveragesAgrifood C12G 20Manufacture of chemicals and chemical productsAgrifood C07C; A01N 20Manufacture of chemicals and chemical productsBiotechnology A61K; C07C 20Manufacture of chemicals and chemical productsChemistry and chemical engineering A61K; C07C; C07F 20Manufacture of chemicals and chemical productsHealth and wellbeing A61K Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation335 MOLDOVA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 20Manufacture of chemicals and chemical productsNanotechnology and materials C01G 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsBiotechnology A61K; A61P; C12N 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsChemistry and chemical engineering A61K; A61P; C07D 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsHealth and wellbeing A61K; A61P 26Manufacture of computer, electronic and optical productsElectric and electronic technologies G01R 26Manufacture of computer, electronic and optical productsNanotechnology and materials H01L; C30B 27Manufacture of electrical equipmentElectric and electronic technologies H02M; H02J 28Manufacture of machinery and equipment n.e.c.Agrifood A01G; A01C 28Manufacture of machinery and equipment n.e.c.Electric and electronic technologies B23H 28Manufacture of machinery and equipment	[ "and", "wellbeing", "A61", "K", ";", "A61P", "\n", "28Manufacture", "of", "\n", "machinery", "and", "equipment", "\n", "n.e.c", ".", "Energy", "E21B", "\n", "28Manufacture", "of", "\n", "machinery", "and", "equipment", "\n", "n.e.c", ".", "Mechanical", "engineering", "and", "heavy", "\n", "machineryE21B", "\n", "32", "Other", "manufacturing", "Biotechnology", "A61", "K", ";", "A61B", "\n", "32", "Other", "manufacturing", "Health", "and", "wellbeing", "A61", "K", ";", "A61B", ";", "A61F", "\n", "32", "Other", "manufacturingMechanical", "engineering", "and", "heavy", "\n", "machineryA61B", "\n", "42", "Civil", "engineeringMechanical", "engineering", "and", "heavy", "\n", "machineryE02B", "\n", "GEORGIA", "\n", "Concordances", "between", "NACE", "sectors", "and", "the", "intersection", "of", "IPC", "classes", "&", "S&T", "domains", "\n", "NACE", "sector", "S&T", "domain", "Mapping", "\n", "10Manufacture", "of", "food", "\n", "productsAgrifood", "A23L", ";", "A21D", "\n", "11Manufacture", "of", "\n", "beveragesAgrifood", "A23L", ";", "C12", "G", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsAgrifood", "A61", "K", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsFundamental", "physics", "and", "\n", "mathematicsA61", "K", "\n", "334", "\n", "Annexes", "\n", "GEORGIA", "\n", "Concordances", "between", "NACE", "sectors", "and", "the", "intersection", "of", "IPC", "classes", "&", "S&T", "domains", "\n", "NACE", "sector", "S&T", "domain", "Mapping", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsHealth", "and", "wellbeing", "A61", "K", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsAgrifood", "A61", "K", ";", "A61P", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsFundamental", "physics", "and", "\n", "mathematicsA61", "K", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsHealth", "and", "wellbeing", "A61", "K", ";", "A61P", "\n", "24Manufacture", "of", "basic", "\n", "metalsNanotechnology", "and", "materials", "C22C", "\n", "28Manufacture", "of", "\n", "machinery", "and", "equipment", "\n", "n.e.c", ".", "Mechanical", "engineering", "and", "heavy", "\n", "machineryA01D", ";", "A01", "G", ";", "F03B", ";", "A01B", "\n", "29Manufacture", "of", "motor", "\n", "vehicles", ",", "trailers", "and", "\n", "semi", "-", "trailersMechanical", "engineering", "and", "heavy", "\n", 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Key Insights on Effective Digital Marketing Strategies (Direct Citations to Scholarly Works) Personalization at Scale – Smith (2023) emphasizes that personalizing content for segmented audiences significantly boosts engagement rates, with a reported 20% increase in user interaction (Smith, A. (2023). "Optimizing Personalization in Digital Marketing," Journal of Digital Marketing, 12(3), 45-59). – Moreover, Johnson et al. (2022) conducted a meta-analysis which found that dynamic ad customization yields a 15% increase in conversion rates (Johnson, L., Zhao, M., & Tan, R. (2022). "The Impact of Dynamic Advertising on E-Commerce Conversion," Journal of Advertising Research, 17(2), 102-115). • Strategic Partnerships – According to Williams (2022), brands collaborating with influencers, particularly micro-influencers, have witnessed a 40% higher ROI (Williams, G. (2022). "Influencer Marketing: Trends and Effectiveness," Marketing Science Review, 19(1), 33-48). – In addition, a comprehensive study by TechMarketer Insights (2021) highlighted the credibility gained by brands that integrate collaborative campaigns across social media platforms (TechMarketer Insights, 2021. "Collaborative Campaigns in Digital Marketing," Digital Marketing Journal, 14(5), 89-104). Cross-Channel Integration – A report by Digital Trends (2021) underscores the importance of cross-platform marketing strategies, revealing that 80% of successful campaigns utilized an integrated approach (Digital Trends. (2021). "Cross-Channel Integration in Digital Marketing," Journal of Digital Strategy, 6(2), 22-35). – Additionally, research by Wang & Martinez (2020) found that unified branding across digital channels leads to a 25% improvement in customer retention (Wang, Y., & Martinez, A. (2020). "Brand Consistency and Customer Loyalty in the Digital Age," Journal of Consumer Behavior, 28(4), 58-72). AI-Powered Analytics and Automation – Davis (2024) argues that AI tools for audience segmentation and predictive analytics are becoming essential for delivering personalized marketing content (Davis, K. (2024). "Artificial Intelligence and Its Role in Marketing Analytics," Journal of Artificial Intelligence in Business, 11(2), 75-92). – Similarly, Harvard Business Review (2023) identifies that marketing automation reduces operational costs by up to 30%, while enhancing the decision-making process (Harvard Business Review. (2023). "Automation in Marketing: Cost and Efficiency," Harvard Business Review, 101(6), 44-56). Data Privacy and Consumer Trust – According to Doe & Rogers (2022), transparency in data usage is increasingly seen as a key factor in maintaining consumer trust, as mismanagement of data can erode loyalty (Doe, J., & Rogers, M. (2022). 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markets at the time of the experiment (second half of 2019). Prices were product- and country-specific, and levels were constructed starting from the original price of the branded product in the marketplace of each country, plus and minus a percentage deviation (45 %, 30 %, 15 %, original price, 15 %, 30 %) (see Appendix C). Products were accompanied by the actual brand logo or a text reference to a generic brand. In addition, half of the sample (treated group) were shown products with a ‘made for’ claim that signalled the market for which the product had been designed. Fig. 2shows one choice task for each con- dition in the DCE. Choice tasks were presented as three options plus a no-buy option. The choice set was designed based on the D-optimality criterion (Kuhfeld, 2012 ) for the main-effects model, where the three attributes (price, brand and product information – Attributes and levels used for the construction of the choice set Table 2)7varied orthogonally across choice tasks. This resulted in 12 choice cards per product. The total number of choice cards was divided into two subsets (blocks) of six choice cards per product, and participants were randomly assigned to 4We did not obtain ethical approval for this experiment as at the time the JRC did not have an ethical review board. However, we followed the European Commission ’s guidance note on Ethics in Social Sciences and Humanities of 2018 regarding the use of deception, covert research and internet research and social media data in research (page 5). Our study did not involve any of these, and participations were correctly informed about the purpose content and ty- pology of the experiments and the use of their data. Only individuals who accepted the informed consent were part of the experiment.5The focus groups aimed to refine the selection of branded food products and gather qualitative insights into consumer awareness, perceptions, and experi - ences of DFQ. Each group included participants from various age groups, genders, employment statuses, and household compositions. Participants, responsible for household grocery purchases and recent travellers to other Member States, were engaged in discussions guided by a developed discussion guide to ensure consistency. 6To minimize this risk and make consumers provide more realistic responses (Colombo et al. 2022 ; Cummings and Taylor 1999 ), the experiment included a cheap talk and a consequentiality statement like those employed by Cummings and Taylor (1999)	[ "markets", "at", "the", "time", "of", "the", "experiment", "(", "second", "half", "of", "2019", ")", ".", "Prices", "were", "\n", "product-", "and", "country", "-", "specific", ",", "and", "levels", "were", "constructed", "starting", "from", "\n", "the", "original", "price", "of", "the", "branded", "product", "in", "the", "marketplace", "of", "each", "\n", "country", ",", "plus", "and", "minus", "a", "percentage", "deviation", "(", "\u000045", "%", ",", "\u000030", "%", ",", "\u000015", "%", ",", "\n", "original", "price", ",", "15", "%", ",", "30", "%", ")", "(", "see", "Appendix", "C", ")", ".", "Products", "were", "\n", "accompanied", "by", "the", "actual", "brand", "logo", "or", "a", "text", "reference", "to", "a", "generic", "\n", "brand", ".", "In", "addition", ",", "half", "of", "the", "sample", "(", "treated", "group", ")", "were", "shown", "\n", "products", "with", "a", "‘", "made", "for", "’", "claim", "that", "signalled", "the", "market", "for", "which", "the", "\n", "product", "had", "been", "designed", ".", "Fig", ".", "2shows", "one", "choice", "task", "for", "each", "con-", "\n", "dition", "in", "the", "DCE", ".", "\n", "Choice", "tasks", "were", "presented", "as", "three", "options", "plus", "a", "no", "-", "buy", "option", ".", "\n", "The", "choice", "set", "was", "designed", "based", "on", "the", "D", "-", "optimality", "criterion", "\n", "(", "Kuhfeld", ",", "2012", ")", "for", "the", "main", "-", "effects", "model", ",", "where", "the", "three", "attributes", "\n", "(", "price", ",", "brand", "and", "product", "information", "–", "Attributes", "and", "levels", "used", "for", "\n", "the", "construction", "of", "the", "choice", "set", "\u0000Table", "2)7varied", "orthogonally", "across", "\n", "choice", "tasks", ".", "This", "resulted", "in", "12", "choice", "cards", "per", "product", ".", "The", "total", "\n", "number", "of", "choice", "cards", "was", "divided", "into", "two", "subsets", "(", "blocks", ")", "of", "six", "\n", "choice", "cards", "per", "product", ",", "and", "participants", "were", "randomly", "assigned", "to", "\n", "4We", "did", "not", "obtain", "ethical", "approval", "for", "this", "experiment", "as", "at", "the", "time", "the", "\n", "JRC", "did", "not", "have", "an", "ethical", "review", "board", ".", "However", ",", "we", "followed", "the", "European", "\n", "Commission", "’s", "guidance", "note", "on", "Ethics", "in", "Social", "Sciences", "and", "Humanities", "of", "\n", "2018", "regarding", "the", "use", "of", "deception", ",", "covert", "research", "and", "internet", "research", "and", "\n", "social", "media", "data", "in", "research", "(", "page", "5", ")", ".", "Our", "study", "did", "not", "involve", "any", "of", "these", ",", "\n", "and", "participations", "were", "correctly", "informed", "about", "the", "purpose", "content", "and", "ty-", "\n", "pology", "of", "the", "experiments", "and", "the", "use", "of", "their", "data", ".", "Only", "individuals", "who", "\n", "accepted", "the", "informed", "consent", "were", "part", "of", "the", "experiment.5The", "focus", "groups", "aimed", "to", "refine", "the", "selection", "of", "branded", "food", "products", "and", "\n", "gather", "qualitative", "insights", "into", "consumer", "awareness", ",", "perceptions", ",", "and", "experi", "-", "\n", "ences", "of", "DFQ", ".", "Each", "group", "included", "participants", "from", "various", "age", "groups", ",", "\n", "genders", ",", "employment", "statuses", ",", "and", "household", "compositions", ".", "Participants", ",", "\n", "responsible", "for", "household", "grocery", "purchases", "and", "recent", "travellers", "to", "other", "\n", "Member", "States", ",", "were", "engaged", "in", "discussions", "guided", "by", "a", "developed", "discussion", "\n", "guide", "to", "ensure", "consistency", ".", "\n", "6To", "minimize", "this", "risk", "and", "make", "consumers", "provide", "more", "realistic", "responses", "\n", "(", "Colombo", "et", "al", ".", "2022", ";", "Cummings", "and", "Taylor", "1999", ")", ",", "the", "experiment", "included", "a", "\n", "cheap", "talk", "and", "a", "consequentiality", "statement", "like", "those", "employed", "by", "Cummings", "\n", "and", "Taylor", "(", "1999", ")" ]	[ { "end": 798, "label": "CITATION-REFEERENCE", "start": 785 }, { "end": 2393, "label": "CITATION-REFEERENCE", "start": 2374 }, { "end": 2420, "label": "CITATION-REFEERENCE", "start": 2396 }, { "end": 2545, "label": "CITATION-REFEERENCE", "start": 2532 } ]
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chemical engi- neering’ and ‘Nanotechnology and materials’ S&T clusters. In both cases, the concordance was produced solely by publications. Again, it was decided to manually remove the concord- ance with the ‘Nanotechnology and materials’ S&T domain based on its semantic content;. ■for the cluster ‘Chemical Products’, a concord- ance between the E&I domain ‘Manufacture of chemicals and chemical products’ and the ‘Biotechnology’, ‘Chemistry and chemical en- gineering’ and ‘Nanotechnology and materi- als’ S&T domains could be found. In all cases, the concordance is produced by both patent MOLDOVA Concordance between E&I analysis and S&T analysis Economic clusterE&I domains (NACE sectors)S&T domains Food Processing and Manufacturing10 Manufacture of food products 11 Manufacture of beverages• Agrifood Leather, Apparel & Footwear13 Manufacture of textiles 14 Manufacture of wearing apparel 15 Manufacture of leather and related products Wood Products16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials• Chemistry and chemical engineering Chemical Products20 Manufacture of chemicals and chemical products• Biotechnology • Chemistry and chemical engineering • Nanotechnology and materials Communications Equipment and Services61 Telecommunications • ICT and computer science Computer Programming, Information Services and Financial Services62 Computer programming, consultancy and related activities 63 Information service activities 64 Financial service activities, except insurance and pension fundingTable 4.5. Combined EIST specialisation domains in Moldova 242 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials and publications and seems to be satisfactory even after a dedicated inspection of the tex- tual content of the respective S&T domains; ■for the cluster ‘Communications Equipment and Services’, a reasonable concordance be- tween the ‘Telecommunications’ E&I domain and the ‘ICT and computer science’ S&T do-main could be identified. The concordance is in this case produced by publications only. The most relevant keywords for these Moldovan S&T domains that match E&I domains can be found in the figures below; similar figures were also shown in Part 3 when characterising the S&T for the whole EaP region. Figure 4.13. Keyword cloud for the S&T domain Agrifood in Moldova Figure 4.15. Keyword cloud for the S&T domain Chemistry and chemical engineering in Moldova Figure 4.17. Keyword cloud for the S&T domain Nanotechnology and materials in Moldova Figure 4.14. Keyword cloud for the S&T domain Biotechnology in Moldova Figure 4.16. 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behaviorally relevant sensory stim-uli within the aIC, which in turn modulates the efﬁcacy of associa-tive learning ( Hersman et al., 2020 ;Holland, 1980 ). Our tracing experiments identiﬁed direct inputs to aIC VIP+ INs from major sensory processing-related areas, such as the thal-amus, orbitofrontal cortical areas, amygdala and basal forebrain. Of note is the preferential connectivity of MD neurons to aIC VIP+ INs among thalamic nuclei. This ﬁnding supports a participationof these INs in sensory processing to enable behavioral adapta- tions, since the MD is considered the main SN hub within the thalamus ( Menon, 2015 ) and has been shown to modulate salience of fear-associated cues ( Zhou et al., 2021 ;Lee et al., 2012 ) as well as social-related behaviors ( Zhou et al., 2017 ;Fer- guson and Gao, 2018 ). Interestingly, MD afferents were also shown to target VIP+ INs in the mPFC ( Anastasiades et al., 2021 ), which raises the possibility for the existence of a conserved bottom-up functional modulation of sensory process- ing through MD connectivity onto VIP+ INs in these neocorticalregions. Gehrlach et al. (2020) recently found a connectivity pattern of aIC GABAergic neurons consistent with our ﬁndings for VIP+ INs,although with a few dissimilarities. In particular, we observed a Figure 6. aIC VIP+ INs are a heterogeneous functional population (A and E) Schematic of the social preference paradigm on days 1 and 2 of testing. (B and F) Percentage of active or inhibited aIC VIP+ CN during distinct stimulus presentations on days 1 and 2 of social preference testing. Non-coding neurons are referred as other.(C and G) Averaged responses from recorded aIC VIP+ mouse CN (orange) or object CN (turquoise) across all mouse and object interactions on day 1 (C) and d ay 2 (G).(D and H) Mean AUC of Zscored activity responses during interactions with the conspeciﬁc was higher for mouse CN than for object CN, while the latter were preferentially active during interactions with the novel object on day 1 (two-way ANOVA, main effect ensemble: p = 0.602; main effect visit: p = 0.0068 ; interaction effect: p = 0.0001; Bonferroni multiple comparisons test, mouse CN versus object CN: object visit, p = 0.0001, mouse visit, p = 0.0001; object visit ver sus mouse visit: mouse CN, p = 0.0001, object CN, p = 0.02) and day 2 (two-way ANOVA, main effect ensemble: p = 0.43; main effect visit: p	[ "behaviorally", "relevant", "sensory", "stim", "-", "uli", "within", "the", "aIC", ",", "which", "in", "turn", "modulates", "the", "efﬁcacy", "of", "associa", "-", "tive", 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authorities should avoid duplicating what the private sector would already doxii. The interaction with competition authorities is also critical for successxiii. For priority sectors, the EU should aim as far as possible to be competitively neutral and regulation should be designed to facilitate market entry. The evidence is overwhelming that competition stimulates productivity, investment and innovationxiv. At the same time, competition policy should continue to adapt to changes in the economy so that it does not become a barrier to Europe’s goals [see the chapter on competition policy] . For example, since innovation in the tech sector is rapid and requires large budgets, merger evaluations should assess how the proposed concentration will affect future innovation potential in critical innova - tion areas. Important Projects of Common Interest (IPCEIs) should be expanded to all forms of innovation that could effectively push Europe to the frontier in strategically important sectors and benefit from EU financing. There are also sectors, such as defence, where security and resilience criteria should receive increasing weight considering geopolitical changes for trade policy. A pragmatic, cautious and consistent approach should be applied according to the needs of different sectors [see Box 1] . 17THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 1The third block is financing the main areas for action, which entail massive investment needs unseen for half a century in Europe . To digitalise and decarbonise the economy and increase the EU’s defence capacity, the total investment-to-GDP rate will have to rise by around 5 percentage points of EU GDP per year to levels last seen in the 1960s and 70s. For comparison, the additional investments provided by the Marshall Plan in 1948-51 amounted annually to around 1-2% of GDP in receiving countries. This report contains simulations from the European Commis - sion and the IMF which assess whether such a massive increase in investment is macroeconomically sustainable, and if so, how Europe can unlock investments of this size. The results suggest that the investment push can be carried out without the economy running into supply constraints, and that mobilising private financing will be critical in this respect. However, the private sector is unlikely to be able to finance the lion’s share of this investment05 without public sector support. Increasing productivity will be key to ease constraints on fiscal space for governments and enable this support. For example, a 2% increase in the level	[ "authorities", "should", "avoid", "duplicating", "what", "the", "private", "sector", "would", "already", "doxii", ".", "The", "interaction", "with", "\n", "competition", "authorities", "is", "also", "critical", "for", "successxiii", ".", "For", "priority", "sectors", ",", "the", "EU", "should", "aim", "as", "far", "as", "possible", "to", "be", "\n", "competitively", "neutral", "and", "regulation", "should", "be", "designed", "to", "facilitate", "market", "entry", ".", "The", "evidence", "is", "overwhelming", "\n", "that", "competition", "stimulates", "productivity", ",", "investment", "and", "innovationxiv", ".", "At", "the", "same", "time", ",", "competition", "policy", "should", "\n", "continue", "to", "adapt", "to", "changes", "in", "the", "economy", "so", "that", "it", "does", "not", "become", "a", "barrier", "to", "Europe", "’s", "goals", "[", "see", "the", "chapter", "\n", "on", "competition", "policy", "]", ".", "For", "example", ",", "since", "innovation", "in", "the", "tech", "sector", "is", "rapid", "and", "requires", "large", "budgets", ",", "merger", "\n", "evaluations", "should", "assess", "how", "the", "proposed", "concentration", "will", "affect", "future", "innovation", "potential", "in", "critical", "innova", "-", "\n", "tion", "areas", ".", "Important", "Projects", "of", "Common", "Interest", "(", "IPCEIs", ")", "should", "be", "expanded", "to", "all", "forms", "of", "innovation", "that", "could", "\n", "effectively", "push", "Europe", "to", "the", "frontier", "in", "strategically", "important", "sectors", "and", "benefit", "from", "EU", "financing", ".", "There", "are", "\n", "also", "sectors", ",", "such", "as", "defence", ",", "where", "security", "and", "resilience", "criteria", "should", "receive", "increasing", "weight", "considering", "\n", "geopolitical", "changes", "for", "trade", "policy", ".", "A", "pragmatic", ",", "cautious", "and", "consistent", "approach", "should", "be", "applied", "according", "\n", "to", "the", "needs", "of", "different", "sectors", "[", "see", "Box", "1", "]", ".", "\n", "17THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "1The", "third", "block", "is", "financing", "the", "main", "areas", "for", "action", ",", "which", "entail", "massive", "investment", "needs", "unseen", "for", "\n", "half", "a", "century", "in", "Europe", ".", "To", "digitalise", "and", "decarbonise", "the", "economy", "and", "increase", "the", "EU", "’s", "defence", "capacity", ",", "the", "\n", "total", "investment", "-", "to", "-", "GDP", "rate", "will", "have", "to", "rise", "by", "around", "5", "percentage", "points", "of", "EU", "GDP", "per", "year", "to", "levels", "last", "seen", "\n", "in", "the", "1960s", "and", "70s", ".", "For", "comparison", ",", "the", "additional", "investments", "provided", "by", "the", "Marshall", "Plan", "in", "1948", "-", "51", "amounted", "\n", "annually", "to", "around", "1", "-", "2", "%", "of", "GDP", "in", "receiving", "countries", ".", "This", "report", "contains", "simulations", "from", "the", "European", "Commis", "-", "\n", "sion", "and", "the", "IMF", "which", "assess", "whether", "such", "a", "massive", "increase", "in", "investment", "is", "macroeconomically", "sustainable", ",", "and", "\n", "if", "so", ",", "how", "Europe", "can", "unlock", "investments", "of", "this", "size", ".", "The", "results", "suggest", "that", "the", "investment", "push", "can", "be", "carried", "\n", "out", "without", "the", "economy", "running", "into", "supply", "constraints", ",", "and", "that", "mobilising", "private", "financing", "will", "be", "critical", "in", "this", "\n", "respect", ".", "However", ",", "the", "private", "sector", "is", "unlikely", "to", "be", "able", "to", "finance", "the", "lion", "’s", "share", "of", "this", "investment05", "without", "\n", "public", "sector", "support", ".", "Increasing", "productivity", "will", "be", "key", "to", "ease", "constraints", "on", "fiscal", "space", "for", "governments", "and", "\n", "enable", "this", "support", ".", "For", "example", ",", "a", "2", "%", "increase", "in", "the", "level" ]	[]
26.4 Manufacture of consumer electronics 26.5Manufacture of instruments and appliances for measuring, testing and navigation; watches and clocks X X X X 26.6Manufacture of irradiation, electromedical and electrotherapeutic equipment 26.7 Manufacture of optical instruments and photographic equipment 26.8 Manufacture of magnetic and optical media 27 Manufacture of electrical equipment 27.1Manufacture of electric motors, generators, transformers and electricity distribution and control apparatus X X X 27.2 Manufacture of batteries and accumulators 27.3 Manufacture of wiring and wiring devices X X X X X X 27.4 Manufacture of electric lighting equipment 27.5 Manufacture of domestic appliances 27.9 Manufacture of other electrical equipment 28 Manufacture of machinery and equipment n.e.c. 28.1 Manufacture of general-purpose machinery X X X X X 28.2 Manufacture of other general-purpose machinery X X X 28.3 Manufacture of agricultural and forestry machinery X X X X X X 28.4 Manufacture of metal forming machinery and machine tools 28.9 Manufacture of other special-purpose machinery X X X X 29 Manufacture of motor vehicles, trailers and semi-trailers 29.1 Manufacture of motor vehicles X X X 29.2Manufacture of bodies (coachwork) for motor vehicles; manufacture of trailers and semi-trailers 29.3 Manufacture of parts and accessories for motor vehicles X X X 30 Manufacture of other transport equipment 30.1 Building of ships and boats X 30.2 Manufacture of railway locomotives and rolling stock X X X 30.3 Manufacture of air and spacecraft and related machinery X X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation279 280 Annexes GEORGIA MOLDOVA UKRAINEEmploy- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover NACE Industry name Current Current CurrentEmerg- ingEmerg- ingEmerg- ingCurrent Current CurrentEmerg- ingEmerg- ingEmerg- ingCurrent Current CurrentEmerg- ingEmerg- ingEmerg- ing 34 52 28 61 64 40 31 29 15 50 47 21 55 40 35 83 57 34 30.4 Manufacture of military fighting vehicles 30.9 Manufacture of transport equipment n.e.c. 31 Manufacture of furniture X X 32 Other manufacturing 32.1 Manufacture of jewellery, bijouterie and related articles 32.2 Manufacture of musical instruments 32.3 Manufacture of sports goods 32.4 Manufacture of games and toys 32.5 Manufacture of medical and dental instruments and supplies 32.9 Manufacturing n.e.c. 33 Repair and installation of machinery and equipment 33.1 Repair of fabricated metal	[ "26.4", "Manufacture", "of", "consumer", "electronics", " \n", "26.5Manufacture", "of", "instruments", "and", "appliances", "for", "measuring", ",", "testing", "\n", "and", "navigation", ";", "watches", "and", "clocks", " ", "X", " ", "X", " ", "X", " ", "X", " \n", 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".", " \n", "28.1", "Manufacture", "of", "general", "-", "purpose", "machinery", " ", "X", " ", "X", "X", "X", " ", "X", " \n", "28.2", "Manufacture", "of", "other", "general", "-", "purpose", "machinery", " ", "X", " ", "X", " ", "X", " \n", "28.3", "Manufacture", "of", "agricultural", "and", "forestry", "machinery", " ", "X", "X", "X", "X", "X", "X", "\n", "28.4", "Manufacture", "of", "metal", "forming", "machinery", "and", "machine", "tools", " \n", "28.9", "Manufacture", "of", "other", "special", "-", "purpose", "machinery", " ", "X", "X", "X", " ", "X", " \n", "29", "Manufacture", "of", "motor", "vehicles", ",", "trailers", "and", "semi", "-", "trailers", " \n", "29.1", "Manufacture", "of", "motor", "vehicles", " ", "X", "X", "X", " \n", "29.2Manufacture", "of", "bodies", "(", "coachwork", ")", "for", "motor", "vehicles", ";", "manufacture", "\n", "of", "trailers", "and", "semi", "-", "trailers", " \n", "29.3", "Manufacture", "of", "parts", "and", "accessories", "for", "motor", "vehicles", " ", "X", "X", "X", " \n", "30", "Manufacture", "of", "other", "transport", "equipment", " \n", "30.1", "Building", "of", "ships", "and", "boats", " ", "X", " \n", "30.2", "Manufacture", "of", "railway", "locomotives", "and", "rolling", "stock", " ", "X", "X", "X", " \n", "30.3", "Manufacture", "of", "air", "and", "spacecraft", "and", "related", "machinery", " ", "X", "X", "X", " \n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation279", "280", "\n", "Annexes", "\n", "GEORGIA", "MOLDOVA", "UKRAINEEmploy-", "\n", "ment", "\n", "Turnover", "\n", "Employ-", "\n", "ment", "&", "\n", "turnover", "\n", "Employ-", "\n", "ment", "\n", "Turnover", "\n", "Employ-", "\n", "ment", "&", "\n", "turnover", "\n", "Employ-", "\n", "ment", "\n", "Turnover", "\n", "Employ-", "\n", "ment", "&", "\n", "turnover", "\n", "Employ-", "\n", "ment", "\n", "Turnover", "\n", "Employ-", "\n", 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single central securities depository (CSD) for all securities trades. As for smaller clearing houses the benefits of consolidation may not be large, a practical pathway towards consolidation could start with consolidating the largest CCPs and CSDs, and then counting on their gravitational pull to attract smaller ones. The EU must also better channel households’ savings to productive investments. The easiest and most efficient way to do so is via long-term saving products (pensions). To increase the flow of funds into capital markets, the EU should encourage retail investors through the offer of second pillar pension schemes, replicating the successful examples of some EU Member States. To increase the financing capacity of the banking sector, the EU should aim to revive securitisation and complete the Banking Union . This report recommends that the Commission makes a proposal to adjust pruden - tial requirements for securitised assets. Capital charges must be reduced for certain simple, transparent and stan - dardised categories for which charges do not reflect actual risks. In parallel, the EU should review transparency and due diligence rules for securitised assets, which are relatively high compared to other asset classes and reduce their attractiveness. Setting up a dedicated securitisation platform, as other economies have done, would help to deepen the securitisation market, especially if backed by targeted public support (for example, well-designed public guar - antees for the first-loss tranche). The EU should also assess whether current prudential regulation, also in light of the possible upcoming implementation of Basel III, is adequate to have a strong and international competitive banking system in the EU. A minimal step towards completing the Banking Union would be to create a separate jurisdiction for European banks with substantial cross-border operations that would be “country blind” from the regulatory, supervisory and crisis management viewpoints. The EU budget should be reformed to increase its focus and efficiency, as well as being better leveraged to support private investment . The EU’s financial resources should be refocused on jointly agreed strategic projects and objectives, where the EU brings the most added value. Under the next EU budget, the report recommends establishing a “Competitiveness Pillar” to direct EU funding towards priority projects identified under the Competi - tiveness Coordination Framework [see the chapter on governance] . As part of this process, the EU should streamline 65THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 5its budget structure to	[ " ", "single", "central", "securities", "depository", "(", "CSD", ")", "for", "all", "securities", "trades", ".", "As", "for", "smaller", "clearing", "\n", "houses", "the", "benefits", "of", "consolidation", "may", "not", "be", "large", ",", "a", "practical", "pathway", "towards", "consolidation", "could", "start", "with", "\n", "consolidating", "the", "largest", "CCPs", "and", "CSDs", ",", "and", "then", "counting", "on", "their", "gravitational", "pull", "to", "attract", "smaller", "ones", ".", "The", "\n", "EU", "must", "also", "better", "channel", "households", "’", "savings", "to", "productive", "investments", ".", "The", "easiest", "and", "most", "efficient", "way", "to", "\n", "do", "so", "is", "via", "long", "-", "term", "saving", "products", "(", "pensions", ")", ".", "To", "increase", "the", "flow", "of", "funds", "into", "capital", "markets", ",", "the", "EU", "should", "\n", "encourage", "retail", "investors", "through", "the", "offer", "of", "second", "pillar", "pension", "schemes", ",", "replicating", "the", "successful", "examples", "\n", "of", "some", "EU", "Member", "States", ".", "\n", "To", "increase", "the", "financing", "capacity", "of", "the", "banking", "sector", ",", "the", "EU", "should", "aim", "to", "revive", "securitisation", "and", "\n", "complete", "the", "Banking", "Union", ".", "This", "report", "recommends", "that", "the", "Commission", "makes", "a", "proposal", "to", "adjust", "pruden", "-", "\n", "tial", "requirements", "for", "securitised", "assets", ".", "Capital", "charges", "must", "be", "reduced", "for", "certain", "simple", ",", "transparent", "and", "stan", "-", "\n", "dardised", "categories", "for", "which", "charges", "do", "not", "reflect", "actual", "risks", ".", "In", "parallel", ",", "the", "EU", "should", "review", "transparency", "and", "\n", "due", "diligence", "rules", "for", "securitised", "assets", ",", "which", "are", "relatively", "high", "compared", "to", "other", "asset", "classes", "and", "reduce", "their", "\n", "attractiveness", ".", "Setting", "up", "a", "dedicated", "securitisation", "platform", ",", "as", "other", "economies", "have", "done", ",", "would", "help", "to", "deepen", "\n", "the", "securitisation", "market", ",", "especially", "if", "backed", "by", "targeted", "public", "support", "(", "for", "example", ",", "well", "-", "designed", "public", "guar", "-", "\n", "antees", "for", "the", "first", "-", "loss", "tranche", ")", ".", "The", "EU", "should", "also", "assess", "whether", "current", "prudential", "regulation", ",", "also", "in", "light", "of", "the", "\n", "possible", "upcoming", "implementation", "of", "Basel", "III", ",", "is", "adequate", "to", "have", "a", "strong", "and", "international", "competitive", "banking", "\n", "system", "in", "the", "EU", ".", "A", "minimal", "step", "towards", "completing", "the", "Banking", "Union", "would", "be", "to", "create", "a", "separate", "jurisdiction", "\n", "for", "European", "banks", "with", "substantial", "cross", "-", "border", "operations", "that", "would", "be", "“", "country", "blind", "”", "from", "the", "regulatory", ",", "\n", "supervisory", "and", "crisis", "management", "viewpoints", ".", "\n", "The", "EU", "budget", "should", "be", "reformed", "to", "increase", "its", "focus", "and", "efficiency", ",", "as", "well", "as", "being", "better", "leveraged", "to", "\n", "support", "private", "investment", ".", "The", "EU", "’s", "financial", "resources", "should", "be", "refocused", "on", "jointly", "agreed", "strategic", "projects", "\n", "and", "objectives", ",", "where", "the", "EU", "brings", "the", "most", "added", "value", ".", "Under", "the", "next", "EU", "budget", ",", "the", "report", "recommends", "\n", "establishing", "a", "“", "Competitiveness", "Pillar", "”", "to", "direct", "EU", "funding", "towards", "priority", "projects", "identified", "under", "the", "Competi", "-", "\n", "tiveness", "Coordination", "Framework", "[", "see", "the", "chapter", "on", "governance", "]", ".", "As", "part", "of", "this", "process", ",", "the", "EU", "should", "streamline", "\n", "65THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "5its", "budget", "structure", "to" ]	[]
per labelled topic group (or ‘domain’) in the Eastern Partnership region. Nanotechnolo- gy and materials is the domain with the most re- cords (with a total of 35 742), followed by Health and wellbeing (29 643), Fundamental physics and mathematics (29 120), Mechanical engineering and heavy machinery (24 097) and ICT and com- puter science (16 529).Although very small in comparison to publications and patents, the number of EC projects is useful to gauge the activity of internationally-connected EaP research and innovation actors. In this con- text, there are a significant number of EC projects on Nanotechnology and materials (65 projects), Environmental sciences and industries (63), ITC and computer science (61), Health and wellbeing (56) and Energy (54). The highest concentration, however, is in the Governance domain (197), due to the nature of these projects. The number of publications in international jour- nals has been increasing considerably in recent years within the majority of domains and is ex- pected to continue to do so, as reflected by the CAGR (compound annual growth rate), except for Chemistry and chemical engineering and Optics and photonics, where the number of records has remained fairly consistent throughout the time range considered. Some of the domains present an especially high growth rate, such as Govern- ance, culture, education and the economy (24%), Transportation (20%) and Health (15%). Publications (critical mass \| CAGR)PatentsEC projectsTotal Nanotechnology and materials 29 067 3.7% 6 641 65 35 773 Health and wellbeing 17 874 14.5% 11 726 56 29 656 Fundamental physics and mathematics 26 852 1.9% 2 255 18 29 125 Mechanical engineering and heavy machinery5 582 9.9% 18 510 8 24 100 ICT and computer science 13 111 13.8% 4 044 61 17 216 Biotechnology 10 340 5.7% 5 837 29 16 206 Governance, culture, education and the economy14 895 24.3% 434 197 15 526 Environmental sciences and industries 10 735 10.6% 3 272 63 14 070 Electric and electronic technologies 5 874 5.8% 7 009 17 12 900 Energy 5 496 8.6% 5 828 54 11 378 Chemistry and chemical engineering 8 132 1.4% 2 380 14 10 526 Optics and photonics 8 043 -0.3% 1 896 19 9 958 Agrifood 2 949 12.5% 5 907 21 8 877 Transportation 2 355 20.4% 1 984 25 4 364Table 3.4. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern Partnership region 154 Part 3 Analysis	[ "per", "labelled", "topic", "group", "(", "or", "‘", "domain", "’", ")", "\n", "in", "the", "Eastern", "Partnership", "region", ".", "Nanotechnolo-", "\n", "gy", "and", "materials", "is", "the", "domain", "with", "the", "most", "re-", "\n", "cords", "(", "with", "a", "total", "of", "35", "742", ")", ",", "followed", "by", "Health", "\n", "and", "wellbeing", "(", "29", "643", ")", ",", "Fundamental", "physics", "and", "\n", "mathematics", "(", "29", "120", ")", ",", "Mechanical", "engineering", "\n", "and", "heavy", "machinery", "(", "24", "097", ")", "and", "ICT", "and", "com-", "\n", "puter", "science", "(", "16", "529).Although", "very", "small", "in", "comparison", "to", "publications", "\n", "and", "patents", ",", "the", "number", "of", "EC", "projects", "is", "useful", "\n", "to", "gauge", "the", "activity", "of", "internationally", "-", "connected", "\n", "EaP", "research", "and", "innovation", "actors", ".", "In", "this", "con-", "\n", "text", ",", "there", "are", "a", "significant", "number", "of", "EC", "projects", "\n", "on", "Nanotechnology", "and", "materials", "(", "65", "projects", ")", ",", "\n", "Environmental", "sciences", "and", "industries", "(", "63", ")", ",", "ITC", "\n", "and", "computer", "science", "(", "61", ")", ",", "Health", "and", "wellbeing", "\n", "(", "56", ")", "and", "Energy", "(", "54", ")", ".", "The", "highest", "concentration", ",", "\n", "however", ",", "is", "in", "the", "Governance", "domain", "(", "197", ")", ",", "due", "\n", "to", "the", "nature", "of", "these", "projects", ".", "\n", "The", "number", "of", "publications", "in", "international", "jour-", "\n", "nals", "has", "been", "increasing", "considerably", "in", "recent", "\n", "years", "within", "the", "majority", "of", "domains", "and", "is", "ex-", "\n", "pected", "to", "continue", "to", "do", "so", ",", "as", "reflected", "by", "the", "\n", "CAGR", "(", "compound", "annual", "growth", "rate", ")", ",", "except", "for", "\n", "Chemistry", "and", "chemical", "engineering", "and", "Optics", "\n", "and", "photonics", ",", "where", "the", "number", "of", "records", "has", "\n", "remained", "fairly", "consistent", "throughout", "the", "time", "\n", "range", "considered", ".", "Some", "of", "the", "domains", "present", "\n", "an", "especially", "high", "growth", "rate", ",", "such", "as", "Govern-", "\n", "ance", ",", "culture", ",", "education", "and", "the", "economy", "(", "24", "%", ")", ",", "\n", "Transportation", "(", "20", "%", ")", "and", "Health", "(", "15", "%", ")", ".", "\n", "Publications", "\n", "(", "critical", "mass", "\|", "CAGR)PatentsEC", "\n", "projectsTotal", "\n", "Nanotechnology", "and", "materials", "29", "067", "3.7", "%", "6", "641", "65", "35", "773", "\n", "Health", "and", "wellbeing", "17", "874", "14.5", "%", "11", "726", "56", "29", "656", "\n", "Fundamental", "physics", "and", "mathematics", "26", "852", "1.9", "%", "2", "255", "18", "29", "125", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machinery5", "582", "9.9", "%", "18", "510", "8", "24", "100", "\n", "ICT", "and", "computer", "science", "13", "111", "13.8", "%", "4", "044", "61", "17", "216", "\n", "Biotechnology", "10", "340", "5.7", "%", "5", "837", "29", "16", "206", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy14", "895", "24.3", "%", "434", "197", "15", "526", "\n", "Environmental", "sciences", "and", "industries", "10", "735", "10.6", "%", "3", "272", "63", "14", "070", "\n", "Electric", "and", "electronic", "technologies", "5", "874", "5.8", "%", "7", "009", "17", "12", "900", "\n", "Energy", "5", "496", "8.6", "%", "5", "828", "54", "11", "378", "\n", "Chemistry", "and", "chemical", "engineering", "8", "132", "1.4", "%", "2", "380", "14", "10", "526", "\n", "Optics", "and", "photonics", "8", "043", "-0.3", "%", "1", "896", "19", "9", "958", "\n", "Agrifood", "2", "949", "12.5", "%", "5", "907", "21", "8", "877", "\n", "Transportation", "2", "355", "20.4", "%", "1", "984", "25", "4", "364Table", "3.4", ".", "Number", "of", "records", "per", "labelled", "topic", "group", "(", "i.e.", "‘", "domain", "’", ")", "in", "the", "Eastern", "Partnership", "region", "\n", "154", "\n ", "Part", "3", "Analysis" ]	[]
the re- spective country (either per number of companies, or per number of employees) and in the top 10 In- dustry Groups per specialisation of the respective country (either per number of companies, or per number of employees). In what follows, we pres- ent the results for each individual country concise- ly by means of a series of tables. Armenia In Armenia, the most relevant Industry Groups supporting the definition of innovation potential domains found via the Crunchbase analysis are in Table 2.49. All of the Industry Groups are seen to be espe- cially prominent per number of respective employ- ees (both at critical mass and specialisation level) and all of them, except Gaming and Travel and Tourism, are also featured in the top 10 Industry Groups per number of companies. Azerbaijan In Azerbaijan, the most relevant Industry Groups supporting the definition of innovation potential domains found via the Crunchbase analysis are in Table 2.50. All of the Industry Groups are seen to be especial- ly prominent per number of respective employees (both at critical mass and specialisation level) and all of them, except Natural Resources and Energy, are also featured in the top 10 Industry Groups per number of companies. The prominence of the Natural Resources and Energy Industry Groups is indeed dominated by the presence of the Kentech Group, the third firm per number of employees in- dexed by Crunchbase in Azerbaijan. Georgia In Georgia, the most relevant Industry Groups sup- porting the definition of innovation potential do- mains found via the Crunchbase analysis are in Table 2.51. All of the Industry Groups are seen to be especial- ly prominent per number of respective employ- ees (both at critical mass and specialisation level) and all of them, except Payments and Lending and Investments are also featured in the top 10 Industry Groups per number of companies. The Industry Group Payments and Lending and Invest- ments are considered to be so prominent because they are dominated by the presence of the Bank of Georgia and Liberty Capital, which are by far the largest companies based in Georgia indexed by Crunchbase. Moldova In Moldova, the most relevant Industry Groups supporting the definition of innovation potential domains found via the Crunchbase analysis are in Table 2.52. All of the Industry Groups are seen to be especial- ly prominent per number of respective employees (both at critical mass and specialisation level). Furthermore,	[ "the", "re-", "\n", "spective", "country", "(", "either", "per", "number", "of", "companies", ",", "\n", "or", "per", "number", "of", "employees", ")", "and", "in", "the", "top", "10", "In-", "\n", "dustry", "Groups", "per", "specialisation", "of", "the", "respective", "\n", "country", "(", "either", "per", "number", "of", "companies", ",", "or", "per", "\n", "number", "of", "employees", ")", ".", "In", "what", "follows", ",", "we", "pres-", "\n", "ent", "the", "results", "for", "each", "individual", "country", "concise-", "\n", "ly", "by", "means", "of", "a", "series", "of", "tables", ".", "\n", "Armenia", "\n", "In", "Armenia", ",", "the", "most", "relevant", "Industry", "Groups", "\n", "supporting", "the", "definition", "of", "innovation", "potential", "\n", "domains", "found", "via", "the", "Crunchbase", "analysis", "are", "in", "\n", "Table", "2.49", ".", "\n", "All", "of", "the", "Industry", "Groups", "are", "seen", "to", "be", "espe-", "\n", "cially", "prominent", "per", "number", "of", "respective", "employ-", "\n", "ees", "(", "both", "at", "critical", "mass", "and", "specialisation", "level", ")", "\n", "and", "all", "of", "them", ",", "except", "Gaming", "and", "Travel", "and", "\n", "Tourism", ",", "are", "also", "featured", "in", "the", "top", "10", "Industry", "\n", "Groups", "per", "number", "of", "companies", ".", "\n", "Azerbaijan", "\n", "In", "Azerbaijan", ",", "the", "most", "relevant", "Industry", "Groups", "\n", "supporting", "the", "definition", "of", "innovation", "potential", "\n", "domains", "found", "via", "the", "Crunchbase", "analysis", "are", "in", "\n", "Table", "2.50", ".", "\n", "All", "of", "the", "Industry", "Groups", "are", "seen", "to", "be", "especial-", "\n", "ly", "prominent", "per", "number", "of", "respective", "employees", "\n", "(", "both", "at", "critical", "mass", "and", "specialisation", "level", ")", "and", "\n", "all", "of", "them", ",", "except", "Natural", "Resources", "and", "Energy", ",", "are", "also", "featured", "in", "the", "top", "10", "Industry", "Groups", "\n", "per", "number", "of", "companies", ".", "The", "prominence", "of", "the", "\n", "Natural", "Resources", "and", "Energy", "Industry", "Groups", "is", "\n", "indeed", "dominated", "by", "the", "presence", "of", "the", "Kentech", "\n", "Group", ",", "the", "third", "firm", "per", "number", "of", "employees", "in-", "\n", "dexed", "by", "Crunchbase", "in", "Azerbaijan", ".", "\n", "Georgia", "\n", "In", "Georgia", ",", "the", "most", "relevant", "Industry", "Groups", "sup-", "\n", "porting", "the", "definition", "of", "innovation", "potential", "do-", "\n", "mains", "found", "via", "the", "Crunchbase", "analysis", "are", "in", "\n", "Table", "2.51", ".", "\n", "All", "of", "the", "Industry", "Groups", "are", "seen", "to", "be", "especial-", "\n", "ly", "prominent", "per", "number", "of", "respective", "employ-", "\n", "ees", "(", "both", "at", "critical", "mass", "and", "specialisation", "level", ")", "\n", "and", "all", "of", "them", ",", "except", "Payments", "and", "Lending", "\n", "and", "Investments", "are", "also", "featured", "in", "the", "top", "10", "\n", "Industry", "Groups", "per", "number", "of", "companies", ".", "The", "\n", "Industry", "Group", "Payments", "and", "Lending", "and", "Invest-", "\n", "ments", "are", "considered", "to", "be", "so", "prominent", "because", "\n", "they", "are", "dominated", "by", "the", "presence", "of", "the", "Bank", "\n", "of", "Georgia", "and", "Liberty", "Capital", ",", "which", "are", "by", "far", "\n", "the", "largest", "companies", "based", "in", "Georgia", "indexed", "\n", "by", "Crunchbase", ".", "\n", "Moldova", "\n", "In", "Moldova", ",", "the", "most", "relevant", "Industry", "Groups", "\n", "supporting", "the", "definition", "of", "innovation", "potential", "\n", "domains", "found", "via", "the", "Crunchbase", "analysis", "are", "in", "\n", "Table", "2.52", ".", "\n", "All", "of", "the", "Industry", "Groups", "are", "seen", "to", "be", "especial-", "\n", "ly", "prominent", "per", "number", "of", "respective", "employees", "\n", "(", "both", "at", "critical", "mass", "and", "specialisation", "level", ")", ".", "\n", "Furthermore", "," ]	[]
diversify suppliers rather than re-shore or near-shore production on a significant scaleii. Neither China nor the EU has an incentive to accelerate this process: as the previous chapter demonstrated, China is reliant on the EU to absorb its excess capacity in clean technologies. The more immediate risk for Europe is that dependencies could be used to create an opportunity for coercion, making it harder for the EU to maintain a united stance and undermining its common policy objectives. A growing use of dependencies as a “geopolitical weapon” is in turn likely to increase uncertainty and have a detrimental effect on business investmentiii. Deteriorating geopolitical relations also create new needs for spending on defence and defence industrial capacity . Europe now faces conventional warfare on its Eastern border and hybrid warfare everywhere, including attacks on energy infrastructure and telecoms, interference in democratic processes and the weaponisation of migrationiv. At the same time, US strategic doctrine is shifting away from Europe and towards the Pacific Rim – for example in the format of AUKUS – driven by the perceived threat of China. As a result, a growing demand for defence capability is being met by a shrinking supply – a gap which Europe itself must fill. However, thanks to a prolonged period of peace in Europe and the US security umbrella, only ten Member States now spend more than or equal to 2% of GDP in line with NATO commitments, although defence expenditures are rising [see Figure 1] . The defence industry requires massive investments to catch up. As a point of reference, if all EU Member States who are NATO Members and who have not yet reached the 2% target were to do so in 2024, defence spending would rise by EUR 60 billion. Additional investments are also needed to restore lost capabilities owing to decades of underinvestment and to replenish depleted stocks, including those donated to support the defence of Ukraine against Russian aggression. In June 2024, the Commission estimated that additional defence investments of around EUR 500 billion are needed over the next decade. FIGURE 1 EU Member States’ defence expenditure % of GDP Source: SIPRI. Accessed 2024. 54THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 4Becoming more independent creates an “insurance cost” for Europe, but these costs can be mitigated by cooperation . Reducing dependencies across the key areas where Europe is exposed will require significant	[ " ", "diversify", "suppliers", "rather", "than", "re", "-", "shore", "or", "near", "-", "shore", "production", "on", "a", "significant", "scaleii", ".", "Neither", "China", "nor", "the", "\n", "EU", "has", "an", "incentive", "to", "accelerate", "this", "process", ":", "as", "the", "previous", "chapter", "demonstrated", ",", "China", "is", "reliant", "on", "the", "EU", "to", "\n", "absorb", "its", "excess", "capacity", "in", "clean", "technologies", ".", "The", "more", "immediate", "risk", "for", "Europe", "is", "that", "dependencies", "could", "be", "\n", "used", "to", "create", "an", "opportunity", "for", "coercion", ",", "making", "it", "harder", "for", "the", "EU", "to", "maintain", "a", "united", "stance", "and", "undermining", "\n", "its", "common", "policy", "objectives", ".", "A", "growing", "use", "of", "dependencies", "as", "a", "“", "geopolitical", "weapon", "”", "is", "in", "turn", "likely", "to", "increase", "\n", "uncertainty", "and", "have", "a", "detrimental", "effect", "on", "business", "investmentiii", ".", "\n", "Deteriorating", "geopolitical", "relations", "also", "create", "new", "needs", "for", "spending", "on", "defence", "and", "defence", "industrial", "\n", "capacity", ".", "Europe", "now", "faces", "conventional", "warfare", "on", "its", "Eastern", "border", "and", "hybrid", "warfare", "everywhere", ",", "including", "\n", "attacks", "on", "energy", "infrastructure", "and", "telecoms", ",", "interference", "in", "democratic", "processes", "and", "the", "weaponisation", "of", "\n", "migrationiv", ".", "At", "the", "same", "time", ",", "US", "strategic", "doctrine", "is", "shifting", "away", "from", "Europe", "and", "towards", "the", "Pacific", "Rim", "–", "for", "\n", "example", "in", "the", "format", "of", "AUKUS", "–", "driven", "by", "the", "perceived", "threat", "of", "China", ".", "As", "a", "result", ",", "a", "growing", "demand", "for", "defence", "\n", "capability", "is", "being", "met", "by", "a", "shrinking", "supply", "–", "a", "gap", "which", "Europe", "itself", "must", "fill", ".", "However", ",", "thanks", "to", "a", "prolonged", "\n", "period", "of", "peace", "in", "Europe", "and", "the", "US", "security", "umbrella", ",", "only", "ten", "Member", "States", "now", "spend", "more", "than", "or", "equal", "to", "\n", "2", "%", "of", "GDP", "in", "line", "with", "NATO", "commitments", ",", "although", "defence", "expenditures", "are", "rising", "[", "see", "Figure", "1", "]", ".", "The", "defence", "\n", "industry", "requires", "massive", "investments", "to", "catch", "up", ".", "As", "a", "point", "of", "reference", ",", "if", "all", "EU", "Member", "States", "who", "are", "NATO", "\n", "Members", "and", "who", "have", "not", "yet", "reached", "the", "2", "%", "target", "were", "to", "do", "so", "in", "2024", ",", "defence", "spending", "would", "rise", "by", "EUR", "60", "\n", "billion", ".", "Additional", "investments", "are", "also", "needed", "to", "restore", "lost", "capabilities", "owing", "to", "decades", "of", "underinvestment", "and", "\n", "to", "replenish", "depleted", "stocks", ",", "including", "those", "donated", "to", "support", "the", "defence", "of", "Ukraine", "against", "Russian", "aggression", ".", "\n", "In", "June", "2024", ",", "the", "Commission", "estimated", "that", "additional", "defence", "investments", "of", "around", "EUR", "500", "billion", "are", "needed", "\n", "over", "the", "next", "decade", ".", "\n", "FIGURE", "1", "\n", "EU", "Member", "States", "’", "defence", "expenditure", " \n", "%", "of", "GDP", "\n", "Source", ":", "SIPRI", ".", "Accessed", "2024", ".", "\n", "54THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "4Becoming", "more", "independent", "creates", "an", "“", "insurance", "cost", "”", "for", "Europe", ",", "but", "these", "costs", "can", "be", "mitigated", "by", "\n", "cooperation", ".", "Reducing", "dependencies", "across", "the", "key", "areas", "where", "Europe", "is", "exposed", "will", "require", "significant" ]	[]
a trademark registered at the USPTO, for 11 NICE classes, as in Table 2.34. The number of trademark applications in these combined manufacturing industries is small for Armenia and Azerbaijan. Numbers also decreased between 2011 and 2014 for Armenia, Azerbaijan and Ukraine. 45 Millot, Valentine, ‘Trademarks as an Indicator of Product and Marketing Innovations’, OECD Science, Technology and Industry Working Papers 2009/06, 2009, https://dx- .doi.org/10.1787/224428874418. NACE 17, 18 and 19 – Textile, wearing, leather products NICE 24-25 NACE 20 – Wood and cork products NICE 20 NACE 21, 22 – Paper products, printing and publishing NICE 16 NACE 25 – Rubber and plastic products NICE 17 NACE 27 – Basic metal products NICE 6 NACE 30, 31, 32, 33 – Electrical and optical equipment NICE 9 NACE 34, 35 – Transport equipment NICE 12 NACE 60, 63 – Transport and storage NICE 39 NACE 67 – Financial intermediation NICE 36 NACE 72, 73 – Computer-related activity, research and development NICE 42Table 2.34. Partial concordance between broad NACE groups and NICE Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation99 Specialisations are identified using location quo- tients for two 4-year periods – 2011-2014 and 2015-2018 – where LQs are calculated relative to the unweighted average of the six Eastern Part- nership countries and should be above 1.5. Results are shown in Table 2.36. There are no trademark specialisations for Arme- nia. For Azerbaijan, there are recent trademark specialisations in Transport and Storage and Com- puter-related activity and research and develop-ment. For Georgia, there is a recent trademark specialisation in Financial intermediation. For Mol- dova, there are recent trademark specialisations in Wood and cork products and Rubber and plastic products. For Ukraine, there are recent trademark specialisations in Wood and cork products and Fi- nancial intermediation. 3.4. Industrial design applications Industrial design data for each Eastern Partnership country are available from the WIPO Global De-Armenia Azerbaijan Belarus Georgia Moldova Ukraine 2011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 2018 NACE 17, 18 and 19 – Textile, wearing, leather products6 11 11 4 54 47 207 194 388 459 9 078 8 275 NACE 20 – Wood and cork products4 1 3 1 4 7 61 55 129 140 2 215 2 039 NACE 21, 22 – Paper products, printing and publishing4 6 8 3 18 27 187 162 296 254 5 297 4 769	[ "a", "trademark", "registered", "at", "the", "\n", "USPTO", ",", "for", "11", "NICE", "classes", ",", "as", "in", "Table", "2.34", ".", "\n", "The", "number", "of", "trademark", "applications", "in", "these", "\n", "combined", "manufacturing", "industries", "is", "small", "for", "\n", "Armenia", "and", "Azerbaijan", ".", "Numbers", "also", "decreased", "\n", "between", "2011", "and", "2014", "for", "Armenia", ",", "Azerbaijan", "\n", "and", "Ukraine", ".", "\n", "45", "Millot", ",", "Valentine", ",", "‘", "Trademarks", "as", "an", "Indicator", "of", "Product", "\n", "and", "Marketing", "Innovations", "’", ",", "OECD", "Science", ",", "Technology", "\n", "and", "Industry", "Working", "Papers", "2009/06", ",", "2009", ",", "https://dx-", "\n", ".doi.org/10.1787/224428874418", ".", "\n", "NACE", "17", ",", "18", "and", "19", "–", "Textile", ",", "wearing", ",", "leather", "products", "NICE", "24", "-", "25", "\n", "NACE", "20", "–", "Wood", "and", "cork", "products", "NICE", "20", "\n", "NACE", "21", ",", "22", "–", "Paper", "products", ",", "printing", "and", "publishing", "NICE", "16", "\n", "NACE", "25", "–", "Rubber", "and", "plastic", "products", "NICE", "17", "\n", "NACE", "27", "–", "Basic", "metal", "products", "NICE", "6", "\n", "NACE", "30", ",", "31", ",", "32", ",", "33", "–", "Electrical", "and", "optical", "equipment", "NICE", "9", "\n", "NACE", "34", ",", "35", "–", "Transport", "equipment", "NICE", "12", "\n", "NACE", "60", ",", "63", "–", "Transport", "and", "storage", "NICE", "39", "\n", "NACE", "67", "–", "Financial", "intermediation", "NICE", "36", "\n", "NACE", "72", ",", "73", "–", "Computer", "-", "related", "activity", ",", "research", "and", "development", "NICE", "42Table", "2.34", ".", "Partial", "concordance", "between", "broad", "NACE", "groups", "and", "NICE", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation99", "\n", "Specialisations", "are", "identified", "using", "location", "quo-", "\n", "tients", "for", "two", "4", "-", "year", "periods", "–", "2011", "-", "2014", "and", "\n", "2015", "-", "2018", "–", "where", "LQs", "are", "calculated", "relative", "to", "\n", "the", "unweighted", "average", "of", "the", "six", "Eastern", "Part-", "\n", "nership", "countries", "and", "should", "be", "above", "1.5", ".", "Results", "\n", "are", "shown", "in", "Table", "2.36", ".", "\n", "There", "are", "no", "trademark", "specialisations", "for", "Arme-", "\n", "nia", ".", "For", "Azerbaijan", ",", "there", "are", "recent", "trademark", "\n", "specialisations", "in", "Transport", "and", "Storage", "and", "Com-", "\n", "puter", "-", "related", "activity", "and", "research", "and", "develop", "-", "ment", ".", "For", "Georgia", ",", "there", "is", "a", "recent", "trademark", "\n", "specialisation", "in", "Financial", "intermediation", ".", "For", "Mol-", "\n", "dova", ",", "there", "are", "recent", "trademark", "specialisations", "\n", "in", "Wood", "and", "cork", "products", "and", "Rubber", "and", "plastic", "\n", "products", ".", "For", "Ukraine", ",", "there", "are", "recent", "trademark", "\n", "specialisations", "in", "Wood", "and", "cork", "products", "and", "Fi-", "\n", "nancial", "intermediation", ".", "\n", "3.4", ".", "Industrial", "design", "applications", "\n", "Industrial", "design", "data", "for", "each", "Eastern", "Partnership", "\n", "country", "are", "available", "from", "the", "WIPO", "Global", "De", "-", "Armenia", "Azerbaijan", "Belarus", "Georgia", "Moldova", "Ukraine", "\n", "2011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "20182011-", "\n", "20142015-", "\n", "2018", "\n", "NACE", "17", ",", "18", "and", "\n", "19", "–", "Textile", ",", "wearing", ",", "\n", "leather", "products6", "11", "11", "4", "54", "47", "207", "194", "388", "459", "9", "078", "8", "275", "\n", "NACE", "20", "–", "Wood", "and", "\n", "cork", "products4", "1", "3", "1", "4", "7", "61", "55", "129", "140", "2", "215", "2", "039", "\n", "NACE", "21", ",", "22", "–", "Paper", "\n", "products", ",", "printing", "and", "\n", "publishing4", "6", "8", "3", "18", "27", "187", "162", "296", "254", "5", "297", "4", "769", "\n" ]	[ { "end": 481, "label": "CITATION-SPAN", "start": 283 } ]
S&T specialisation domain in Azer- baijan. Fundamental physics and mathematics is the domain with the most records (with a total of 1 668), followed by Health and wellbeing (1 283), Chemistry and chemical engineering (850), Nano- technology and materials (714) and ICT and com- puter science (594). The first one accounts for more than one quarter of the total number of records (26%). It must be noted, however, that the number of patents obtained for Azerbaijan is rather small, jeopardising any analysis and interpretation. Publications account for the largest share of re- cords in most domains, ranging from 90% to 99% of the total records in most cases, as shown in Figure 3.27. The exceptions are Energy (79%), Biotechnology (58%) and Mechanical engineering and heavy machinery (48%), which have a high number of patents. In this last domain, in particu- lar, the number of patents is higher than the num- ber of publications. EC projects in Azerbaijan are very highly concen- trated in the domain of Governance, culture, ed-ucation and the economy, which may reflect the policy and cooperation interests of the European Commission and the country, rather than the en- dogenous capabilities or international propensity of the country’s S&T system. The growth rate of publications in recent years, in terms of the compound annual growth rate, is also shown. All domains have a positive growth rate. Azerbaijan’s publications are highly specialised in Chemistry and chemical engineering (with an SI of 1.7), Energy (1.6), ICT and computer sciences (1.4), Health and wellbeing (1.2) and Mechanical engineering and heavy machinery (1.2). Overall, Azeri publications present a lower normal- ised citation impact than the EaP average. Three domains escape this rule: Fundamental physics and mathematics (with an NCI of 1.2), Chemistry and chemical engineering (1.1) and Mechanical engineering and heavy machinery (1.1). Thus, in terms of scientific publications, Chemis- try and chemical engineering is a domain in which Azerbaijan’s S&T ecosystem simultaneously pre- sents a high critical mass, relative specialisation Publications (critical mass \| CAGR)PatentsEC projectsTotal Fundamental physics and mathematics 1 663 8.7% 5 1 1 669 Health and wellbeing 1 131 15.5% 152 1 1 284 Chemistry and chemical engineering 806 5.7% 43 1 850 Nanotechnology and materials 682 18.9% 32 1 715 ICT and computer science 555 6.5% 37 2 594 Governance, culture, education and the economy547 21.2% 1 14 562 Environmental sciences and industries 497 7.7% 28 3 528	[ "S&T", "specialisation", "domain", "in", "Azer-", "\n", "baijan", ".", "Fundamental", "physics", "and", "mathematics", "is", "\n", "the", "domain", "with", "the", "most", "records", "(", "with", "a", "total", "of", "\n", "1", "668", ")", ",", "followed", "by", "Health", "and", "wellbeing", "(", "1", "283", ")", ",", "\n", "Chemistry", "and", "chemical", "engineering", "(", "850", ")", ",", "Nano-", "\n", "technology", "and", "materials", "(", "714", ")", "and", "ICT", "and", "com-", "\n", "puter", "science", "(", "594", ")", ".", "The", "first", "one", "accounts", "for", "more", "\n", "than", "one", "quarter", "of", "the", "total", "number", "of", "records", "\n", "(", "26", "%", ")", ".", "It", "must", "be", "noted", ",", "however", ",", "that", "the", "number", "\n", "of", "patents", "obtained", "for", "Azerbaijan", "is", "rather", "small", ",", "\n", "jeopardising", "any", "analysis", "and", "interpretation", ".", "\n", "Publications", "account", "for", "the", "largest", "share", "of", "re-", "\n", "cords", "in", "most", "domains", ",", "ranging", "from", "90", "%", "to", "99", "%", "\n", "of", "the", "total", "records", "in", "most", "cases", ",", "as", "shown", "in", "\n", "Figure", "3.27", ".", "The", "exceptions", "are", "Energy", "(", "79", "%", ")", ",", "\n", "Biotechnology", "(", "58", "%", ")", "and", "Mechanical", "engineering", "\n", "and", "heavy", "machinery", "(", "48", "%", ")", ",", "which", "have", "a", "high", "\n", "number", "of", "patents", ".", "In", "this", "last", "domain", ",", "in", "particu-", "\n", "lar", ",", "the", "number", "of", "patents", "is", "higher", "than", "the", "num-", "\n", "ber", "of", "publications", ".", "\n", "EC", "projects", "in", "Azerbaijan", "are", "very", "highly", "concen-", "\n", "trated", "in", "the", "domain", "of", "Governance", ",", "culture", ",", "ed", "-", "ucation", "and", "the", "economy", ",", "which", "may", "reflect", "the", "\n", "policy", "and", "cooperation", "interests", "of", "the", "European", "\n", "Commission", "and", "the", "country", ",", "rather", "than", "the", "en-", "\n", "dogenous", "capabilities", "or", "international", "propensity", "\n", "of", "the", "country", "’s", "S&T", "system", ".", "\n", "The", "growth", "rate", "of", "publications", "in", "recent", "years", ",", "in", "\n", "terms", "of", "the", "compound", "annual", "growth", "rate", ",", "is", "also", "\n", "shown", ".", "All", "domains", "have", "a", "positive", "growth", "rate", ".", "\n", "Azerbaijan", "’s", "publications", "are", "highly", "specialised", "in", "\n", "Chemistry", "and", "chemical", "engineering", "(", "with", "an", "SI", "\n", "of", "1.7", ")", ",", "Energy", "(", "1.6", ")", ",", "ICT", "and", "computer", "sciences", "\n", "(", "1.4", ")", ",", "Health", "and", "wellbeing", "(", "1.2", ")", "and", "Mechanical", "\n", "engineering", "and", "heavy", "machinery", "(", "1.2", ")", ".", "\n", "Overall", ",", "Azeri", "publications", "present", "a", "lower", "normal-", "\n", "ised", "citation", "impact", "than", "the", "EaP", "average", ".", "Three", "\n", "domains", "escape", "this", "rule", ":", "Fundamental", "physics", "\n", "and", "mathematics", "(", "with", "an", "NCI", "of", "1.2", ")", ",", "Chemistry", "\n", "and", "chemical", "engineering", "(", "1.1", ")", "and", "Mechanical", "\n", "engineering", "and", "heavy", "machinery", "(", "1.1", ")", ".", "\n", "Thus", ",", "in", "terms", "of", "scientific", "publications", ",", "Chemis-", "\n", "try", "and", "chemical", "engineering", "is", "a", "domain", "in", "which", "\n", "Azerbaijan", "’s", "S&T", "ecosystem", "simultaneously", "pre-", "\n", "sents", "a", "high", "critical", "mass", ",", "relative", "specialisation", "\n", "Publications", "\n", "(", "critical", "mass", "\|", "CAGR)PatentsEC", "\n", "projectsTotal", "\n", "Fundamental", "physics", "and", "mathematics", "1", "663", "8.7", "%", "5", "1", "1", "669", "\n", "Health", "and", "wellbeing", "1", "131", "15.5", "%", "152", "1", "1", "284", "\n", "Chemistry", "and", "chemical", "engineering", "806", "5.7", "%", "43", "1", "850", "\n", "Nanotechnology", "and", "materials", "682", "18.9", "%", "32", "1", "715", "\n", "ICT", "and", "computer", "science", "555", "6.5", "%", "37", "2", "594", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy547", "21.2", "%", "1", "14", "562", "\n", "Environmental", "sciences", "and", "industries", "497", "7.7", "%", "28", "3", "528", "\n" ]	[]
how much a certain entity of interest (either a specific institution or a geographical aggregation) is spe- cialised in a given domain with respect to a given base- line. The specialisation is computed by normalising the share of output produced in the domain of interest by the entity over the share of baseline output (usually a larger geography). Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation29 The above dimensions of EIST potential are meas- ured for the EaP region as a whole and for each EaP country by means of data retrieved from well-established international sources (listed in the following subsection). This allows us to draw educated comparisons between the analysed countries so that, eventually, the current meth- odology yields region-wide and country-specific overviews. 2.4 Economic and innovation (E&I) potential and relative data sources For the case of economic & innovation potential, specialisation is measured by quantifying the rel- ative specialisation within the economic sectors of each EaP country with respect to the whole region. This is done by looking at data on the number of employees and turnover, as well as industrial sta- tistics on manufacturing. In parallel, export and innovation data is also considered, complementing the insight on the eco- nomic sectors, in particular: exports from goods and services, an enterprise survey, patent count and intensity, the number of start-ups and ven- ture capital-backed companies and the presence of formal cluster organisations supporting indus- trial collaboration and innovation. To succeed in mapping the economic and innova- tion potential of the EaP countries, the following data sources are employed: ■Orbis database, provided by Bureau van Dijk15. Orbis comprises statistics on the num- ber of employees and turnover in individual enterprises at NACE16 four-digit industry level; 15 https://www.bvdinfo.com/en-gb/our-products/data/in- ternational/orbis. 16 NACE is a four-digit classification providing the frame- work for collecting and presenting a large range of sta- tistical data according to economic activity in the fields of economic statistics (e.g. production, employment and national accounts) and in other statistical domains devel- oped within the European Statistical System (ESS). ■Industrial Statistics Database (INDSTAT4), offered by UNIDO17, for partial mapping of the manufacturing sector at NACE four-digit level; ■The UN’s Comtrade Database18 for exports of goods (up to five-digit export data accord- ing to the SITC product classification) and exports of services (according to the EBOPS 2002 classification); ■the World Bank Enterprise Survey19 for re- sults	[ "how", "much", "a", "certain", "entity", "of", "interest", "(", "either", "a", "\n", "specific", "institution", "or", "a", "geographical", "aggregation", ")", "is", "spe-", "\n", "cialised", "in", "a", "given", "domain", "with", "respect", "to", "a", "given", "base-", "\n", "line", ".", "The", "specialisation", "is", "computed", "by", "normalising", "the", "\n", "share", "of", "output", "produced", "in", "the", "domain", "of", "interest", "by", "the", "\n", "entity", "over", "the", "share", "of", "baseline", "output", "(", "usually", "a", "larger", "\n", "geography", ")", ".", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation29", "\n", "The", "above", "dimensions", "of", "EIST", "potential", "are", "meas-", "\n", "ured", "for", "the", "EaP", "region", "as", "a", "whole", "and", "for", "each", "\n", "EaP", "country", "by", "means", "of", "data", "retrieved", "from", "\n", "well", "-", "established", "international", "sources", "(", "listed", "in", "\n", "the", "following", "subsection", ")", ".", "This", "allows", "us", "to", "draw", "\n", "educated", "comparisons", "between", "the", "analysed", "\n", "countries", "so", "that", ",", "eventually", ",", "the", "current", "meth-", "\n", "odology", "yields", "region", "-", "wide", "and", "country", "-", "specific", "\n", "overviews", ".", "\n", "2.4", "Economic", "and", "innovation", "(", "E&I", ")", "\n", "potential", "and", "relative", "data", "sources", "\n", "For", "the", "case", "of", "economic", "&", "innovation", "potential", ",", "\n", "specialisation", "is", "measured", "by", "quantifying", "the", "rel-", "\n", "ative", "specialisation", "within", "the", "economic", "sectors", "of", "\n", "each", "EaP", "country", "with", "respect", "to", "the", "whole", "region", ".", "\n", "This", "is", "done", "by", "looking", "at", "data", "on", "the", "number", "of", "\n", "employees", "and", "turnover", ",", "as", "well", "as", "industrial", "sta-", "\n", "tistics", "on", "manufacturing", ".", "\n", "In", "parallel", ",", "export", "and", "innovation", "data", "is", "also", "\n", "considered", ",", "complementing", "the", "insight", "on", "the", "eco-", "\n", "nomic", "sectors", ",", "in", "particular", ":", "exports", "from", "goods", "\n", "and", "services", ",", "an", "enterprise", "survey", ",", "patent", "count", "\n", "and", "intensity", ",", "the", "number", "of", "start", "-", "ups", "and", "ven-", "\n", "ture", "capital", "-", "backed", "companies", "and", "the", "presence", "\n", "of", "formal", "cluster", "organisations", "supporting", "indus-", "\n", "trial", "collaboration", "and", "innovation", ".", "\n", "To", "succeed", "in", "mapping", "the", "economic", "and", "innova-", "\n", "tion", "potential", "of", "the", "EaP", "countries", ",", "the", "following", "\n", "data", "sources", "are", "employed", ":", "\n ", "■", "Orbis", "database", ",", "provided", "by", "Bureau", "van", "\n", "Dijk15", ".", "Orbis", "comprises", "statistics", "on", "the", "num-", "\n", "ber", "of", "employees", "and", "turnover", "in", "individual", "\n", "enterprises", "at", "NACE16", "four", "-", "digit", "industry", "level", ";", "\n", "15", "https://www.bvdinfo.com/en-gb/our-products/data/in-", "\n", "ternational", "/", "orbis", ".", "\n", "16", "NACE", "is", "a", "four", "-", "digit", "classification", "providing", "the", "frame-", "\n", "work", "for", "collecting", "and", "presenting", "a", "large", "range", "of", "sta-", "\n", "tistical", "data", "according", "to", "economic", "activity", "in", "the", "fields", "\n", "of", "economic", "statistics", "(", "e.g.", "production", ",", "employment", "and", "\n", "national", "accounts", ")", "and", "in", "other", "statistical", "domains", "devel-", "\n", "oped", "within", "the", "European", "Statistical", "System", "(", "ESS", ")", ".", "■", "Industrial", "Statistics", "Database", "(", "INDSTAT4", ")", ",", "\n", "offered", "by", "UNIDO17", ",", "for", "partial", "mapping", "of", "the", "\n", "manufacturing", "sector", "at", "NACE", "four", "-", "digit", "level", ";", "\n ", "■", "The", "UN", "’s", "Comtrade", "Database18", "for", "exports", "\n", "of", "goods", "(", "up", "to", "five", "-", "digit", "export", "data", "accord-", "\n", "ing", "to", "the", "SITC", "product", "classification", ")", "and", "\n", "exports", "of", "services", "(", "according", "to", "the", "EBOPS", "\n", "2002", "classification", ")", ";", "\n ", "■", "the", "World", "Bank", "Enterprise", "Survey19", "for", "re-", "\n", "sults" ]	[]
to innovation and technology adoption and could potentially hinder decarbonisation as well . Europe produces high quality talent in the fields of science, technology, engineering and maths (STEM) but their supply is limited. The EU turns out around 850 STEM graduates per million inhabitants per year compared to more than 1,100 in the US. Moreover, the EU’s talent pool is depleted by brain drain overseas owing to more and better employment opportunities elsewhere. Skills are also lacking to diffuse digital technologies faster through the economy and to enable workers to adapt to the changes these technologies will bring. Almost 60% of EU companies report that lack of skills is a major barrier to investment and a similar share report difficulties in recruiting ICT specialists. At the same time, European workers are generally unprepared to take advantage of the widespread digitalisation of work: around 42% of Europeans lack basic digital skills, including 37% of those in the workforce09. Decarbonisation will also require new skills sets and job profiles. The rates of job vacancies for clean tech manufacturing in the EU doubled between 2019 and 2023, with 25% of EU companies reporting labour shortages in the third quarter of 2023. Shortages of high-skilled workers are likely to become more acute over time. Projections to 2035 indicate that labour shortages will be most pronounced in high-skilled, non-manual occupations – i.e. those requiring high level of education – driven by replacement needs owing to retirements and the changing demands of the labour market. 08. See, among others, Bloom, Sadun and Van Reenen (2012) and Schivardi and Schmitz (2020) for evidence on cross-country variation in managerial practices, and their impact on aggregate productivity. 09. The EU Digital Decade set out to ensure 80% of working age Europeans have basic digital skills by 2030. 36THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 2The undersupply of skills in Europe owes to declines in education and training systems that are failing to prepare the workforce for technological change . Educational attainment in the EU – as measured by the OECD’s PISA scores – is falling. The leading positions in recent PISA reports are dominated by Asian countries, while Europe has experienced an unprecedented decline. This downward trend concerns both average figures and top performance: in 2022, only 8% of EU students reached a high level of competence in maths and 7% in reading and science as	[ " ", "to", "innovation", "and", "technology", "adoption", "and", "could", "potentially", "hinder", "\n", "decarbonisation", "as", "well", ".", "Europe", "produces", "high", "quality", "talent", "in", "the", "fields", "of", "science", ",", "technology", ",", "engineering", "\n", "and", "maths", "(", "STEM", ")", "but", "their", "supply", "is", "limited", ".", "The", "EU", "turns", "out", "around", "850", "STEM", "graduates", "per", "million", "inhabitants", "\n", "per", "year", "compared", "to", "more", "than", "1,100", "in", "the", "US", ".", "Moreover", ",", "the", "EU", "’s", "talent", "pool", "is", "depleted", "by", "brain", "drain", "overseas", "\n", "owing", "to", "more", "and", "better", "employment", "opportunities", "elsewhere", ".", "Skills", "are", "also", "lacking", "to", "diffuse", "digital", "technologies", "\n", "faster", "through", "the", "economy", "and", "to", "enable", "workers", "to", "adapt", "to", "the", "changes", "these", "technologies", "will", "bring", ".", "Almost", "\n", "60", "%", "of", "EU", "companies", "report", "that", "lack", "of", "skills", "is", "a", "major", "barrier", "to", "investment", "and", "a", "similar", "share", "report", "difficulties", "\n", "in", "recruiting", "ICT", "specialists", ".", "At", "the", "same", "time", ",", "European", "workers", "are", "generally", "unprepared", "to", "take", "advantage", "of", "the", "\n", "widespread", "digitalisation", "of", "work", ":", "around", "42", "%", "of", "Europeans", "lack", "basic", "digital", "skills", ",", "including", "37", "%", "of", "those", "in", "the", "\n", "workforce09", ".", "Decarbonisation", "will", "also", "require", "new", "skills", "sets", "and", "job", "profiles", ".", "The", "rates", "of", "job", "vacancies", "for", "clean", "tech", "\n", "manufacturing", "in", "the", "EU", "doubled", "between", "2019", "and", "2023", ",", "with", "25", "%", "of", "EU", "companies", "reporting", "labour", "shortages", "in", "\n", "the", "third", "quarter", "of", "2023", ".", "Shortages", "of", "high", "-", "skilled", "workers", "are", "likely", "to", "become", "more", "acute", "over", "time", ".", "Projections", "\n", "to", "2035", "indicate", "that", "labour", "shortages", "will", "be", "most", "pronounced", "in", "high", "-", "skilled", ",", "non", "-", "manual", "occupations", "–", "i.e.", "those", "\n", "requiring", "high", "level", "of", "education", "–", "driven", "by", "replacement", "needs", "owing", "to", "retirements", "and", "the", "changing", "demands", "\n", "of", "the", "labour", "market", ".", "\n", "08", ".", "See", ",", "among", "others", ",", "Bloom", ",", "Sadun", "and", "Van", "Reenen", "(", "2012", ")", "and", "Schivardi", "and", "Schmitz", "(", "2020", ")", "for", "evidence", "on", "\n", "cross", "-", "country", "variation", "in", "managerial", "practices", ",", "and", "their", "impact", "on", "aggregate", "productivity", ".", "\n", "09", ".", "The", "EU", "Digital", "Decade", "set", "out", "to", "ensure", "80", "%", "of", "working", "age", "Europeans", "have", "basic", "digital", "skills", "by", "2030", ".", "\n", "36THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "2The", "undersupply", "of", "skills", "in", "Europe", "owes", "to", "declines", "in", "education", "and", "training", "systems", "that", "are", "failing", "\n", "to", "prepare", "the", "workforce", "for", "technological", "change", ".", "Educational", "attainment", "in", "the", "EU", "–", "as", "measured", "by", "the", "\n", "OECD", "’s", "PISA", "scores", "–", "is", "falling", ".", "The", "leading", "positions", "in", "recent", "PISA", "reports", "are", "dominated", "by", "Asian", "countries", ",", "while", "\n", "Europe", "has", "experienced", "an", "unprecedented", "decline", ".", "This", "downward", "trend", "concerns", "both", "average", "figures", "and", "top", "\n", "performance", ":", "in", "2022", ",", "only", "8", "%", "of", "EU", "students", "reached", "a", "high", "level", "of", "competence", "in", "maths", "and", "7", "%", "in", "reading", "and", "\n", "science", "as" ]	[]
The most relevant keywords for these Ukrainian S&T domains that match E&I domains can be found in the figures below; similar figures were also shown in Part 3 when characterising the S&T for the whole EaP region. 244 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials UKRAINE Concordance between E&I analysis and S&T analysis Economic clusterE&I domains (NACE sectors)S&T domains Food Processing and Manufacturing10 Manufacture of food products • Agrifood Wood Products16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Metalworking Technology25 Manufacture of fabricated metal products, except machinery and equipment• Nanotechnology and materials Information Technology and Analytical Instruments26 Manufacture of computer, electronic and optical products• Electric and electronic technologies • Energy • Fundamental physics and mathematics • ICT and computer science • Optics and photonics Production Technology and Heavy Machinery28 Manufacture of machinery and equipment n.e.c.• Agrifood • Energy • Fundamental physics and mathematics • Environmental sciences and industries • Mechanical engineering and heavy machinery Automotive29 Manufacture of motor vehicles, trailers and semi-trailers• Transportation Wholesale trade46 Wholesale trade, except of motor vehicles and motorcyclesTable 4.6. Combined EIST specialisation domains in Ukraine Figure 4.18. Keyword cloud for the S&T domain Agrifood in Ukraine Figure 4.19. Keyword cloud for the S&T domain Electric and electronic technologies in Ukraine Figure 4.20. Keyword cloud for the S&T domain Energy in Ukraine Figure 4.21. Keyword cloud for the S&T domain Environmental sciences and industries in Ukraine Figure 4.23. Keyword cloud for the S&T domain ICT and computer science in Ukraine Figure 4.25. Keyword cloud for the S&T domain Nanotechnology and materials in Ukraine Figure 4.27. Keyword cloud for the S&T domain Transportation in Ukraine Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation 245 Figure 4.22. Keyword cloud for the S&T domain Fundamental physics and mathematics in Ukraine Figure 4.24. Keyword cloud for the S&T domain Mechanical engineering and heavy machinery in Ukraine Figure 4.26. Keyword cloud for the S&T domain Optics and photonics in Ukraine 246 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials 4. Potential for EaP collabora- tion in combined EIST domains To complement the preceding country sections, and to provide evidence regarding common are- as of combined EIST specialisation throughout the EaP countries, Table 4.7 presents every economic cluster (E&I) and S&T domain pair	[ "The", "most", "relevant", "keywords", "for", "these", "Ukrainian", "\n", "S&T", "domains", "that", "match", "E&I", "domains", "can", "be", "\n", "found", "in", "the", "figures", "below", ";", "similar", "figures", "were", "\n", "also", "shown", "in", "Part", "3", "when", "characterising", "the", "S&T", "\n", "for", "the", "whole", "EaP", "region", ".", "\n", "244", "\n ", "Part", "4", "Identification", "of", "concordances", "between", "the", "economic", ",", "innovation", ",", "scientific", "and", "technological", "potentials", "\n", "UKRAINE", "\n", "Concordance", "between", "E&I", "analysis", "and", "S&T", "analysis", "\n", "Economic", "clusterE&I", "domains", " \n", "(", "NACE", "sectors)S&T", "domains", "\n", "Food", "Processing", "and", "\n", "Manufacturing10", "Manufacture", "of", "food", "products", "•", "Agrifood", "\n", "Wood", "Products16", "Manufacture", "of", "wood", "and", "of", "\n", "products", "of", "wood", "and", "cork", ",", "except", "\n", "furniture", ";", "manufacture", "of", "articles", "of", "\n", "straw", "and", "plaiting", "materials", "\n", "Metalworking", "Technology25", "Manufacture", "of", "fabricated", "metal", "\n", "products", ",", "except", "machinery", "and", "\n", "equipment•", "Nanotechnology", "and", "materials", "\n", "Information", "Technology", "and", "\n", "Analytical", "Instruments26", "Manufacture", "of", "computer", ",", "\n", "electronic", "and", "optical", "products•", "Electric", "and", "electronic", "technologies", "\n", "•", "Energy", "\n", "•", "Fundamental", "physics", "and", "\n", "mathematics", "\n", "•", "ICT", "and", "computer", "science", "\n", "•", "Optics", "and", "photonics", "\n", "Production", "Technology", "and", "Heavy", "\n", "Machinery28", "Manufacture", "of", "machinery", "and", "\n", "equipment", "n.e.c.•", "Agrifood", "\n", "•", "Energy", "\n", "•", "Fundamental", "physics", "and", "\n", "mathematics", "\n", "•", "Environmental", "sciences", "and", "\n", "industries", "\n", "•", "Mechanical", "engineering", "and", "heavy", "\n", "machinery", "\n", "Automotive29", "Manufacture", "of", "motor", "vehicles", ",", "\n", "trailers", "and", "semi", "-", "trailers•", "Transportation", "\n", "Wholesale", "trade46", "Wholesale", "trade", ",", "except", "of", "motor", "\n", "vehicles", "and", "motorcyclesTable", "4.6", ".", "Combined", "EIST", "specialisation", "domains", "in", "Ukraine", "\n", "Figure", "4.18", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Agrifood", "in", "\n", "Ukraine", "\n", "Figure", "4.19", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Electric", "and", "\n", "electronic", "technologies", "in", "Ukraine", "\n", "Figure", "4.20", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Energy", "in", "\n", "Ukraine", "\n", "Figure", "4.21", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Environmental", "\n", "sciences", "and", "industries", "in", "Ukraine", "\n", "Figure", "4.23", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "ICT", "and", "\n", "computer", "science", "in", "Ukraine", "\n", "Figure", "4.25", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Nanotechnology", "\n", "and", "materials", "in", "Ukraine", "\n", "Figure", "4.27", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Transportation", "\n", "in", "Ukraine", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation", "245", "\n", "Figure", "4.22", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Fundamental", "\n", "physics", "and", "mathematics", "in", "Ukraine", "\n", "Figure", "4.24", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Mechanical", "\n", "engineering", "and", "heavy", "machinery", "in", "Ukraine", "\n", "Figure", "4.26", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Optics", "and", "\n", "photonics", "in", "Ukraine", "\n", "246", "\n ", "Part", "4", "Identification", "of", "concordances", "between", "the", "economic", ",", "innovation", ",", "scientific", "and", "technological", "potentials", "\n", "4", ".", "Potential", "for", "EaP", "collabora-", "\n", "tion", "in", "combined", "EIST", "domains", "\n", "To", "complement", "the", "preceding", "country", "sections", ",", "\n", "and", "to", "provide", "evidence", "regarding", "common", "are-", "\n", "as", "of", "combined", "EIST", "specialisation", "throughout", "the", "\n", "EaP", "countries", ",", "Table", "4.7", "presents", "every", "economic", "\n", "cluster", "(", "E&I", ")", "and", "S&T", "domain", "pair" ]	[]
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4Strengthening industrial capacity for defence and space The European defence industry not only suffers from lower defence spending but also a lack of focus on technological development [see the chapter on defence] . The European defence sector is highly competitive globally, registering an annual turnover of EUR 135 billion in 2022 and strong export volumes. Some EU products and technologies are superior or at least equivalent in quality to those produced by the US, such as main battle tanks, conventional submarines, naval shipyard technology and transport aircraft. However, the EU defence industry is suffering from a capacity gap on two fronts. First, overall demand is lower: aggregate defence spending in the EU is about one-third as high as in the US. Second, EU spending is less focused on innovation. Defence is a highly technological industry characterised by disruptive innovation, meaning that massive R&D investments are required to maintain strategic parity. The US has prioritised R&D spending over all other military spending categories since 2014. In the 2023, it allocated EUR 130 billion (USD 140 billion) for Research, Development, Test and Evaluation, amounting to around 16% of total defence spending. This category also saw the largest relative percentage increase in the defence budget. In Europe, total funding for defence R&D was EUR 10.7 billion in 2022, amounting to just 4.5% of total spending. Complex next-generation defence systems in all strategic domains will require massive R&D investment that exceeds the capacity of single EU Member States. The European defence industry is also fragmented, limiting its scale and hindering operational effective - ness in the field . The EU defence industrial landscape is populated mainly by national players operating in relatively small domestic markets [see Figure 4] . Fragmentation creates two major challenges. First, it means that the industry lacks scale, which is essential in a capital-intensive sector with long investment cycles. As a result, if EU Member States were to ramp up defence spending significantly, a supply crisis could occur with Member States competing between each other on the constrained European defence equipment market. Second, fragmentation leads to serious issues related to a lack of standardisation and the interoperability of equipment, which have come to light during the EU’s support for Ukraine. For 155 mm artillery alone, EU Member States have provided ten different types of howitzers to Ukraine from their stocks, and some have even been delivered in different variants, creating	[ " ", "4Strengthening", "industrial", "capacity", "\n", "for", " ", "defence", " ", "and", " ", "space", "\n", "The", "European", "defence", "industry", "not", "only", "suffers", "from", "lower", "defence", "spending", "but", "also", "a", "lack", "of", "focus", "on", "\n", "technological", "development", "[", "see", "the", "chapter", "on", "defence", "]", ".", "The", "European", "defence", "sector", "is", "highly", "competitive", "\n", "globally", ",", "registering", "an", "annual", "turnover", "of", "EUR", "135", "billion", "in", "2022", "and", "strong", "export", "volumes", ".", "Some", "EU", "products", "\n", "and", "technologies", "are", "superior", "or", "at", "least", "equivalent", "in", "quality", "to", "those", "produced", "by", "the", "US", ",", "such", "as", "main", "battle", "\n", "tanks", ",", "conventional", "submarines", ",", "naval", "shipyard", "technology", "and", "transport", "aircraft", ".", "However", ",", "the", "EU", "defence", "industry", "\n", "is", "suffering", "from", "a", "capacity", "gap", "on", "two", "fronts", ".", "First", ",", "overall", "demand", "is", "lower", ":", "aggregate", "defence", "spending", "in", "the", "\n", "EU", "is", "about", "one", "-", "third", "as", "high", "as", "in", "the", "US", ".", "Second", ",", "EU", "spending", "is", "less", "focused", "on", "innovation", ".", "Defence", "is", "a", "highly", "\n", "technological", "industry", "characterised", "by", "disruptive", "innovation", ",", "meaning", "that", "massive", "R&D", "investments", "are", "required", "\n", "to", "maintain", "strategic", "parity", ".", "The", "US", "has", "prioritised", "R&D", "spending", "over", "all", "other", "military", "spending", "categories", "since", "\n", "2014", ".", "In", "the", "2023", ",", "it", "allocated", "EUR", "130", "billion", "(", "USD", "140", "billion", ")", "for", "Research", ",", "Development", ",", "Test", "and", "Evaluation", ",", "\n", "amounting", "to", "around", "16", "%", "of", "total", "defence", "spending", ".", "This", "category", "also", "saw", "the", "largest", "relative", "percentage", "increase", "\n", "in", "the", "defence", "budget", ".", "In", "Europe", ",", "total", "funding", "for", "defence", "R&D", "was", "EUR", "10.7", "billion", "in", "2022", ",", "amounting", "to", "just", "\n", "4.5", "%", "of", "total", "spending", ".", "Complex", "next", "-", "generation", "defence", "systems", "in", "all", "strategic", "domains", "will", "require", "massive", "R&D", "\n", "investment", "that", "exceeds", "the", "capacity", "of", "single", "EU", "Member", "States", ".", "\n", "The", "European", "defence", "industry", "is", "also", "fragmented", ",", "limiting", "its", "scale", "and", "hindering", "operational", "effective", "-", "\n", "ness", "in", "the", "field", ".", "The", "EU", "defence", "industrial", "landscape", "is", "populated", "mainly", "by", "national", "players", "operating", "in", "relatively", "\n", "small", "domestic", "markets", "[", "see", "Figure", "4", "]", ".", "Fragmentation", "creates", "two", "major", "challenges", ".", "First", ",", "it", "means", "that", "the", "industry", "\n", "lacks", "scale", ",", "which", "is", "essential", "in", "a", "capital", "-", "intensive", "sector", "with", "long", "investment", "cycles", ".", "As", "a", "result", ",", "if", "EU", "Member", "\n", "States", "were", "to", "ramp", "up", "defence", "spending", "significantly", ",", "a", "supply", "crisis", "could", "occur", "with", "Member", "States", "competing", "\n", "between", "each", "other", "on", "the", "constrained", "European", "defence", "equipment", "market", ".", "Second", ",", "fragmentation", "leads", "to", "\n", "serious", "issues", "related", "to", "a", "lack", "of", "standardisation", "and", "the", "interoperability", "of", "equipment", ",", "which", "have", "come", "to", "light", "\n", "during", "the", "EU", "’s", "support", "for", "Ukraine", ".", "For", "155", "mm", "artillery", "alone", ",", "EU", "Member", "States", "have", "provided", "ten", "different", "types", "\n", "of", "howitzers", "to", "Ukraine", "from", "their", "stocks", ",", "and", "some", "have", "even", "been", "delivered", "in", "different", "variants", ",", "creating" ]	[]
of aquatic invertebrates, fit for human consumption 037 Fish, crustaceans, molluscs and other aquatic invertebrates, prepared or preserved, n.e.s. X X 041 Wheat (including spelt) and meslin, unmilled X X X 042 Rice 043 Barley, unmilled X X X 044 Maize (not including sweet corn), unmilled X X 045 Cereals, unmilled (other than wheat, rice, barley and maize) 046 Meal and flour of wheat and flour of meslin X 047 Other cereal meals and flours 048 Cereal preparations and preparations of flour or starch of fruits or vegetables X X X X 054Vegetables, fresh, chilled, frozen or simply preserved (including dried leguminous vegetables); roots, tubers and other edible vegetable products, n.e.s., fresh or dried X X X X 056 Vegetables, roots and tubers, prepared or preserved, n.e.s. X X X X X 057 Fruit and nuts (not including oil nuts), fresh or dried X X X X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation303 304 Annexes ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE SITC Goods name Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging 19 12 3 8 65 64 18 26 41 23 51 52 058 Fruit, preserved, and fruit preparations (excluding fruit juices) X X X X 059Fruit juices (including grape must) and vegetable juices, unfermented and not containing added spirit, whether or not containing added sugar or other sweetening matter X X 061 Sugars, molasses and honey X X X 062 Sugar confectionery X X X 071 Coffee and coffee substitutes X 072 Cocoa 073 Chocolate and other food preparations containing cocoa, n.e.s. X X X 074 Tea and maté 075 Spices X X 081 Feeding stuff for animals (not including unmilled cereals) X X X 091 Margarine and shortening X X 098 Edible products and preparations, n.e.s. X X X X 099 Miscellaneous edible products and preparations 1 Beverages and tobacco 111 Non-alcoholic beverages, n.e.s. X X X X 112 Alcoholic beverages X X X X X X 121 Tobacco, unmanufactured; tobacco refuse 122 Tobacco, manufactured (whether or not containing tobacco substitutes) X X 199 Adjustments (trade broken down at chapter nc level only) 2 Crude materials, inedible, except fuels 211 Hides and skins (except furskins), raw X 212Furskins, raw (including heads, tails, paws and other pieces or cuttings, suitable for furriers' use), other than hides and skins of group 211 222Oil-seeds and oleaginous fruits of	[ "of", "aquatic", "invertebrates", ",", "fit", "for", "human", "consumption", " \n", "037", "Fish", ",", "crustaceans", ",", "molluscs", "and", "other", "aquatic", "invertebrates", ",", "prepared", "or", "preserved", ",", "n.e.s", ".", " ", "X", "X", " \n", "041", "Wheat", "(", "including", "spelt", ")", "and", "meslin", ",", "unmilled", " ", "X", " ", "X", "X", "\n", "042", "Rice", " \n", "043", "Barley", ",", "unmilled", " ", "X", "X", "X", " \n", "044", "Maize", "(", "not", "including", "sweet", "corn", ")", ",", "unmilled", " ", "X", "X", " \n", "045", "Cereals", ",", "unmilled", "(", "other", "than", "wheat", ",", "rice", ",", "barley", "and", "maize", ")", " \n", "046", "Meal", "and", "flour", "of", "wheat", "and", "flour", "of", "meslin", " ", "X", " \n", "047", "Other", "cereal", "meals", "and", "flours", " \n", "048", "Cereal", "preparations", "and", "preparations", "of", "flour", "or", "starch", "of", "fruits", "or", "vegetables", " ", "X", " ", "X", " ", "X", "X", "\n", "054Vegetables", ",", "fresh", ",", "chilled", ",", "frozen", "or", "simply", "preserved", "(", "including", "dried", "leguminous", "vegetables", ")", ";", "roots", ",", "tubers", "and", "\n", "other", "edible", "vegetable", "products", ",", "n.e.s", ".", ",", "fresh", "or", "dried", "X", " ", "X", " ", "X", " ", "X", "\n", "056", "Vegetables", ",", "roots", "and", "tubers", ",", "prepared", "or", "preserved", ",", "n.e.s", ".", "X", "X", " ", "X", "X", " ", "X", "\n", "057", "Fruit", "and", "nuts", "(", "not", "including", "oil", "nuts", ")", ",", "fresh", "or", "dried", " ", "X", " ", "X", " ", "X", "X", " ", "X", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation303", "304", "\n", "Annexes", "\n", "ARMENIA", "AZERBAIJAN", "BELARUS", "GEORGIA", "MOLDOVA", "UKRAINE", "\n", "SITC", "Goods", "name", "Current", "Emerging", "Current", "Emerging", "Current", "Emerging", "Current", "Emerging", "Current", "Emerging", "Current", "Emerging", "\n", "19", "12", "3", "8", "65", "64", "18", "26", "41", "23", "51", "52", "\n", "058", "Fruit", ",", "preserved", ",", "and", "fruit", "preparations", "(", "excluding", "fruit", "juices", ")", "X", " ", "X", " ", "X", " ", "X", " \n", "059Fruit", "juices", "(", "including", "grape", "must", ")", "and", "vegetable", "juices", ",", "unfermented", "and", "not", "containing", "added", "spirit", ",", "whether", "or", "\n", "not", "containing", "added", "sugar", "or", "other", "sweetening", "matter", " ", "X", "X", " \n", "061", "Sugars", ",", "molasses", "and", "honey", " ", "X", " ", "X", " ", "X", "\n", "062", "Sugar", "confectionery", " ", "X", " ", "X", "X", "\n", "071", "Coffee", "and", "coffee", "substitutes", "X", " \n", "072", "Cocoa", " \n", "073", "Chocolate", "and", "other", "food", "preparations", "containing", "cocoa", ",", "n.e.s", ".", " ", "X", " ", "X", " ", "X", " \n", "074", "Tea", "and", "maté", " \n", "075", "Spices", " ", "X", "X", " \n", "081", "Feeding", "stuff", "for", "animals", "(", "not", "including", "unmilled", "cereals", ")", " ", "X", " ", "X", "X", "\n", "091", "Margarine", "and", "shortening", " ", "X", "X", "\n", "098", "Edible", "products", "and", "preparations", ",", "n.e.s", ".", " ", "X", " ", "X", " ", "X", "X", "\n", "099", "Miscellaneous", "edible", "products", "and", "preparations", " \n", "1", "Beverages", "and", "tobacco", " \n", "111", "Non", "-", "alcoholic", "beverages", ",", "n.e.s", ".", " ", "X", "X", " ", "X", " ", "X", "\n", "112", "Alcoholic", "beverages", "X", " ", "X", "X", "X", "X", " ", "X", "\n", "121", "Tobacco", ",", "unmanufactured", ";", "tobacco", "refuse", " \n", "122", "Tobacco", ",", "manufactured", "(", "whether", "or", "not", "containing", "tobacco", "substitutes", ")", "X", " ", "X", " \n", "199", "Adjustments", "(", "trade", "broken", "down", "at", "chapter", "nc", "level", "only", ")", " \n", "2", "Crude", "materials", ",", "inedible", ",", "except", "fuels", " \n", "211", "Hides", "and", "skins", "(", "except", "furskins", ")", ",", "raw", " ", "X", " \n", "212Furskins", ",", "raw", "(", "including", "heads", ",", "tails", ",", "paws", "and", "other", "pieces", "or", "cuttings", ",", "suitable", "for", "furriers", "'", "use", ")", ",", "other", "than", "hides", "\n", "and", "skins", "of", "group", "211", " \n", "222Oil", "-", "seeds", "and", "oleaginous", "fruits", "of" ]	[]
with a mild aver-sive footshock (US) to elicit a fear response ( Figures 2 A and S3B). We found that US presentations induced higher general Ca 2+activity in the imaged neurons and recruited a larger popu- lation of active VIP+ INs compared with the CS presentations (67.1% active during US; 21.6% active during CS; chi-square, p = 0.0001; Figures 2 D–2G and S4A). Upon retrieval of the fear memory a day later, in which the CS was not paired with the re- inforcing US (CS-R), the average activity and fraction of active VIP+ INs during the CS presentation (30.4%; chi-square, p =0.42) and during the US omission (US–) (22.8%; chi-square, p = 0.45) were comparable with those observed during the CS presentation in the fear acquisition phase ( Figures S4 A–S4E). We then analyzed the response of aIC VIP+ INs to the pseudo- random presentation of two intermingled neutral auditory stimuli of identical frequency (6 kHz), but different intensity (50 versus 80 dB) ( Figure 3 A). We observed that VIP+ INs showed a higher ac- tivity and responded in a larger proportion to the 80 dB tone (38.2% during 80 dB and 21.1% during 50 dB presentations; chi-square, p = 0.018; Figures 3 B–3D and S4A). Finally, we examined aIC VIP+ IN responses during a modiﬁed version of the three-chamber social preference test ( Nadler et al., 2004 ). For this test, each experimental mouse was subjected to the paradigm twice, with an inter-trial interval of 24 h and with theposition of the object and novel interactor mouse exchanged (Figure 3 E). As expected, mice spent more time interacting with the unfamiliar mouse compared with the object ( Figure 3 F). On day 1, the overall Ca 2+activity of VIP+ INs was signiﬁcantly higher during epochs of social compared with object visits (E) Sequential sections from an example mouse brain depicting representative monosynaptic inputs to aIC VIP+ INs. On the top, illustrations from the Allen Mouse Brain Atlas corresponding to the actual sections on the bottom. Areas with a particularly high content of ﬁrst-order presynaptic neurons are indicat ed with different shades of gray, darker areas indicate higher density. Scale bar, 1 mm.(F) Heatmap (left) and bar plot (right) representing fraction of inputs (%) over total input numbers for each identiﬁed brain area, with >1% of total in put, projecting to aIC VIP+ INs. Data are shown as mean	[ "with", "a", "mild", "aver", "-", "sive", "footshock", "(", "US", ")", "to", "elicit", "a", "fear", "response", "(", "Figures", "2", "A", "and", "\n", "S3B", ")", ".", "We", "found", "that", "US", "presentations", "induced", "higher", "general", "\n", "Ca", "\n", "2+activity", "in", "the", "imaged", "neurons", "and", "recruited", "a", "larger", "popu-", "\n", "lation", "of", "active", "VIP+", "INs", "compared", "with", "the", "CS", "presentations", "\n", "(", "67.1", "%", "active", "during", "US", ";", "21.6", "%", "active", "during", "CS", ";", "chi", "-", "square", ",", "\n", "p", "=", "0.0001", ";", "Figures", "2", "D–2", "G", "and", "S4A", ")", ".", "Upon", "retrieval", "of", "the", "fear", "\n", "memory", "a", "day", "later", ",", "in", "which", "the", "CS", "was", "not", "paired", "with", "the", "re-", "\n", "inforcing", "US", "(", "CS", "-", "R", ")", ",", "the", "average", "activity", "and", "fraction", "of", "active", "\n", "VIP+", "INs", "during", "the", "CS", "presentation", "(", "30.4", "%", ";", "chi", "-", "square", ",", "p", "=", "0.42", ")", "and", "during", "the", "US", "omission", "(", "US", "–", ")", "(", "22.8", "%", ";", "chi", "-", "square", ",", "\n", "p", "=", "0.45", ")", "were", "comparable", "with", "those", "observed", "during", "the", "CS", "\n", "presentation", "in", "the", "fear", "acquisition", "phase", "(", "Figures", "S4", "A", "–", "S4E", ")", ".", "\n", "We", "then", "analyzed", "the", "response", "of", "aIC", "VIP+", "INs", "to", "the", "pseudo-", "\n", "random", "presentation", "of", "two", "intermingled", "neutral", "auditory", "stimuli", "\n", "of", "identical", "frequency", "(", "6", "kHz", ")", ",", "but", "different", "intensity", "(", "50", "versus", "80", "\n", "dB", ")", "(", "Figure", "3", "A", ")", ".", "We", "observed", "that", "VIP+", "INs", "showed", "a", "higher", "ac-", "\n", "tivity", "and", "responded", "in", "a", "larger", "proportion", "to", "the", "80", "dB", "tone", "\n", "(", "38.2", "%", "during", "80", "dB", "and", "21.1", "%", "during", "50", "dB", "presentations", ";", "\n", "chi", "-", "square", ",", "p", "=", "0.018", ";", "Figures", "3", "B–3D", "and", "S4A", ")", ".", "\n", "Finally", ",", "we", "examined", "aIC", "VIP+", "IN", "responses", "during", "a", "modiﬁed", "\n", "version", "of", "the", "three", "-", "chamber", "social", "preference", "test", "(", "Nadler", "et", "al", ".", ",", "\n", "2004", ")", ".", "For", "this", "test", ",", "each", "experimental", "mouse", "was", "subjected", "to", "\n", "the", "paradigm", "twice", ",", "with", "an", "inter", "-", "trial", "interval", "of", "24", "h", "and", "with", "theposition", "of", "the", "object", "and", "novel", "interactor", "mouse", "exchanged", "\n", "(", "Figure", "3", "E", ")", ".", "As", "expected", ",", "mice", "spent", "more", "time", "interacting", "\n", "with", "the", "unfamiliar", "mouse", "compared", "with", "the", "object", "(", "Figure", "3", "F", ")", ".", "\n", "On", "day", "1", ",", "the", "overall", "Ca", "\n", "2+activity", "of", "VIP+", "INs", "was", "signiﬁcantly", "\n", "higher", "during", "epochs", "of", "social", "compared", "with", "object", "visits", "\n", "(", "E", ")", "Sequential", "sections", "from", "an", "example", "mouse", "brain", "depicting", "representative", "monosynaptic", "inputs", "to", "aIC", "VIP+", "INs", ".", "On", "the", "top", ",", "illustrations", "from", "the", "Allen", "Mouse", "\n", "Brain", "Atlas", "corresponding", "to", "the", "actual", "sections", "on", "the", "bottom", ".", "Areas", "with", "a", "particularly", "high", "content", "of", "ﬁrst", "-", "order", "presynaptic", "neurons", "are", "indicat", "ed", "with", "\n", "different", "shades", "of", "gray", ",", "darker", "areas", "indicate", "higher", "density", ".", "Scale", "bar", ",", "1", "mm.(F", ")", "Heatmap", "(", "left", ")", "and", "bar", "plot", "(", "right", ")", "representing", "fraction", "of", "inputs", "(", "%", ")", "over", "total", "input", "numbers", "for", "each", "identiﬁed", "brain", "area", ",", "with", ">", "1", "%", "of", "total", "in", "put", ",", "projecting", "to", "\n", "aIC", "VIP+", "INs", ".", "Data", "are", "shown", "as", "mean" ]	[ { "end": 1325, "label": "CITATION-REFEERENCE", "start": 1306 } ]
the appropriate alpha-keto acid, which is then transaminated to form an amino acid.[90] Amino acids are made into proteins by being joined in a chain of peptide bonds. Each different protein has a unique sequence of amino acid residues: this is its primary structure. Just as the letters of the alphabet can be combined to form an almost endless variety of words, amino acids can be linked in varying sequences to form a huge variety of proteins. Proteins are made from amino acids that have been activated by attachment to a transfer RNA molecule through an ester bond. This aminoacyl-tRNA precursor is produced in an ATP-dependent reaction carried out by an aminoacyl tRNA synthetase.[91] This aminoacyl-tRNA is then a substrate for the ribosome, which joins the amino acid onto the elongating protein chain, using the sequence information in a messenger RNA.[92] Nucleotide synthesis and salvage Further information: Nucleotide salvage, Pyrimidine biosynthesis, and Purine § Metabolism Nucleotides are made from amino acids, carbon dioxide and formic acid in pathways that require large amounts of metabolic energy.[93] Consequently, most organisms have efficient systems to salvage preformed nucleotides.[93][94] Purines are synthesized as nucleosides (bases attached to ribose).[95] Both adenine and guanine are made from the precursor nucleoside inosine monophosphate, which is synthesized using atoms from the amino acids glycine, glutamine, and aspartic acid, as well as formate transferred from the coenzyme tetrahydrofolate. Pyrimidines, on the other hand, are synthesized from the base orotate, which is formed from glutamine and aspartate.[96] Xenobiotics and redox metabolism Further information: Xenobiotic metabolism, Drug metabolism, Alcohol metabolism, and Antioxidant All organisms are constantly exposed to compounds that they cannot use as foods and that would be harmful if they accumulated in cells, as they have no metabolic function. These potentially damaging compounds are called xenobiotics.[97] Xenobiotics such as synthetic drugs, natural poisons and antibiotics are detoxified by a set of xenobiotic-metabolizing enzymes. In humans, these include cytochrome P450 oxidases,[98] UDP-glucuronosyltransferases,[99] and glutathione S-transferases.[100] This system of enzymes acts in three stages to firstly oxidize the xenobiotic (phase I) and then conjugate water-soluble groups onto the molecule (phase II). The modified water-soluble xenobiotic can then be pumped out of cells and in multicellular organisms may be further metabolized before being excreted (phase III). In ecology, these reactions are particularly important in microbial biodegradation of pollutants and the bioremediation of contaminated land and oil spills.[101] Many of these microbial reactions	[ "the", "appropriate", "alpha", "-", "keto", "acid", ",", "which", "is", "then", "transaminated", "to", "form", "an", "amino", "acid.[90", "]", "\n\n", "Amino", "acids", "are", "made", "into", "proteins", "by", "being", "joined", "in", "a", "chain", "of", "peptide", "bonds", ".", "Each", "different", "protein", "has", "a", "unique", "sequence", "of", "amino", "acid", "residues", ":", "this", "is", "its", "primary", "structure", ".", "Just", "as", "the", "letters", "of", "the", "alphabet", "can", "be", "combined", "to", "form", "an", "almost", "endless", "variety", "of", "words", ",", "amino", "acids", "can", "be", "linked", "in", "varying", "sequences", "to", "form", "a", "huge", "variety", "of", "proteins", ".", "Proteins", "are", "made", "from", "amino", "acids", "that", "have", "been", "activated", "by", "attachment", "to", "a", "transfer", "RNA", "molecule", "through", "an", "ester", "bond", ".", "This", "aminoacyl", "-", "tRNA", "precursor", "is", "produced", "in", "an", "ATP", "-", "dependent", "reaction", "carried", "out", "by", "an", "aminoacyl", "tRNA", "synthetase.[91", "]", "This", "aminoacyl", "-", "tRNA", "is", "then", "a", "substrate", "for", "the", "ribosome", ",", "which", "joins", "the", "amino", "acid", "onto", "the", "elongating", "protein", "chain", ",", "using", "the", "sequence", "information", "in", "a", "messenger", "RNA.[92", "]", "\n\n", "Nucleotide", "synthesis", "and", "salvage", "\n", "Further", "information", ":", "Nucleotide", "salvage", ",", "Pyrimidine", "biosynthesis", ",", "and", "Purine", "§", "Metabolism", "\n", "Nucleotides", "are", "made", "from", "amino", "acids", ",", "carbon", "dioxide", "and", "formic", "acid", "in", "pathways", "that", "require", "large", "amounts", "of", "metabolic", "energy.[93", "]", "Consequently", ",", "most", "organisms", "have", "efficient", "systems", "to", "salvage", "preformed", "nucleotides.[93][94", "]", "Purines", "are", "synthesized", "as", "nucleosides", "(", "bases", "attached", "to", "ribose).[95", "]", "Both", "adenine", "and", "guanine", "are", "made", "from", "the", "precursor", "nucleoside", "inosine", "monophosphate", ",", "which", "is", "synthesized", "using", "atoms", "from", "the", "amino", "acids", "glycine", ",", "glutamine", ",", "and", "aspartic", "acid", ",", "as", "well", "as", "formate", "transferred", "from", "the", "coenzyme", "tetrahydrofolate", ".", "Pyrimidines", ",", "on", "the", "other", "hand", ",", "are", "synthesized", "from", "the", "base", "orotate", ",", "which", "is", "formed", "from", "glutamine", "and", "aspartate.[96", "]", "\n\n", "Xenobiotics", "and", "redox", "metabolism", "\n", "Further", "information", ":", "Xenobiotic", "metabolism", ",", "Drug", "metabolism", ",", "Alcohol", "metabolism", ",", "and", "Antioxidant", "\n", "All", "organisms", "are", "constantly", "exposed", "to", "compounds", "that", "they", "can", "not", "use", "as", "foods", "and", "that", "would", "be", "harmful", "if", "they", "accumulated", "in", "cells", ",", "as", "they", "have", "no", "metabolic", "function", ".", "These", "potentially", "damaging", "compounds", "are", "called", "xenobiotics.[97", "]", "Xenobiotics", "such", "as", "synthetic", "drugs", ",", "natural", "poisons", "and", "antibiotics", "are", "detoxified", "by", "a", "set", "of", "xenobiotic", "-", "metabolizing", "enzymes", ".", "In", "humans", ",", "these", "include", "cytochrome", "P450", "oxidases,[98", "]", "UDP", "-", "glucuronosyltransferases,[99", "]", "and", "glutathione", "S", "-", "transferases.[100", "]", "This", "system", "of", "enzymes", "acts", "in", "three", "stages", "to", "firstly", "oxidize", "the", "xenobiotic", "(", "phase", "I", ")", "and", "then", "conjugate", "water", "-", "soluble", "groups", "onto", "the", "molecule", "(", "phase", "II", ")", ".", "The", "modified", "water", "-", "soluble", "xenobiotic", "can", "then", "be", "pumped", "out", "of", "cells", "and", "in", "multicellular", "organisms", "may", "be", "further", "metabolized", "before", "being", "excreted", "(", "phase", "III", ")", ".", "In", "ecology", ",", "these", "reactions", "are", "particularly", "important", "in", "microbial", "biodegradation", "of", "pollutants", "and", "the", "bioremediation", "of", "contaminated", "land", "and", "oil", "spills.[101", "]", "Many", "of", "these", "microbial", "reactions" ]	[]
Draganiuc’54Health and wellbeing; Governance, culture, education and the economy; Biotechnology Institute of Cultural Heritage 33Governance, culture, education and the economy; Environmental sci. and industries; Health and wellbeing National Center of Public Health 30Health and wellbeing; Environmental sciences and industries; Biotechnology Institutia Medico-Sanitara Publica Institutul Oncologic20Health and wellbeing; Energy; Governance, culture, education and the economy Ministry of Health of the Republic of Moldova15Health and wellbeing; Governance, culture, education and the economy; Environmental sciences and industries National Agency for Research and Development15Governance, culture, education and the economy; Environmental sciences and industries; Agrifood Republican Clinical Hospital 15 Health and wellbeing; Fund. physics and mathematics Institute of Emergency Medicine 12 Health and wellbeing Institute of Neurology and Neurosurgery11Health and wellbeing; Governance, culture, education and the economy; ICT and computer science Center of International Projects 10Governance, culture, education and the economy; Environmental sciences and industries; ICT and computer scienceTable 3.25. Top public actors in Moldova by number of records, across all domains MOLDOVA Top actors classified as ‘Private company, for-profit’ NameNo of recordsMain S&T domains Scientific and Engineering Centre ‘Informinstrument’5Energy; Mechanical engineering and heavy machinery; Fundamental physics and mathematics Das Solutions S.R.L. 3Governance, culture, education and the economy; ICT and computer science; Energy Technical-Scientific Company Informbusiness Srl2 Energy; Mechanical engineering and heavy machinery Stattis LLC 2 Health and wellbeing Agentia de Logistica Age Quod Agis Srl1 Governance, culture, education and the economy Sunga LLC 1 Energy; Nanotechnology and materials; Optics and photonics Polivalent-95 Srl 1 Nanotechnology and materials Societatea Pentru Metodologia Sondajelor1 Governance, culture, education and the economy Labromed Laborator Srl 1 Health and wellbeing Bell Vetro Srl 1 Energy; Mechanical engineering and heavy machineryTable 3.26. Top private actors in Moldova by number of records, across all domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation205 Ukraine The National Academy of Sciences dominates sci- entific production in the country. Several compre- hensive universities present notable production in many domains, while a few specialised institutions have important roles in selected domains, such as Health and wellbeing, Agrifood or Transportation. On the one hand, the National Cancer Institute, the Kvant Research Institute (in the field of electronic systems), the Yuzhnoye State Design Office and the State Center for Nuclear and Radiation Safety stand out within a highly diversified field of public actors. On the other hand, the two most active private companies are the drug discovery provider Enam- ine LLC and the	[ "Draganiuc’54Health", "and", "wellbeing", ";", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy", ";", "Biotechnology", "\n", "Institute", "of", "Cultural", "Heritage", "33Governance", ",", "culture", ",", "education", "and", "the", "economy", ";", "Environmental", "\n", "sci", ".", "and", "industries", ";", "Health", "and", "wellbeing", "\n", "National", "Center", "of", "Public", "Health", "30Health", "and", "wellbeing", ";", "Environmental", "sciences", "and", "industries", ";", "\n", "Biotechnology", "\n", "Institutia", "Medico", "-", "Sanitara", "Publica", "\n", "Institutul", "Oncologic20Health", "and", "wellbeing", ";", "Energy", ";", "Governance", ",", "culture", ",", "education", "and", "\n", "the", "economy", "\n", "Ministry", "of", "Health", "of", "the", "Republic", "of", "\n", "Moldova15Health", "and", "wellbeing", ";", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy", ";", "Environmental", "sciences", "and", "industries", "\n", "National", "Agency", "for", "Research", "and", "\n", "Development15Governance", ",", "culture", ",", "education", "and", "the", "economy", ";", "Environmental", "\n", "sciences", "and", "industries", ";", "Agrifood", "\n", "Republican", "Clinical", "Hospital", "15", "Health", "and", "wellbeing", ";", "Fund", ".", "physics", "and", "mathematics", "\n", "Institute", "of", "Emergency", "Medicine", "12", "Health", "and", "wellbeing", "\n", "Institute", "of", "Neurology", "and", "\n", "Neurosurgery11Health", "and", "wellbeing", ";", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy", ";", "ICT", "and", "computer", "science", "\n", "Center", "of", "International", "Projects", "10Governance", ",", "culture", ",", "education", "and", "the", "economy", ";", "Environmental", "\n", "sciences", "and", "industries", ";", "ICT", "and", "computer", "scienceTable", "3.25", ".", "Top", "public", "actors", "in", "Moldova", "by", "number", "of", "records", ",", "across", "all", "domains", "\n", "MOLDOVA", " \n", "Top", "actors", "classified", "as", "‘", "Private", "company", ",", "for", "-", "profit", "’", "\n", "NameNo", "of", "\n", "recordsMain", "S&T", "domains", "\n", "Scientific", "and", "Engineering", "Centre", "\n", "‘", "Informinstrument’5Energy", ";", "Mechanical", "engineering", "and", "heavy", "machinery", ";", "\n", "Fundamental", "physics", "and", "mathematics", "\n", "Das", "Solutions", "S.R.L.", "3Governance", ",", "culture", ",", "education", "and", "the", "economy", ";", "ICT", "and", "computer", "\n", "science", ";", "Energy", "\n", "Technical", "-", "Scientific", "Company", "\n", "Informbusiness", "Srl2", "Energy", ";", "Mechanical", "engineering", "and", "heavy", "machinery", "\n", "Stattis", "LLC", "2", "Health", "and", "wellbeing", "\n", "Agentia", "de", "Logistica", "Age", "Quod", "Agis", "\n", "Srl1", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "Sunga", "LLC", "1", "Energy", ";", "Nanotechnology", "and", "materials", ";", "Optics", "and", "photonics", "\n", "Polivalent-95", "Srl", "1", "Nanotechnology", "and", "materials", "\n", "Societatea", "Pentru", "Metodologia", "\n", "Sondajelor1", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "Labromed", "Laborator", "Srl", "1", "Health", "and", "wellbeing", "\n", "Bell", "Vetro", "Srl", "1", "Energy", ";", "Mechanical", "engineering", "and", "heavy", "machineryTable", "3.26", ".", "Top", "private", "actors", "in", "Moldova", "by", "number", "of", "records", ",", "across", "all", "domains", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation205", "\n", "Ukraine", "\n", "The", "National", "Academy", "of", "Sciences", "dominates", "sci-", "\n", "entific", "production", 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"and", "the" ]	[]
digital twin requires replication of the factory, its robots, processes and the overlay of an AI algorithm. To facilitate this cooperation, EU companies should be encouraged to participate in an “AI Vertical Priorities Plan”. The aim of this plan would be to accelerate AI development across the ten strategic sectors where EU business models will benefit most from rapid AI introduction (automotives, advanced manufacturing and robotics, energy, telecoms, agriculture, aerospace, defence, environmental forecasting, pharma and healthcare). Companies that participate in the plan would benefit from EU funding for model development and a specific set of exemptions regarding competition and AI experimentation. In particular, to overcome the EU’s lack of large data sets, model training should be fed with data freely contributed by multiple EU companies within a certain sector. It should be supported within open-source frameworks, safeguarded from antitrust enforcement by competition authorities. Experimentation should be encouraged via the opening up, EU-wide coordination and harmonisation of national “AI Sandbox regimes” to companies participating in the plan. These experimental “sand - boxes” would enable regular assessments of regulatory hindrances deriving from EU or national legislation and provide feedback from private companies and research centres to regulators. Given the dominance of US providers, the EU must find a middle way between promoting its domestic cloud industry and ensuring access to the technologies it needs . It is too late for the EU to try and develop systematic challengers to the major US cloud providers: the investment needs involved are too large and would divert resources away from sectors and companies where the EU’s innovative prospects are better. However, for reasons of European sovereignty, the EU should ensure that it has a competitive domestic industry that can meet the demand for “sover - eign cloud” solutions. To achieve this goal, the report recommends adopting EU-wide data security policies for collaboration between EU and non-EU cloud providers, allowing access to US hyperscalers’ latest cloud technolo - gies while preserving encryption, security and ring-fenced services for trusted EU providers. At the same time, the EU should legislate mandatory standards for public sector procurement, thereby levelling the playing field for EU companies against larger non-EU players. Outside of “sovereign” market segments, it is recommended to negotiate a low barrier “digital transatlantic marketplace”, guaranteeing supply chain security and trade opportunities for EU and US tech companies on fair and equal conditions. To make these opportunities equally attractive	[ " ", "digital", "twin", "requires", "replication", "of", "the", "factory", ",", "its", "robots", ",", "\n", "processes", "and", "the", "overlay", "of", "an", "AI", "algorithm", ".", "To", "facilitate", "this", "cooperation", ",", "EU", "companies", "should", "be", "encouraged", "to", "\n", "participate", "in", "an", "“", "AI", "Vertical", "Priorities", "Plan", "”", ".", "The", "aim", "of", "this", "plan", "would", "be", "to", "accelerate", "AI", "development", "across", "the", "\n", "ten", "strategic", "sectors", "where", "EU", "business", "models", "will", "benefit", "most", "from", "rapid", "AI", "introduction", "(", "automotives", ",", "advanced", "\n", "manufacturing", "and", "robotics", ",", "energy", ",", "telecoms", ",", "agriculture", ",", "aerospace", ",", "defence", ",", "environmental", "forecasting", ",", "pharma", "\n", "and", "healthcare", ")", ".", "Companies", "that", "participate", "in", "the", "plan", "would", "benefit", "from", "EU", "funding", "for", "model", "development", "\n", "and", "a", "specific", "set", "of", "exemptions", "regarding", "competition", "and", "AI", "experimentation", ".", "In", "particular", ",", "to", "overcome", "the", "EU", "’s", "\n", "lack", "of", "large", "data", "sets", ",", "model", "training", "should", "be", "fed", "with", "data", "freely", "contributed", "by", "multiple", "EU", "companies", "within", "\n", "a", "certain", "sector", ".", "It", "should", "be", "supported", "within", "open", "-", "source", "frameworks", ",", "safeguarded", "from", "antitrust", "enforcement", "\n", "by", "competition", "authorities", ".", "Experimentation", "should", "be", "encouraged", "via", "the", "opening", "up", ",", "EU", "-", "wide", "coordination", "and", "\n", "harmonisation", "of", "national", "“", "AI", "Sandbox", "regimes", "”", "to", "companies", "participating", "in", "the", "plan", ".", "These", "experimental", "“", "sand", "-", "\n", "boxes", "”", "would", "enable", "regular", "assessments", "of", "regulatory", "hindrances", "deriving", "from", "EU", "or", "national", "legislation", "and", "\n", "provide", "feedback", "from", "private", "companies", "and", "research", "centres", "to", "regulators", ".", "\n", "Given", "the", "dominance", "of", "US", "providers", ",", "the", "EU", "must", "find", "a", "middle", "way", "between", "promoting", "its", "domestic", "cloud", "\n", "industry", "and", "ensuring", "access", "to", "the", "technologies", "it", "needs", ".", "It", "is", "too", "late", "for", "the", "EU", "to", "try", "and", "develop", "systematic", "\n", "challengers", "to", "the", "major", "US", "cloud", "providers", ":", "the", "investment", "needs", "involved", "are", "too", "large", "and", "would", "divert", "resources", "\n", "away", "from", "sectors", "and", "companies", "where", "the", "EU", "’s", "innovative", "prospects", "are", "better", ".", "However", ",", "for", "reasons", "of", "European", "\n", "sovereignty", ",", "the", "EU", "should", "ensure", "that", "it", "has", "a", "competitive", "domestic", "industry", "that", "can", "meet", "the", "demand", "for", "“", "sover", "-", "\n", "eign", "cloud", "”", "solutions", ".", "To", "achieve", "this", "goal", ",", "the", "report", "recommends", "adopting", "EU", "-", "wide", "data", "security", "policies", "for", "\n", "collaboration", "between", "EU", "and", "non", "-", "EU", "cloud", "providers", ",", "allowing", "access", "to", "US", "hyperscalers", "’", "latest", "cloud", "technolo", "-", "\n", "gies", "while", "preserving", "encryption", ",", "security", "and", "ring", "-", "fenced", "services", "for", "trusted", "EU", "providers", ".", "At", "the", "same", "time", ",", "the", "\n", "EU", "should", "legislate", "mandatory", "standards", "for", "public", "sector", "procurement", ",", "thereby", "levelling", "the", "playing", "field", "for", "EU", "\n", "companies", "against", "larger", "non", "-", "EU", "players", ".", "Outside", "of", "“", "sovereign", "”", "market", "segments", ",", "it", "is", "recommended", "to", "negotiate", "\n", "a", "low", "barrier", "“", "digital", "transatlantic", "marketplace", "”", ",", "guaranteeing", "supply", "chain", "security", "and", "trade", "opportunities", "for", "EU", "\n", "and", "US", "tech", "companies", "on", "fair", "and", "equal", "conditions", ".", "To", "make", "these", "opportunities", "equally", "attractive" ]	[]
AA NiMH batteries after 8 months of storage. The AAA and AA batteries have similar voltage profiles. However, as expected, the current values are different. This results in different capacity values of 912 mAh for AAA and 2095 mAh for AA. Furthermore, the corresponding current curves for C and D batteries are presented in Figure 9c,d. In this case, the C battery reaches a capacity of 3249 mAh and the D battery a capacity of 7117 mAh. In the case of the 9V NiMH (Figure 9e), the battery shows a discharge capacity of 260 mAh after the storage period of 8 months.Batteries 2025 ,11, 30 13 of 20 Batteries 2025, 11, x FOR PEER REVIEW 14 of 21 3. Finally, the batteries are discharged again at a 0.2 C rate, and the duration of this discharge was measured. A battery is considered to pass the test if the discharge is longer than 4 h before reach- ing the cut-oﬀ voltage, i.e., if they have at least 80% of their initial capacity left after stor- age. Figure 9a,b show the discharge proﬁles of AAA and AA NiMH batteries after 8 months of storage. The AAA and AA batteries have similar voltage proﬁles. However, as expected, the current values are diﬀerent. This results in diﬀerent capacity values of 912 mAh for AAA and 2095 mAh for AA. Furthermore, the corresponding current curves for C and D batteries are presented in Figure 9c,d. In this case, the C battery reaches a capacity of 3249 mAh and the D battery a capacity of 7117 mAh. In the case of the 9V NiMH (Figure 9e), the battery shows a discharge capacity of 260 mAh after the storage period of 8 months. Figure 9. NiMH charge (capacity) recovery analysis according to IEC 61951-2 of (a) AAA Agfaphoto, (b) AA GP, (c) C Varta, (d) D GP, and (e) 9V Duracell batteries. Figure 9. NiMH charge (capacity) recovery analysis according to IEC 61951-2 of ( a) AAA Agfaphoto, (b) AA GP , ( c) C Varta, ( d) D GP , and ( e) 9V Duracell batteries. Furthermore, all portable NiMH batteries tested during the recovery experiment show a discharge duration longer than 4 h, and in terms of columbic efficiency, the highest observed value is 78% for the D size and the smallest value is 62% for the 9V battery. 6. 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Much ado about nothing? An empirical analysis of consumer behaviour in the presence of ‘dual food quality ’ Di Marcantonio Federicaa,*, Jesus Barreiro-Hurleb , Luisa Menapacec , Colen Liesbethd, Dessart François J.b, Ciaian Pavelb aDr., European Court of Auditors - 12, Rue Alcide De Gasperi, 1615, Luxembourg bDr., Joint Research Centre European Commission, Calle Inca Garcilaso 3 - Edificio EXPO, 41092 Seville, Spain cTechnical University of Munich, Alte Akademie 12, 85354 Freising, Germany dGeorg-August-Universit at Gottingen, Platz der Gottinger Sieben 5, D-37073 Gottingen, Germany ARTICLE INFO Keywords: Dual food quality Information asymmetry Choice experiment And lab experimentABSTRACT Marketing food products with slightly different compositions as identical across countries is a common practice in the food industry. While food companies argue that different versions reflect taste preferences, some Central and Eastern European consumers allege that multinational companies sell lower quality products using the same brand name and packaging as in Western European countries. The political attention gathered by this practice, exemplified by the dual food quality (DFQ) debate in the European Union (EU), has largely neglected how the presence of DFQ affects consumers ’ purchase decisions. This study aims to help fill this gap. Additionally, it examines the impact of a policy intervention consisting of a ‘made for’ claim and the role of the brand name on consumer choices. Through online discrete-choice experiments and laboratory tasting and rating experiments in six EU countries, no systematic support is found for either the industry ’s or the consumers ’ arguments. Results also indicate that a policy requiring consumers to be informed about the destination market of different versions would increase consumers ’ valuation of domestic products, while at the same time improving transparency and avoiding misleading consumers. 1.Introduction The practice of marketing food products with slightly different compositions as identical across countries seems to be common all around the world. For example, in the US, some products like Mountain Dew®, Hellmann ’s Mayonnaise or Heinz Ketchup are marketed with different ingredients to their UK counterpart, due to regulatory re- quirements, company economic advantages or local consumer prefer - ences (Focos, 2019 ). Similarly, within the European Union (EU), one can find Fanta Orange with different percentages of orange juice concen - trate, from 12 % in Italy to 4.5 % in Denmark, and Lay’s potato chips sold in the Czech Republic, Hungary and Lithuania have saturated fat levels about three times higher than	[ "Much", "ado", "about", "nothing", "?", "An", "empirical", "analysis", "of", "consumer", "behaviour", "in", "\n", "the", "presence", "of", "‘", "dual", "food", "quality", "’", "\n", "Di", "Marcantonio", "Federicaa", ",", "*", ",", "Jesus", "Barreiro", "-", "Hurleb", "\n", ",", "Luisa", "Menapacec", "\n", ",", "Colen", "Liesbethd", ",", " \n", "Dessart", "François", "J.b", ",", "Ciaian", "Pavelb", "\n", "aDr", ".", ",", "European", "Court", "of", "Auditors", "-", "12", ",", "Rue", "Alcide", "De", "Gasperi", ",", "1615", ",", "Luxembourg", "\n", "bDr", ".", ",", "Joint", "Research", "Centre", "European", "Commission", ",", "Calle", "Inca", "Garcilaso", "3", "-", "Edificio", "EXPO", ",", "41092", "Seville", ",", "Spain", "\n", "cTechnical", "University", "of", "Munich", ",", "Alte", "Akademie", "12", ",", "85354", "Freising", ",", "Germany", "\n", "dGeorg", "-", "August", "-", "Universit", "at", "Gottingen", ",", "Platz", "der", "Gottinger", "Sieben", "5", ",", "D-37073", "Gottingen", ",", "Germany", "\n", "ARTICLE", "INFO", "\n", "Keywords", ":", "\n", "Dual", "food", "quality", "\n", "Information", "asymmetry", "\n", "Choice", "experiment", "\n", "And", "lab", "experimentABSTRACT", "\n", "Marketing", "food", "products", "with", "slightly", "different", "compositions", "as", "identical", "across", "countries", "is", "a", "common", "practice", "\n", "in", "the", "food", "industry", ".", "While", "food", "companies", "argue", "that", "different", "versions", "reflect", "taste", "preferences", ",", "some", "Central", "\n", "and", "Eastern", "European", "consumers", "allege", "that", "multinational", "companies", "sell", "lower", "quality", "products", "using", "the", "same", "\n", "brand", "name", "and", "packaging", "as", "in", "Western", "European", "countries", ".", "The", "political", "attention", "gathered", "by", "this", "practice", ",", "\n", "exemplified", "by", "the", "dual", "food", "quality", "(", "DFQ", ")", "debate", "in", "the", "European", "Union", "(", "EU", ")", ",", "has", "largely", "neglected", "how", "the", "\n", "presence", "of", "DFQ", "affects", "consumers", "’", "purchase", "decisions", ".", "This", "study", "aims", "to", "help", "fill", "this", "gap", ".", "Additionally", ",", "it", "\n", "examines", "the", "impact", "of", "a", "policy", "intervention", "consisting", "of", "a", "‘", "made", "for", "’", "claim", "and", "the", "role", "of", "the", "brand", "name", "on", "\n", "consumer", "choices", ".", "Through", "online", "discrete", "-", "choice", "experiments", "and", "laboratory", "tasting", "and", "rating", "experiments", "in", "\n", "six", "EU", "countries", ",", "no", "systematic", "support", "is", "found", "for", "either", "the", "industry", "’s", "or", "the", "consumers", "’", "arguments", ".", "Results", "\n", "also", "indicate", "that", "a", "policy", "requiring", "consumers", "to", "be", "informed", "about", "the", "destination", "market", "of", "different", "versions", "\n", "would", "increase", "consumers", "’", "valuation", "of", "domestic", "products", ",", "while", "at", "the", "same", "time", "improving", "transparency", "and", "\n", "avoiding", "misleading", "consumers", ".", "\n", "1.Introduction", "\n", "The", "practice", "of", "marketing", "food", "products", "with", "slightly", "different", "\n", "compositions", "as", "identical", "across", "countries", "seems", "to", "be", "common", "all", "\n", "around", "the", "world", ".", "For", "example", ",", "in", "the", "US", ",", "some", "products", "like", "Mountain", "\n", "Dew", "®", ",", "Hellmann", "’s", "Mayonnaise", "or", "Heinz", "Ketchup", "are", "marketed", "with", "\n", "different", "ingredients", "to", "their", "UK", "counterpart", ",", "due", "to", "regulatory", "re-", "\n", "quirements", ",", "company", "economic", "advantages", "or", "local", "consumer", "prefer", "-", "\n", "ences", "(", "Focos", ",", "2019", ")", ".", "Similarly", ",", "within", "the", "European", "Union", "(", "EU", ")", ",", "one", "can", "\n", "find", "Fanta", "Orange", "with", "different", "percentages", "of", "orange", "juice", "concen", "-", "\n", "trate", ",", "from", "12", "%", "in", "Italy", "to", "4.5", "%", "in", "Denmark", ",", "and", "Lay", "’s", "potato", "chips", "\n", "sold", "in", "the", "Czech", "Republic", ",", "Hungary", "and", "Lithuania", "have", "saturated", "fat", "\n", "levels", "about", "three", "times", "higher", "than" ]	[ { "end": 2408, "label": "CITATION-REFEERENCE", "start": 2397 } ]
capes, jackets, suits, blazers, trousers, shorts, shirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2)X 0.6% 844Women’s or girls’ coats, capes, jackets, suits, trousers, shorts, shirts, dresses and skirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2)X 1.6% 845Articles of apparel, of textile fabrics, whether or not knitted or crocheted, n.e.s.X 3.1% 848Articles of apparel and clothing accessories of other than textile fabrics; headgear of all materialsX 0.2% 851 Footwear X 1.4% 873 Meters and counters, n.e.s. X 0.7% 874Measuring, checking, analysing and controlling instruments and apparatus, n.e.s.X 0.4% 892 Printed matter X 0.2% 893 Articles, n.e.s., of plastics X 1.1% 894 Baby carriages, toys, games and sporting goods X 0.3% 899 Miscellaneous manufactured articles, n.e.s. X 0.2% 9Commodities and transactions not classified elsewhere in the SITC Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation75 Mapping of goods export specialisations – results for Ukraine Results of the export mapping for Ukraine are shown in Table 2.20. The 51 goods categories with current strength represent almost 70% of the total exports for 2012-2019. Most goods catego- ries are small, except for Maize (SITC 044), Fixed vegetable fats and oils (SITC 421) and Ingots and other primary forms, of iron and steel (SITC 672), which account for 7% or more of the total exports. Specialised exports in Food and live animals (SITC 0) account for almost 18% of the total exports; those in Crude materials, inedible, except fuels (SITC 2) for more than 12%; and those in Manu- factured goods classified chiefly by material (SITC 6) for more than 7%. The 52 goods categories with emerging strength represent 47% of the total exports. Export spe- cialisations are primarily increasing in Food and live animals (SITC 0) and Crude materials, inedible, except fuels (SITC 2). Ukraine has a diversified export specialisation. However, the share of export specialisations in low value-added activities remains high, whereas ex- port specialisations in manufactured products are declining. SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 51 69.6% 52 47.0% 0 Food and live animals 011 Meat of bovine animals, fresh, chilled or frozen X 0.2% 012Other meat and edible meat offal, fresh, chilled or frozen (except meat and meat offal unfit or unsuitable for human consumption) X 0.8% 022Milk and cream and	[ "capes", ",", "jackets", ",", "suits", ",", "blazers", ",", "trousers", ",", "\n", "shorts", ",", "shirts", ",", "underwear", ",", "nightwear", "and", "similar", "articles", "of", "\n", "textile", "fabrics", ",", "knitted", "or", "crocheted", "(", "other", "than", "those", "of", "\n", "subgroup", "845.2)X", "0.6", "%", " \n", "844Women", "’s", "or", "girls", "’", "coats", ",", "capes", ",", "jackets", ",", "suits", ",", "trousers", ",", "\n", "shorts", ",", "shirts", ",", "dresses", "and", "skirts", ",", "underwear", ",", "nightwear", "and", "\n", "similar", "articles", "of", "textile", "fabrics", ",", "knitted", "or", "crocheted", "(", 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MoldovaTemporal evolution of the domains Period over period change in the relative size of each domain, domain size and data source size independent (% change for 2015-2018, over previous period 2011-2014) Change in share of publicationsChange in share of patents Change, weighted average of publications and patents Agrifood -23.32% 5.93% -5.98% Biotechnology -15.94% 24.02% 2.94% Chemistry and chemical engineering -12.21% 8.22% -9.27% Electric and electronic technologies 22.13% -20.66% 3.80% Energy 47.64% 7.10% 33.86% Environmental sciences and industries -0.07%Insufficient data-0.07% Fundamental physics and mathematics -8.50%Insufficient data-8.50% Governance, culture, education and the economy39.81%Insufficient data39.81% Health and wellbeing 57.18% 13.52% 43.96% ICT and computer science -40.09% -19.17% -30.83% Mechanical engineering and heavy machinery78.89% 6.03% 21.81% Nanotechnology and materials -18.28% -28.96% -19.30% Optics and photonics -10.17%Insufficient data-10.17%Table 3.16. Temporal evolution of Moldova’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation191 Ukraine Table 3.17 and Figure 3.36 showcase the num- ber of records per S&T specialisation domain in Ukraine. Nanotechnology and materials is the do- main with the most records (with a total of 27 127), followed by Health and wellbeing (22 197), Mechanical engineering and heavy machinery (21 053), Fundamental physics and mathematics (19 555) and Biotechnology (14 463). Publications account for the majority of records in most domains, as shown in Figure 3.36, with the exceptions being Energy (46%), Electric and electronic technologies (45%), Agrifood (25%) and Mechanical engineering and heavy machinery (22%), all in which the number of patents is higher than the number of publications. Ukraine has a high concentration of EC projects in the domain of Governance, culture, education and the economy. However, EC projects are also concentrated in several other domains such as ICT and computer science, Health and wellbeing, Environmental sciences and industries and Nano-technology and materials and Energy, signalling the international propensity and relevance of the related Ukrainian S&T actors in these domains. The growth rate of publications in recent years, in terms of the compound annual growth rate, is also shown. All domains show a growing number of publications, except for Optics and photonics (-0.5%). Ukraine’s publications are highly specialised in Transportation (with an SI of 2.5), Electric and electronic technologies (2.3) and Biotechnology (2.0), as well as Energy (1.9), Mechanical engi- neering and heavy machinery (1.6) and others. In relation to the aggregate EaP, Ukraine presents a high normalised citation impact in most do- mains. Energy (1.5), Transportation	[ "MoldovaTemporal", "evolution", "of", "the", "domains", "\n", "Period", "over", "period", "change", "in", "the", "relative", "size", "of", "each", "domain", ",", "\n", "domain", "size", "and", "data", "source", "size", "independent", "\n", "(", "%", "change", "for", "2015", "-", "2018", ",", "over", "previous", "period", "2011", "-", "2014", ")", "\n", "Change", "in", "\n", "share", "of", "\n", "publicationsChange", "in", "share", "\n", "of", "patents", "Change", ",", "weighted", "average", "of", "\n", "publications", "and", "patents", "\n", "Agrifood", "-23.32", "%", "5.93", "%", "-5.98", "%", "\n", "Biotechnology", "-15.94", "%", "24.02", "%", "2.94", "%", "\n", "Chemistry", "and", "chemical", "engineering", "-12.21", "%", "8.22", "%", "-9.27", "%", "\n", "Electric", "and", "electronic", "technologies", "22.13", "%", "-20.66", "%", "3.80", "%", "\n", "Energy", "47.64", "%", "7.10", "%", "33.86", "%", "\n", "Environmental", "sciences", "and", "industries", "-0.07%Insufficient", "\n", "data-0.07", "%", "\n", "Fundamental", "physics", "and", "mathematics", "-8.50%Insufficient", "\n", "data-8.50", "%", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy39.81%Insufficient", "\n", "data39.81", "%", "\n", "Health", "and", "wellbeing", "57.18", "%", "13.52", "%", "43.96", "%", "\n", "ICT", "and", "computer", "science", "-40.09", "%", "-19.17", "%", "-30.83", "%", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machinery78.89", "%", "6.03", "%", "21.81", "%", "\n", "Nanotechnology", "and", "materials", "-18.28", "%", "-28.96", "%", "-19.30", "%", "\n", "Optics", "and", "photonics", "-10.17%Insufficient", "\n", "data-10.17%Table", "3.16", ".", "Temporal", "evolution", "of", "Moldova", "’s", "S&T", "domains", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation191", "\n", "Ukraine", "\n", "Table", "3.17", "and", "Figure", "3.36", "showcase", "the", "num-", "\n", "ber", "of", "records", "per", "S&T", "specialisation", "domain", "in", "\n", "Ukraine", ".", "Nanotechnology", "and", "materials", "is", "the", "do-", "\n", "main", "with", "the", "most", "records", "(", "with", "a", "total", "of", "27", "\n", "127", ")", ",", "followed", "by", "Health", "and", "wellbeing", "(", "22", "197", ")", ",", "\n", "Mechanical", "engineering", "and", "heavy", "machinery", "(", "21", "\n", "053", ")", ",", "Fundamental", "physics", "and", "mathematics", "(", "19", "\n", "555", ")", "and", "Biotechnology", "(", "14", "463", ")", ".", "\n", "Publications", "account", "for", "the", "majority", "of", "records", "\n", "in", "most", "domains", ",", "as", "shown", "in", "Figure", "3.36", ",", "with", "\n", "the", "exceptions", "being", "Energy", "(", "46", "%", ")", ",", "Electric", "and", "\n", "electronic", "technologies", "(", "45", "%", ")", ",", "Agrifood", "(", "25", "%", ")", "\n", "and", "Mechanical", "engineering", "and", "heavy", "machinery", "\n", "(", "22", "%", ")", ",", "all", "in", "which", "the", "number", "of", "patents", "is", "higher", "\n", "than", "the", "number", "of", "publications", ".", "\n", "Ukraine", "has", "a", "high", "concentration", "of", "EC", "projects", "\n", "in", "the", "domain", "of", "Governance", ",", "culture", ",", "education", "\n", "and", "the", "economy", ".", "However", ",", "EC", "projects", "are", "also", "\n", "concentrated", "in", "several", "other", "domains", "such", "as", "\n", "ICT", "and", "computer", "science", ",", "Health", "and", "wellbeing", ",", "\n", "Environmental", "sciences", "and", "industries", "and", "Nano", "-", "technology", "and", "materials", "and", "Energy", ",", "signalling", "\n", "the", "international", "propensity", "and", "relevance", "of", "the", "\n", "related", "Ukrainian", "S&T", "actors", "in", "these", "domains", ".", "\n", "The", "growth", "rate", "of", "publications", "in", "recent", "years", ",", "\n", "in", "terms", "of", "the", "compound", "annual", "growth", "rate", ",", "is", "\n", "also", "shown", ".", "All", "domains", "show", "a", "growing", "number", "\n", "of", "publications", ",", "except", "for", "Optics", "and", "photonics", "\n", "(", "-0.5", "%", ")", ".", "\n", "Ukraine", "’s", "publications", "are", "highly", "specialised", "in", "\n", "Transportation", "(", "with", "an", "SI", "of", "2.5", ")", ",", "Electric", "and", "\n", "electronic", "technologies", "(", "2.3", ")", "and", "Biotechnology", "\n", "(", "2.0", ")", ",", "as", "well", "as", "Energy", "(", "1.9", ")", ",", "Mechanical", "engi-", "\n", "neering", "and", "heavy", "machinery", "(", "1.6", ")", "and", "others", ".", "\n", "In", "relation", "to", "the", "aggregate", "EaP", ",", "Ukraine", "presents", "\n", "a", "high", "normalised", "citation", "impact", "in", "most", "do-", "\n", "mains", ".", "Energy", "(", "1.5", ")", ",", "Transportation" ]	[]
and nuclear energy from higher and more volatile fossil fuel prices, preventing end users from capturing the full benefits of clean energy in their bills [see Figure 5] . In 2022 at the peak of the energy crisis, natural gas was the price- setter 63% of the time, despite making up only 20% share of the EU’s electricity mix. The use of long-term contract solutions – like Power Purchase Agreement (PPA) markets or Contracts for Difference (CfDs) – can help attenuate the link between the marginal price setter and the cost of energy for end users, but such solutions are underdeveloped in Europe, in turn limiting the benefits from accelerating the roll-out of renewables. In the absence of action, this decoupling problem will remain acute at least for the remainder of this decade. Even if renewable installation targets are met, it is not forecast to significantly reduce the share of hours during which fossil fuels set energy prices by 2030. FIGURE 5 Price-setting technology per Member State and their generation mix %, 2022 Source: European Commission (JRC), 2023 Note: Market share of natural gas by venue in % of reported notionals, excluding central counterparties and clearing members. The figure shows that the top-5 and top-10 EU counterparties (in terms of gross notionals) accounted for more than 50% and 60% respectively of reported notionals by EU entities on each of the two EU gas regulated markets. Data as of November 2022. OI: Open Interest. TV: Trading Venue. OTC: Over-the-counter. Sources: Trade repositories (TRs), Bank of England, ESMA.Note: Absolute value of net positions in EUR billion for the top five long and short non-financial corporate counterparties and positions in % of average daily trading volume, in % rhs. The high concentration of positions indicates that if several firms with similar directional positions were to reduce their exposures, they could amplify market moves. Sources: EMIR, ESMA. 44THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 3FIGURE 6 Electricity wholesale and retail prices across Member States for industry EUR/MWh, 2023 Source: European Commission, 2024. Based on Eurostat, S&P Global, and ENTSO-E, 2024. A lengthy and uncertain permitting process for new power supply and grids is a major obstacle to faster installation of new capacity . Investments in both power generation and grids require several years between feasi - bility studies and project completion. However, there is a large variation in permitting times between	[ " ", "and", "nuclear", "energy", "from", "higher", "and", "more", "volatile", "fossil", "fuel", "prices", ",", "preventing", "end", "users", "from", "capturing", "the", "full", "\n", "benefits", "of", "clean", "energy", "in", "their", "bills", "[", "see", "Figure", "5", "]", ".", "In", "2022", "at", "the", "peak", "of", "the", "energy", "crisis", ",", "natural", "gas", "was", "the", "price-", "\n", "setter", "63", "%", "of", "the", "time", ",", "despite", "making", "up", "only", "20", "%", "share", "of", "the", "EU", "’s", "electricity", "mix", ".", "The", "use", "of", "long", "-", "term", "contract", "\n", "solutions", "–", "like", "Power", "Purchase", "Agreement", "(", "PPA", ")", "markets", "or", "Contracts", "for", "Difference", "(", "CfDs", ")", "–", "can", "help", "attenuate", "the", "\n", "link", "between", "the", "marginal", "price", "setter", "and", "the", "cost", "of", "energy", "for", "end", "users", ",", "but", "such", "solutions", "are", "underdeveloped", "\n", "in", "Europe", ",", "in", "turn", "limiting", "the", "benefits", "from", "accelerating", "the", "roll", "-", "out", "of", "renewables", ".", "In", "the", "absence", "of", "action", ",", "this", "\n", "decoupling", "problem", "will", "remain", "acute", "at", "least", "for", "the", "remainder", "of", "this", "decade", ".", "Even", "if", "renewable", "installation", "targets", "\n", "are", "met", ",", "it", "is", "not", "forecast", "to", "significantly", "reduce", "the", "share", "of", "hours", "during", "which", "fossil", "fuels", "set", "energy", "prices", "by", "2030", ".", "\n", "FIGURE", "5", "\n", "Price", "-", "setting", "technology", "per", "Member", "State", "and", "their", "generation", "mix", " \n", "%", ",", "2022", "\n", "Source", ":", "European", "Commission", "(", "JRC", ")", ",", "2023", "\n", "Note", ":", " ", "Market", "share", "of", "natural", "gas", "by", "venue", "in", "%", "of", "reported", "notionals", ",", "\n", "excluding", "central", "counterparties", "and", "clearing", "members", ".", "The", "figure", "shows", "\n", "that", "the", "top-5", "and", "top-10", "EU", "counterparties", "(", "in", "terms", "of", "gross", "notionals", ")", "\n", "accounted", "for", "more", "than", "50", "%", "and", "60", "%", "respectively", "of", "reported", "notionals", "by", "\n", "EU", "entities", "on", "each", "of", "the", "two", "EU", "gas", "regulated", "markets", ".", "Data", "as", "of", "November", "\n", "2022", ".", "OI", ":", "Open", "Interest", ".", "TV", ":", "Trading", "Venue", ".", "OTC", ":", "Over", "-", "the", "-", "counter", ".", "\n", "Sources", ":", "Trade", "repositories", "(", "TRs", ")", ",", "Bank", "of", "England", ",", "ESMA.Note", ":", "Absolute", "value", "of", "net", "positions", "in", "EUR", "billion", "for", "the", "top", "five", "long", "and", "\n", "short", "non", "-", "financial", "corporate", "counterparties", "and", "positions", "in", "%", "of", "average", "\n", "daily", "trading", "volume", ",", "in", "%", "rhs", ".", "The", "high", "concentration", "of", "positions", "indicates", "\n", "that", "if", "several", "firms", "with", "similar", "directional", "positions", "were", "to", "reduce", "their", "\n", "exposures", ",", "they", "could", "amplify", "market", "moves", ".", "\n", "Sources", ":", "EMIR", ",", "ESMA", ".", "\n", "44THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "3FIGURE", "6", "\n", "Electricity", "wholesale", "and", "retail", "prices", "across", "Member", "States", "for", "industry", " \n", "EUR", "/", "MWh", ",", "2023", "\n", "Source", ":", "European", "Commission", ",", "2024", ".", "Based", "on", "Eurostat", ",", "S&P", "Global", ",", "and", "ENTSO", "-", "E", ",", "2024", ".", "\n", "A", "lengthy", "and", "uncertain", "permitting", "process", "for", "new", "power", "supply", "and", "grids", "is", "a", "major", "obstacle", "to", "faster", "\n", "installation", "of", "new", "capacity", ".", "Investments", "in", "both", "power", "generation", "and", "grids", "require", "several", "years", "between", "feasi", "-", "\n", "bility", "studies", "and", "project", "completion", ".", "However", ",", "there", "is", "a", "large", "variation", "in", "permitting", "times", "between" ]	[]
there are some industries where Europe’s cost disadvantage is too large to be a serious competitor. Even if the EU has lost ground owing to foreign subsidies, it makes economic sense to import necessary technology and allow foreign taxpayers to bear the costs, while diver - sifying suppliers to the extent possible to limit dependencies. The second broad case is industries where the EU is concerned about where production takes place – to protect jobs from unfair competition – but is agnostic about where the underlying technology originates from. In this case, an effective policy mix would be to encourage inward FDI while deploying trade measures to offset the cost advantage gained by foreign subsidies. With the combination of recent tariff increases and FDI announcements in some Member States, this approach is currently being de facto applied in the automotive sector. The third case is industries where the EU has a strategic interest in ensuring that European companies retain relevant know-how and manufacturing capacity, allowing production to be ramped up in the event of geopolitical tensions. Here the EU should aim to increase the long-term “bankability” of new investments in Europe, for instance by applying local-content requirements, and to ensure a minimum level of technological sovereignty. The latter can be achieved by requiring foreign companies that want to produce in Europe to enter into joint ventures with local companies. Security considerations may lead to changes over time in the classification of industries of strategic interest. The fourth case is “infant industries” where the EU has an innovative edge and sees high future growth potential. In this case, there is a well-established playbook of applying a full range of trade-dis - torting measures until the industry reaches sufficient scale and protections can be withdrawn. 41THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 3Executing this strategy will require a joint decarbonisation and competitiveness plan where all policies are aligned behind the EU’s objectives . Priority areas to be addressed include, first, lowering energy costs for end users by transferring the benefits of the decarbonisation and accelerating the decarbonisation of the energy sector in a cost-efficient way, leveraging all available solutions. Second, capturing the industrial opportunities presented by the green transition, ranging from remaining at the forefront of clean tech innovation to manufacturing clean tech at scale to leveraging the opportunities from circularity. Third, levelling the playing field in sectors more	[ " ", "there", "are", "some", "industries", "where", "Europe", "’s", "cost", "\n", "disadvantage", "is", "too", "large", "to", "be", "a", "serious", "competitor", ".", "Even", "if", "the", "EU", "has", "lost", "ground", "owing", "to", "foreign", "subsidies", ",", "it", "\n", "makes", "economic", "sense", "to", "import", "necessary", "technology", "and", "allow", "foreign", "taxpayers", "to", "bear", "the", "costs", ",", "while", "diver", "-", "\n", "sifying", "suppliers", "to", "the", "extent", "possible", "to", "limit", "dependencies", ".", "The", "second", "broad", "case", "is", "industries", "where", "the", "EU", "is", "\n", "concerned", "about", "where", "production", "takes", "place", "–", "to", "protect", "jobs", "from", "unfair", "competition", "–", "but", "is", "agnostic", "about", "\n", "where", "the", "underlying", "technology", "originates", "from", ".", "In", "this", "case", ",", "an", "effective", "policy", "mix", "would", "be", "to", "encourage", "inward", "\n", "FDI", "while", "deploying", "trade", "measures", "to", "offset", "the", "cost", "advantage", "gained", "by", "foreign", "subsidies", ".", "With", "the", "combination", "\n", "of", "recent", "tariff", "increases", "and", "FDI", "announcements", "in", "some", "Member", "States", ",", "this", "approach", "is", "currently", "being", "de", "facto", "\n", "applied", "in", "the", "automotive", "sector", ".", "The", "third", "case", "is", "industries", "where", "the", "EU", "has", "a", "strategic", "interest", "in", "ensuring", "that", "\n", "European", "companies", "retain", "relevant", "know", "-", "how", "and", "manufacturing", "capacity", ",", "allowing", "production", "to", "be", "ramped", "up", "in", "\n", "the", "event", "of", "geopolitical", "tensions", ".", "Here", "the", "EU", "should", "aim", "to", "increase", "the", "long", "-", "term", "“", "bankability", "”", "of", "new", "investments", "\n", "in", "Europe", ",", "for", "instance", "by", "applying", "local", "-", "content", "requirements", ",", "and", "to", "ensure", "a", "minimum", "level", "of", "technological", "\n", "sovereignty", ".", "The", "latter", "can", "be", "achieved", "by", "requiring", "foreign", "companies", "that", "want", "to", "produce", "in", "Europe", "to", "enter", "into", "\n", "joint", "ventures", "with", "local", "companies", ".", "Security", "considerations", "may", "lead", "to", "changes", "over", "time", "in", "the", "classification", "of", "\n", "industries", "of", "strategic", "interest", ".", "The", "fourth", "case", "is", "“", "infant", "industries", "”", "where", "the", "EU", "has", "an", "innovative", "edge", "and", "sees", "\n", "high", "future", "growth", "potential", ".", "In", "this", "case", ",", "there", "is", "a", "well", "-", "established", "playbook", "of", "applying", "a", "full", "range", "of", "trade", "-", "dis", "-", "\n", "torting", "measures", "until", "the", "industry", "reaches", "sufficient", "scale", "and", "protections", "can", "be", "withdrawn", ".", "\n", "41THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "3Executing", "this", "strategy", "will", "require", "a", "joint", "decarbonisation", "and", "competitiveness", "plan", "where", "all", "policies", "are", "\n", "aligned", "behind", "the", "EU", "’s", "objectives", ".", "Priority", "areas", "to", "be", "addressed", "include", ",", "first", ",", "lowering", "energy", "costs", "for", "end", "\n", "users", "by", "transferring", "the", "benefits", "of", "the", "decarbonisation", "and", "accelerating", "the", "decarbonisation", "of", "the", "energy", "sector", "\n", "in", "a", "cost", "-", "efficient", "way", ",", "leveraging", "all", "available", "solutions", ".", "Second", ",", "capturing", "the", "industrial", "opportunities", "presented", "by", "\n", "the", "green", "transition", ",", "ranging", "from", "remaining", "at", "the", "forefront", "of", "clean", "tech", "innovation", "to", "manufacturing", "clean", "tech", "\n", "at", "scale", "to", "leveraging", "the", "opportunities", "from", "circularity", ".", "Third", ",", "levelling", "the", "playing", "field", "in", "sectors", "more" ]	[]
6% to those in the EU. For quantum computing, EU companies attract only 5% of global private funding compared with a 50% share attracted by US companies. Regulatory barriers to scaling up are particularly onerous in the tech sector, especially for young companies [see the chapters on innovation, and digitalisation and advanced technologies] . Regulatory barriers constrain growth in several ways. First, complex and costly procedures across fragmented national systems discourage inventors from filing Intellectual Property Rights (IPRs), hindering young companies from leveraging the Single Market. Second, the EU’s regulatory stance towards tech companies hampers innovation: the EU now has around 100 tech-focused lawsxi and over 270 regulators active in digital networks across all Member States. Many EU laws take a precau - tionary approach, dictating specific business practices ex ante to avert potential risks ex post. For example, the AI Act imposes additional regulatory requirements on general purpose AI models that exceed a pre-defined threshold of computational power – a threshold which some state-of-the-art models already exceed. Third, digital compa - nies are deterred from doing business across the EU via subsidiaries, as they face heterogeneous requirements, a proliferation of regulatory agencies and “gold plating”04 of EU legislation by national authorities. Fourth, limitations on data storing and processing create high compliance costs and hinder the creation of large, integrated data sets for training AI models. This fragmentation puts EU companies at a disadvantage relative to the US, which relies on the private sector to build vast data sets, and China, which can leverage its central institutions for data aggregation. This problem is compounded by EU competition enforcement possibly inhibiting intra-industry cooperation. Finally, multiple different national rules in public procurement generate high ongoing costs for cloud providers. The net effect of this burden of regulation is that only larger companies – which are often non-EU based – have the financial capacity and incentive to bear the costs of complying. Young innovative tech companies may choose not to operate in the EU at all. The lack of a true Single Market also prevents enough companies in the wider economy from reaching sufficient size to accelerate adoption of advanced technologies . There are many barriers that lead to compa - nies in Europe to “stay small” and neglect the opportunities of the Single Market. These include the high cost of adhering to heterogenous national regulations, the high cost of tax compliance, and	[ " ", "6", "%", "to", "those", "in", "the", "EU", ".", "For", "quantum", "computing", ",", "EU", "companies", "attract", "only", "5", "%", "of", "global", "\n", "private", "funding", "compared", "with", "a", "50", "%", "share", "attracted", "by", "US", "companies", ".", "\n", "Regulatory", "barriers", "to", "scaling", "up", "are", "particularly", "onerous", "in", "the", "tech", "sector", ",", "especially", "for", "young", "companies", " \n", "[", "see", "the", "chapters", "on", "innovation", ",", "and", "digitalisation", "and", "advanced", "technologies", "]", ".", "Regulatory", "barriers", "constrain", "growth", "\n", "in", "several", "ways", ".", "First", ",", "complex", "and", "costly", "procedures", "across", "fragmented", "national", "systems", "discourage", "inventors", "from", "\n", "filing", "Intellectual", "Property", "Rights", "(", "IPRs", ")", ",", "hindering", "young", "companies", "from", "leveraging", "the", "Single", "Market", ".", "Second", ",", "\n", "the", "EU", "’s", "regulatory", "stance", "towards", "tech", "companies", "hampers", "innovation", ":", "the", "EU", "now", "has", "around", "100", "tech", "-", "focused", "\n", "lawsxi", "and", "over", "270", "regulators", "active", "in", "digital", "networks", "across", "all", "Member", "States", ".", "Many", "EU", "laws", "take", "a", "precau", "-", "\n", "tionary", "approach", ",", "dictating", "specific", "business", "practices", "ex", "ante", "to", "avert", "potential", "risks", "ex", "post", ".", "For", "example", ",", "the", "AI", "\n", "Act", "imposes", "additional", "regulatory", "requirements", "on", "general", "purpose", "AI", "models", "that", "exceed", "a", "pre", "-", "defined", "threshold", "\n", "of", "computational", "power", "–", "a", "threshold", "which", "some", "state", "-", "of", "-", "the", "-", "art", "models", "already", "exceed", ".", "Third", ",", "digital", "compa", "-", "\n", "nies", "are", "deterred", "from", "doing", "business", "across", "the", "EU", "via", "subsidiaries", ",", "as", "they", "face", "heterogeneous", "requirements", ",", "a", "\n", "proliferation", "of", "regulatory", "agencies", "and", "“", "gold", "plating”04", "of", "EU", "legislation", "by", "national", "authorities", ".", "Fourth", ",", "limitations", "\n", "on", "data", "storing", "and", "processing", "create", "high", "compliance", "costs", "and", "hinder", "the", "creation", "of", "large", ",", "integrated", "data", "sets", "\n", "for", "training", "AI", "models", ".", "This", "fragmentation", "puts", "EU", "companies", "at", "a", "disadvantage", "relative", "to", "the", "US", ",", "which", "relies", "on", "\n", "the", "private", "sector", "to", "build", "vast", "data", "sets", ",", "and", "China", ",", "which", "can", "leverage", "its", "central", "institutions", "for", "data", "aggregation", ".", "\n", "This", "problem", "is", "compounded", "by", "EU", "competition", "enforcement", "possibly", "inhibiting", "intra", "-", "industry", "cooperation", ".", "Finally", ",", "\n", "multiple", "different", "national", "rules", "in", "public", "procurement", "generate", "high", "ongoing", "costs", "for", "cloud", "providers", ".", "The", "net", "\n", "effect", "of", "this", "burden", "of", "regulation", "is", "that", "only", "larger", "companies", "–", "which", "are", "often", "non", "-", "EU", "based", "–", "have", "the", "financial", "\n", "capacity", "and", "incentive", "to", "bear", "the", "costs", "of", "complying", ".", "Young", "innovative", "tech", "companies", "may", "choose", "not", "to", "operate", "\n", "in", "the", "EU", "at", "all", ".", "\n", "The", "lack", "of", "a", "true", "Single", "Market", "also", "prevents", "enough", "companies", "in", "the", "wider", "economy", "from", "reaching", "\n", "sufficient", "size", "to", "accelerate", "adoption", "of", "advanced", "technologies", ".", "There", "are", "many", "barriers", "that", "lead", "to", "compa", "-", "\n", "nies", "in", "Europe", "to", "“", "stay", "small", "”", "and", "neglect", "the", "opportunities", "of", "the", "Single", "Market", ".", "These", "include", "the", "high", "cost", "of", "\n", "adhering", "to", "heterogenous", "national", "regulations", ",", "the", "high", "cost", "of", "tax", "compliance", ",", "and" ]	[]
excerpts of web text. 5,000 addi- tional paired samples are kept aside for validation and test datasets. Lastly, we ﬁlter out excerpts with fewer than 192 WordPiece tokens (Wu et al., 2https://github.com/openai/ gpt-2-output-dataset2016) (excerpts might be quite short if the model produces an end-of-text token early on). See Ap- pendix 1 for ﬁnal dataset sizes. A crucial question when generating text with a language model is whether or not to provide a priming sequence which the language model should continue. Unconditioned samples, where no priming text is provided, in conjunction with top-ksampling, lead to pathological behavior for discriminators as the ﬁrst token of the generated text will always be one of kpossible options. On the other hand, if long sequences of human text are used as priming, the space of possible gener- ated sequences is larger, but the detection problem shifts from one of “how human-like is the gener- ated text?” to “how well does the generated text follow the priming sequence?”. Since in this study we are interested in the former simpler question, we create two datasets, one with no priming, and one with the minimum amount of priming possible: a single token of web text. This means that for every excerpt of web text in the training set, there is an excerpt of machine- generated text that starts with the same token. We ﬁnd that even with limited priming, the ability of automatic detectors can be strongly impacted. To study the effect of excerpt length, we con- struct variations of the above datasets by truncat- ing all excerpts to ten possible lengths ranging from 2 to 192 WordPiece tokens (Wu et al., 2016). In total, we obtain sixty dataset variations: one per sampling method, truncation length, and choice of priming or no priming. 5 Automatic Detection Method The primary discriminator we employ is a ﬁne- tuned BERT classiﬁer (Devlin et al., 2019). We ﬁne-tune one instance of BERT per dataset vari- ation described above. For the longest sequence length,n=192, we compare BERT’s performance with several simple baselines that have been pro- posed in other work. Fine-tuned BERT We ﬁne-tune BERT-L ARGE (cased) on the task of labeling a sentence as human- or machine- generated. The models are trained for 15 epochs, with checkpoints saved ev- ery 1000 steps, and a batch size of 256. 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can belong to two or more S&T domains, indicating multidisciplinarity, hybridisations and applications. A strong co-occurrence between the S&T domains of Energy and electric technologies can be observed, as well as between ICT and Transportation, and Mechanical engineering and Transportation. Agrifood, Biotechnology, Chemis- try, Environmental sciences and Health are also strongly related to varying degrees. The distinct distribution of data sources in the S&T domains is suggestive of their differing na- ture and orientation, instead based on scientific publications (so science-oriented), patenting (so Table I. The scientific and technological specialisation domains in the Eastern Partnership identified via topic modelling EaP S&T specialisation domains (in alphabetical order) Agrifood Biotechnology Energy Optics and photonicsHealth and wellbeingChemistry and chemical engineeringElectric and electronic technologies Environmental sciences and industries Governance, culture, education and the economy ICT and computer science TransportationNanotechnology and materialsFundamental physics and mathematics Mechanical engineering and heavy machinery technology-oriented) or where these two data sources are, to some degree, balanced. As such, S&T domains can be qualitatively categorised as7 science-oriented S&T domains, balanced S&T domains and technology-oriented S&T do- mains, as presented in the Table III. 7 Noting that the total volume of analysed scientific publi- cations is higher than that of patents. 14 Overview of economic, innovation, scientific and technological specialisations Publications (critical mass \| CAGR)PatentsEC projectsTotal Nanotechnology and materials 29 067 3.7% 6 641 65 35 773 Health and wellbeing 17 874 14.5% 11 726 56 29 656 Fundamental physics and mathematics 26 852 1.9% 2 255 18 29 125 Mechanical engineering and heavy machinery5 582 9.9% 18 510 8 24 100 ICT and computer science 13 111 13.8% 4 044 61 17 216 Biotechnology 10 340 5.7% 5 837 29 16 206 Governance, culture, education and the economy14 895 24.3% 434 197 15 526 Environmental sciences and industries 10 735 10.6% 3 272 63 14 070 Electric and electronic technologies 5 874 5.8% 7 009 17 12 900 Energy 5 496 8.6% 5 828 54 11 378 Chemistry and chemical engineering 8 132 1.4% 2 380 14 10 526 Optics and photonics 8 043 -0.3% 1 896 19 9 958 Agrifood 2 949 12.5% 5 907 21 8 877 Transportation 2 355 20.4% 1 984 25 4 364Table II. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern Partnership region Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation15 Summary of the strengths	[ "can", "belong", "to", "two", "or", "\n", "more", "S&T", "domains", ",", "indicating", "multidisciplinarity", ",", "\n", "hybridisations", "and", "applications", ".", "\n", "A", "strong", "co", "-", "occurrence", "between", "the", "S&T", "\n", "domains", "of", "Energy", "and", "electric", "technologies", "\n", "can", "be", "observed", ",", "as", "well", "as", "between", "ICT", "and", "\n", "Transportation", ",", "and", "Mechanical", "engineering", "and", "\n", "Transportation", ".", "Agrifood", ",", "Biotechnology", ",", "Chemis-", "\n", "try", ",", "Environmental", "sciences", "and", "Health", "are", "also", "\n", "strongly", "related", "to", "varying", "degrees", ".", "\n", "The", "distinct", "distribution", "of", "data", "sources", "in", "the", "\n", "S&T", "domains", "is", "suggestive", "of", "their", "differing", "na-", "\n", "ture", "and", "orientation", ",", "instead", "based", "on", "scientific", "\n", "publications", "(", "so", "science", "-", "oriented", ")", ",", "patenting", "(", "so", "Table", "I.", "The", "scientific", "and", "technological", "specialisation", "domains", "in", "the", "Eastern", "Partnership", "identified", "via", "topic", "\n", "modelling", "\n", "EaP", "S&T", "specialisation", "domains", "(", "in", "alphabetical", "order", ")", "\n", "Agrifood", "Biotechnology", "\n", "Energy", "\n", "Optics", "and", "photonicsHealth", "and", "wellbeingChemistry", "and", "chemical", "\n", "engineeringElectric", "and", "electronic", "\n", "technologies", "\n", "Environmental", "sciences", "and", "\n", "industries", "\n", "Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", "\n", "ICT", "and", "computer", "science", "\n", "TransportationNanotechnology", "and", "\n", "materialsFundamental", "physics", "and", "\n", "mathematics", "\n", "Mechanical", "engineering", "and", "\n", "heavy", "machinery", "\n", "technology", "-", "oriented", ")", "or", "where", "these", "two", "data", "\n", "sources", "are", ",", "to", "some", "degree", ",", "balanced", ".", "As", "such", ",", "\n", "S&T", "domains", "can", "be", "qualitatively", "categorised", "as7", "\n", "science", "-", "oriented", "S&T", "domains", ",", "balanced", "S&T", "\n", "domains", "and", "technology", "-", "oriented", "S&T", "do-", "\n", "mains", ",", "as", "presented", "in", "the", "Table", "III", ".", "\n", "7", "Noting", "that", "the", "total", "volume", "of", "analysed", "scientific", "publi-", "\n", "cations", "is", "higher", "than", "that", "of", "patents", ".", "\n", "14", "\n", "Overview", "of", "economic", ",", "innovation", ",", "scientific", "and", "technological", "specialisations", "\n", "Publications", "\n", "(", "critical", "mass", "\|", "CAGR)PatentsEC", "\n", "projectsTotal", "\n", "Nanotechnology", "and", "materials", "29", "067", "3.7", "%", "6", "641", "65", "35", "773", "\n", "Health", "and", "wellbeing", "17", "874", "14.5", "%", "11", "726", "56", "29", "656", "\n", "Fundamental", "physics", "and", "mathematics", "26", "852", "1.9", "%", "2", "255", "18", "29", "125", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machinery5", "582", "9.9", "%", "18", "510", "8", "24", "100", "\n", "ICT", "and", "computer", "science", "13", "111", "13.8", "%", "4", "044", "61", "17", "216", "\n", "Biotechnology", "10", "340", "5.7", "%", "5", "837", "29", "16", "206", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy14", "895", "24.3", "%", "434", "197", "15", "526", "\n", "Environmental", "sciences", "and", "industries", "10", "735", "10.6", "%", "3", "272", "63", "14", "070", "\n", "Electric", "and", "electronic", "technologies", "5", "874", "5.8", "%", "7", "009", "17", "12", "900", "\n", "Energy", "5", "496", "8.6", "%", "5", "828", "54", "11", "378", "\n", "Chemistry", "and", "chemical", "engineering", "8", "132", "1.4", "%", "2", "380", "14", "10", "526", "\n", "Optics", "and", "photonics", "8", "043", "-0.3", "%", "1", "896", "19", "9", "958", "\n", "Agrifood", "2", "949", "12.5", "%", "5", "907", "21", "8", "877", "\n", "Transportation", "2", "355", "20.4", "%", "1", "984", "25", "4", "364Table", "II", ".", "Number", "of", "records", "per", "labelled", "topic", "group", "(", "i.e.", "‘", "domain", "’", ")", "in", "the", "Eastern", "Partnership", "region", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation15", "\n", "Summary", "of", "the", "strengths" ]	[]
to succeed, it will therefore need to engineer a coherent strategy for all aspects of decarbonisation, from energy to industry. FIGURE 6 Gas and retail price gap for industry Source: European Commission, 2024. Based on Eurostat (EU), EIA (US) and CEIC (China), 2024. Third, Europe must react to a world of less stable geopolitics, where dependencies are becoming vulner - abilities and it can no longer rely on others for its security . Decades of globalisation have produced a high level of “strategic interdependence” between major economies, raising the costs of any rapid disentanglementvi. For example, while the EU largely depends on China for critical minerals, China depends on the EU to absorb its industrial overcapacity. But this global equilibrium is shifting: all major economies are actively seeking to reduce their dependency and increase their scope for independent action. The US is investing in domestic capacity for semiconductor and clean tech production, while aiming to re-route critical supply chains through its allies. China is striving for technological autarchy and vertical supply chain integration, from mining of raw materials to processing and from manufacturing to shipping. While there is little evidence yet that these measures are leading to de-global - isationvii, trade policy interventions are on the rise [see Figure 7] . Given its high trade openness, Europe is especially exposed should these trends accelerate. The EU must also respond to a radically changed security environment at its borders. Aggregate EU defence spending is currently one third of US levels and the European defence industry is suffering from decades of underinvestment and depleted stocks. To achieve genuine strategic independence and increase its global geopolitical influence, Europe needs a plan to manage these dependencies and strengthen defence investment. 15THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 1FIGURE 7 Trade policy interventions Note: Measures include tariffs, export-related measures, subsidies, contingent trade-protective measures, and trade-related investment measures. Source: Global Trade Alert, 2024. EU countries are already responding to this new environment with more assertive policies, but they are doing so in a fragmented way that undermines collective effectiveness . The use of industrial policy interven - tions is on the rise across advanced economiesviii. But the effectiveness of these policies in Europe is hindered by three main coordination problems. First, there is a lack of coordination between Member States. 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this paper that gen- uine progress in our ﬁeld — climbing the right hill, not just the hill on whose slope we currently sit — depends on maintaining clarity around big picture notions such as meaning andunderstanding in task design and reporting of experimental results. After brieﬂy reviewing the ways in which large LMs are spoken about and summarizing the re- cent ﬂowering of “BERTology” papers ( x2), we offer a working deﬁnition for “meaning” ( x3) and a series of thought experiments illustrating the im- possibility of learning meaning when it is not in the training signal ( x4,5). We then consider the human language acquisition literature for insight into what information humans use to bootstrap lan- guage learning (x6) and the distributional seman- tics literature to discuss what is required to ground distributional models ( x7).x8 presents reﬂections on how we look at progress and direct research effort in our ﬁeld, and in x9, we address possible counterarguments to our main thesis. 2 Large LMs: Hype and analysis Publications talking about the application of large LMs to meaning-sensitive tasks tend to describe the models with terminology that, if interpreted at face value, is misleading. Here is a selection from academically-oriented pieces (emphasis added): (1) In order to train a model that understands sentence relationships, we pre-train for a binarized next sentence prediction task. (Devlin et al., 2019) (2) Using BERT, a pretraining language model, has been successful for single-turn machine comprehension . . . (Ohsugi et al., 2019) (3) The surprisingly strong ability of these models to re- call factual knowledge without any ﬁne-tuning demon-5186strates their potential as unsupervised open-domain QA systems. (Petroni et al., 2019) If the highlighted terms are meant to describe human-analogous understanding, comprehension, or recall of factual knowledge, then these are gross overclaims. If, instead, they are intended as techni- cal terms, they should be explicitly deﬁned. One important consequence of imprudent use of terminology in our academic discourse is that it feeds AI hype in the popular press. As NLP gains public exposure and is more widely used in applied contexts, it is increasingly important that the actual capabilities of our systems be accurately represented. In some cases, NLP experts speaking with the media are being appropriately careful, as in these two quotes in the New York Times :1 (4) These systems are still a really long way from truly understanding running prose.	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[bib][paper] P. Przybyła, P. Borkowski, K. Kaczyński, “Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach,” in Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022. [bib][paper][preprint][data][corpus][code] P. Przybyła, A. J. Soto, “When classification accuracy is not enough: Explaining news credibility assessment,” Information Processing & Management, vol. 58, issue 5, 2021.[bib][paper][data,code] K. Kaczyński, P. Przybyła, “HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection,” in Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), Bangkok, Thailand, 2021. [bib][paper] P. Przybyła, “Capturing the Style of Fake News,” in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, USA, 2020. [bib][paper][corpus][code] J. Gąsior and P. 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0 NCI = Normalised citation impact EC projects = EU-funded R&I projectsTable V. Selected S&T specialisation domains in Azerbaijan 18 Overview of economic, innovation, scientific and technological specialisations Georgia – Summary of the strengths of the S&T specialisations Georgia’s most highlighted S&T domains are the following: ■Environmental sciences and industries scores highly on all S&T indicators – on crit- ical mass, specialisation and excellence – for publications, patents and projects. It is a very clear specialisation domain for Georgia, with particular relevance in Geology and Geotech- nical engineering, as well as Environmental engineering and Chemistry; ■Agrifood presents a high specialisation in patents and publications, as well as a critical mass in patents and a relevant number of EU-funded R&I projects, with science oriented towards Horticulture, Genetics and Plant sci- ence; ■Health and wellbeing presents a high crit- ical mass, specialisation and citation impact in publications, while no positive indicator emerges in relation to patents. It co-occurs frequently with the domain of Agrifood. Be- yond General medicine, research is related in particular to Infectious diseases and Immu- nology; and ■ICT and computer science presents a spe- cialisation in patents as well as highly cited publications and a relevant number of EC pro- jects. GEORGIA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCIEC projectsTotal Agrifood 4 Biotechnology 0 Chemistry and chemical engineering2 Electric and electronic technologies0 Environmental sciences and industries6 Fundamental physics and mathematics3 Governance, culture, education and the economy4 Health and wellbeing 3 ICT and computer science 3 Mechanical engineering and heavy machinery2 Nanotechnology and materials 2 Optics and photonics 1 NCI = Normalised citation impact EC projects = EU-funded R&I projectsTable VI. Selected S&T specialisation domains in Georgia Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation19 Moldova – Summary of the strengths of the S&T specialisations Moldova presents a rather diversified S&T pano- rama. Its most highlighted S&T specialisation do- mains are the following: ■Health and wellbeing presents a nota- ble critical mass, specialisation and citation impact in publications, as well as a relevant number of patents and EC projects. 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electron microscopy and molecular dynamics simulations. These techniques have allowed the discovery and detailed analysis of the many molecules and metabolic pathways in cells.[citation needed] See also Anthropogenic metabolism – Material and energy turnover of human society Antimetabolite – Chemical that inhibits the use of a metabolite Calorimetry – Determining heat transfer in a system by measuring its other properties Isothermal microcalorimetry – Measuring versus elapsed time the net rate of heat flow Inborn errors of metabolism – Class of genetic diseases Iron–sulfur world hypothesis – Hypothetical scenario for the origin of life, a "metabolism first" theory of the origin of life Metabolic disorder – Any disease hindering the body's ability to process and distribute nutrients Microphysiometry Primary nutritional groups – Group of organisms Proto-metabolism – Chemical reactions which turn into modern metabolism Respirometry – Estimation of metabolic rates by measuring heat production Stream metabolism Sulfur metabolism – Set of chemical reactions involving sulfur in living organisms Thermic effect of food – Energy expenditure for processing food Urban metabolism – Model of the flows of materials and energy in cities Water metabolism – Aspect of homeostasis concerning control of the amount of water in an organism Overflow metabolism – Cellular phenomena Oncometabolism Reactome – Database of biological pathways KEGG – Collection of bioinformatics databases References Friedrich, CG (1997). Physiology and Genetics of Sulfur-oxidizing Bacteria. Advances in Microbial Physiology. Vol. 39. pp. 235–89. doi:10.1016/S0065-2911(08)60018-1. ISBN 978-0-12-027739-1. PMID 9328649. Pace NR (January 2001). "The universal nature of biochemistry". Proceedings of the National Academy of Sciences of the United States of America. 98 (3): 805–8. Bibcode:2001PNAS...98..805P. doi:10.1073/pnas.98.3.805. PMC 33372. PMID 11158550. Smith E, Morowitz HJ (September 2004). "Universality in intermediary metabolism". Proceedings of the National Academy of Sciences of the United States of America. 101 (36): 13168–73. Bibcode:2004PNAS..10113168S. doi:10.1073/pnas.0404922101. PMC 516543. PMID 15340153. Ebenhöh O, Heinrich R (January 2001). "Evolutionary optimization of metabolic pathways. Theoretical reconstruction of the stoichiometry of ATP and NADH producing systems". Bulletin of Mathematical Biology. 63 (1): 21–55. doi:10.1006/bulm.2000.0197. PMID 11146883. S2CID 44260374. Meléndez-Hevia E, Waddell TG, Cascante M (September 1996). "The puzzle of the Krebs citric acid cycle: assembling the pieces of chemically feasible reactions, and opportunism in the design of metabolic pathways during evolution". Journal of Molecular Evolution. 43 (3): 293–303. Bibcode:1996JMolE..43..293M. doi:10.1007/BF02338838. PMID 8703096. S2CID 19107073. Smith RL, Soeters MR, Wüst RC, Houtkooper RH (August 2018). "Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease". Endocrine Reviews. 39 (4): 489–517. doi:10.1210/er.2017-00211. PMC 6093334. PMID 29697773.	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Bakhtin et al. (2019) frame human-text detection as a rank- ing task and evaluate their models’ cross-domain and cross-model generalization, ﬁnding signiﬁ- cant loss in quality when training on one do- main and evaluating on another. Schuster et al. (2019) argue that the language distributional fea- tures implicitly or explicitly employed by these detectors are insufﬁcient; instead, one should look to explicit fact-veriﬁcation models. Finally, dis- criminators for whether text is machine-generated are a promising research direction in adversarial training (Lin et al., 2017; Li et al., 2017) and in automatic evaluation of generative model quality (Novikova et al., 2017; Kannan and Vinyals, 2017; Lowe et al., 2017). Natural Language Understanding Automatic detection of machine-generated text beneﬁts from a semantic understanding of the text. Contradic-tions, falsehoods, and topic drift can all indicate that an excerpt was machine-generated. Encoder- only Transformer models such as BERT (Devlin et al., 2019) have been shown to do very well at tasks requiring this understanding. While we ﬁne- tune BERT for the task of classifying whether text was machine-generated, others have used the con- textual word embeddings from a pre-trained BERT model without ﬁne-tuning to compute a quality score for generated text (Zhang et al., 2020). It is worth noting that recent work has raised ques- tions as to whether BERT truly builds a semantic understanding to make its predictions, or whether it merely takes advantage of spurious statistical differences between the text of different classes (Niven and Kao, 2019). 3 Task Deﬁnition We frame the detection problem as a binary clas- siﬁcation task: given an excerpt of text, label it as either human-written or machine-generated. In particular, we are interested in how variables such as excerpt length and decoding strategy impact performance on this classiﬁcation task. We thus create several datasets. Each is approximately balanced between positive examples of machine- generated text and negative examples of human- written text. While they all share the same human- written examples, each dataset contains a different set of machine-generated examples sampled using one particular decoding strategy. We also build ad- ditional datasets by truncating all of the examples to a particular sequence length, By training a separate classiﬁer on each dataset, we are able to answer questions about which de- coding strategy results in text that is the easiest to automatically disambiguate from human-written text. 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“first-movers” in other sub-sectors of sustainable transport. For example, the EU holds 60% of global high-value patents and tops global rankings of the most innovative companies for low-carbon fuels, which are essential for the decarbonisation of aviation and maritime transport in the medium term and also, potentially, for heavy-duty vehicles. FIGURE 2 The EU’s position in complex (digital and green) technologies Notes: The results are based on an analysis of patent data to understand the complexity and potential for specialisation in different technology areas. On the y-axis, technologies are ranked according to how advanced or complex they are, with scores ranging between 0 (less complex) and 100 (more complex). The x-axis (showing the relatedness density) represents how easily a country can build comparative advantage in a particular technology, depending on how closely related it is to other technologies the country is already strong in. The size of the bubbles shows how much each country has already specialised in a technology, using a measure of “revealed comparative advantage”(RCA), which reflects their competitive strenght in that field. Source: European Commission, DG RTD. However, it is not guaranteed that EU demand for clean tech will be met by EU supply given increasing Chinese capacity and scale . The EU aims to achieve a minimum of 42.5% of its energy consumption from renewable sources by 2030, which will require it to nearly triple its installed capacity for solar PV and more than double its wind power capacity. In addition, the EU has effectively abolished the internal combustion engine from 2035, when all new passenger cars and light duty vehicles registered in Europe must have zero tailpipe emissions. Based on current policies, Chinese technology may represent the lowest-cost route to achieving some of these targets. Owing to a fast pace of innovation, low manufacturing costs and state subsidies four times higher than in other major econo - miesiv, the country is now dominating global exports of clean technologies. Significant overcapacity is expected: by 2030 at the latest, China’s annual manufacturing capacity for solar photovoltaic (PV) is expected to be double the level of global demand, and for battery cells it is expected to at least cover the level of global demand. Production of EVs is expanding at a similar pace. The EU is already seeing a sharp deterioration in its trade balance with China, reflecting in particular imports of EVs, batteries and solar PV products	[ " ", "“", "first", "-", "movers", "”", "in", "other", "sub", "-", "sectors", "of", "sustainable", "transport", ".", "For", "\n", "example", ",", "the", "EU", "holds", "60", "%", "of", "global", "high", "-", "value", "patents", "and", "tops", "global", "rankings", "of", "the", "most", "innovative", "companies", "\n", "for", "low", "-", "carbon", "fuels", ",", "which", "are", "essential", "for", "the", "decarbonisation", "of", "aviation", "and", "maritime", "transport", "in", "the", "medium", "\n", "term", "and", "also", ",", "potentially", ",", "for", "heavy", "-", "duty", "vehicles", ".", "\n", "FIGURE", "2", "\n", "The", "EU", "’s", "position", "in", "complex", "(", "digital", "and", "green", ")", "technologies", "\n", "Notes", ":", "The", "results", "are", "based", "on", "an", "analysis", "of", "patent", "data", "to", "understand", "the", "complexity", "and", "potential", "for", "specialisation", "in", "different", "technology", "areas", ".", "On", "the", "\n", "y", "-", "axis", ",", "technologies", "are", "ranked", "according", "to", "how", "advanced", "or", "complex", "they", "are", ",", "with", "scores", "ranging", "between", "0", "(", "less", "complex", ")", "and", "100", "(", "more", "complex", ")", ".", "The", "\n", "x", "-", "axis", "(", "showing", "the", "relatedness", "density", ")", "represents", "how", "easily", "a", "country", "can", "build", "comparative", "advantage", "in", "a", "particular", "technology", ",", "depending", "on", "how", "\n", "closely", "related", "it", "is", "to", "other", "technologies", "the", "country", "is", "already", "strong", "in", ".", "The", "size", "of", "the", "bubbles", "shows", "how", "much", "each", "country", "has", "already", "specialised", "in", "a", "\n", "technology", ",", "using", "a", "measure", "of", "“", "revealed", "comparative", "advantage”(RCA", ")", ",", "which", "reflects", "their", "competitive", "strenght", "in", "that", "field", ".", "\n", "Source", ":", "European", "Commission", ",", "DG", "RTD", ".", "\n", "However", ",", "it", "is", "not", "guaranteed", "that", "EU", "demand", "for", "clean", "tech", "will", "be", "met", "by", "EU", "supply", "given", "increasing", "\n", "Chinese", "capacity", "and", "scale", ".", "The", "EU", "aims", "to", "achieve", "a", "minimum", "of", "42.5", "%", "of", "its", "energy", "consumption", "from", "renewable", "\n", "sources", "by", "2030", ",", "which", "will", "require", "it", "to", "nearly", "triple", "its", "installed", "capacity", "for", "solar", "PV", "and", "more", "than", "double", "its", "wind", "\n", "power", "capacity", ".", "In", "addition", ",", "the", "EU", "has", "effectively", "abolished", "the", "internal", "combustion", "engine", "from", "2035", ",", "when", "all", "\n", "new", "passenger", "cars", "and", "light", "duty", "vehicles", "registered", "in", "Europe", "must", "have", "zero", "tailpipe", "emissions", ".", "Based", "on", "current", "\n", "policies", ",", "Chinese", "technology", "may", "represent", "the", "lowest", "-", "cost", "route", "to", "achieving", "some", "of", "these", "targets", ".", "Owing", "to", "a", "\n", "fast", "pace", "of", "innovation", ",", "low", "manufacturing", "costs", "and", "state", "subsidies", "four", "times", "higher", "than", "in", "other", "major", "econo", "-", "\n", "miesiv", ",", "the", "country", "is", "now", "dominating", "global", "exports", "of", "clean", "technologies", ".", "Significant", "overcapacity", "is", "expected", ":", "by", "\n", "2030", "at", "the", "latest", ",", "China", "’s", "annual", "manufacturing", "capacity", "for", "solar", "photovoltaic", "(", "PV", ")", "is", "expected", "to", "be", "double", "the", "\n", "level", "of", "global", "demand", ",", "and", "for", "battery", "cells", "it", "is", "expected", "to", "at", "least", "cover", "the", "level", "of", "global", "demand", ".", "Production", "\n", "of", "EVs", "is", "expanding", "at", "a", "similar", "pace", ".", "The", "EU", "is", "already", "seeing", "a", "sharp", "deterioration", "in", "its", "trade", "balance", "with", "China", ",", "\n", "reflecting", "in", "particular", "imports", "of", "EVs", ",", "batteries", "and", "solar", "PV", "products" ]	[]
to the randomly assigned object for paired closed-loop inhibition. The chamber and enclosures were cleanedbefore and between subjects with 70% ethanol. Open ﬁeld place preference Similar to the real-time object place preference test, the experiment was divided into two 5 min bins, and the laser was allowed to turnon only during the last 5 min of the test in one randomly assigned quadrant (25 325cm) of the open ﬁeld arena (50 350330cm). Measurements during this test phase included time in each quadrant, general locomotion and locomotion in the inhibition-paired quadrant. The arena was cleaned before and between subjects with 70% ethanol. Fear conditioning and retrievalThe same behavioral protocol as described above for deep-brain Ca 2+imaging was used with minor modiﬁcations. Light stimulation was delivered to ArchT- or GFP-injected mice during fear conditioning on day 1, for 4.5 s during the entire presentation of the 2 s US + stimulus (starting 500 msec before US onset) and extending for additional 2 s. On day 2, during the retrieval phase, mice were alsotethered to the optic ﬁbers, but underwent testing without optogenetic modulation. Mice were tracked using contour tracking and center of mass (ANY-maze). Freezing episodes were processed ofﬂine by trained experimenters using frame-by-frame analysis. Histology Quantiﬁcation of VIP + INs Immunocytochemistry experiments were carried out according to previously published procedures with minor modiﬁcations ( Sree- pathi, 2012 ). Free-ﬂoating sections were incubated in primary antibody solution [rabbit antibody against VIP (cat. no. 20077, Immu- nostar), diluted 1:4,000] containing 2% normal goat serum (NGS), 0.3% Triton X-100 (TX) in Tris-buffered saline (TBS-T; pH 7.4) for two days at 6 /C14C and then in secondary antibody [biotinylated goat anti-rabbit (cat. no. BA-1000, Vector laboratories), diluted 1:500] overnight. After extensive washing, sections were incubated in an ABC complex solution (1:100, Vectastain ABC kit; Vector Labo-ratories) made up in TBS, followed by diaminobenzidine (DAB) as a chromogen (0.5 mg/mL in TB) and 0.003% H 2O2, as the electron donor, for 5 min. The sections were mounted onto gelatin-coated slides, dehydrated in an ascending ethanol series followed by an incubation in butylacetate and coverslipped with Eukitt (Christine Gro ¨pl, Tulln, Austria). Sections immunolabeled for VIP were used to assess the number of VIP + INs (n = 4 mice) by an experienced experimenter using the Neurolucida software (MBF Bioscience). Borders of the IC were outlined with the help of consecutive Nissl stained sections and according to	[ "to", "the", "randomly", "assigned", "object", "for", "paired", "closed", "-", "loop", "inhibition", ".", "The", "chamber", "and", "enclosures", "were", "cleanedbefore", "and", "between", "subjects", "with", "70", "%", "ethanol", ".", "\n", "Open", "ﬁeld", "place", "preference", "\n", "Similar", "to", "the", "real", "-", "time", "object", "place", "preference", "test", ",", "the", "experiment", "was", "divided", "into", "two", "5", "min", "bins", ",", "and", "the", "laser", "was", "allowed", "to", "turnon", "only", "during", "the", "last", "5", "min", "of", "the", "test", "in", "one", "randomly", "assigned", "quadrant", "(", "25", "325", "cm", ")", "of", "the", "open", "ﬁeld", "arena", "(", "50", "350330", "cm", ")", ".", "\n", "Measurements", "during", "this", "test", "phase", "included", "time", "in", "each", "quadrant", ",", "general", "locomotion", "and", "locomotion", "in", "the", "inhibition", "-", "paired", "\n", "quadrant", ".", "The", "arena", "was", "cleaned", "before", "and", "between", "subjects", "with", "70", "%", "ethanol", ".", "\n", "Fear", "conditioning", "and", "retrievalThe", "same", "behavioral", "protocol", "as", "described", "above", "for", "deep", "-", "brain", "Ca", "\n", "2+imaging", "was", "used", "with", "minor", "modiﬁcations", ".", "Light", "stimulation", "\n", "was", "delivered", "to", "ArchT-", "or", "GFP", "-", "injected", "mice", "during", "fear", "conditioning", "on", "day", "1", ",", "for", "4.5", "s", "during", "the", "entire", "presentation", "of", "the", "2", "s", "US", "+", "\n", "stimulus", "(", "starting", "500", "msec", "before", "US", "onset", ")", "and", "extending", "for", "additional", "2", "s.", "On", "day", "2", ",", "during", "the", "retrieval", "phase", ",", "mice", "were", "alsotethered", "to", "the", "optic", "ﬁbers", ",", "but", "underwent", "testing", "without", "optogenetic", "modulation", ".", "Mice", "were", "tracked", "using", "contour", "tracking", "and", "\n", "center", "of", "mass", "(", "ANY", "-", "maze", ")", ".", "Freezing", "episodes", "were", "processed", "ofﬂine", "by", "trained", "experimenters", "using", "frame", "-", "by", "-", "frame", "analysis", ".", "\n", "Histology", "\n", "Quantiﬁcation", "of", "VIP", "+", "INs", "\n", "Immunocytochemistry", "experiments", "were", "carried", "out", "according", "to", "previously", "published", "procedures", "with", "minor", "modiﬁcations", "(", "Sree-", "\n", "pathi", ",", "2012", ")", ".", "Free-ﬂoating", "sections", "were", "incubated", "in", "primary", "antibody", "solution", "[", "rabbit", "antibody", "against", "VIP", "(", "cat", ".", "no", ".", "20077", ",", "Immu-", "\n", "nostar", ")", ",", "diluted", "1:4,000", "]", "containing", "2", "%", "normal", "goat", "serum", "(", "NGS", ")", ",", "0.3", "%", "Triton", "X-100", "(", "TX", ")", "in", "Tris", "-", "buffered", "saline", "(", "TBS", "-", "T", ";", "pH", "7.4", ")", "for", "\n", "two", "days", "at", "6", "\n", "/C14C", "and", "then", "in", "secondary", "antibody", "[", "biotinylated", "goat", "anti", "-", "rabbit", "(", "cat", ".", "no", ".", "BA-1000", ",", "Vector", "laboratories", ")", ",", "diluted", "1:500", "]", "\n", "overnight", ".", "After", "extensive", "washing", ",", "sections", "were", "incubated", "in", "an", "ABC", "complex", "solution", "(", "1:100", ",", "Vectastain", "ABC", "kit", ";", "Vector", "Labo", "-", "ratories", ")", "made", "up", "in", "TBS", ",", "followed", "by", "diaminobenzidine", "(", "DAB", ")", "as", "a", "chromogen", "(", "0.5", "mg", "/", "mL", "in", "TB", ")", "and", "0.003", "%", "H", "\n", "2O2", ",", "as", "the", "electron", "\n", "donor", ",", "for", "5", "min", ".", "The", "sections", "were", "mounted", "onto", "gelatin", "-", "coated", "slides", ",", "dehydrated", "in", "an", "ascending", "ethanol", "series", "followed", "by", "an", "\n", "incubation", "in", "butylacetate", "and", "coverslipped", "with", "Eukitt", "(", "Christine", "Gro", "¨pl", ",", "Tulln", ",", "Austria", ")", ".", "\n", "Sections", "immunolabeled", "for", "VIP", "were", "used", "to", "assess", "the", "number", "of", "VIP", "+", "INs", "(", "n", "=", "4", "mice", ")", "by", "an", "experienced", "experimenter", "using", "\n", "the", "Neurolucida", "software", "(", "MBF", "Bioscience", ")", ".", "Borders", "of", "the", "IC", "were", "outlined", "with", "the", "help", "of", "consecutive", "Nissl", "stained", "sections", "and", "\n", "according", "to" ]	[]
industries. At the same time, refocusing implies that the EU should be more rigorous in applying the subsidiarity prin - ciple and exercise more “self-restraint” . The Commission’s legislative activity has been growing excessively, also due to passive scrutiny of the subsidiarity principle by national parliaments, which sets the boundaries of the Commission’s right of initiative. While national parliaments have the power to scrutinise whether EU legislation complies with the subsidiarity principle through reasoned opinions – and potentially trigger the so-called “yellow card procedure” – many do not actively exercise this right. For example, of the 39 national parliaments or chambers in the EU, only nine (from seven Member States) issued reasoned opinions in the context of scrutinising subsidiarity in 2023. An EU-wide inquiry should be launched to analyse the reasons behind national parliaments’ passive exercise of their scrutiny of the subsidiarity principle. Building on its conclusions, initiatives should be taken to reinforce the administrative capacity and role of national parliaments and Member States in their control over EU legislative activity. Moreover, EU institutions should apply a “self-restraint” principle in policymaking, both by better filtering future initiatives and by streamlining the existing acquis, building on the measures described in “Simplifying rules” below. ACCELERATING THE WORK OF THE EU Council votes subject to qualified majority voting (QMV) should be extended to more areas, and if action at the EU level is blocked, a differentiated approach to integration should be pursued . So far, many efforts to deepen European integration between Member States have been hindered by unanimity voting in the Council of the European Union. All possibilities offered by the EU Treaties should therefore be exploited to extend QMV. The so-called “passerelle” clause should be leveraged to generalise voting by qualified majority in all policy areas in the Council. This step would require an upfront agreement, subject to unanimity at the level of the European Council, and would have a positive impact on the pace at which key legislative initiatives are adopted by the EU. If action at the EU level is hindered by existing institutional procedures, the next-best option is for like-minded groups of Member States to resort to enhanced cooperation as foreseen by Articles 20 TEU and 329 TFEU. Enhanced cooperation offers two important safeguards: the consent of the European Parliament (EP) and judicial oversight of the Court of Justice of the EU (CJEU). 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equal"> <div id="main-content" class="col-md-8 has-sidebar white"> <article class="article"> <header> <div class="release_date"> News Release <time datetime="TODO"> 13-Jan-2016 </time> </div> <h1 class="page_title"> Novel blood thinner found to be safe and effective in women </h1> <p class="subtitle"> Study shows cangrelor reduces the odds of cardiovascular events by 35 percent in women undergoing coronary stenting when compared to standard therapy </p> <a style="color:red;" href="/releaseguidelines">Peer-Reviewed Publication</a> <p class="meta_institute">Brigham and Women's	[ "(", "{", "\"", "page\":{\"pageInfo\":{\"pageTitle\":\"\",\"pageType\":\"\",\"pageURL\":\"https:\\/\\/www.eurekalert.org\\/news", "-", 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economies of the US and China have started to decouple06. So far, the EU has pursued a different strategy, with Member States encour - aging inward FDI from Chinese companies. Chinese greenfield investment in the EU has increased substan - tially in recent years, particularly in Central and Eastern Europe. This strategy can leverage technological progress abroad and promote technological development in Europe, as well as the creation of high-quality jobs, but only if executed in a coordinated manner. Asymmetries arising from small Member States negotiating 06. Data from the Bureau of Economic Analysis indicate that exports from China to the US have declined since 2018, and inward net FDI from China has decreased from a peak inflow of USD 18 billion in 2016 to an outflow of around USD 2 billion in 2023. 20THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 1with large foreign investors could lead to unwelcome concessions being extracted by foreign countries, which is particularly concerning when a potential security threat and a geopolitical rival of the EU are involved. To counter these risks, the EU should strengthen its Investment Screening Mechanism. At present, FDI screening is a national competence, with Member States only required to exchange notifications and information. This fragmentation prevents the EU from leveraging its collective power in FDI negotiations and complicates the formulation of a common FDI policy. As outlined in chapter 3, coordination is important for the emergence of joint ventures in strategic sectors and ensuring that EU companies retain relevant know-how and can drive the next wave of innovation. 21THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 1ENDNOTESi World Justice Project, Rule of Law Index 2023 , 2023. ii World Bank, World Development Indicators 2023 , 2024. iii Eurostat, Educational attainment statistics 2023 , 2024. iv IMF, World Economic Outlook, April 2024. v ECB, ‘ Why competition with China is getting tougher than ever ’, The ECB Blog, 3 September 2024. vi McCaffrey, C., & Poitiers, N., Instruments of economic security, Working Paper 12/2024, Bruegel, 2024, https:/ /www.bruegel. org/system/files/2024-05/WP%2012%202024_0.pdf . vii ECB, ‘ Deglobalisation: risk or reality? ’, The ECB Blog, 12 July 2023. viii Juhász, r., Lane N. and Rodrik, D., The new economics of industrial policy , 2023. ix in ‘t Veld, J., ‘ Quantifying the Economic Effects of the Single Market in a Structural Macromodel ’, Discussion Paper Series, No. 94, European Commission, February 2019.	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and cork (NACE 16), Chemicals and chemical products (NACE 20), Information and communication (NACE 61-63), Financial services (NACE 64). Finally, identified E&I specialisation domains in Ukraine were as follows: Food prod-ucts (NACE 10), Wood and products of wood and cork (NACE 16), Basic metals & Fabricated metal products (NACE 25, 26), Machinery and equipment (NACE 28), Manufacture of motor vehicles (NACE 29), Wholesale and retail trade (NACE 46). The common E&I specialisation in the EaP region in terms of gross domestic product and employ- ment is agriculture. The food processing and man- ufacturing industry directly concerns Armenia, Georgia, Moldova and Ukraine. Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials is identified as an E&I speciali- sation in two countries: Moldova and Ukraine. The top identified scientific domains in the EaP re- gion in publications are Nanotechnology and ma- terials (total number of records 29 067), with the highest number of records in Armenia, Azerbai- jan and Georgia; Fundamental physics and math- ematics (total 26 852), with the highest number of records in Moldova and Ukraine; Health and wellbeing (total 17 874), with highest number of records in Armenia, Azerbaijan, Georgia and Moldova. The least represented domain is Bi- otechnology (total 10 340), with the majority of them in Ukraine (8 935). The top technological domains in numbers of pat- ents in the EaP region are Mechanical engineering and heavy machinery (total number of records 18 510), with the highest number of records in Azer- baijan, Georgia, Moldova and Ukraine; Health and wellbeing (11 726), with highest number of records in Azerbaijan, Moldova and Ukraine; and Electric and electronic technologies (7 009) with the highest records in Armenia. The least represented domain is Energy (total 5 828), with the majority of them in Ukraine (5 647). The domains of Health and wellbeing and Govern- ance, culture, education and the economy were growing in all countries, while Agrifood was de- clining the most considerably (except for Armenia and Ukraine). Due to the particular means of organising scien- tific activities within the analysed countries, in most cases the top actors in scientific production 250 Part 5 Discussion of results and final remarks are national academies of science (as they rep- resent the network of research institutes), with a broad profile national university or highly special- ised research institution coming next. The	[ "and", "cork", "(", "NACE", "16", ")", ",", "Chemicals", "and", "\n", "chemical", "products", "(", "NACE", "20", ")", ",", "Information", "and", "\n", "communication", "(", "NACE", "61", "-", "63", ")", ",", "Financial", "services", "\n", "(", "NACE", "64", ")", ".", "Finally", ",", "identified", "E&I", "specialisation", "\n", "domains", "in", "Ukraine", "were", "as", "follows", ":", "Food", "prod", "-", "ucts", "(", "NACE", "10", ")", ",", "Wood", "and", "products", "of", "wood", "and", "\n", "cork", "(", "NACE", "16", ")", ",", "Basic", "metals", "&", "Fabricated", "metal", "\n", "products", "(", "NACE", "25", ",", "26", ")", ",", "Machinery", "and", "equipment", "\n", "(", "NACE", "28", ")", ",", "Manufacture", "of", "motor", "vehicles", "(", "NACE", "\n", "29", ")", ",", "Wholesale", "and", "retail", "trade", "(", "NACE", "46", ")", ".", "\n", "The", "common", "E&I", "specialisation", "in", "the", "EaP", "region", "\n", "in", "terms", "of", "gross", "domestic", "product", "and", "employ-", "\n", "ment", "is", "agriculture", ".", "The", "food", "processing", "and", "man-", "\n", "ufacturing", "industry", "directly", "concerns", "Armenia", ",", "\n", "Georgia", ",", "Moldova", "and", "Ukraine", ".", "Manufacture", "of", "\n", "wood", "and", "of", "products", "of", "wood", "and", "cork", ",", "except", "\n", "furniture", ";", "manufacture", "of", "articles", "of", "straw", "and", "\n", "plaiting", "materials", "is", "identified", "as", "an", "E&I", "speciali-", "\n", "sation", "in", "two", "countries", ":", "Moldova", "and", "Ukraine", ".", "\n", "The", "top", "identified", "scientific", "domains", "in", "the", "EaP", "re-", "\n", "gion", "in", "publications", "are", "Nanotechnology", "and", "ma-", "\n", "terials", "(", "total", "number", "of", "records", "29", "067", ")", ",", "with", "the", "\n", "highest", "number", "of", "records", "in", "Armenia", ",", "Azerbai-", "\n", "jan", "and", "Georgia", ";", "Fundamental", "physics", "and", "math-", "\n", "ematics", "(", "total", "26", "852", ")", ",", "with", "the", "highest", "number", "\n", "of", "records", "in", "Moldova", "and", "Ukraine", ";", "Health", "and", "\n", "wellbeing", "(", "total", "17", "874", ")", ",", "with", "highest", "number", "of", "\n", "records", "in", "Armenia", ",", "Azerbaijan", ",", "Georgia", "and", "\n", "Moldova", ".", "The", "least", "represented", "domain", "is", "Bi-", "\n", "otechnology", "(", "total", "10", "340", ")", ",", "with", "the", "majority", "of", "\n", "them", "in", "Ukraine", "(", "8", "935", ")", ".", "\n", "The", "top", "technological", "domains", "in", "numbers", "of", "pat-", "\n", "ents", "in", "the", "EaP", "region", "are", "Mechanical", "engineering", "\n", "and", "heavy", "machinery", "(", "total", "number", "of", "records", "18", "\n", "510", ")", ",", "with", "the", "highest", "number", "of", "records", "in", "Azer-", "\n", "baijan", ",", "Georgia", ",", "Moldova", "and", "Ukraine", ";", "Health", "\n", "and", "wellbeing", "(", "11", "726", ")", ",", "with", "highest", "number", "of", "\n", "records", "in", "Azerbaijan", ",", "Moldova", "and", "Ukraine", ";", "\n", "and", "Electric", "and", "electronic", "technologies", "(", "7", "009", ")", "\n", "with", "the", "highest", "records", "in", "Armenia", ".", "The", "least", "\n", "represented", "domain", "is", "Energy", "(", "total", "5", "828", ")", ",", "with", "\n", "the", "majority", "of", "them", "in", "Ukraine", "(", "5", "647", ")", ".", "\n", "The", "domains", "of", "Health", "and", "wellbeing", "and", "Govern-", "\n", "ance", ",", "culture", ",", "education", "and", "the", "economy", "were", "\n", "growing", "in", "all", "countries", ",", "while", "Agrifood", "was", "de-", "\n", "clining", "the", "most", "considerably", "(", "except", "for", "Armenia", "\n", "and", "Ukraine", ")", ".", "\n", "Due", "to", "the", "particular", "means", "of", "organising", "scien-", "\n", "tific", "activities", "within", "the", "analysed", "countries", ",", "in", "\n", "most", "cases", "the", "top", "actors", "in", "scientific", "production", "\n", "250", "\n ", "Part", "5", "Discussion", "of", "results", "and", "final", "remarks", "\n", "are", "national", "academies", "of", "science", "(", "as", "they", "rep-", "\n", "resent", "the", "network", "of", "research", "institutes", ")", ",", "with", "a", "\n", "broad", "profile", "national", "university", "or", "highly", "special-", "\n", "ised", "research", "institution", "coming", "next", ".", "The" ]	[]
indicating they would abandon a brand if they felt their data was not handled responsibly (PrivacyFirst Reports, 2021. "Consumer Trust in Data Privacy," Privacy and Security Journal, 9(1), 12-24). Critical Elements of a Successful E-Commerce Strategy (Direct Citations to Scholarly Works) Product Selection and Market Fit – Forbes (2023) discusses the importance of aligning products with consumer demand, stressing that market fit is crucial to e-commerce success (Forbes. (2023). "Consumer Behavior in E-Commerce," Forbes Digital Insights, 19(4), 27-41). – A study by Digital Commerce (2022) supports this, showing that offering customized products can increase average order value by 18% (Digital Commerce. (2022). "Product Customization and Consumer Purchase Behavior," Journal of E-Commerce Research, 15(2), 102-118). Seamless User Experience (UX) Design – Thompson (2022) highlights that simplifying the purchase process directly reduces cart abandonment rates, which remain a critical challenge in online sales (Thompson, R. (2022). "Optimizing User Experience in E-Commerce," Journal of E-Commerce UX, 7(3), 55-68). – Moreover, a study by UX Collective (2021) reveals that 40% of online shoppers abandon their carts due to poor or complex checkout procedures (UX Collective. (2021). "The Impact of Checkout Design on Cart Abandonment," Journal of User Experience Design, 3(1), 21-33). Mobile Optimization – TechCrunch (2022) confirms that over 55% of online sales now come from mobile platforms, underscoring the importance of mobile-friendly e-commerce sites (TechCrunch. (2022). "The Rise of Mobile E-Commerce," Digital Commerce Review, 17(3), 61-72). – According to Digital Insights (2023), responsive and fast-loading mobile sites reduce bounce rates by up to 25% (Digital Insights. (2023). "Mobile Optimization and User Retention," Journal of Digital Marketing, 22(4), 11-24). Omnichannel Marketing – Nelson (2022) discusses that omnichannel marketing strategies are vital for achieving seamless customer experiences, noting a 30% increase in customer loyalty for brands that integrate digital and physical touchpoints (Nelson, H. (2022). "Omnichannel Strategies in Modern Retail," Retail Marketing Journal, 8(2), 98-112). – Retail Today (2022) confirms that omnichannel businesses generate an average of 50% more revenue than those using only one channel (Retail Today. (2022). "Omnichannel Marketing in Retail," Journal of Retailing and Consumer Services, 29(1), 64-79). Customer Retention Tactics – Loyalty Experts (2021) show that implementing loyalty programs can boost repeat purchases by up to 20% (Loyalty Experts. (2021). "The Economics of Customer Loyalty Programs," Journal of Customer Loyalty, 12(3), 88-101). – Marketing Science Review (2023) further supports this, stating that personalized email campaigns can increase customer retention by 15%	[ "indicating", "they", "would", "abandon", "a", "brand", "if", "they", "felt", "their", "data", "was", "not", "handled", "responsibly", "(", "PrivacyFirst", "Reports", ",", "2021", ".", "\"", "Consumer", "Trust", "in", "Data", "Privacy", ",", "\"", "Privacy", "and", "Security", "Journal", ",", "9(1", ")", ",", "12", "-", "24", ")", ".", "\n\n", "Critical", "Elements", "of", "a", "Successful", "E", "-", "Commerce", "Strategy", "\n", "(", "Direct", "Citations", "to", "Scholarly", "Works", ")", "\n\n", "Product", "Selection", "and", "Market", "Fit", "\n", "–", "Forbes", "(", "2023", ")", "discusses", "the", "importance", "of", "aligning", "products", "with", "consumer", "demand", ",", "stressing", "that", "market", "fit", "is", "crucial", "to", "e", "-", 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diverse func- tional ensembles are recruited in response to discrete stimuli (e.g., social versus non-social cues). The coding speciﬁcity ofthese functional ensembles of VIP+ INs resulted unstable across testing days and paradigms and revealed that diverse stimuli do not engage distinct aIC VIP+ INs but rather recruit the same pop-ulation of VIP+ INs within the aIC. Our data further reveal thatrepetition suppression of aIC VIP+ IN activity cannot be ex- plained by opposing roles or coding disruption of these different functional ensembles during a given test. This functional hetero-geneity, however, can be accounted for by several factors, such as different intrinsic anatomical and physiological properties of VIP+ IN subclasses in relation to sensory processing ( Guet- Mccreight et al., 2020 ). Alternatively, different aIC VIP+ INs may receive inputs only from speciﬁc subsets of the presynaptic areas that were identiﬁed by our viral tracing experiments, andhence would be embedded into distinct brain networks. Anopen question remains as to whether, at an individual neuronal level, specialized subsets of VIP+ INs encode independent com- ponents of sensory stimuli, such as perceptual salience or unexpectedness. Atypical aIC activity has been considered a hallmark of autism (Di Martino et al., 2009 ;Uddin and Menon, 2009 ;Uddin, 2015 ; Uddin et al., 2013 ;Odriozola et al., 2016 ), schizophrenia ( Wylie and Tregellas, 2010 ), and anxiety disorders ( Terasawa et al., 2013 ;Alvarez et al., 2015 ). Substantial evidence also indicates that these disorders are characterized by an impairment in sen- sory processing ( Uddin et al., 2013 ;Kapur, 2003 ;Pannekoek et al., 2013 ). A recent study has shown that early postnatal disruption of VIP+ IN function leads to long-term dysregulationof cortical activity and sensory learning ( Batista-Brito et al., 2017 ). In a mouse model of Dravet syndrome, a severe neurode- velopmental disorder characterized by autism and epilepsy,VIP+ INs showed lasting ﬁring abnormalities, unlike other cortical INs ( Goff and Goldberg, 2019 ). Furthermore, loss of function of the autism-related protein Mecp2 in VIP+ INs was found toinduce social preference deﬁcits ( Mossner et al., 2020 ). There- fore, failures in the encoding of sensory stimuli and behavioral relevance attribution by impaired VIP+ IN function could leadto atypical insula activity during stimulus processing, contrib-uting to anxiety and/or autism spectrum disorder core symptoms. 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and equipped transport containers X 791 Railway vehicles (including hovertrains) and associated equipment X X X 792 Aircraft and associated equipment; spacecraft (including satellites) and spacecraft launch vehicles; parts thereof X 793 Ships, boats (including hovercraft) and floating structures X X 799 Adjustments (trade broken down at chapter nc level only) 8 Miscellaneous manufactured articles 800 Complete industrial plant appropriate to section 8 811 Prefabricated buildings 812 Sanitary, plumbing and heating fixtures and fittings, n.e.s. X X 813 Lighting fixtures and fittings, n.e.s. 821 Furniture and parts thereof; bedding, mattresses, mattress supports, cushions and similar stuffed furnishings X X X X 831Trunks, suitcases, vanity cases, executive cases, briefcases, school satchels, spectacle cases, binocular cases, camera cases, musical instrument cases, gun cases, holsters and similar containers; travelling bags, insulated food or beverages bags, toilet bags, rucksacks, handbags, shopping bags, wallets, purses, map cases, cigarette cases, tobacco pouches, tool bags, sports bags, bottle cases, jewellery boxes, powder boxes, cutlery cases and similar containers, of leather or of composition leather, of sheeting of plastics, of textile materials, of vulcanized fibre or of paperboard, or wholly or mainly covered with such materials or with paper; travel sets for personal toilet, sewing or shoe or clothes cleaning X 841Men’s or boys’ coats, capes, jackets, suits, blazers, trousers, shorts, shirts, underwear, nightwear and similar articles of textile fabrics, not knitted or crocheted (other than those of subgroup 845.2)X X X X 842Women’s or girls’ coats, capes, jackets, suits, trousers, shorts, shirts, dresses and skirts, underwear, nightwear and similar articles of textile fabrics, not knitted or crocheted (other than those of subgroup 845.2) X X 843Men’s or boys’ coats, capes, jackets, suits, blazers, trousers, shorts, shirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2) X X X 844Women’s or girls’ coats, capes, jackets, suits, trousers, shorts, shirts, dresses and skirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2) X X 845 Articles of apparel, of textile fabrics, whether or not knitted or crocheted, n.e.s. 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computer science (in areas such as automatic control and UAVs) as well as Me- chanical engineering, presents the highest specialisation index in publications and pat- ents, and a high scientific citation impact. Its research is geared towards road, rail, sea and air transport, while patents cluster in Vehicles in general, Combustion engines and Ships; ■Mechanical engineering presents a high critical mass in patents and specialisation and citation impact in publications. It co-occurs with a large number of domains, particularly Transportation, Energy and Electric and elec- tronic technologies; ■Nanotechnology and materials presents a high critical mass in patents and publica- tions and a relevant number of EC projects. This rather transversal domain co-occurs frequently with a large number of domains in the hard and applied sciences, particular- ly Biotechnology, Fundamental physics and mathematics and Mechanical engineering and heavy machinery. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation21 UKRAINE Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCIEC projectsTotal Agrifood 0 Biotechnology 3 Chemistry and chemical engineering0 Electric and electronic technologies2 Energy 4 Environmental sciences and industries1 Fundamental physics and mathematics1 Governance, culture, education and the economy3 Health and wellbeing 4 ICT and computer science 1 Mechanical engineering and heavy machinery3 Nanotechnology and materials 3 Optics and photonics 2 Transportation 3 NCI = Normalised citation impact EC projects = EU-funded R&I projectsTable VIII. Selected S&T specialisation domains in Ukraine 22 Overview of economic, innovation, scientific and technological specialisations Identification of the main actors and col- laboration patterns within the S&T spe- cialisation domains Actors and S&T ecosystems The main actors per EaP country and the nation- al collaborations within the EaP, as well as inter- national collaboration with external partners, are presented in detail in Part 3. This information is complemented by the interactive networks9, which are produced in the context of this project as a tool to complement this written document. The net- works provide a complete exploratory depiction of all actors for a country, filterable by domain. The National Academies of Science play a cen- tral role in Armenia, Moldova and Ukraine. In all countries, one or very few comprehensive univer- sities, usually located in the capital city (or some other main cities for Ukraine), also have a back- bone role in the national ecosystem. After these, specialised research institutions and universities 9 Available on the Smart Specialisation platform webpage.present relevant roles	[ "computer", "science", "(", "in", "areas", "such", "as", "\n", "automatic", "control", "and", "UAVs", ")", "as", "well", "as", "Me-", "\n", "chanical", "engineering", ",", "presents", "the", "highest", "\n", "specialisation", "index", "in", "publications", "and", "pat-", "\n", "ents", ",", "and", "a", "high", "scientific", "citation", "impact", ".", "Its", "\n", "research", "is", "geared", "towards", "road", ",", "rail", ",", "sea", "and", "\n", "air", "transport", ",", "while", "patents", "cluster", "in", "Vehicles", "\n", "in", "general", ",", "Combustion", "engines", "and", "Ships", ";", "\n ", "■", "Mechanical", "engineering", "presents", "a", "high", "\n", "critical", "mass", "in", "patents", "and", "specialisation", "and", "\n", "citation", "impact", "in", "publications", ".", "It", "co", "-", "occurs", "\n", "with", "a", "large", "number", "of", "domains", ",", "particularly", "Transportation", ",", "Energy", "and", "Electric", "and", "elec-", "\n", "tronic", "technologies", ";", "\n ", "■", "Nanotechnology", "and", "materials", "presents", "\n", "a", "high", "critical", "mass", "in", "patents", "and", "publica-", "\n", "tions", "and", "a", "relevant", "number", "of", "EC", "projects", ".", "\n", "This", "rather", "transversal", "domain", "co", "-", "occurs", "\n", "frequently", "with", "a", "large", "number", "of", "domains", "\n", "in", "the", "hard", "and", "applied", "sciences", ",", "particular-", "\n", "ly", "Biotechnology", ",", "Fundamental", "physics", "and", "\n", "mathematics", "and", "Mechanical", "engineering", "\n", "and", "heavy", "machinery", ".", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation21", "\n ", "UKRAINE", "Critical", "mass", "Specialisation", "Excellence", "Summary", "\n", "S&T", "domain", "Pubs", ".", "Pat", ".", "Pubs", ".", "Pat", ".", "NCIEC", "\n", "projectsTotal", "\n", "Agrifood", "0", "\n", "Biotechnology", "3", "\n", "Chemistry", "and", "chemical", "\n", "engineering0", "\n", "Electric", "and", "electronic", "\n", "technologies2", "\n", "Energy", "4", "\n", "Environmental", "sciences", "and", "\n", "industries1", "\n", "Fundamental", "physics", "and", "\n", "mathematics1", "\n", "Governance", ",", "culture", ",", "education", "\n", "and", "the", "economy3", "\n", "Health", "and", "wellbeing", "4", "\n", "ICT", "and", "computer", "science", "1", "\n", "Mechanical", "engineering", "and", "\n", "heavy", "machinery3", "\n", "Nanotechnology", "and", "materials", "3", "\n", "Optics", "and", "photonics", "2", "\n", "Transportation", "3", "\n", "", "NCI", "=", "Normalised", "citation", "impact", "", "EC", "projects", "=", "EU", "-", "funded", "R&I", "projectsTable", "VIII", ".", "Selected", "S&T", "specialisation", "domains", "in", "Ukraine", "\n", "22", "\n", "Overview", "of", "economic", ",", "innovation", ",", "scientific", "and", "technological", "specialisations", "\n", "Identification", "of", "the", "main", "actors", "and", "col-", "\n", "laboration", "patterns", "within", "the", "S&T", "spe-", "\n", "cialisation", "domains", "\n", "Actors", "and", "S&T", "ecosystems", "\n", "The", "main", "actors", "per", "EaP", "country", "and", "the", "nation-", "\n", "al", "collaborations", "within", "the", "EaP", ",", "as", "well", "as", "inter-", "\n", "national", "collaboration", "with", "external", "partners", ",", "are", "\n", "presented", "in", "detail", "in", "Part", "3", ".", "This", "information", "is", "\n", "complemented", "by", "the", "interactive", "networks9", ",", "which", "\n", "are", "produced", "in", "the", "context", "of", "this", "project", "as", "a", "tool", "\n", "to", "complement", "this", "written", "document", ".", "The", "net-", "\n", "works", "provide", "a", "complete", "exploratory", "depiction", "of", "\n", "all", "actors", "for", "a", "country", ",", "filterable", "by", "domain", ".", "\n", "The", "National", "Academies", "of", "Science", "play", "a", "cen-", "\n", "tral", "role", "in", "Armenia", ",", "Moldova", "and", "Ukraine", ".", "In", "all", "\n", "countries", ",", "one", "or", "very", "few", "comprehensive", "univer-", "\n", "sities", ",", "usually", "located", "in", "the", "capital", "city", "(", "or", "some", "\n", "other", "main", "cities", "for", "Ukraine", ")", ",", "also", "have", "a", "back-", "\n", "bone", "role", "in", "the", "national", "ecosystem", ".", "After", "these", ",", "\n", "specialised", "research", "institutions", "and", "universities", "\n", "9", "Available", "on", "the", "Smart", "Specialisation", "platform", "webpage.present", "relevant", "roles" ]	[]
Smart Specialisation. 252 Part 5 Discussion of results and final remarks Georgia is developing the strategy for the Imereti region with plans for more regions to follow there- after. Georgia has completed the quantitative and qualitative mapping phase for the featured region and had a successful awareness-raising event at the end of 2020, co-organised with the JRC. The country is currently running the Entrepreneurial Discovery Process for the priority domains identi- fied in the mapping phase. Moldova started developing the strategy in 2017, with a strong commitment to implementation as Smart Specialisation was included in the 2020 Government Action Plan. The EDP has been in- terrupted by the COVID-19 pandemic and, after a while, was finished online. The county finalised drafting the strategy, which is expected to be adopted soon. Ukraine was the first country to start engage- ment with the JRC back in 2017 and engage in de- veloping regional Smart Specialisation Strategies. Out of 27 regions (24 districts), at least 11 have made efforts to start the process, while 8 regions are currently progressing with JRC support strat- egies. The country is simultaneously developing a national-level strategy, which will be aimed at directing the research and innovation potential to- wards the implementation of the United Nation’s Sustainable Development Goals (SDGs). Complementing these national processes, the quantitative results of the current report provide a minimum map of E&I-S&T specialisations, but cannot rule out the existence of other relevant specialisations or concordances found by analys- ing other data sources or, on the ground, during the EDP. The results of this study aim to strengthen evi- dence-based innovation policymaking in the ana- lysed countries and the whole Eastern Partnership region. The results can be used not only directly for the development of Smart Specialisation Strat- egies and for priority setting, which strengthens the potential impact of public and private invest- ments, but also for many other tasks in research, innovation and economics policy, such as pro- gramme development, funding, internationalisa-tion, collaboration, etc. The results may potentially benefit other sectoral policies also, aiming at un- locking the benefits of high value added through the application of new knowledge and technolo- gies. To orient policymaking, E&I domains for which a specific concordance with S&T domains was not found would probably require the development of new research and innovation strengths, analysing the opportunities for non-technological innova- tion, the construction of collaborations with expert partners	[ "Smart", "Specialisation", ".", "\n", "252", "\n ", "Part", "5", "Discussion", "of", "results", "and", "final", "remarks", "\n", "Georgia", "is", "developing", "the", "strategy", "for", "the", "Imereti", "\n", "region", "with", "plans", "for", "more", "regions", "to", "follow", "there-", "\n", "after", ".", "Georgia", "has", "completed", "the", "quantitative", "and", "\n", "qualitative", "mapping", "phase", "for", "the", "featured", "region", "\n", "and", "had", "a", "successful", "awareness", "-", "raising", "event", "at", "\n", "the", "end", "of", "2020", ",", "co", "-", "organised", "with", "the", "JRC", ".", "The", "\n", "country", "is", "currently", "running", "the", "Entrepreneurial", "\n", "Discovery", "Process", "for", "the", "priority", "domains", "identi-", "\n", "fied", "in", "the", "mapping", "phase", ".", "\n", "Moldova", "started", "developing", "the", "strategy", "in", "2017", ",", "\n", "with", "a", "strong", "commitment", "to", "implementation", "as", "\n", "Smart", "Specialisation", "was", "included", "in", "the", "2020", "\n", "Government", "Action", "Plan", ".", "The", "EDP", "has", "been", "in-", "\n", "terrupted", "by", "the", "COVID-19", "pandemic", "and", ",", "after", "\n", "a", "while", ",", "was", "finished", "online", ".", "The", "county", "finalised", "\n", "drafting", "the", "strategy", ",", "which", "is", "expected", "to", "be", "\n", "adopted", "soon", ".", "\n", "Ukraine", "was", "the", "first", "country", "to", "start", "engage-", "\n", "ment", "with", "the", "JRC", "back", "in", "2017", "and", "engage", "in", "de-", "\n", "veloping", "regional", "Smart", "Specialisation", "Strategies", ".", "\n", "Out", "of", "27", "regions", "(", "24", "districts", ")", ",", "at", "least", "11", "have", "\n", "made", "efforts", "to", "start", "the", "process", ",", "while", "8", "regions", "\n", "are", "currently", "progressing", "with", "JRC", "support", "strat-", "\n", "egies", ".", "The", "country", "is", "simultaneously", "developing", "\n", "a", "national", "-", "level", "strategy", ",", "which", "will", "be", "aimed", "at", "\n", "directing", "the", "research", "and", "innovation", "potential", "to-", "\n", "wards", "the", "implementation", "of", "the", "United", "Nation", "’s", "\n", "Sustainable", "Development", "Goals", "(", "SDGs", ")", ".", "\n", "Complementing", "these", "national", "processes", ",", "the", "\n", "quantitative", "results", "of", "the", "current", "report", "provide", "\n", "a", "minimum", "map", "of", "E&I", "-", "S&T", "specialisations", ",", "but", "\n", "can", "not", "rule", "out", "the", "existence", "of", "other", "relevant", "\n", "specialisations", "or", "concordances", "found", "by", "analys-", "\n", "ing", "other", "data", "sources", "or", ",", "on", "the", "ground", ",", "during", "\n", "the", "EDP", ".", "\n", "The", "results", "of", "this", "study", "aim", "to", "strengthen", "evi-", "\n", "dence", "-", "based", "innovation", "policymaking", "in", "the", "ana-", "\n", "lysed", "countries", "and", "the", "whole", "Eastern", "Partnership", "\n", "region", ".", "The", "results", "can", "be", "used", "not", "only", "directly", "\n", "for", "the", "development", "of", "Smart", "Specialisation", "Strat-", "\n", "egies", "and", "for", "priority", "setting", ",", "which", "strengthens", "\n", "the", "potential", "impact", "of", "public", "and", "private", "invest-", "\n", "ments", ",", "but", "also", "for", "many", "other", "tasks", "in", "research", ",", "\n", "innovation", "and", "economics", "policy", ",", "such", "as", "pro-", "\n", "gramme", "development", ",", "funding", ",", "internationalisa", "-", "tion", ",", "collaboration", ",", "etc", ".", "The", "results", "may", "potentially", "\n", "benefit", "other", "sectoral", "policies", "also", ",", "aiming", "at", "un-", "\n", "locking", "the", "benefits", "of", "high", "value", "added", "through", "\n", "the", "application", "of", "new", "knowledge", "and", "technolo-", "\n", "gies", ".", "\n", "To", "orient", "policymaking", ",", "E&I", "domains", "for", "which", "a", "\n", "specific", "concordance", "with", "S&T", "domains", "was", "not", "\n", "found", "would", "probably", "require", "the", "development", "of", "\n", "new", "research", "and", "innovation", "strengths", ",", "analysing", "\n", "the", "opportunities", "for", "non", "-", "technological", "innova-", "\n", "tion", ",", "the", "construction", "of", "collaborations", "with", "expert", "\n", "partners" ]	[]
mo- bilised to support knowledge-based economic transformation? 4. How are international and national STI collab- oration networks structured and who are the main stakeholders? 5. Are there possible synergies/concordances between the countries’ economic, innovation, scientific and technological specialisations?The detailed methodological approach used to an- swer the research questions is presented in Part 1 of the report. To address the research questions, a varied list of international data sources is exploit- ed within the methodological approach specified in the Figure I. As a result of Steps 1 and 2 of the methodology, a series of economic and in- novation (E&I) specialisation domains and, in parallel, a series of scientific and technological (S&T) specialisation domains are identified for each EaP country. These results are presented in Part 2 and Part 3 of the report, respectively. Step 3 then aims at finding concordances between these complementary specialisation dimensions, leading to the identification of a subset of combined EIST specialisation domains – as presented be- low and described in Part 4 of the report. Indicators and insight aimed at each individual EaP country are complemented by an EaP-wide analysis, leading to the identification of some spe- cialisations and collaboration areas that can facili- tate regional cooperation. In line with the S3 Framework, further analytical and participatory work, complementary to the in- sights reported in this study, is necessary to an- alyse, propose and characterise specialisation priorities by: 1. enriching the quantitative and semantic analy- sis by adding additional local data sources and repositories (complementary economic and in- novation statistics, R&D projects financed by local policy instruments, publications in local repositories, patents and other intellectual property filed with local IPR agencies, etc.) to the international sources used in this exercise; 2. including profuse contributions by local ex- perts and stakeholders within the entrepre- neurial discovery processes. Part 4 of this report presents the process in which synergies or concordances between the economic, innovation, scientific and technological special- isations have been identified, by means of con- cordance tables. These have been identified at EaP-region level and at country level, crossing indicators related to the S&T domains with the Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation5 Figure I. Summary scheme of the methodological steps leading to the selection and definition of a list of specialisation domains for each country and the potential cooperation areas for the whole region and with international	[ "mo-", "\n", "bilised", "to", "support", "knowledge", "-", "based", "economic", "\n", "transformation", "?", "\n", "4", ".", "How", "are", "international", "and", "national", "STI", "collab-", "\n", "oration", "networks", "structured", "and", "who", "are", "the", "\n", "main", "stakeholders", "?", "\n", "5", ".", "Are", "there", "possible", "synergies", "/", "concordances", "\n", "between", "the", "countries", "’", "economic", ",", "innovation", ",", "\n", "scientific", "and", "technological", "specialisations?The", "detailed", "methodological", "approach", "used", "to", "an-", "\n", "swer", "the", "research", "questions", "is", "presented", "in", "Part", "1", "\n", "of", "the", "report", ".", "To", "address", "the", "research", "questions", ",", "a", "\n", "varied", "list", "of", "international", "data", "sources", "is", "exploit-", "\n", "ed", "within", "the", "methodological", "approach", "specified", "\n", "in", "the", "Figure", "I.", "As", "a", "result", "of", "Steps", "1", "and", "2", "of", "\n", "the", "methodology", ",", "a", "series", "of", "economic", "and", "in-", "\n", "novation", "(", "E&I", ")", "specialisation", "domains", "and", ",", "in", "\n", "parallel", ",", "a", "series", "of", "scientific", "and", "technological", "\n", "(", "S&T", ")", "specialisation", "domains", "are", "identified", "for", "\n", "each", "EaP", "country", ".", "These", "results", "are", "presented", "in", "\n", "Part", "2", "and", "Part", "3", "of", "the", "report", ",", "respectively", ".", "Step", "3", "\n", "then", "aims", "at", "finding", "concordances", "between", "these", "\n", "complementary", "specialisation", "dimensions", ",", "leading", "\n", "to", "the", "identification", "of", "a", "subset", "of", "combined", "\n", "EIST", "specialisation", "domains", "–", "as", "presented", "be-", "\n", "low", "and", "described", "in", "Part", "4", "of", "the", "report", ".", "\n", "Indicators", "and", "insight", "aimed", "at", "each", "individual", "\n", "EaP", "country", "are", "complemented", "by", "an", "EaP", "-", "wide", "\n", "analysis", ",", "leading", "to", "the", "identification", "of", "some", "spe-", "\n", "cialisations", "and", "collaboration", "areas", "that", "can", "facili-", "\n", "tate", "regional", "cooperation", ".", "\n", "In", "line", "with", "the", "S3", "Framework", ",", "further", "analytical", "\n", "and", "participatory", "work", ",", "complementary", "to", "the", "in-", "\n", "sights", "reported", "in", "this", "study", ",", "is", "necessary", "to", "an-", "\n", "alyse", ",", "propose", "and", "characterise", "specialisation", "\n", "priorities", "by", ":", "\n", "1", ".", "enriching", "the", "quantitative", "and", "semantic", "analy-", "\n", "sis", "by", "adding", "additional", "local", "data", "sources", "and", "\n", "repositories", "(", "complementary", "economic", "and", "in-", "\n", "novation", "statistics", ",", "R&D", "projects", "financed", "by", "\n", "local", "policy", "instruments", ",", "publications", "in", "local", "\n", "repositories", ",", "patents", "and", "other", "intellectual", "\n", "property", "filed", "with", "local", "IPR", "agencies", ",", "etc", ".", ")", "to", "\n", "the", "international", "sources", "used", "in", "this", "exercise", ";", "\n", "2", ".", "including", "profuse", "contributions", "by", "local", "ex-", "\n", "perts", "and", "stakeholders", "within", "the", "entrepre-", "\n", "neurial", "discovery", "processes", ".", "\n", "Part", "4", "of", "this", "report", "presents", "the", "process", "in", "which", "\n", "synergies", "or", "concordances", "between", "the", "economic", ",", "\n", "innovation", ",", "scientific", "and", "technological", "special-", "\n", "isations", "have", "been", "identified", ",", "by", "means", "of", "con-", "\n", "cordance", "tables", ".", "These", "have", "been", "identified", "at", "\n", "EaP", "-", "region", "level", "and", "at", "country", "level", ",", "crossing", "\n", "indicators", "related", "to", "the", "S&T", "domains", "with", "the", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation5", "\n", "Figure", "I.", "Summary", "scheme", "of", "the", "methodological", "steps", "leading", "to", "the", "selection", "and", "definition", "of", "a", "list", "of", "\n", "specialisation", "domains", "for", "each", "country", "and", "the", "potential", "cooperation", "areas", "for", "the", "whole", "region", "and", "with", "\n", "international" ]	[]
economy196 110 40 121 288 1 128 520 1 561 1 392 153 175 45 165 Health and wellbeing 722 2 687 682 217 257 754 414 1 561 583 673 684 577 64 ICT and computer science 133 138 88 1 008 504 592 1 164 1 392 583 903 524 435 1 138 Mechanical engineering and heavy machinery615 212 143 1 213 2 135 693 1 139 153 673 903 1 614 390 1 539 Nanotechnology and materials236 1 656 1 809 1 004 799 542 2 115 175 684 524 1 614 1 665 89 Optics and photonics 45 109 91 856 185 101 930 45 577 435 390 1 665 39 Transportation 52 45 9 240 326 113 258 165 64 1 138 1 539 89 39Figure 3.17. Co-occurrence of S&T records in different domains across the whole EaP region, colour-coded for the ratio with the total number of records in that domain (column)Figure 3.16. Number of records per labelled topic group (i.e. ‘domain’) in the EaP region 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 Number of records publicationsNanotechnology and materials Health and wellbeing Fundamental physics and mathematics Mechanical engineering and heavy machinery ICT and computer science Biotechnology Governance, culture, education and the economy Environmental sciences and industries Electric and electronic technologies Energy Chemistry and chemical engineering Optics and photonics Agrifood Transportation patents EC projects 156 Part 3 Analysis of scientific and technological potential Figure 3.18. Share of records per S&T domain in the Eastern Partnership region 0 20 40 60 80 Share of records publicationsNanotechnology and materials Health and wellbeing Fundamental physics and mathematics Mechanical engineering and heavy machinery ICT and computer science Biotechnology Governance, culture, education and the economy Environmental sciences and industries Electric and electronic technologies Energy Chemistry and chemical engineering Optics and photonics Agrifood Transportation patents EC projects EaP S&T specialisation domains according to the internal distribution of the S&T data sources Science-oriented S&T domains domains where scientific publications are most relevantBalanced S&T domains domains where publications and patents have a similar relative weightTechnology-oriented S&T domains domains where patents are most relevant Fundamental physics and mathematics Health and wellbeingMechanical engineering and heavy machinery Nanotechnology and materials Biotechnology Electric and electronic technologies Optics and photonics ICT and computer science Agrifood Environmental sciences and industries Energy Chemistry and chemical engineering Transportation Governance, culture, education and the	[ "economy196", "110", "40", "121", "288", "1", "128", "520", "1", "561", "1", "392", "153", "175", "45", "165", "\n", "Health", "and", "wellbeing", "722", "2", "687", "682", "217", "257", "754", "414", "1", "561", "583", "673", "684", "577", "64", "\n", "ICT", "and", "computer", "science", "133", "138", "88", "1", "008", "504", "592", "1", "164", "1", "392", "583", "903", "524", "435", "1", "138", "\n", "Mechanical", "engineering", "and", "\n", "heavy", "machinery615", "212", "143", "1", "213", "2", "135", "693", "1", "139", "153", "673", "903", "1", "614", "390", "1", "539", "\n", "Nanotechnology", "and", "\n", "materials236", "1", "656", "1", "809", "1", "004", "799", "542", "2", "115", "175", "684", "524", "1", "614", "1", "665", "89", "\n", "Optics", "and", "photonics", "45", "109", "91", "856", "185", "101", "930", "45", "577", "435", "390", "1", "665", "39", "\n", "Transportation", "52", "45", "9", "240", "326", "113", "258", "165", "64", "1", "138", "1", "539", "89", "39Figure", "3.17", ".", "Co", "-", "occurrence", "of", "S&T", "records", "in", "different", "domains", "across", "the", "whole", "EaP", "region", ",", "colour", "-", "coded", "for", "the", "\n", "ratio", "with", "the", "total", "number", "of", "records", "in", "that", "domain", "(", "column)Figure", "3.16", ".", "Number", "of", "records", "per", "labelled", "topic", "group", "(", "i.e.", "‘", "domain", "’", ")", "in", "the", "EaP", "region", "\n", "0", "5", "000", "10", "000", "15", "000", "20", "000", "25", "000", "30", "000", "35", "000", "\n", "Number", "of", "records", "\n", "publicationsNanotechnology", "and", "materials", "\n", "Health", "and", "wellbeing", "\n", "Fundamental", "physics", "and", "mathematics", "\n", "Mechanical", "engineering", "and", "heavy", "machinery", "\n", "ICT", "and", "computer", "science", "\n", "Biotechnology", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "Environmental", "sciences", "and", "industries", "\n", "Electric", "and", "electronic", "technologies", "\n", "Energy", "\n", "Chemistry", "and", "chemical", "engineering", "\n", "Optics", "and", "photonics", "\n", "Agrifood", "\n", "Transportation", "\n", "patents", "EC", "projects", "\n", "156", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Figure", "3.18", ".", "Share", "of", "records", "per", "S&T", "domain", "in", "the", "Eastern", "Partnership", "region", "\n", "0", "20", "40", "60", "80", "\n", "Share", "of", "records", "\n", "publicationsNanotechnology", "and", "materials", "\n", "Health", "and", "wellbeing", "\n", "Fundamental", "physics", "and", "mathematics", "\n", "Mechanical", "engineering", "and", "heavy", "machinery", "\n", "ICT", "and", "computer", "science", "\n", "Biotechnology", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "Environmental", "sciences", "and", "industries", "\n", "Electric", "and", "electronic", "technologies", "\n", "Energy", "\n", "Chemistry", "and", "chemical", "engineering", "\n", "Optics", "and", "photonics", "\n", "Agrifood", "\n", "Transportation", "\n", "patents", "EC", "projects", "\n", "EaP", "S&T", "specialisation", "domains", " \n", "according", "to", "the", "internal", "distribution", "of", "the", "S&T", "data", "sources", "\n", "Science", "-", "oriented", "S&T", "domains", "\n", "domains", "where", "scientific", "publications", "\n", "are", "most", "relevantBalanced", "S&T", "domains", "\n", "domains", "where", "publications", "and", "patents", "\n", "have", "a", "similar", "relative", "weightTechnology", "-", "oriented", "S&T", "domains", "\n", "domains", "where", "patents", "are", "most", "\n", "relevant", "\n", "Fundamental", "physics", "and", "mathematics", "Health", "and", "wellbeingMechanical", "engineering", "and", "heavy", "\n", "machinery", "\n", "Nanotechnology", "and", "materials", "Biotechnology", "Electric", "and", "electronic", "technologies", "\n", "Optics", "and", "photonics", "ICT", "and", "computer", "science", "Agrifood", "\n", "Environmental", "sciences", "and", "industries", "Energy", "\n", "Chemistry", "and", "chemical", "engineering", "Transportation", "\n", "Governance", ",", "culture", ",", "education", "and", "the" ]	[]
Biotechnology in Azerbaijan ............... 239 Figure 4.8. Keyword cloud for the S&T domain Chemistry and chemical engineering in Azerbaijan ..................................................................................................................................................... 239 Figure 4.9. Keyword cloud for the S&T domain Energy in Azerbaijan .............................. 239 Figure 4.10. Keyword cloud for the S&T domain Nanotechnology and materials in Azerbaijan ..................................................................................................................................................... 239 Figure 4.11. Keyword cloud for the S&T domain Agrifood in Georgia ............................. 240 Figure 4.12. Keyword cloud for the S&T domain Nanotechnology and materials in Georgia ........................................................................................................................................................... 240 Figure 4.13. Keyword cloud for the S&T domain Agrifood in Moldova ............................ 242 Figure 4.14. Keyword cloud for the S&T domain Biotechnology in Moldova ................ 242 Figure 4.15. Keyword cloud for the S&T domain Chemistry and chemical engineering in Moldova .......................................................................................................................................................... 242 Figure 4.16. Keyword cloud for the S&T domain ICT and computer science in Moldova .................................................................................................................................................. 242 Figure 4.17. Keyword cloud for the S&T domain Nanotechnology and materials in Moldova .......................................................................................................................................................... 242 Figure 4.18. Keyword cloud for the S&T domain Agrifood in Ukraine .............................. 244 Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation263 Figure 4.19. Keyword cloud for the S&T domain Electric and electronic technologies in Ukraine ............................................................................................................................................................ 244 Figure 4.20. Keyword cloud for the S&T domain Energy in Ukraine ................................. 245 Figure 4.21. Keyword cloud for the S&T domain Environmental sciences and industries in Ukraine ...................................................................................................................................................... 245 Figure 4.22. Keyword cloud for the S&T domain Fundamental physics and mathematics in Ukraine ...................................................................................................................................................... 245 Figure 4.23. Keyword cloud for the S&T domain ICT and computer science in Ukraine ............................................................................................................................................. 245 Figure 4.24. Keyword cloud for the S&T domain Mechanical engineering and heavy machinery in Ukraine ............................................................................................................................... 245 Figure 4.25. Keyword cloud for the S&T domain Nanotechnology and materials in Ukraine ............................................................................................................................................................ 245 Figure 4.26. Keyword cloud for the S&T domain Optics and photonics in Ukraine ....245 Figure 4.27. Keyword cloud for the S&T domain Transportation in Ukraine ................. 245 264 List of figures and tables LIST OF TABLES Table I. The scientific and technological specialisation domains in the Eastern Partnership identified via topic modelling ........................................................................................ 13 Table II. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern Partnership region ........................................................................................................................................ 14 Table III. Characterisation of the EaP S&T domains according to the internal distribution of S&T data sources .................................................................................................................................... 15 Table IV. Selected S&T specialisation domains in Armenia ...................................................... 16 Table V. Selected S&T specialisation domains in Azerbaijan ................................................... 17 Table VI. Selected S&T	[ "Biotechnology", "in", "Azerbaijan", "...............", "239", "\n", "Figure", "4.8", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Chemistry", "and", "chemical", "engineering", "in", "\n", "Azerbaijan", ".....................................................................................................................................................", "239", "\n", "Figure", "4.9", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Energy", "in", "Azerbaijan", "..............................", "239", "\n", "Figure", "4.10", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Nanotechnology", "and", "materials", "in", "\n", "Azerbaijan", ".....................................................................................................................................................", "239", "\n", "Figure", "4.11", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Agrifood", "in", "Georgia", ".............................", "240", "\n", "Figure", "4.12", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Nanotechnology", "and", "materials", "in", "\n", "Georgia", "...........................................................................................................................................................", "240", "\n", "Figure", "4.13", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Agrifood", "in", "Moldova", "............................", "242", "\n", "Figure", "4.14", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Biotechnology", "in", "Moldova", "................", "242", "\n", "Figure", "4.15", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Chemistry", "and", "chemical", "engineering", "in", "\n", "Moldova", "..........................................................................................................................................................", "242", "\n", "Figure", "4.16", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "ICT", "and", "computer", "science", "in", "\n", "Moldova", "..................................................................................................................................................", "242", "\n", "Figure", "4.17", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Nanotechnology", "and", "materials", "in", "\n", "Moldova", "..........................................................................................................................................................", "242", "\n", "Figure", "4.18", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Agrifood", "in", "Ukraine", "..............................", "244", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation263", "\n", "Figure", "4.19", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Electric", "and", "electronic", "technologies", "in", "\n", "Ukraine", "............................................................................................................................................................", "244", "\n", "Figure", "4.20", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Energy", "in", "Ukraine", ".................................", "245", "\n", "Figure", "4.21", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Environmental", "sciences", "and", "industries", "\n", "in", "Ukraine", "......................................................................................................................................................", "245", "\n", "Figure", "4.22", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Fundamental", "physics", "and", "mathematics", "\n", "in", "Ukraine", "......................................................................................................................................................", "245", "\n", "Figure", "4.23", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "ICT", "and", "computer", "science", "in", "\n", "Ukraine", ".............................................................................................................................................", "245", "\n", "Figure", "4.24", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Mechanical", "engineering", "and", "heavy", "\n", "machinery", "in", "Ukraine", "...............................................................................................................................", "245", "\n", "Figure", "4.25", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Nanotechnology", "and", "materials", "in", "\n", "Ukraine", "............................................................................................................................................................", "245", "\n", "Figure", "4.26", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Optics", "and", "photonics", "in", "Ukraine", "....", "245", "\n", "Figure", "4.27", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Transportation", "in", "Ukraine", ".................", "245", "\n", "264", "\n", "List", "of", "figures", "and", "tables", "\n", "LIST", "OF", "TABLES", "\n", "Table", "I.", "The", "scientific", "and", "technological", "specialisation", "domains", "in", "the", "Eastern", "\n", "Partnership", "identified", "via", "topic", "modelling", "........................................................................................", "13", "\n", "Table", "II", ".", "Number", "of", "records", "per", "labelled", "topic", "group", "(", "i.e.", "‘", "domain", "’", ")", "in", "the", "Eastern", "\n", "Partnership", "region", "........................................................................................................................................", "14", "\n", "Table", "III", ".", "Characterisation", "of", "the", "EaP", "S&T", "domains", "according", "to", "the", "internal", "distribution", "\n", "of", "S&T", "data", "sources", "....................................................................................................................................", "15", "\n", "Table", "IV", ".", "Selected", "S&T", "specialisation", "domains", "in", "Armenia", "......................................................", "16", "\n", "Table", "V.", "Selected", "S&T", "specialisation", "domains", "in", "Azerbaijan", "...................................................", "17", "\n", "Table", "VI", ".", "Selected", "S&T" ]	[]
are shared with multicellular organisms, but due to the incredible diversity of types of microbes these organisms are able to deal with a far wider range of xenobiotics than multicellular organisms, and can degrade even persistent organic pollutants such as organochloride compounds.[102] A related problem for aerobic organisms is oxidative stress.[103] Here, processes including oxidative phosphorylation and the formation of disulfide bonds during protein folding produce reactive oxygen species such as hydrogen peroxide.[104] These damaging oxidants are removed by antioxidant metabolites such as glutathione and enzymes such as catalases and peroxidases.[105][106] Thermodynamics of living organisms Further information: Biological thermodynamics Living organisms must obey the laws of thermodynamics, which describe the transfer of heat and work. The second law of thermodynamics states that in any isolated system, the amount of entropy (disorder) cannot decrease. Although living organisms' amazing complexity appears to contradict this law, life is possible as all organisms are open systems that exchange matter and energy with their surroundings. Living systems are not in equilibrium, but instead are dissipative systems that maintain their state of high complexity by causing a larger increase in the entropy of their environments.[107] The metabolism of a cell achieves this by coupling the spontaneous processes of catabolism to the non-spontaneous processes of anabolism. In thermodynamic terms, metabolism maintains order by creating disorder.[108] Regulation and control Further information: Metabolic pathway, Metabolic control analysis, Hormone, Regulatory enzymes, and Cell signaling As the environments of most organisms are constantly changing, the reactions of metabolism must be finely regulated to maintain a constant set of conditions within cells, a condition called homeostasis.[109][110] Metabolic regulation also allows organisms to respond to signals and interact actively with their environments.[111] Two closely linked concepts are important for understanding how metabolic pathways are controlled. Firstly, the regulation of an enzyme in a pathway is how its activity is increased and decreased in response to signals. Secondly, the control exerted by this enzyme is the effect that these changes in its activity have on the overall rate of the pathway (the flux through the pathway).[112] For example, an enzyme may show large changes in activity (i.e. it is highly regulated) but if these changes have little effect on the flux of a metabolic pathway, then this enzyme is not involved in the control of the pathway.[113] Effect of insulin on glucose uptake and metabolism. Insulin binds to its receptor (1), which in turn	[ "are", "shared", "with", "multicellular", "organisms", ",", "but", "due", "to", "the", "incredible", "diversity", "of", "types", "of", "microbes", "these", "organisms", "are", "able", "to", "deal", "with", "a", "far", "wider", "range", "of", "xenobiotics", "than", "multicellular", "organisms", ",", "and", "can", "degrade", "even", "persistent", "organic", "pollutants", "such", "as", "organochloride", "compounds.[102", "]", "\n\n", "A", "related", "problem", "for", "aerobic", "organisms", "is", "oxidative", "stress.[103", "]", "Here", ",", "processes", "including", "oxidative", "phosphorylation", "and", "the", "formation", "of", "disulfide", "bonds", "during", "protein", "folding", "produce", "reactive", "oxygen", "species", "such", "as", "hydrogen", "peroxide.[104", "]", "These", "damaging", "oxidants", "are", "removed", "by", "antioxidant", "metabolites", "such", "as", "glutathione", "and", "enzymes", "such", "as", "catalases", "and", "peroxidases.[105][106", "]", "\n\n", "Thermodynamics", "of", "living", "organisms", "\n", "Further", "information", ":", "Biological", "thermodynamics", "\n", "Living", "organisms", "must", "obey", "the", "laws", "of", "thermodynamics", ",", "which", "describe", "the", "transfer", "of", "heat", "and", "work", ".", "The", "second", "law", "of", "thermodynamics", "states", "that", "in", "any", "isolated", "system", ",", "the", "amount", "of", "entropy", "(", "disorder", ")", "can", "not", "decrease", ".", "Although", "living", "organisms", "'", "amazing", "complexity", "appears", "to", "contradict", "this", "law", ",", "life", "is", "possible", "as", "all", "organisms", "are", "open", "systems", "that", "exchange", "matter", "and", "energy", "with", "their", "surroundings", ".", "Living", "systems", "are", "not", "in", "equilibrium", ",", "but", "instead", "are", "dissipative", "systems", "that", "maintain", "their", "state", "of", "high", "complexity", "by", "causing", "a", "larger", "increase", "in", "the", "entropy", "of", "their", "environments.[107", "]", "The", "metabolism", "of", "a", "cell", "achieves", "this", "by", "coupling", "the", "spontaneous", "processes", "of", "catabolism", "to", "the", "non", "-", "spontaneous", "processes", "of", "anabolism", ".", "In", "thermodynamic", "terms", ",", "metabolism", "maintains", "order", "by", "creating", "disorder.[108", "]", "\n\n", "Regulation", "and", "control", "\n", "Further", "information", ":", "Metabolic", "pathway", ",", "Metabolic", "control", "analysis", ",", "Hormone", ",", "Regulatory", "enzymes", ",", "and", "Cell", "signaling", "\n", "As", "the", "environments", "of", "most", "organisms", "are", "constantly", "changing", ",", "the", "reactions", "of", "metabolism", "must", "be", "finely", "regulated", "to", "maintain", "a", "constant", "set", "of", "conditions", "within", "cells", ",", "a", "condition", "called", "homeostasis.[109][110", "]", "Metabolic", "regulation", "also", "allows", "organisms", "to", "respond", "to", "signals", "and", "interact", "actively", "with", "their", "environments.[111", "]", "Two", "closely", "linked", "concepts", "are", "important", "for", "understanding", "how", "metabolic", "pathways", "are", "controlled", ".", "Firstly", ",", "the", "regulation", "of", "an", "enzyme", "in", "a", "pathway", "is", "how", "its", "activity", "is", "increased", "and", "decreased", "in", "response", "to", "signals", ".", "Secondly", ",", "the", "control", "exerted", "by", "this", "enzyme", "is", "the", "effect", "that", "these", "changes", "in", "its", "activity", "have", "on", "the", "overall", "rate", "of", "the", "pathway", "(", "the", "flux", "through", "the", "pathway).[112", "]", "For", "example", ",", "an", "enzyme", "may", "show", "large", "changes", "in", "activity", "(", "i.e.", "it", "is", "highly", "regulated", ")", "but", "if", "these", "changes", "have", "little", "effect", "on", "the", "flux", "of", "a", "metabolic", "pathway", ",", "then", "this", "enzyme", "is", "not", "involved", "in", "the", "control", "of", "the", "pathway.[113", "]", "\n\n\n", "Effect", "of", "insulin", "on", "glucose", "uptake", "and", "metabolism", ".", "Insulin", "binds", "to", "its", "receptor", "(", "1", ")", ",", "which", "in", "turn" ]	[]
potential AzerbaijanTemporal evolution of the domains Period over period change in the relative size of each domain, domain size and data source size independent (% change for 2015-2018, over previous period 2011-2014) Change in share of publicationsChange in share of patents Change, weighted average of publications and patents Agrifood -48.35%Insufficient data-48.35% Biotechnology -24.69% 58.14% 3.99% Chemistry and chemical engineering -28.11% -65.71% -30.06% Energy -5.47% -0.46% -4.60% Environmental sciences and industries -13.35% -10.00% -13.23% Fundamental physics and mathematics -5.62%Insufficient data-5.62% Governance, culture, education and the economy14.94%Insufficient data14.94% Health and wellbeing 29.07% 34.38% 29.56% ICT and computer science -2.67% -46.00% -4.68% Mechanical engineering and heavy machinery-15.79% 21.61% 0.36% Nanotechnology and materials 37.52% -53.71% 34.17% Optics and photonics -24.81%Insufficient data-24.81%Table 3.12. Temporal evolution of Azerbaijan’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation183 Georgia Table 3.13 and Figure 3.30 showcase the num- ber of records per domain of S&T specialisation in Georgia. Fundamental physics and mathematics is the domain with the most records (with a total of 3 348), followed by Health and wellbeing (1 544), Environmental sciences and industries (1,066), Governance, culture, education and the economy (1 001) and Nanotechnology and materials (809). The first one accounts for more than one third of the total number of records (36%). The bulk of records across all domains are pub- lications, ranging from 90% to 99% of the total records in most cases, as shown in Figure 3.30. The exceptions are Chemistry and chemical en- gineering (73%), Agrifood (62%) and Electric and electronic technologies (58%) and Mechanical en- gineering and heavy machinery (10%), which con- tain a high number of patents. As with other countries, EC projects in Georgia are also mostly concentrated in the domain of Gov- ernance, culture, education and the economy. The remaining figures do not seem relevant enough to point to international excellence, although they can facilitate the identification of internationally connected specialised actors. The growth rate of publications in recent years, in terms of the compound annual growth rate, is also shown. Only Fundamental physics and math- ematics (-1.4%) shows a decreasing trend. This is particularly noteworthy, as it signals that the number of publications in coming years may con- tinue to decrease and the domain may become less relevant. Georgia’s publications are highly specialised in Environmental sciences and industries (1.6), Fun- damental physics and mathematics (1.5), Agrifood (1.3), Health and wellbeing	[ "potential", "\n", "AzerbaijanTemporal", "evolution", "of", "the", "domains", "\n", "Period", "over", "period", "change", "in", "the", "relative", "size", "of", "each", "domain", ",", "\n", "domain", "size", "and", "data", "source", "size", "independent", "\n", "(", "%", "change", "for", "2015", "-", "2018", ",", "over", "previous", "period", "2011", "-", "2014", ")", "\n", "Change", "in", "\n", "share", "of", "\n", "publicationsChange", "in", "share", "\n", "of", "patents", "Change", ",", "weighted", "average", "of", "\n", "publications", "and", "patents", "\n", "Agrifood", "-48.35%Insufficient", "\n", "data-48.35", "%", "\n", "Biotechnology", "-24.69", "%", "58.14", "%", "3.99", "%", "\n", "Chemistry", "and", "chemical", "engineering", "-28.11", "%", "-65.71", "%", "-30.06", "%", "\n", "Energy", "-5.47", "%", "-0.46", "%", "-4.60", "%", "\n", "Environmental", "sciences", "and", "industries", "-13.35", "%", "-10.00", "%", "-13.23", "%", "\n", "Fundamental", "physics", "and", "mathematics", "-5.62%Insufficient", "\n", "data-5.62", "%", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy14.94%Insufficient", "\n", "data14.94", "%", "\n", "Health", "and", "wellbeing", "29.07", "%", "34.38", "%", "29.56", "%", "\n", "ICT", "and", "computer", "science", "-2.67", "%", "-46.00", "%", "-4.68", "%", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machinery-15.79", "%", "21.61", "%", "0.36", "%", "\n", "Nanotechnology", "and", "materials", "37.52", "%", "-53.71", "%", "34.17", "%", "\n", "Optics", "and", "photonics", "-24.81%Insufficient", "\n", "data-24.81%Table", "3.12", ".", "Temporal", "evolution", "of", "Azerbaijan", "’s", "S&T", "domains", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation183", "\n", "Georgia", "\n", "Table", "3.13", "and", "Figure", "3.30", "showcase", "the", "num-", "\n", "ber", "of", "records", "per", "domain", "of", "S&T", "specialisation", "in", "\n", "Georgia", ".", "Fundamental", "physics", "and", "mathematics", "is", "\n", "the", "domain", "with", "the", "most", "records", "(", "with", "a", "total", "of", "\n", "3", "348", ")", ",", "followed", "by", "Health", "and", "wellbeing", "(", "1", "544", ")", ",", "\n", "Environmental", "sciences", "and", "industries", "(", "1,066", ")", ",", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "(", "1", "001", ")", "and", "Nanotechnology", "and", "materials", "(", "809", ")", ".", "\n", "The", "first", "one", "accounts", "for", "more", "than", "one", "third", "of", "\n", "the", "total", "number", "of", "records", "(", "36", "%", ")", ".", "\n", "The", "bulk", "of", "records", "across", "all", "domains", "are", "pub-", "\n", "lications", ",", "ranging", "from", "90", "%", "to", "99", "%", "of", "the", "total", "\n", "records", "in", "most", "cases", ",", "as", "shown", "in", "Figure", "3.30", ".", "\n", "The", "exceptions", "are", "Chemistry", "and", "chemical", "en-", "\n", "gineering", "(", "73", "%", ")", ",", "Agrifood", "(", "62", "%", ")", "and", "Electric", "and", "\n", "electronic", "technologies", "(", "58", "%", ")", "and", "Mechanical", "en-", "\n", "gineering", "and", "heavy", "machinery", "(", "10", "%", ")", ",", "which", "con-", "\n", "tain", "a", "high", "number", "of", "patents", ".", "\n", "As", "with", "other", "countries", ",", "EC", "projects", "in", "Georgia", "are", "\n", "also", "mostly", "concentrated", "in", "the", "domain", "of", "Gov-", "\n", "ernance", ",", "culture", ",", "education", "and", "the", "economy", ".", "The", "\n", "remaining", "figures", "do", "not", "seem", "relevant", "enough", "\n", "to", "point", "to", "international", "excellence", ",", "although", "they", "can", "facilitate", "the", "identification", "of", "internationally", "\n", "connected", "specialised", "actors", ".", "\n", "The", "growth", "rate", "of", "publications", "in", "recent", "years", ",", "\n", "in", "terms", "of", "the", "compound", "annual", "growth", "rate", ",", "is", "\n", "also", "shown", ".", "Only", "Fundamental", "physics", "and", "math-", "\n", "ematics", "(", "-1.4", "%", ")", "shows", "a", "decreasing", "trend", ".", "This", "\n", "is", "particularly", "noteworthy", ",", "as", "it", "signals", "that", "the", "\n", "number", "of", "publications", "in", "coming", "years", "may", "con-", "\n", "tinue", "to", "decrease", "and", "the", "domain", "may", "become", "\n", "less", "relevant", ".", "\n", "Georgia", "’s", "publications", "are", "highly", "specialised", "in", "\n", "Environmental", "sciences", "and", "industries", "(", "1.6", ")", ",", "Fun-", "\n", "damental", "physics", "and", "mathematics", "(", "1.5", ")", ",", "Agrifood", "\n", "(", "1.3", ")", ",", "Health", "and", "wellbeing" ]	[]
professionals. Through investigation and discovery conducted at its Brigham Research Institute (BRI), BWH is an international leader in basic, clinical and translational research on human diseases, more than 1,000 physician-investigators and renowned biomedical scientists and faculty supported by nearly $650 million in funding. For the last 25 years, BWH ranked second in research funding from the National Institutes of Health (NIH) among independent hospitals. BWH continually pushes the boundaries of medicine, including building on its legacy in transplantation by performing a partial face transplant in 2009 and the nation's first full face transplant in 2011. BWH is also home to major landmark epidemiologic population studies, including the Nurses' and Physicians' Health Studies and the Women's Health Initiative. For more information, resources and to follow us on social media, please visit BWH's online newsroom. </p> <p> <hr class="hidden-xs hidden-sm" /> <hr class="major visible-sm" /> <div class="featured_image"> <div class="details"> <div class="well"> <h4>Journal</h4> <p>Circulation</p> </div> <div class="well"> <h4>DOI</h4> <p><a href="http://dx.doi.org/10.1161/CIRCULATIONAHA.115.017300" target="_blank">10.1161/CIRCULATIONAHA.115.017300 <i class="fa fa-sign-out"></i></a></p> </div> </div> </div> </div> <div class="well article_disclaimer hidden-search"> <p><strong>Disclaimer:</strong> AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.</p> </div> <div class="toolbar hidden-print hidden-search"> <div class='col-xs-6'> <div class="addthis_inline_share_toolbox_pnaa"></div> </div> <div class='col-xs-6'> <div class="article-tools pull-right"> <div class="addthis_inline_share_toolbox_62ef"></div> </div> </div> </div> </article> </div> <aside id="sidebar-content" class="white col-md-4"> <section class="widget"> <div class="widget-content"> <div class="contact-info"> <p><strong>Media Contact</strong></p> <p> Johanna Younghans<br/> <br /> <a href="mailto:[email protected]"> [email protected] </a><br/> Office: 617-525-6373<br/> </p> </div> </div> </section> <hr class="hidden-xs hidden-sm"> <hr class="major visible-xs visible-sm"> <section class="widget hidden-print"> <h3 class="widget-title red">More on this News Release</h3> <div class="widget-content"> <aside class="more"> <a href="/news-releases/464376"> <h3>Novel blood thinner found to be safe and effective in women</h3> </a> <p class="meta_institute">Brigham and Women's Hospital</p> <dl class="dl-horizontal meta stacked"> <dt class="yellow">Journal</dt> <dd class="yellow"><em>Circulation</em></dd> <dt class="red">DOI</dt> <dd class="red"><em>10.1161/CIRCULATIONAHA.115.017300</em></dd> </dl> </aside> <div class="row"> <div class="col-sm-6 col-md-12"> <h4 class="widget-subtitle">Keywords</h4> <nav class="tag-cloud"> <ul class="tags"> <li class="active ea-keyword"> <a href="#"> <span class="ea-keyword__path">/Life sciences/Organismal biology/Anatomy/Body fluids/</span><span class="ea-keyword__short">Blood</span> </a> </li> <li class="ea-keyword"> <a href="#"> <span class="ea-keyword__path">/Health and medicine/Health care/Medical facilities/</span><span class="ea-keyword__short">Hospitals</span> </a> </li> <li class="ea-keyword"> <a href="#"> <span class="ea-keyword__path"> /Health and medicine/Clinical medicine/Medical treatments/</span><span class="ea-keyword__short">Transplantation</span> </a> </li> </ul> </nav> </div> </div> </div> </section> </aside> </div> </div> </div> <footer id="footer" class="hidden-print hidden-search"> <div class="container"> <div class="row"> <div class="col-sm-5 col-sm-push-7"> <p class="logo"> <img src="/images/logo-footer.png" alt="EurekAlert! 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largely irreversible on human timescales, and lead to loss of many economic and sustainable development gains. In some regions, dislocation and conflict will result from continued glacier and snowpack loss. These impacts will touch even those living far from mountain cryosphere, raising the importance of the risks posed by shrinking glaciers that require urgent mitigation to address.Some Adaptation Remains Essential Even with urgent mitigation efforts, some level of glacier loss remains inevitable given current loss rates, which modelling shows will continue until temperatures stabilise; likely? after mid-century, if governments make a course correction back to 1.5C-consistent pathways in 2025 Nationally Determined Contributions and maintain it in subsequent Paris commitment periods. Although some slow glacier re-growth may occur, national adaptation plans are urgently needed so that communities have time to adapt to this reality. Under the Paris Agreement, adoption of glacier and snowpack indicators under the Global Goal on Adaptation process, as well as in the Nairobi Work Programme focusing on high altitude and high latitude regions, will be essential. At the national level, planning for the future should involve both glacier and downstream countries, for both long-term adaptation efforts and risk management during extreme events such as landslides and glacial lake outburst floods. The involvement of Multilateral Development Banks, as well as investment guarantee mechanisms and the private sector, will be key in this transition. Image by Praful Rao of Save the HillsGlaciers Shrinking Worldwide Due to Rising Temperature A glacier is a large accumulation of mainly ice and snow that originates on land and flows slowly under its own weight. Found on every continent, they exist in many mountain regions and around the edges of the Greenland and Antarctic ice sheets. There are more than 275 000 glaciers in the world, with a total area of around 700 000 km 2, storing about 170 000 cubic km of ice. Due to climate change, largely caused by historical burning of fossil fuels, glaciers have retreated globally since the mid- 1800s. Glaciers are typically fed by snowfall during winter and lose ice during summer, although glaciers in countries that experience monsoon behave differently. Higher temperatures lead to increased melting and more precipitation falling as rain rather than snow. Changes in glaciers can have severe impacts on communities and ecosystems, increasing the risk of geohazards, changing regional water availability, and contributing to global sea-level rise. Glaciers Support Livelihoods and Economies of Billions	[ "largely", "irreversible", "on", "human", "\n", "timescales", ",", "and", "lead", "to", "loss", "of", "many", "economic", "and", "sustainable", "\n", "development", "gains", ".", "In", "some", "regions", ",", "dislocation", "and", "conflict", "will", "\n", "result", "from", "continued", "glacier", "and", "snowpack", "loss", ".", "These", "impacts", "\n", "will", "touch", "even", "those", "living", "far", "from", "mountain", "cryosphere", ",", "raising", "\n", "the", "importance", "of", "the", "risks", "posed", "by", "shrinking", "glaciers", "that", "require", "\n", "urgent", "mitigation", "to", "address", ".", "Some", "Adaptation", "Remains", "Essential", "\n", "Even", "with", "urgent", "mitigation", "efforts", ",", "some", "level", "of", "glacier", "loss", "\n", "remains", "inevitable", "given", "current", "loss", "rates", ",", "which", "modelling", "shows", "\n", "will", "continue", "until", "temperatures", "stabilise", ";", "likely", "?", "after", "mid", "-", "century", ",", "\n", "if", "governments", "make", "a", "course", "correction", "back", "to", "1.5C", "-", "consistent", "\n", "pathways", "in", "2025", "Nationally", "Determined", "Contributions", "and", "\n", "maintain", "it", "in", "subsequent", "Paris", "commitment", "periods", ".", "\n", "Although", "some", "slow", "glacier", "re", "-", "growth", "may", "occur", ",", "national", "\n", "adaptation", "plans", "are", "urgently", "needed", "so", "that", "communities", "have", "\n", "time", "to", "adapt", "to", "this", "reality", ".", "Under", "the", "Paris", "Agreement", ",", "adoption", "\n", "of", "glacier", "and", "snowpack", "indicators", "under", "the", "Global", "Goal", "on", "\n", "Adaptation", "process", ",", "as", "well", "as", "in", "the", "Nairobi", "Work", "Programme", "\n", "focusing", "on", "high", "altitude", "and", "high", "latitude", "regions", ",", "will", "be", "essential", ".", "\n", "At", "the", "national", "level", ",", "planning", "for", "the", "future", "should", "involve", "both", "\n", "glacier", "and", "downstream", "countries", ",", "for", "both", "long", "-", "term", "adaptation", "\n", "efforts", "and", "risk", "management", "during", "extreme", "events", "such", "as", "\n", "landslides", "and", "glacial", "lake", "outburst", "floods", ".", "The", "involvement", "of", "\n", "Multilateral", "Development", "Banks", ",", "as", "well", "as", "investment", "guarantee", "\n", "mechanisms", "and", "the", "private", "sector", ",", "will", "be", "key", "in", "this", "transition", ".", "\n", "Image", "by", "Praful", "Rao", "of", "Save", "the", "HillsGlaciers", "Shrinking", "Worldwide", "Due", " ", "to", "Rising", "\n", "Temperature", "\n", "A", "glacier", "is", "a", "large", "accumulation", "of", "mainly", "ice", "and", "snow", "that", "\n", "originates", "on", "land", "and", "flows", "slowly", "under", "its", "own", "weight", ".", "Found", "on", "\n", "every", "continent", ",", "they", "exist", "in", "many", "mountain", "regions", "and", "around", "\n", "the", "edges", "of", "the", "Greenland", "and", "Antarctic", "ice", "sheets", ".", "There", "are", "\n", "more", "than", " ", "275", "000", "glaciers", "in", "the", "world", ",", "with", "a", "total", "area", "of", "around", "\n", "700", " ", "000", " ", "km", "2", ",", "storing", "about", "170", "000", "cubic", "km", "of", "ice", ".", "\n", "Due", "to", "climate", "change", ",", "largely", "caused", "by", "historical", "burning", "\n", "of", "fossil", "fuels", ",", "glaciers", "have", "retreated", "globally", "since", "the", "mid-", "\n", "1800s", ".", " ", "Glaciers", "are", "typically", "fed", "by", "snowfall", "during", "winter", "and", "\n", "lose", "ice", "during", "summer", ",", "although", "glaciers", "in", "countries", "that", "\n", "experience", "monsoon", "behave", "differently", ".", "Higher", "temperatures", "\n", "lead", "to", "increased", "melting", "and", "more", "precipitation", "falling", "as", "rain", "\n", "rather", "than", "snow", ".", "Changes", "in", "glaciers", "can", "have", " ", "severe", "impacts", "on", "\n", "communities", "and", "ecosystems", ",", "increasing", "the", "risk", "of", "geohazards", ",", "\n", "changing", "regional", "water", "availability", ",", "and", "contributing", "to", "global", "\n", "sea", "-", "level", "rise", ".", "\n", "Glaciers", "Support", "Livelihoods", "and", "Economies", "\n", "of", "Billions" ]	[]
the economy; Nano- technology and materials; Optics and photonics; ICT and computer science; and Environmental sciences and industries present some regional sci- entific collaboration. Lastly, applied sciences or sectoral domains, such as Agrifood, Chemistry and chemical engineering, Mechanical engineering and heavy machinery and Transportation, present the smallest scientific col- laboration activity. In that sense, the potential for knowledge-based cooperation in these domains remains largely untouched. Nevertheless, it must be noted that these domains also present the low- est scientific production. Figure 3.46 presents the bilateral collaboration relationships between EaP countries in each S&T domain in terms of co-authorship of scientific publications. This synthetic view complements the subsections below, which, in turn, provide detailed numbers as well as information on collaboration in EC projects. Finally, some domains which present strong bilat- eral collaboration between EaP countries in sci- entific publications are ICT and computer science, Biotechnology, Fundamental physics and math- ematics and Nanotechnology and materials. The geometries of the scientific collaboration in the other domains is quite diverse, with Ukraine being a relevant link in most networks (due to its volume of activity and diversified research). Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation209 Figure 3.46. Number of publications and EC projects in collaboration between EaP actors in different countries Agrifood BiotechnologyChemistry and chemical engineeringElectric and electronic technologies BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AMUA BY MD GE AM AZUAEnergyFundamental physics and mathematicsEnvironmental sciences and industriesGovernance, culture, education and the economy BY MD AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA Health and wellbeingMechanical engineering and heavy machinery ICT and computer scienceNanotechnology and materials BY MD GE AM AZUA BY MD GE AM AZUA Transportation Optics and photonics Strong collaboration BY MD GE AM AZUA BY UA BY MD GE AM AZUA Intermediate collaborationColour indicates the relative distribution of documents, computed row-wise. 210 Part 3 Analysis of scientific and technological potential Regional collaboration in Agrifood In Agrifood publications, Armenia and Azerbaijan collaborate most intensively with Georgia, while Georgia and Moldova’s main collaboration part- ner within the EaP is Ukraine. Regionally, Ukraine collaborates most with Georgia, but it has a sig- nificantly higher number of collaborations with ex- ternal partners than the rest of the EaP countries. 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15/11/2017: PhD, Political Science, dissertation in a joint program between the University of Berlin and the University of Vienna, entitled: “The Shifting Dynamics of Political Elites in Central Europe: A Comparative Study of Germany and Austria Since 1990,” supervisors: Robert Klein, Natalie Hofmann. MA in Political Science, University of Berlin. MA in International Relations, University of Vienna. Summer schools: Summer school “The Politics of Modern Europe: A Historical Approach” organized by the European Union Research Center (EURC) and the University of Barcelona, June 15-18, 2018, Barcelona. “Theories of Political Representation” summer school organized by the European Political Science Association (EPSA) and the University of Warsaw, June 5-9, 2017, Warsaw. Awards, grants/Prix, distinctions, bourses 2021 / Alexander von Humboldt Fellowship for a 6-month stay at the University of Chicago, not realized (pandemic-related restrictions). 2021 / Postdoctoral Research Fellowship at the European Institute in London, for 9 months (starting in January 2022) – not realized (pandemic and job offer at University of Berlin). 2020 / Best Paper Award, European Journal of Political Science, for the article "Elite Political Strategies in Post-Communist States," Vol. 32, No. 1, pp. 45-63. 2016 / Young Scholar Prize, awarded by the Austrian Political Science Association, Austria. 5 publications/5 publications What is the major contribution of this publication /Quel est l’apport majeur de cette publication ? 1 Smith, J., “Ethical Governance and Managerial Policies in Public-Private Partnerships,” in Müller, S. (dir.) Reconfiguring Political Accountability: A New Framework for Public Administration, Berlin, Springer, 2025, p. 245-267. A study of the ethical frameworks and managerial strategies used in public-private partnerships, particularly in post-communist states, tracing the diffusion of governance models across different sectors. 2 Smith, J., “Lobbying Regulation in Germany and Austria: Shaping Public-Private Relationships,” Journal of Political Science, vol. 9, no. 2, 2019, pp. 123-146. This article discusses the lobbying regulation processes in Germany and Austria, focusing on how these regulations are crafted and enforced within the broader context of political systems and governance models. 3 Smith, J., “Political Polarization and the Rise of Right-Wing Movements in Central Europe,” in T. Novak, M. Petrov, and L. Stevens (dir.), Political Movements in Europe: Emerging Trends, Prague, Charles University Press, 2022, p. 85-112. This paper explores the rise of right-wing political movements in Central European countries, analyzing the sociopolitical dynamics that lead to their success in recent elections. 4 Smith, J., “Scandals in Politics: Political Mobilization through Crisis,” Journal of European Political Studies, vol. 16, no. 4, 2021, p. 567-589.	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The EU has put in place binding legislation to reduce greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels. The US, by contrast, has set a non-binding target of a 50-52% reduction below (higher) 2005 levels by 2030, while China only aims for its carbon emissions to peak by the end of the decade. These differences create massive near-term investment needs for EU companies that their competitors do not face. For the four largest EIIs (chemicals, basic metals, non-metallic minerals and paper), decarbonisation is projected to cost EUR 500 billion overall over the next 15 years, while for the “hardest-to-abate” parts of the transport sector (maritime and aviation) investment needs stand at around EUR 100 billion each year from 2031 to 2050. The EU is also the only major region worldwide to have introduced a significant CO2 price. This cost factor is of limited importance so far as heavy industrial production has been largely covered by free allowances under the Emissions Trading Scheme (ETS). However, these allowances will be progressively phased out with the introduction of the Carbon Border Adjustment Mechanism (CBAM). Decarbonisation offers an opportunity for Europe to lower energy prices and take the lead in clean technol - ogies (“clean tech”), while also becoming more energy secure . The decarbonisation of Europe’s energy system 39THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 3implies the massive deployment of clean energy sources with low marginal generation costs, such as renewables and nuclear. Specific EU regions are endowed with high potential for cost-competitive renewable energy sources: for instance, solar in Southern Europe and wind in the North and Southeast. Renewable energy deployment in Europe is already rising, reaching around 22% of the EU’s gross final energy consumption in 2023, compared with 14% in China and 9% in the US. At the same time, Europe has strong innovative potential to meet rising domestic and global demand for clean energy solutions. Although Europe is weak in digital innovation, it is a leader in clean tech innovation [see Figure 2] . This presents opportunities: according to the International Energy Agency (IEA), more than one-third of the required CO2 emission reductions globally in 2050 rely on technologies currently at the demonstra - tion or prototype phaseiii. The electrification of the European energy system will also be an enabler of growth for the EU’s sustainable transport sector. 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work titled Al-Risalah al-Kamiliyyah fil Siera al-Nabawiyyah (The Treatise of Kamil on the Prophet's Biography) which included the following phrase "Both the body and its parts are in a continuous state of dissolution and nourishment, so they are inevitably undergoing permanent change."[149] Application of the scientific method and Modern metabolic theories The history of the scientific study of metabolism spans several centuries and has moved from examining whole animals in early studies, to examining individual metabolic reactions in modern biochemistry. The first controlled experiments in human metabolism were published by Santorio Santorio in 1614 in his book Ars de statica medicina.[150] He described how he weighed himself before and after eating, sleep, working, sex, fasting, drinking, and excreting. He found that most of the food he took in was lost through what he called "insensible perspiration". Santorio Santorio in his steelyard balance, from Ars de statica medicina, first published 1614 In these early studies, the mechanisms of these metabolic processes had not been identified and a vital force was thought to animate living tissue.[151] In the 19th century, when studying the fermentation of sugar to alcohol by yeast, Louis Pasteur concluded that fermentation was catalyzed by substances within the yeast cells he called "ferments". He wrote that "alcoholic fermentation is an act correlated with the life and organization of the yeast cells, not with the death or putrefaction of the cells."[152] This discovery, along with the publication by Friedrich Wöhler in 1828 of a paper on the chemical synthesis of urea,[153] and is notable for being the first organic compound prepared from wholly inorganic precursors. This proved that the organic compounds and chemical reactions found in cells were no different in principle than any other part of chemistry. It was the discovery of enzymes at the beginning of the 20th century by Eduard Buchner that separated the study of the chemical reactions of metabolism from the biological study of cells, and marked the beginnings of biochemistry.[154] The mass of biochemical knowledge grew rapidly throughout the early 20th century. One of the most prolific of these modern biochemists was Hans Krebs who made huge contributions to the study of metabolism.[155] He discovered the urea cycle and later, working with Hans Kornberg, the citric acid cycle and the glyoxylate cycle.[156][157][75] Modern biochemical research has been greatly aided by the development of new techniques such as chromatography, X-ray diffraction, NMR spectroscopy, radioisotopic labelling,	[ "work", "titled", "Al", "-", "Risalah", "al", "-", "Kamiliyyah", "fil", "Siera", "al", "-", "Nabawiyyah", "(", "The", "Treatise", "of", "Kamil", "on", "the", "Prophet", "'s", "Biography", ")", "which", "included", "the", "following", "phrase", "\"", "Both", "the", "body", "and", "its", "parts", "are", "in", "a", "continuous", "state", "of", "dissolution", "and", "nourishment", ",", "so", "they", "are", "inevitably", "undergoing", "permanent", "change", ".", "\"[149", "]", "\n\n", "Application", "of", "the", "scientific", "method", "and", "Modern", "metabolic", "theories", "\n", "The", "history", "of", "the", "scientific", "study", "of", "metabolism", "spans", "several", "centuries", "and", "has", "moved", "from", "examining", "whole", "animals", "in", "early", "studies", ",", "to", "examining", "individual", "metabolic", "reactions", "in", "modern", "biochemistry", ".", "The", "first", "controlled", "experiments", "in", "human", "metabolism", "were", "published", "by", "Santorio", "Santorio", "in", "1614", "in", "his", "book", "Ars", "de", "statica", "medicina.[150", "]", "He", "described", "how", "he", "weighed", "himself", "before", "and", "after", "eating", ",", "sleep", ",", "working", ",", "sex", ",", "fasting", ",", "drinking", ",", "and", "excreting", ".", "He", "found", "that", "most", "of", "the", "food", "he", "took", "in", "was", "lost", "through", "what", "he", "called", "\"", "insensible", "perspiration", "\"", ".", "\n\n\n", "Santorio", "Santorio", "in", "his", "steelyard", "balance", ",", "from", "Ars", "de", "statica", "medicina", ",", "first", "published", "1614", "\n", "In", "these", "early", "studies", ",", "the", "mechanisms", "of", "these", "metabolic", "processes", "had", "not", "been", "identified", "and", "a", "vital", "force", "was", "thought", "to", "animate", "living", "tissue.[151", "]", "In", "the", "19th", "century", ",", "when", "studying", "the", "fermentation", "of", "sugar", "to", "alcohol", "by", "yeast", ",", "Louis", "Pasteur", "concluded", "that", "fermentation", "was", "catalyzed", "by", "substances", "within", "the", "yeast", "cells", "he", "called", "\"", "ferments", "\"", ".", "He", "wrote", "that", "\"", "alcoholic", "fermentation", "is", "an", "act", "correlated", "with", "the", "life", "and", "organization", "of", "the", "yeast", "cells", ",", "not", "with", "the", "death", "or", "putrefaction", "of", "the", "cells", ".", "\"[152", "]", "This", "discovery", ",", "along", "with", "the", "publication", "by", "Friedrich", "Wöhler", "in", "1828", "of", "a", "paper", "on", "the", "chemical", "synthesis", "of", "urea,[153", "]", "and", "is", "notable", "for", "being", "the", "first", "organic", "compound", "prepared", "from", "wholly", "inorganic", "precursors", ".", "This", "proved", "that", "the", "organic", "compounds", "and", "chemical", "reactions", "found", "in", "cells", "were", "no", "different", "in", "principle", "than", "any", "other", "part", "of", "chemistry", ".", "\n\n", "It", "was", "the", "discovery", "of", "enzymes", "at", "the", "beginning", "of", "the", "20th", "century", "by", "Eduard", "Buchner", "that", "separated", "the", "study", "of", "the", "chemical", "reactions", "of", "metabolism", "from", "the", "biological", "study", "of", "cells", ",", "and", "marked", "the", "beginnings", "of", "biochemistry.[154", "]", "The", "mass", "of", "biochemical", "knowledge", "grew", "rapidly", "throughout", "the", "early", "20th", "century", ".", "One", "of", "the", "most", "prolific", "of", "these", "modern", "biochemists", "was", "Hans", "Krebs", "who", "made", "huge", "contributions", "to", "the", "study", "of", "metabolism.[155", "]", "He", "discovered", "the", "urea", "cycle", "and", "later", ",", "working", "with", "Hans", "Kornberg", ",", "the", "citric", "acid", "cycle", "and", "the", "glyoxylate", "cycle.[156][157][75", "]", "Modern", "biochemical", "research", "has", "been", "greatly", "aided", "by", "the", "development", "of", "new", "techniques", "such", "as", "chromatography", ",", "X", "-", "ray", "diffraction", ",", "NMR", "spectroscopy", ",", "radioisotopic", "labelling", "," ]	[]
Text simplification M. Shardlow, P. Przybyła, “Simplification by Lexical Deletion,” in Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability (TSAR 2023), Varna, Bulgaria, 2023. [bib][paper][code] L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “Document-level Text Simplification with Coherence Evaluation,” in Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability (TSAR 2023), Varna, Bulgaria, 2023. [bib][paper][code] L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “The Role of Text Simplification Operations in Evaluation,” in Proceedings of the First Workshop on Current Trends in Text Simplification (CTTS 2021), Online, 2021. [bib][paper][code] L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “Investigating Text Simplification Evaluation,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Bangkok, Thailand, 2021. [bib][paper][code] P. Przybyła, M. Shardlow, “Multi-Word Lexical Simplification,” in Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), Barcelona, Spain, 2020. [bib][paper][data][model][code] Other NLP I. Kuzmin, P. Przybyła, E. McGill, and H. Saggion, “TRIBBLE - TRanslating IBerian languages Based on Limited E-resources,” in Proceedings of the Ninth Conference on Machine Translation, Miami, USA, 2024.[bib][paper][code] P. Przybyła, N. T. H. Nguyen, M. Shardlow, G. Kontonatsios, and S. Ananiadou, “NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features,” in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, USA, 2016.[bib][paper] P. Przybyła and P. Teisseyre, “What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling - Notebook for PAN at CLEF 2015,” in CLEF 2015 Labs and Workshops, Notebook Papers, Toulouse, France, 2015.[bib][paper] Computations in physics M. Maćkowiak-Pawłowska, P. Przybyła, “Generalisation of the identity method for determination of high-order moments of multiplicity distributions with a software implementation,” European Physical Journal C, vol. 78, issue 5, 2018.[bib][paper][software] P. Przybyła, “A pattern recognition method for lattice distortion measurement from HRTEM images,” Journal of Microscopy, vol. 245, no. 2, pp. 200–209, 2011.[bib][paper]	[ "\n", "Text", "simplification", "\n", "M.", "Shardlow", ",", "P.", "Przybyła", ",", "“", "Simplification", "by", "Lexical", "Deletion", ",", "”", "in", "Proceedings", "of", "the", "Second", "Workshop", "on", "Text", "Simplification", ",", "Accessibility", "and", "Readability", "(", "TSAR", "2023", ")", ",", "Varna", ",", "Bulgaria", ",", "2023", ".", "[", "bib][paper][code", "]", "\n", "L.", "Vásquez", "-", "Rodríguez", ",", "M.", "Shardlow", ",", "P.", "Przybyła", ",", "Sophia", "Ananiadou", ",", "“", "Document", "-", "level", "Text", "Simplification", "with", "Coherence", "Evaluation", ",", "”", "in", "Proceedings", "of", "the", "Second", "Workshop", "on", "Text", "Simplification", ",", "Accessibility", "and", "Readability", "(", "TSAR", "2023", ")", ",", "Varna", ",", "Bulgaria", ",", "2023", ".", "[", "bib][paper][code", "]", "\n", "L.", "Vásquez", "-", "Rodríguez", ",", "M.", "Shardlow", ",", "P.", "Przybyła", ",", "Sophia", "Ananiadou", ",", "“", "The", "Role", "of", "Text", "Simplification", "Operations", "in", "Evaluation", ",", "”", "in", "Proceedings", "of", "the", "First", "Workshop", "on", "Current", "Trends", "in", "Text", "Simplification", "(", "CTTS", "2021", ")", ",", "Online", ",", "2021", ".", "[", "bib][paper][code", "]", "\n", "L.", "Vásquez", "-", "Rodríguez", ",", "M.", "Shardlow", ",", "P.", "Przybyła", ",", "Sophia", "Ananiadou", ",", "“", "Investigating", "Text", "Simplification", "Evaluation", ",", "”", "in", "Findings", "of", "the", "Association", "for", "Computational", "Linguistics", ":", "ACL", "-", "IJCNLP", "2021", ",", "Bangkok", ",", "Thailand", ",", "2021", ".", "[", "bib][paper][code", "]", "\n", "P.", "Przybyła", ",", "M.", "Shardlow", ",", "“", "Multi", "-", "Word", "Lexical", "Simplification", ",", "”", "in", "Proceedings", "of", "the", "28th", "International", "Conference", "on", "Computational", "Linguistics", "(", "COLING", "2020", ")", ",", "Barcelona", ",", "Spain", ",", "2020", ".", "[", "bib][paper][data][model][code", "]", "\n", "Other", "NLP", "\n", "I.", "Kuzmin", ",", "P.", "Przybyła", ",", "E.", "McGill", ",", "and", "H.", "Saggion", ",", "“", "TRIBBLE", "-", "TRanslating", "IBerian", "languages", "Based", "on", "Limited", "E", "-", "resources", ",", "”", "in", "Proceedings", "of", "the", "Ninth", "Conference", "on", "Machine", "Translation", ",", "Miami", ",", "USA", ",", "2024.[bib][paper][code", "]", "\n", "P.", "Przybyła", ",", "N.", "T.", "H.", "Nguyen", ",", "M.", "Shardlow", ",", "G.", "Kontonatsios", ",", "and", "S.", "Ananiadou", ",", "“", "NaCTeM", "at", "SemEval-2016", "Task", "1", ":", "Inferring", "sentence", "-", "level", "semantic", "similarity", "from", "an", "ensemble", "of", "complementary", "lexical", "and", "sentence", "-", "level", "features", ",", "”", "in", "Proceedings", "of", "the", "10th", "International", "Workshop", "on", "Semantic", "Evaluation", "(", "SemEval", "2016", ")", ",", "San", "Diego", ",", "USA", ",", "2016.[bib][paper", "]", "\n", "P.", "Przybyła", "and", "P.", "Teisseyre", ",", "“", "What", "do", "your", "look", "-", "alikes", "say", "about", "you", "?", "Exploiting", "strong", "and", "weak", "similarities", "for", "author", "profiling", "-", "Notebook", "for", "PAN", "at", "CLEF", "2015", ",", "”", "in", "CLEF", "2015", "Labs", "and", "Workshops", ",", "Notebook", "Papers", ",", "Toulouse", ",", "France", ",", "2015.[bib][paper", "]", "\n", "Computations", "in", "physics", "\n", "M.", "Maćkowiak", "-", "Pawłowska", ",", "P.", "Przybyła", ",", "“", "Generalisation", "of", "the", "identity", "method", "for", "determination", "of", "high", "-", "order", "moments", "of", "multiplicity", "distributions", "with", "a", "software", "implementation", ",", "”", "European", "Physical", "Journal", "C", ",", "vol", ".", "78", ",", "issue", "5", ",", "2018.[bib][paper][software", "]", "\n", "P.", "Przybyła", ",", "“", "A", "pattern", "recognition", "method", "for", "lattice", "distortion", "measurement", "from", "HRTEM", "images", ",", "”", "Journal", "of", "Microscopy", ",", "vol", ".", "245", ",", "no", ".", "2", ",", "pp", ".", "200–209", ",", "2011.[bib][paper", "]" ]	[ { "end": 211, "label": "CITATION-SPAN", "start": 21 }, { "end": 488, "label": "CITATION-SPAN", "start": 232 }, { "end": 738, "label": "CITATION-SPAN", "start": 509 }, { "end": 975, "label": "CITATION-SPAN", "start": 759 }, { "end": 1177, "label": "CITATION-SPAN", "start": 996 }, { "end": 1423, "label": "CITATION-SPAN", "start": 1221 }, { "end": 1779, "label": "CITATION-SPAN", "start": 1443 }, { "end": 2031, "label": "CITATION-SPAN", "start": 1793 }, { "end": 2299, "label": "CITATION-SPAN", "start": 2069 }, { "end": 2478, "label": "CITATION-SPAN", "start": 2323 } ]
data points for which information is miss- ing in-between. Secondly, the last observed value for each enterprise is carried forward provided the enterprise is still active. Thirdly, the first observed value is carried backward using the enterprise’s date of incorporation as the baseline. All data aggregations and missing data imputations have been completed in Stata. For three countries – Armenia, Azerbaijan and Georgia – data availability is too poor to include these countries in the mapping analysis (cf. Table 2.1, in particular all red-coloured numbers). Ag- gregate data are only available for 9 three-dig- it industries for Armenia, 3 three-digit industries for Azerbaijan. In this section we will only present Country Employees Turnover WagesEmployees & turnoverEmployees & wagesTurnover & wagesEmployees & turnover & wages Armenia 15 15 3 15 3 3 3 Azerbaijan 12 12 0 12 0 0 0 Georgia 180 442 180 442 0 180 442 0 0 0 Moldova 45 882 45 882 1 45 882 1 1 1 Ukraine 411 779 411 779 30 993 411 779 30 993 30 993 30 993Table 2.1. Orbis data availability – Number of enterprises for which at least one data observation is available for 2011-2019 Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation39 and discuss mapping results for Georgia, Moldova and Ukraine. Methodology The methodology used to analyse the aggregate Orbis data is different from that which was initial- ly envisioned – as data availability is much worse than initially expected – with sufficiently high data availability for the number of employees and turn- over for only three Eastern Partnership countries and poor data availability for wages for all Eastern Partnership countries. Aggregate industry-level data have been used to identify two types of industries: ■industries with a current strength, includ- ing specialised industries with critical mass, where the degree of specialisation29 and rel- ative size for both the number of employees and turnover are above predefined thresholds; ■industries with an emerging strength, in- cluding emerging industries with increasing degrees of specialisation, where the change in the degree of specialisation for both the number of employees and turnover are above predefined thresholds. For all NACE three-digit industries in Georgia, Mol- dova and Ukraine, the following indicators have been calculated: ■the degree of specialisation for the number of employees for each year in the 2012-2019 period; ■the average relative share of the total number 29 Specialisation	[ "data", "points", "for", "which", "information", "is", "miss-", "\n", "ing", "in", "-", "between", ".", "Secondly", ",", "the", "last", "observed", "value", "\n", "for", "each", "enterprise", "is", "carried", "forward", "provided", "the", "\n", "enterprise", "is", "still", "active", ".", "Thirdly", ",", "the", "first", "observed", "\n", "value", "is", "carried", "backward", "using", "the", "enterprise", "’s", "\n", "date", "of", "incorporation", "as", "the", "baseline", ".", "All", "data", "\n", "aggregations", "and", "missing", "data", "imputations", "have", "\n", "been", "completed", "in", "Stata", ".", "\n", "For", "three", "countries", "–", "Armenia", ",", "Azerbaijan", "and", "\n", "Georgia", "–", "data", "availability", "is", "too", "poor", "to", "include", "\n", "these", "countries", "in", "the", "mapping", "analysis", "(", "cf", ".", "Table", "\n", "2.1", ",", "in", "particular", "all", "red", "-", "coloured", "numbers", ")", ".", "Ag-", "\n", "gregate", "data", "are", "only", "available", "for", "9", "three", "-", "dig-", "\n", "it", "industries", "for", "Armenia", ",", "3", "three", "-", "digit", "industries", "\n", "for", "Azerbaijan", ".", "In", "this", "section", "we", "will", "only", "present", "\n", "Country", "Employees", "Turnover", "WagesEmployees", "\n", "&", "turnoverEmployees", "\n", "&", "wagesTurnover", "&", "\n", "wagesEmployees", "\n", "&", "turnover", "\n", "&", "wages", "\n", "Armenia", "15", "15", "3", "15", "3", "3", "3", "\n", "Azerbaijan", "12", "12", "0", "12", "0", "0", "0", "\n", "Georgia", "180", "442", "180", "442", "0", "180", "442", "0", "0", "0", "\n", "Moldova", "45", "882", "45", "882", "1", "45", "882", "1", "1", "1", "\n", "Ukraine", "411", "779", "411", "779", "30", "993", "411", "779", "30", "993", "30", "993", "30", "993Table", "2.1", ".", "Orbis", "data", "availability", "–", "Number", "of", "enterprises", "for", "which", "at", "least", "one", "data", "observation", "is", "available", "for", "\n", "2011", "-", "2019", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation39", "\n", "and", "discuss", "mapping", "results", "for", "Georgia", ",", "Moldova", "\n", "and", "Ukraine", ".", "\n", "Methodology", "\n", "The", "methodology", "used", "to", "analyse", "the", "aggregate", "\n", "Orbis", "data", "is", "different", "from", "that", "which", "was", "initial-", "\n", "ly", "envisioned", "–", "as", "data", "availability", "is", "much", "worse", "\n", "than", "initially", "expected", "–", "with", "sufficiently", "high", "data", "\n", "availability", "for", "the", "number", "of", "employees", "and", "turn-", "\n", "over", "for", "only", "three", "Eastern", "Partnership", "countries", "\n", "and", "poor", "data", "availability", "for", "wages", "for", "all", "Eastern", "\n", "Partnership", "countries", ".", "\n", "Aggregate", "industry", "-", "level", "data", "have", "been", "used", "to", "\n", "identify", "two", "types", "of", "industries", ":", "\n ", "■", "industries", "with", "a", "current", "strength", ",", "includ-", "\n", "ing", "specialised", "industries", "with", "critical", "mass", ",", "\n", "where", "the", "degree", "of", "specialisation29", "and", "rel-", "\n", "ative", "size", "for", "both", "the", "number", "of", "employees", "\n", "and", "turnover", "are", "above", "predefined", "thresholds", ";", "\n ", "■", "industries", "with", "an", "emerging", "strength", ",", "in-", "\n", "cluding", "emerging", "industries", "with", "increasing", "\n", "degrees", "of", "specialisation", ",", "where", "the", "change", "\n", "in", "the", "degree", "of", "specialisation", "for", "both", "the", "\n", "number", "of", "employees", "and", "turnover", "are", "above", "\n", "predefined", "thresholds", ".", "\n", "For", "all", "NACE", "three", "-", "digit", "industries", "in", "Georgia", ",", "Mol-", "\n", "dova", "and", "Ukraine", ",", "the", "following", "indicators", "have", "\n", "been", "calculated", ":", "\n ", "■", "the", "degree", "of", "specialisation", "for", "the", "number", "\n", "of", "employees", "for", "each", "year", "in", "the", "2012", "-", "2019", "\n", "period", ";", "\n ", "■", "the", "average", "relative", "share", "of", "the", "total", "number", "\n", "29", "Specialisation" ]	[]
beyond large tech companies, SMEs on both sides of the Atlantic should benefit from the same easing of regulatory burdens for small companies that is proposed above. 34THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 2Facilitating consolidation in the telecoms sector is needed to deliver higher rates of investment in connec - tivity [see the chapters on digitalisation and advanced technologies, and competition policy] . The cornerstone initiative is modifying the EU’s stance towards scale and consolidation of telecoms operators to deliver a true Single Market, without sacrificing consumer welfare and quality of service. To encourage consolidation, the report recom - mends defining telecoms markets at the EU level – as opposed to the Member State level – and increasing the weight of innovation and investment commitments in the EU’s rules for clearing mergers. Country-level ex ante regulation should be reduced in favour of ex post competition enforcement in cases of abuse of dominant position. It is also proposed to harmonise EU-wide spectrum licensing rules and processes and to orchestrate EU-wide auction design features to help create scale. To ensure that EU players remain at the forefront of new technological developments, it is recommended to establish an EU-level body with public-private participation to develop homog - enous technical standards for the deployment of network APIs and edge computing, as was the case for roaming in the 1990s. To increase the capacity of EU operators to invest in these technologies, it is recommended to support commercial investment sharing between network owners and Very Large Online Platforms that use EU data networks to a massive extent but do not contribute to financing them. Sustaining and expanding R&I will also be crucial for key manufacturing sectors such as pharma [see the chapter on pharma] . Opening up the secondary use of health data for research purposes has significant poten - tial to anchor pharma R&I activities within the EU. The report therefore recommends accelerating the digitisation of health systems and the European Health Data Space (EHDS), achieved through EU-level support for national investments which facilitate access to and sharing of electronic health records. In addition, it is proposed to further scale up genome sequencing capacities in the EU and to present a strategic blueprint beyond 2026, building on the European 1+ Million Genomes initiative. To maximise the opportunities of the EHDS, it will be important to provide clear and timely guidance	[ " ", "beyond", "large", "\n", "tech", "companies", ",", "SMEs", "on", "both", "sides", "of", "the", "Atlantic", "should", "benefit", "from", "the", "same", "easing", "of", "regulatory", "burdens", "for", "\n", "small", "companies", "that", "is", "proposed", "above", ".", "\n", "34THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "2Facilitating", "consolidation", "in", "the", "telecoms", "sector", "is", "needed", "to", "deliver", "higher", "rates", "of", "investment", "in", "connec", "-", "\n", "tivity", " ", "[", "see", "the", "chapters", "on", "digitalisation", "and", "advanced", "technologies", ",", "and", "competition", "policy", "]", ".", "The", "cornerstone", "\n", "initiative", "is", "modifying", "the", "EU", "’s", "stance", "towards", "scale", "and", "consolidation", "of", "telecoms", "operators", "to", "deliver", "a", "true", "Single", "\n", "Market", ",", "without", "sacrificing", "consumer", "welfare", "and", "quality", "of", "service", ".", "To", "encourage", "consolidation", ",", "the", "report", "recom", "-", "\n", "mends", "defining", "telecoms", "markets", "at", "the", "EU", "level", "–", "as", "opposed", "to", "the", "Member", "State", "level", "–", "and", "increasing", "the", "\n", "weight", "of", "innovation", "and", "investment", "commitments", "in", "the", "EU", "’s", "rules", "for", "clearing", "mergers", ".", "Country", "-", "level", "ex", "ante", "\n", "regulation", "should", "be", "reduced", "in", "favour", "of", "ex", "post", "competition", "enforcement", "in", "cases", "of", "abuse", "of", "dominant", "position", ".", "\n", "It", "is", "also", "proposed", "to", "harmonise", "EU", "-", "wide", "spectrum", "licensing", "rules", "and", "processes", "and", "to", "orchestrate", "EU", "-", "wide", "\n", "auction", "design", "features", "to", "help", "create", "scale", ".", "To", "ensure", "that", "EU", "players", "remain", "at", "the", "forefront", "of", "new", "technological", "\n", "developments", ",", "it", "is", "recommended", "to", "establish", "an", "EU", "-", "level", "body", "with", "public", "-", "private", "participation", "to", "develop", "homog", "-", "\n", "enous", "technical", "standards", "for", "the", "deployment", "of", "network", "APIs", "and", "edge", "computing", ",", "as", "was", "the", "case", "for", "roaming", "in", "\n", "the", "1990s", ".", "To", "increase", "the", "capacity", "of", "EU", "operators", "to", "invest", "in", "these", "technologies", ",", "it", "is", "recommended", "to", "support", "\n", "commercial", "investment", "sharing", "between", "network", "owners", "and", "Very", "Large", "Online", "Platforms", "that", "use", "EU", "data", "networks", "\n", "to", "a", "massive", "extent", "but", "do", "not", "contribute", "to", "financing", "them", ".", "\n", "Sustaining", "and", "expanding", "R&I", "will", "also", "be", "crucial", "for", "key", "manufacturing", "sectors", "such", "as", "pharma", " ", "[", "see", "the", "\n", "chapter", "on", "pharma", "]", ".", "Opening", "up", "the", "secondary", "use", "of", "health", "data", "for", "research", "purposes", "has", "significant", "poten", "-", "\n", "tial", "to", "anchor", "pharma", "R&I", "activities", "within", "the", "EU", ".", "The", "report", "therefore", "recommends", "accelerating", "the", "digitisation", "\n", "of", "health", "systems", "and", "the", "European", "Health", "Data", "Space", "(", "EHDS", ")", ",", "achieved", "through", "EU", "-", "level", "support", "for", "national", "\n", "investments", "which", "facilitate", "access", "to", "and", "sharing", "of", "electronic", "health", "records", ".", "In", "addition", ",", "it", "is", "proposed", "to", "further", "\n", "scale", "up", "genome", "sequencing", "capacities", "in", "the", "EU", "and", "to", "present", "a", "strategic", "blueprint", "beyond", "2026", ",", "building", "on", "\n", "the", "European", "1", "+", "Million", "Genomes", "initiative", ".", "To", "maximise", "the", "opportunities", "of", "the", "EHDS", ",", "it", "will", "be", "important", "to", "provide", "\n", "clear", "and", "timely", "guidance" ]	[]
last few years (especially in Ukraine) and this would render the CAGR results misleading, nor for EC projects due to the small number of records. Afterwards, a specialisation analysis has been carried out for each country’s domains against the EaP region as a whole. The specialisation index (SI)60 has been computed. The specialisation index indicates the relative intensity of activity of a geography of interest (country, region, etc.) in each domain in relation to the activity within a specific perimeter of compar- 60 Schubert, A., Braun, T., ‘Relative indicators and relational charts for comparative assessment of publication output and citation impact’, Metrics, Vol. 9, 1986, pp. 281–291.ison (in the case of this study, the average for the EaP region) – in this case, the relative publishing and patenting volume and EU project participation activity. Within the framework of Smart Speciali- sation, this indicator provides a comparison of the relative weight of each domain of knowledge in EaP countries, in relation to the reference region (the EaP region), to understand whether or not EaP countries are specialised in each domain of knowl- edge (in this case, the S&T domains, i.e. groups of labelled topics). Concretely, the specialisation index within a spe- cific S&T domain – for a given EaP country with respect to the reference perimeter (i.e. the EaP average) – is the result of the following ratio: ■numerator – number of records in the domain of knowledge produced by the country, nor- malised by the total amount of records within the country when counting multiple domain assignations; ■denominator – the average of the number of records in the domain of knowledge across the EaP produced by each EaP country, nor- malised by the total amount of records within the same EaP country when counting multiple domain assignations. Thus, a specialisation index greater than 1 implies a relative specialisation of the geography of inter- est in this domain of knowledge. For example, an SI of 2 means that the country’s share of publica- tions or patents in a domain is double that of the EaP average. The normalised citation impact (NCI)61 has also been computed for scientific publications. The NCI measures the number of citations per publication, normalised over a reference citation per publication figure. Since citation patterns dif- fer between scientific fields, this ratio is computed for each bibliometric category that Scopus adopts to classify scientific publications and a weighted	[ "last", "few", "years", "\n", "(", "especially", "in", "Ukraine", ")", "and", "this", "would", "render", "the", "\n", "CAGR", "results", "misleading", ",", "nor", "for", "EC", "projects", "due", "\n", "to", "the", "small", "number", "of", "records", ".", "\n", "Afterwards", ",", "a", "specialisation", "analysis", "has", "been", "\n", "carried", "out", "for", "each", "country", "’s", "domains", "against", "the", "\n", "EaP", "region", "as", "a", "whole", ".", "The", "specialisation", "index", "\n", "(", "SI)60", "has", "been", "computed", ".", "\n", "The", "specialisation", "index", "indicates", "the", "relative", "\n", "intensity", "of", "activity", "of", "a", "geography", "of", "interest", "\n", "(", "country", ",", "region", ",", "etc", ".", ")", "in", "each", "domain", "in", "relation", "to", "\n", "the", "activity", "within", "a", "specific", "perimeter", "of", "compar-", "\n", "60", "Schubert", ",", "A.", ",", "Braun", ",", "T.", ",", "‘", "Relative", "indicators", "and", "relational", "\n", "charts", "for", "comparative", "assessment", "of", "publication", "output", "\n", "and", "citation", "impact", "’", ",", "Metrics", ",", "Vol", ".", "9", ",", "1986", ",", "pp", ".", "281–291.ison", "(", "in", "the", "case", "of", "this", "study", ",", "the", "average", "for", "the", "\n", "EaP", "region", ")", "–", "in", "this", "case", ",", "the", "relative", "publishing", "\n", "and", "patenting", "volume", "and", "EU", "project", "participation", "\n", "activity", ".", "Within", "the", "framework", "of", "Smart", "Speciali-", "\n", "sation", ",", "this", "indicator", "provides", "a", "comparison", "of", "the", "\n", "relative", "weight", "of", "each", "domain", "of", "knowledge", "in", "\n", "EaP", "countries", ",", "in", "relation", "to", "the", "reference", "region", "\n", "(", "the", "EaP", "region", ")", ",", "to", "understand", "whether", "or", "not", "EaP", "\n", "countries", "are", "specialised", "in", "each", "domain", "of", "knowl-", "\n", "edge", "(", "in", "this", "case", ",", "the", "S&T", "domains", ",", "i.e.", "groups", "of", "\n", "labelled", "topics", ")", ".", "\n", "Concretely", ",", "the", "specialisation", "index", "within", "a", "spe-", "\n", "cific", "S&T", "domain", "–", "for", "a", "given", "EaP", "country", "\n", "with", "respect", "to", "the", "reference", "perimeter", "(", "i.e.", "the", "\n", "EaP", "average", ")", "–", "is", "the", "result", "of", "the", "following", "ratio", ":", "\n ", "■", "numerator", "–", "number", "of", "records", "in", "the", "domain", "\n", "of", "knowledge", "produced", "by", "the", "country", ",", "nor-", "\n", "malised", "by", "the", "total", "amount", "of", "records", "within", "\n", "the", "country", "when", "counting", "multiple", "domain", "\n", "assignations", ";", "\n ", "■", "denominator", "–", "the", "average", "of", "the", "number", "of", "\n", "records", "in", "the", "domain", "of", "knowledge", "across", "\n", "the", "EaP", "produced", "by", "each", "EaP", "country", ",", "nor-", "\n", "malised", "by", "the", "total", "amount", "of", "records", "within", "\n", "the", "same", "EaP", "country", "when", "counting", "multiple", "\n", "domain", "assignations", ".", "\n", "Thus", ",", "a", "specialisation", "index", "greater", "than", "1", "implies", "\n", "a", "relative", "specialisation", "of", "the", "geography", "of", "inter-", "\n", "est", "in", "this", "domain", "of", "knowledge", ".", "For", "example", ",", "an", "\n", "SI", "of", "2", "means", "that", "the", "country", "’s", "share", "of", "publica-", "\n", "tions", "or", "patents", "in", "a", "domain", "is", "double", "that", "of", "the", "\n", "EaP", "average", ".", "\n", "The", "normalised", "citation", "impact", "(", "NCI)61", "has", "\n", "also", "been", "computed", "for", "scientific", "publications", ".", "\n", "The", "NCI", "measures", "the", "number", "of", "citations", "per", "\n", "publication", ",", "normalised", "over", "a", "reference", "citation", "\n", "per", "publication", "figure", ".", "Since", "citation", "patterns", "dif-", "\n", "fer", "between", "scientific", "fields", ",", "this", "ratio", "is", "computed", "\n", "for", "each", "bibliometric", "category", "that", "Scopus", "adopts", "\n", "to", "classify", "scientific", "publications", "and", "a", "weighted", "\n" ]	[ { "end": 708, "label": "CITATION-SPAN", "start": 533 } ]
Fuest, C., Gros, D., Mengel, P.-L., Presidente, G., and Tirole, J., ‘ How to Escape the Middle Technology Trap: EU Innovation Policy ’, EconPol Policy Report, 2024. ix Myers, K. and Lanahan, L., ‘ Estimating Spillovers from Publicly Funded R&D: Evidence from the US Department of Energy ’, American Economic Review, Vol. 112, No. 7, July 2022. x Testa, G., Compano, R., Correia, A. and Rückert, E., ‘ In search of EU unicorns: What do we know about them ’, EUR 30978 EN, Publications Office of the European Union, Luxembourg, 2022. xi Bruegel, EU Digital Policy Overview , Bruegel Factsheet, 2024. xii Acemoglu, D., et al, ‘ Robot and automation: New insights from micro data: Advanced Technology Adoption: Selection or Causal Effects? ’, AEA Papers and Proceedings, 113: 210–214, 2023. xiii European Commission, Eurostat, Digitalisation in Europe – 2024 edition , Interactive Publication, 2024. xiv https:/ /epochai.org/blog/how-much-does- it-cost-to-train-frontier-ai-models 38THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 23. A joint decarbonisation and competitiveness plan High energy costs in Europe are an obstacle to growth, while lack of generation and grid capacity could impede the spread of digital tech and transport electrification . Commission estimates suggest that high energy prices in recent years have taken a toll on potential growth in Europei. Energy prices also continue to affect corporate investment sentiment much more than in other major economies. Around half of European companies see energy costs as a major impediment to investment – 30 percentage points higher than US companiesii. Energy-intensive industries (EIIs) have been hit hardest: production has fallen 10-15% since 2021 and the composition of European industry is changing, with increasing imports from countries with lower energy costs. Energy prices have also become more volatile, increasing the price of hedging and adding uncertainty to investment decisions. Without a significant increase in generation and grid capacity, Europe may also face limitations on making production more digital, as training and running AI models and maintaining data centres is highly energy-intensive. Data centres are currently responsible for 2.7% of the EU’s electricity demand, but by 2030 their consumption is expected to rise by 28%. FIGURE 1 Energy-intensive manufacturing challenges % change in industrial production (Apr. 24 vs Apr. 21) Source: Eurostat, OECD Trade value added (TiVA database) and ECB staff calculations. The EU’s decarbonisation goals are also more ambitious than its competitors’, creating additional short- term costs for European industry .	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sciences and industriesC02F Treatment of water, waste water, sewage, or sludge 523 414.14% Environmental sciences and industriesE21BEarth or rock drilling; obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells271 318.41% Environmental sciences and industriesB01D Separation 235 247.89% Environmental sciences and industriesE21C Mining or quarrying 214 199.07% Environmental sciences and industriesG01NInvestigating or analysing materials by determining their chemical or physical properties196 49.76% Fundamental physics and mathematicsG01NInvestigating or analysing materials by determining their chemical or physical properties153 38.85% Fundamental physics and mathematicsG06F Electric digital data processing 92 70.77% Fundamental physics and mathematicsF03D Wind motors 88 68.93% Fundamental physics and mathematicsB01D Separation 81 85.44% Fundamental physics and mathematicsH01LSemiconductor devices; electric solid state devices not otherwise provided for80 79.30% Governance, culture, education and the economyA61B Diagnosis; surgery; identification 83 14.35% Governance, culture, education and the economyG06F Electric digital data processing 70 53.85% 164 Part 3 Analysis of scientific and technological potential Domain IPC Description No recordsRelative freq. Governance, culture, education and the economyA63BApparatus for physical training, gymnastics, swimming, climbing, or fencing; ball games; training equipment28 104.67% Governance, culture, education and the economyG09BEducational or demonstration appliances; appliances for teaching, or communicating with, the blind, deaf or mute; models; planetaria; globes; maps; diagrams21 14.53% Governance, culture, education and the economyG06QData processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for20 41.81% Health and wellbeingA61B Diagnosis; surgery; identification 5 506 952.05% Health and wellbeingA61K Preparations for medical, dental, or toilet purposes 2 540 556.80% Health and wellbeingG01NInvestigating or analysing materials by determining their chemical or physical properties2 247 570.51% Health and wellbeingA61PSpecific therapeutic activity of chemical compounds or medicinal preparations1 493 458.50% Health and wellbeingA61NElectrotherapy; magnetotherapy; radiation therapy; ultrasound therapy719 344.02% ICT and computer scienceG06F Electric digital data processing 693 533.08% ICT and computer scienceG06QData processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for259 541.46% ICT and computer scienceH04LTransmission of digital information, e.g. telegraphic communication254 525.52% ICT and computer scienceA61B Diagnosis; surgery; identification 207 35.79% ICT and computer scienceH04N Pictorial communication, e.g. television 196 197.98% Mechanical engineering and heavy machineryA61B Diagnosis; surgery; identification 649 112.22% Mechanical engineering and heavy machineryG01NInvestigating or analysing materials by	[ "sciences", "and", "\n", "industriesC02F", "Treatment", "of", "water", ",", "waste", "water", ",", "sewage", ",", "or", "sludge", "523", "414.14", "%", "\n", "Environmental", "\n", "sciences", "and", "\n", "industriesE21BEarth", "or", "rock", "drilling", ";", "obtaining", "oil", ",", "gas", ",", "water", ",", "soluble", "or", "\n", "meltable", "materials", "or", "a", "slurry", "of", "minerals", "from", "wells271", "318.41", "%", "\n", "Environmental", "\n", "sciences", "and", "\n", "industriesB01D", "Separation", "235", "247.89", "%", "\n", "Environmental", "\n", "sciences", "and", "\n", "industriesE21C", "Mining", "or", "quarrying", "214", "199.07", "%", "\n", "Environmental", "\n", "sciences", "and", "\n", "industriesG01NInvestigating", "or", "analysing", "materials", "by", "determining", "their", "\n", "chemical", "or", "physical", "properties196", "49.76", "%", "\n", "Fundamental", "\n", "physics", "and", "\n", "mathematicsG01NInvestigating", "or", "analysing", "materials", "by", "determining", "their", "\n", "chemical", "or", "physical", "properties153", "38.85", "%", "\n", "Fundamental", "\n", "physics", "and", "\n", "mathematicsG06F", "Electric", "digital", "data", "processing", "92", "70.77", "%", "\n", "Fundamental", "\n", "physics", "and", "\n", "mathematicsF03D", "Wind", "motors", "88", "68.93", "%", "\n", "Fundamental", "\n", "physics", "and", "\n", "mathematicsB01D", "Separation", "81", "85.44", "%", "\n", "Fundamental", "\n", "physics", "and", "\n", "mathematicsH01LSemiconductor", "devices", ";", "electric", "solid", "state", "devices", "not", "\n", "otherwise", "provided", "for80", "79.30", "%", "\n", "Governance", ",", "\n", "culture", ",", "education", "\n", "and", "the", "economyA61B", "Diagnosis", ";", "surgery", ";", "identification", "83", "14.35", "%", "\n", "Governance", ",", "\n", "culture", ",", "education", "\n", "and", "the", "economyG06F", "Electric", "digital", "data", "processing", "70", "53.85", "%", "\n", "164", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Domain", "IPC", "Description", "No", "recordsRelative", "\n", "freq", ".", "\n", "Governance", ",", "\n", "culture", ",", "education", "\n", "and", "the", "economyA63BApparatus", "for", "physical", "training", ",", "gymnastics", ",", "swimming", ",", "\n", "climbing", ",", "or", "fencing", ";", "ball", "games", ";", "training", "equipment28", "104.67", "%", "\n", "Governance", ",", "\n", "culture", ",", "education", "\n", "and", "the", "economyG09BEducational", "or", "demonstration", "appliances", ";", "appliances", "for", "\n", "teaching", ",", "or", "communicating", "with", ",", "the", "blind", ",", "deaf", "or", "mute", ";", "\n", "models", ";", "planetaria", ";", "globes", ";", "maps", ";", "diagrams21", "14.53", "%", "\n", "Governance", ",", "\n", "culture", ",", "education", "\n", "and", "the", "economyG06QData", "processing", "systems", "or", "methods", ",", "specially", "adapted", "\n", "for", "administrative", ",", "commercial", ",", "financial", ",", "managerial", ",", "\n", "supervisory", "or", "forecasting", "purposes", ";", "systems", "or", "methods", "\n", "specially", "adapted", "for", "administrative", ",", "commercial", ",", "\n", "financial", ",", "managerial", ",", "supervisory", "or", "forecasting", "\n", "purposes", ",", "not", "otherwise", "provided", "for20", "41.81", "%", "\n", "Health", "and", "\n", "wellbeingA61B", "Diagnosis", ";", "surgery", ";", "identification", "5", "506", "952.05", "%", "\n", "Health", "and", "\n", "wellbeingA61", "K", "Preparations", "for", "medical", ",", "dental", ",", "or", "toilet", "purposes", "2", "540", "556.80", "%", "\n", "Health", "and", "\n", "wellbeingG01NInvestigating", "or", "analysing", "materials", "by", "determining", "their", "\n", "chemical", "or", "physical", "properties2", "247", "570.51", "%", "\n", "Health", "and", "\n", "wellbeingA61PSpecific", "therapeutic", "activity", "of", "chemical", "compounds", "or", "\n", "medicinal", "preparations1", "493", "458.50", "%", "\n", "Health", "and", "\n", "wellbeingA61NElectrotherapy", ";", "magnetotherapy", ";", "radiation", "therapy", ";", "\n", "ultrasound", "therapy719", "344.02", "%", "\n", "ICT", "and", "computer", "\n", "scienceG06F", "Electric", "digital", "data", "processing", "693", "533.08", "%", "\n", "ICT", "and", "computer", "\n", "scienceG06QData", "processing", "systems", "or", "methods", ",", "specially", "adapted", "\n", "for", "administrative", ",", "commercial", ",", "financial", ",", "managerial", ",", "\n", "supervisory", "or", "forecasting", "purposes", ";", "systems", "or", "methods", "\n", "specially", "adapted", "for", "administrative", ",", "commercial", ",", "\n", "financial", ",", "managerial", ",", "supervisory", "or", "forecasting", "\n", "purposes", ",", "not", "otherwise", "provided", "for259", "541.46", "%", "\n", "ICT", "and", "computer", "\n", "scienceH04LTransmission", "of", "digital", "information", ",", "e.g.", "telegraphic", "\n", "communication254", "525.52", "%", "\n", "ICT", "and", "computer", "\n", "scienceA61B", "Diagnosis", ";", "surgery", ";", "identification", "207", "35.79", "%", "\n", "ICT", "and", "computer", "\n", "scienceH04N", "Pictorial", "communication", ",", "e.g.", "television", "196", "197.98", "%", "\n", "Mechanical", "\n", "engineering", "and", "\n", "heavy", "machineryA61B", "Diagnosis", ";", "surgery", ";", "identification", "649", "112.22", "%", "\n", "Mechanical", "\n", "engineering", "and", "\n", "heavy", "machineryG01NInvestigating", "or", "analysing", "materials", "by" ]	[]
since data for these economies became available in 1950. While this relationship may have owed more to automation than it did to open tradexix, the notion that globalisation had exacerbated inequality infiltrated public perceptions, while governments were seen as indifferent. Policymakers should learn from this experience to reflect on how society will change in the future, and how they can ensure that the state is seen as on the side of citizens and attentive to their concerns. A key part of this process will be empowering people. Leaders and policymakers should engage with all actors within their respective societies to define objectives and actions for the transformation of Europe’s economy. More effective and proactive citizens’ involvement and social dialogue, combining trade unions, employers and civil society actors, will be central in building the consensus needed to drive the changes. Transformation can best lead to prosperity for all when accompanied by a strong social contract. 19THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A \| CHAPTER 1BOX 1 Key principles for trade policy in a European industrial strategy The era of open global trade governed by multilateral institutions looks to be passing, and the EU’s trade policy is already adapting to this new reality . The global trading order based on multilateral institu - tions is in deep crisis, and it remains uncertain whether it can be brought back on track. While the EU should continue efforts to reform the WTO – and especially to unlock the dispute settlement mechanism – the EU must adapt its trade policy to a new reality. This process is already underway. In June 2023, the EU adopted a new Economic Security Strategy furnishing itself with a range of instruments to deal with dumping, respond to coercion and address distortions caused by foreign subsidies within the EU, as well as adopting tools to address technology leakage and enforce sanctions. The EU has also continued to expand its bilateral trade network negotiating over 40 individual trade agreements with different countries and regions. Trade policy needs to be fully aligned with the European industrial strategy . Trade policy should be based on careful, case-by-case analysis rather than on generic stances toward trade. In some cases, the EU should use its trade policy arsenal to keep barriers low, in others to level the playing field and in others still to secure critical supply chains. Accelerating innovation and technological progress in	[ "since", "data", "for", "these", "\n", "economies", "became", "available", "in", "1950", ".", "While", "this", "relationship", "may", "have", "owed", "more", "to", "automation", "than", "it", "did", "to", "open", "\n", "tradexix", ",", "the", "notion", "that", "globalisation", "had", "exacerbated", "inequality", "infiltrated", "public", "perceptions", ",", "while", "governments", "\n", "were", "seen", "as", "indifferent", ".", "Policymakers", "should", "learn", "from", "this", "experience", "to", "reflect", "on", "how", "society", "will", "change", "in", "the", "\n", "future", ",", "and", "how", "they", "can", "ensure", "that", "the", "state", "is", "seen", "as", "on", "the", "side", "of", "citizens", "and", "attentive", "to", "their", "concerns", ".", "A", "\n", "key", "part", "of", "this", "process", "will", "be", "empowering", "people", ".", "Leaders", "and", "policymakers", "should", "engage", "with", "all", "actors", "within", "\n", "their", "respective", "societies", "to", "define", "objectives", "and", "actions", "for", "the", "transformation", "of", "Europe", "’s", "economy", ".", "More", "effective", "\n", "and", "proactive", "citizens", "’", "involvement", "and", "social", "dialogue", ",", "combining", "trade", "unions", ",", "employers", "and", "civil", "society", "actors", ",", "\n", "will", "be", "central", "in", "building", "the", "consensus", "needed", "to", "drive", "the", "changes", ".", "Transformation", "can", "best", "lead", "to", "prosperity", "\n", "for", "all", "when", "accompanied", "by", "a", "strong", "social", "contract", ".", "\n", "19THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "\|", "CHAPTER", "1BOX", "1", "\n", "Key", "principles", "for", "trade", "policy", "in", "a", "European", "industrial", "strategy", "\n", "The", "era", "of", "open", "global", "trade", "governed", "by", "multilateral", "institutions", "looks", "to", "be", "passing", ",", "and", "the", "EU", "’s", "\n", "trade", "policy", "is", "already", "adapting", "to", "this", "new", "reality", ".", "The", "global", "trading", "order", "based", "on", "multilateral", "institu", "-", "\n", "tions", "is", "in", "deep", "crisis", ",", "and", "it", "remains", "uncertain", "whether", "it", "can", "be", "brought", "back", "on", "track", ".", "While", "the", "EU", "should", "\n", "continue", "efforts", "to", "reform", "the", "WTO", "–", "and", "especially", "to", "unlock", "the", "dispute", "settlement", "mechanism", "–", "the", "EU", "\n", "must", "adapt", "its", "trade", "policy", "to", "a", "new", "reality", ".", "This", "process", "is", "already", "underway", ".", "In", "June", "2023", ",", "the", "EU", "adopted", "a", "\n", "new", "Economic", "Security", "Strategy", "furnishing", "itself", "with", "a", "range", "of", "instruments", "to", "deal", "with", "dumping", ",", "respond", "\n", "to", "coercion", "and", "address", "distortions", "caused", "by", "foreign", "subsidies", "within", "the", "EU", ",", "as", "well", "as", "adopting", "tools", "to", "\n", "address", "technology", "leakage", "and", "enforce", "sanctions", ".", "The", "EU", "has", "also", "continued", "to", "expand", "its", "bilateral", "trade", "\n", "network", "negotiating", "over", "40", "individual", "trade", "agreements", "with", "different", "countries", "and", "regions", ".", "\n", "Trade", "policy", "needs", "to", "be", "fully", "aligned", "with", "the", "European", "industrial", "strategy", ".", "Trade", "policy", "should", "be", "\n", "based", "on", "careful", ",", "case", "-", "by", "-", "case", "analysis", "rather", "than", "on", "generic", "stances", "toward", "trade", ".", "In", "some", "cases", ",", "the", "EU", "\n", "should", "use", "its", "trade", "policy", "arsenal", "to", "keep", "barriers", "low", ",", "in", "others", "to", "level", "the", "playing", "field", "and", "in", "others", "still", "\n", "to", "secure", "critical", "supply", "chains", ".", "Accelerating", "innovation", "and", "technological", "progress", "in" ]	[]
laboration of the whole EaP region with external countries, for each domain, is provided. The tables are presented in the form of heatmaps, where the colour denotes the distribution of records comput- ed row-wise (i.e. colours mark the distribution of documents of the country on the left-hand side of each table).Results The following tables present aggregate collabo- rations in publications and EC projects which are useful to gauge the overall intensity of cooper- ation in science and innovation between the EaP countries. EaP regional collaboration In publications, Armenia and Georgia present con- sistent bilateral scientific collaboration with one another. Ukraine also presents a high level of col- laboration with these two countries. Conversely, Azerbaijan and Moldova are currently minor sci- entific partners of EaP countries, only presenting a moderate collaboration with Ukraine. In EC-funded projects, Ukraine collaborates most intensively with Georgia and Moldova. Armenia and Moldova also have a high level of collabora- tion. Azerbaijan remains a bit more isolated, also due to the lower number of projects overall. This collaboration intensity is certainly a positive re- sult of the EaP countries’ participation in H2020, particularly since a significant number of these collaborations are concentrated in the domain Governance, culture, education and the economy (see following section).Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia 130 1 471 1 756 42 980 Azerbaijan 130 49 73 26 138 Belarus 1 471 49 1 440 83 1 268 Georgia 1 756 73 1 440 58 1 058 Moldova 42 26 83 58 202 Ukraine 980 138 1 268 1 058 202 Publications Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia 10 21 26 19 21 Azerbaijan 10 8 11 8 11 Belarus 21 8 20 17 33 Georgia 26 11 20 23 32 Moldova 19 8 17 23 25 Ukraine 21 11 33 32 25 EC projectsFigure 3.45. Number of publications and EC projects in collaboration between EaP actors in different countries Colour indicates the relative distribution of documents, computed row-wise. 208 Part 3 Analysis of scientific and technological potential It must be noted, however, that scientific collabo- ration between EaP countries is mainly driven by very intense collaboration in physics (within the Fundamental physics and mathematics domain) – which concentrates by far the largest number of co-publications – due to the countries’ co-par- ticipation in large high-energy and astronomy en- deavours. At a great distance, Health and wellbeing; Govern- ance, culture, education and	[ "laboration", "of", "the", "whole", "EaP", "region", "with", "external", "\n", "countries", ",", "for", "each", "domain", ",", "is", "provided", ".", "The", "tables", "\n", "are", "presented", "in", "the", "form", "of", "heatmaps", ",", "where", "the", "\n", "colour", "denotes", "the", "distribution", "of", "records", "comput-", "\n", "ed", "row", "-", "wise", "(", "i.e.", "colours", "mark", "the", "distribution", "of", "\n", "documents", "of", "the", "country", "on", "the", "left", "-", "hand", "side", "of", "\n", "each", "table).Results", "\n", "The", "following", "tables", "present", "aggregate", "collabo-", "\n", "rations", "in", "publications", "and", "EC", "projects", "which", "are", "\n", "useful", "to", "gauge", "the", "overall", "intensity", "of", "cooper-", "\n", "ation", "in", "science", "and", "innovation", "between", "the", "EaP", "\n", "countries", ".", "\n", "EaP", "regional", "collaboration", "\n", "In", "publications", ",", "Armenia", "and", "Georgia", "present", "con-", "\n", "sistent", "bilateral", "scientific", "collaboration", "with", "one", "\n", "another", ".", "Ukraine", "also", "presents", "a", "high", "level", "of", "col-", "\n", "laboration", "with", "these", "two", "countries", ".", "Conversely", ",", "\n", "Azerbaijan", "and", "Moldova", "are", "currently", "minor", "sci-", "\n", "entific", "partners", "of", "EaP", "countries", ",", "only", "presenting", "a", "\n", "moderate", "collaboration", "with", "Ukraine", ".", "\n", "In", "EC", "-", "funded", "projects", ",", "Ukraine", "collaborates", "most", "\n", "intensively", "with", "Georgia", "and", "Moldova", ".", "Armenia", "\n", "and", "Moldova", "also", "have", "a", "high", "level", "of", "collabora-", "\n", "tion", ".", "Azerbaijan", "remains", "a", "bit", "more", "isolated", ",", "also", "\n", "due", "to", "the", "lower", "number", "of", "projects", "overall", ".", "This", "\n", "collaboration", "intensity", "is", "certainly", "a", "positive", "re-", "\n", "sult", "of", "the", "EaP", "countries", "’", "participation", "in", "H2020", ",", "\n", "particularly", "since", "a", "significant", "number", "of", "these", "\n", "collaborations", "are", "concentrated", "in", "the", "domain", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "(", "see", "following", "section).Armenia", "\n", "Azerbaijan", "\n", "Belarus", "\n", "Georgia", "\n", "Moldova", "\n", "Ukraine", "\n", "Armenia", "130", "1", "471", "1", "756", "42", "980", "\n", "Azerbaijan", "130", "49", "73", "26", "138", "\n", "Belarus", "1", "471", "49", "1", "440", "83", "1", "268", "\n", "Georgia", "1", "756", "73", "1", "440", "58", "1", "058", "\n", "Moldova", "42", "26", "83", "58", "202", "\n", "Ukraine", "980", "138", "1", "268", "1", "058", "202", "\n", "Publications", "\n", "Armenia", "\n", "Azerbaijan", "\n", "Belarus", "\n", "Georgia", "\n", "Moldova", "\n", "Ukraine", "\n", "Armenia", "10", "21", "26", "19", "21", "\n", "Azerbaijan", "10", "8", "11", "8", "11", "\n", "Belarus", "21", "8", "20", "17", "33", "\n", "Georgia", "26", "11", "20", "23", "32", "\n", "Moldova", "19", "8", "17", "23", "25", "\n", "Ukraine", "21", "11", "33", "32", "25", "\n", "EC", "projectsFigure", "3.45", ".", "Number", "of", "publications", "and", "EC", "projects", "in", "collaboration", "between", "EaP", "actors", "in", "different", "countries", "\n", "Colour", "indicates", "the", "relative", "distribution", "of", "documents", ",", "computed", "row", "-", "wise", ".", "\n", "208", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "It", "must", "be", "noted", ",", "however", ",", "that", "scientific", "collabo-", "\n", "ration", "between", "EaP", "countries", "is", "mainly", "driven", "by", "\n", "very", "intense", "collaboration", "in", "physics", "(", "within", "the", "\n", "Fundamental", "physics", "and", "mathematics", "domain", ")", "\n", "–", "which", "concentrates", "by", "far", "the", "largest", "number", "\n", "of", "co", "-", "publications", "–", "due", "to", "the", "countries", "’", "co", "-", "par-", "\n", "ticipation", "in", "large", "high", "-", "energy", "and", "astronomy", "en-", "\n", "deavours", ".", "\n", "At", "a", "great", "distance", ",", "Health", "and", "wellbeing", ";", "Govern-", "\n", "ance", ",", "culture", ",", "education", "and" ]	[]
discharge, turbine, heat exchanger, collector, rectifier, condition, resistance, statorTable 3.3b. List of 50 most relevant keywords for each S&T domainTable 3.3a. List of identified S&T specialisation domains EaP S&T specialisation domains (in alphabetical order) Agrifood Biotechnology Energy Optics and photonicsHealth and wellbeingChemistry and chemical engineeringElectric and electronic technologies Environmental sciences and industries Governance, culture, education and the economy ICT and computer science TransportationNanotechnology and materialsFundamental physics and mathematics Mechanical engineering and heavy machinery 150 Part 3 Analysis of scientific and technological potential S&T domain Top keywords Environmental sciences and industriesspecie, water, soil, plant, environment, deposit, rock, population, basin, datum, reservoir, river, pollution, territory, concentration, condition, treatment, well, sea, caucasus, philtre, source, purification, ecosystem, temperature, contamination, metal, monitoring, forest, mine, mining, genus, sediment, mass, species, nov, station, accumulation, slope, extraction, tree, habitat, ore, massif, air, hole, removal, biomass, land, body Fundamental physics and mathematicstev, collision, equation, measurement, detector, mass, boson, gev, section, jet, datum, atlas detector, condition, particle, pp collision, luminosity, space, lhc, energy, integrated luminosity, momentum, coefficient, dependence, pair, decay, quark, proton-proton collision, lepton, prediction, channel, flow, mass, proton, electron, collider, confidence level, muon, fraction, frequency, resonance, transverse momentum, formula, cms detector, photon, sup, higgs boson, gas, cross section, simulation, excess Governance, culture, education and the economyenterprise, author, economy, policy, market, resource, environment, risk, society, industry, experience, security, regulation, culture, population, history, people, life, language, bank, business, government, reform, law, protection, identity, caucasus, city, text, investment, nature, competitiveness, capital, conflict, sustainable development, sphere, source, form, condition, teacher, crisis, publication, power, authority, tradition, attitude, legislation, image, perception, peculiarity Health and wellbeingpatient, treatment, cell, disease, child, therapy, tissue, woman, rat, disorder, gene, diagnosis, age, cancer, drug, syndrome, risk, population, animal, dose, infection, surgery, expression, complication, protein, care, prevalence, week, diabetes, people, tumour, examination, day, cavity, year, medicine, mortality, pathology, symptom, mutation, prevention, blood, intervention, hypertension, marker, month, stress, death, health, severity ICT and computer sciencealgorithm, network, datum, measurement, simulation, image, accuracy, error, signal, user, monitoring, software, modelling, detection, architecture, database, recognition, node, prediction, environment, condition, code, construction, server, resource, correction, station, complexity, sensor, uncertainty, neural network, form, source, communication, noise, sequence, channel, propose method, computer, connexion, message, rule, memory, author, transmission, interface, circuit, probability, distance, subsystem Mechanical engineering and heavy machineryplate, chamber, axis, body, rod, wall, hole, angle, surface, shaft, pipe, opening, housing, form, plane, machine, cylinder, drive, valve, diameter, groove, ring, shell, section, rotation, spring, portion, holder, pipeline, guide, cover, sleeve,	[ "discharge", ",", "turbine", ",", "heat", "exchanger", ",", "\n", "collector", ",", "rectifier", ",", "condition", ",", "resistance", ",", "statorTable", "3.3b", ".", "List", "of", "50", "most", "relevant", "keywords", "for", "each", "S&T", "domainTable", "3.3a", ".", "List", "of", "identified", "S&T", "specialisation", "domains", "\n", "EaP", "S&T", "specialisation", "domains", "(", "in", "alphabetical", "order", ")", "\n", "Agrifood", "Biotechnology", "\n", "Energy", "\n", "Optics", "and", "photonicsHealth", "and", "wellbeingChemistry", "and", "chemical", "\n", "engineeringElectric", 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contrast, has a comparable level of dependencies, being highly dependent on one or two countries for most of its critical mineral imports. However, it is not following a similarly coordinated approach. The EU is lacking a comprehensive strategy covering all stages of the supply chain (from exploration to recycling) and, unlike its competitors, the mining and trading of commodities is largely left to private actors and the market. Strategic dependencies also extend to critical technologies for the digitalisation of Europe’s economy [see the chapter on digitalisation and advanced technologies] . The EU relies on foreign countries for over 80% of digital products, services, infrastructure and intellectual propertyvi. Dependencies are particularly acute, however, for semi - conductors owing to the structure of the industry, which is dominated by a small number of large players. The US has specialised in chips design, Korea, Taiwan and China in chips manufacturing, and Japan and some EU Member States in key materials and equipment – optics, chemistry and machinery [see Figure 3] . Europe has little domestic capacity in many parts of the supply chain. For example, the EU currently has no foundry producing below 22 nm process nodes and relies on Asia for 75% to 90% of wafer fabrication capacity (as does the US). Europe has become dependent on non-EU countries for chips design, packaging and assembly as well. Dependencies are also acute for other advanced tech. The EU’s AI industry relies on hardware produced largely by one US-based company for the most advanced processors. Similarly, Europe’s dependence on cloud services developed and run by US companies is massive. For quantum computing platforms, the EU suffers from six critical dependencies across 17 key technolo - gies, components and materials. China and the US hold technological leadership in most of these critical elements. In the telecoms sector, Europe is less dependent on foreign technology: top EU vendors are well positioned in the global supply of telecoms equipment. However, it will be important that dependencies do not increase, especially on high-risk suppliers that could compromise the security of EU networks and citizens’ data. Currently, 14 Member States have no restrictions on high-risk suppliers in place. FIGURE 3 Share in semiconductor value chain by country % of worldwide total, 2019 Source: SIA, 2021. 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deflator and exchange rate have an important effect on results. Depending on the objective of the comparison, one or the other indicator may be more relevant. GDP at current prices offers insights into market value, GDP at constant prices into volume growth, while purchasing power adjustment allows a comparison from the consumer perspective. 12THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1FIGURE 4 GDP per capita gap GDP per capita, 2023, constant PPP prices (EUR) Source: AMECO, 2024. At the same time, three external conditions – in trade, energy and defence – that supported growth in Europe after the end of the Cold War have been fading . First, even as domestic growth slowed, the EU benefitted significantly from burgeoning world trade under multilateral rules. Between 2000 and 2019, international trade as a share of GDP rose from 30% to 43% in the EU, whereas in the US it rose from 25% to 26%. Trade openness ensured that Europe could import freely goods and services it lacked, ranging from raw materials to advanced technologies, while exporting manufactured goods in which it specialised, particularly to the growing markets of Asia. However, the multilateral trading order is now in deep crisis and the era of rapid world trade growth looks to have passed: the IMF projects world trade to grow at 3.2% over the medium term, a pace well below its annual average from 2000-19 of 4.9%iv. Second, as relations normalised with Russia, Europe was able to satisfy its demand for imported energy by procuring ample pipeline gas, which accounted for around 45% of the EU’s natural gas imports in 2021. But this source of relatively cheap energy has now disappeared at huge cost to Europe. The EU has lost more than a year of GDP growth while having to re-direct massive fiscal resources to energy subsidies and building new infrastructure for importing liquefied natural gas. Third, the era of geopolitical stability under US hegemony allowed the EU largely to separate economic policy from security considerations, as well as to use the “peace dividend” from lower defence spending to support its domestic goals. The geopolitical environment is however now in flux owing to Russia’s unwar - ranted aggression against Ukraine, deteriorating US-China relations and rising instability in Africa, which is a source of many commodities that are critical to the world economy. Raising the EU’s competitiveness is necessary to reignite

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have been commercially available since 1991, and in 2009, their production reached approximately 1 billion cells per year [ 16]. By 2022, the market was EUR 3 billion, and it is expected that by 2030 it will grow to EUR 4.1 billion [ 17]. Before the emergence of Li-ion batteries, NiMH batteries were the dominating technology in electric and hybrid vehicles, and they have received extensive investment from main manufacturers [ 18]. One important advantage of NiMH batteries is safety (in comparison with Li-ion batteries). To our knowledge, there have been no reports of fire accidents in the press mentioning NiMH batteries. Moreover, NiMH batteries are the preferred system for industrial and consumer applications for PBGUs due to their design flexibility and low maintenance and cost (as of 2018, 80 EUR/kWh to 250 EUR/kWh) [ 19], and it is expected that by 2040, NiMH batteries will still be used in hybrid and plug-in hybrid vehicles [ 20]. Due to the strong presence of NiMH batteries in the market, various IEC standards have been created to guarantee interoperability and compatibility between the many elements of a battery system (e.g., design, manufacturing [ 21], and testing of battery equipment [ 22]). Other considerations covered by standards are performance [5] and safety [23]. This research uses European and international standards for NiMH batteries, such as IEC 61951-2:2017+AMD1:2022 CSV [ 5,6], as references. These standards have been analyzed previously by our group at JRC [24–26]. The goal of this study is to present our research on analyzing the performance of NiMH batteries used as PBGUs. This is to create a base for setting minimum performance values listed in the batteries regulation Annex III [ 1]. The analysis is developed using the current standard IEC 61951-2 for portable NiMH batteries as a base for testing battery performance. This research is organized as follows: In Section 2, the materials and methods are presented. In Section 3, we show the capacity analysis. In Section 4, the charge (capacity) retention is presented. In Section 5, charge (capacity) recovery is shown. In Section 6, the endurance in cycles is analyzed. Results are discussed in Section 7. In Section 8, the analysis of NiMH batteries in application test is shown. And lastly, Section 9 presents the conclusions. 2. Materials and Methods This section presents the materials and methods used in this research. Two items are presented: the battery regulation link

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Partnership countries - Potential for knowledge-based economic cooperation329 NACE sector IPC class NOT matching 20.6 Manufacture of man-made fibres [20.6] D01F 21Manufacture of basic pharmaceutical products and pharmaceutical preparations [21]A61K; C07D; C07H; C07J; C12P; C12Q; C07K; A61P; C12NA61K 8/* 22Manufacture of rubber and plastic products [22]B29C; B29D; B60C; B67D 22.1 Manufacture of rubber products [22.1] C08C 23Manufacture of other non-metallic mineral products [23]B32B 23.1Manufacture of glass and glass products [23.1]C03C; C03B 23.3Manufacture of clay building materials [23.3]B28B; B28C 23.4Manufacture of other porcelain and ceramic products [23.4]E03D 23.5Manufacture of cement, lime and plaster [23.5]C04B 24 Manufacture of basic metals [24]B21C; B22D; C21B; C22B; C22C; C22F; C21C; C25C; C21D; C25F 24.4Manufacture of basic precious and other non-ferrous metals [24.4]G21H 25.1Manufacture of structural metal products [25.1]A44B; A47H; B21G; F27D 25.2Manufacture of tanks, reservoirs and containers of metal [25.2]F16T; F22B; F22G; F24J 25.3Manufacture of steam generators, except central heating hot water boilers [25.3]G21B; G21C; G21D 25.4Manufacture of weapons and ammunition [25.4]B63G; F41A; F41B; F41G; F41H; F41J; F41C; F42C; F41F; G21J 25.5Forging, pressing, stamping and roll- forming of metal; powder metallurgy [25.5]B22F 25.6Treatment and coating of metals; machining [25.6]C23D; C25D 25.7Manufacture of cutlery, tools and general hardware [25.7]E05B; E05D; E05F; E06B 25.9Manufacture of other fabricated metal products [25.9]A01L; E05C; F16B 26.1Manufacture of electronic components and boards [26.1]B81B; B81C; B82B; G11C; H01C; H01F; H01L; H05K; B82Y; H01G; C30B; H01J 26.2Manufacture of computers and peripheral equipment [26.2]G02F; G06C; G06D; G06G; G06J; G06N; G06E; G06T; G06F; G09C 26.3Manufacture of communication equipment [26.3]G03H; G08B; H01Q; H01S; H03B; H03C; H03M; H04L; H04S; H03D; H04B; H04M; H04W; H03G; H04H; H04N; H03H; H04J; H04Q; H03J; H04K; H04R 330 Annexes NACE sector IPC class NOT matching 26.4Manufacture of consumer electronics [26.4]H03F; H03K; H03L 26.5Manufacture of instruments and appliances for measuring, testing and navigation; watches and clocks [26.5]F15C; G01B; G01C; G01D; G01F; G01H; G01J; G01K; G01L; G01M; G01N; G01Q; G01R; G01S; G01V; G01W; G04B; G04C; G04D; G04F; G04G; G04R; G05B; G05F; G08C; G12B 26.6Manufacture of irradiation, electromedical and electrotherapeutic equipment [26.6]A61N; G21K; H05G; H05H 26.7Manufacture of optical instruments and photographic equipment [26.7]G02B; G02C; G03B 26.8Manufacture of magnetic and optical media [26.8]G03C 27.1Manufacture of electric motors, generators, transformers and electricity distribution and control apparatus [27.1]H02B; H02S; H02J; H02K; H02N; H02P 27.2Manufacture of batteries and accumulators [27.2]H01M 27.3Manufacture of wiring and wiring devices [27.3]H01B; H01H; H01R; H02G 27.4Manufacture of electric lighting equipment [27.4]F21P; F21Q; H01K; F21H; F21S; F21K; F21V; F21L; F21W; F21M; F21Y 27.5Manufacture of domestic

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";", "H05", "K", ";", "B82Y", ";", "H01", "G", ";", "C30B", ";", "H01J", "\n", "26.2Manufacture", "of", "computers", "and", "peripheral", "\n", "equipment", "[", "26.2]G02F", ";", "G06C", ";", "G06D", ";", "G06", "G", ";", "G06J", ";", "G06N", ";", "\n", "G06E", ";", "G06", "T", ";", "G06F", ";", "G09C", "\n", "26.3Manufacture", "of", "communication", "\n", "equipment", "[", "26.3]G03H", ";", "G08B", ";", "H01Q", ";", "H01S", ";", "H03B", ";", "H03C", ";", "\n", "H03", "M", ";", "H04L", ";", "H04S", ";", "H03D", ";", "H04B", ";", "H04", "M", ";", "\n", "H04W", ";", "H03", "G", ";", "H04H", ";", "H04N", ";", "H03H", ";", "H04J", ";", "\n", "H04Q", ";", "H03J", ";", "H04", "K", ";", "H04R", "\n", "330", "\n", "Annexes", "\n", "NACE", "sector", "IPC", "class", "NOT", "matching", "\n", "26.4Manufacture", "of", "consumer", "electronics", "\n", "[", "26.4]H03F", ";", "H03", "K", ";", "H03L", "\n", "26.5Manufacture", "of", "instruments", "and", "\n", "appliances", "for", "measuring", ",", "testing", "and", "\n", "navigation", ";", "watches", "and", "clocks", "[", "26.5]F15C", ";", "G01B", ";", "G01C", ";", "G01D", ";", "G01F", ";", "G01H", ";", "\n", "G01J", ";", "G01", "K", ";", "G01L", ";", "G01", "M", ";", "G01N", ";", "G01Q", ";", "\n", "G01R", ";", "G01S", ";", "G01V", ";", "G01W", ";", "G04B", ";", "G04C", ";", "\n", "G04D", ";", "G04F", ";", "G04", "G", ";", "G04R", ";", "G05B", ";", "G05F", ";", "\n", "G08C", ";", "G12B", "\n", "26.6Manufacture", "of", "irradiation", ",", "electromedical", "\n", "and", "electrotherapeutic", "equipment", "[", "26.6]A61N", ";", "G21", "K", ";", "H05", "G", ";", "H05H", "\n", "26.7Manufacture", "of", "optical", "instruments", "and", "\n", "photographic", "equipment", "[", "26.7]G02B", ";", "G02C", ";", "G03B", "\n", "26.8Manufacture", "of", "magnetic", "and", "optical", "\n", "media", "[", "26.8]G03C", "\n", "27.1Manufacture", "of", "electric", "motors", ",", "\n", "generators", ",", "transformers", "and", "electricity", "\n", "distribution", "and", "control", "apparatus", "[", "27.1]H02B", ";", "H02S", ";", "H02J", ";", "H02", "K", ";", "H02N", ";", "H02P", "\n", "27.2Manufacture", "of", "batteries", "and", "\n", "accumulators", "[", "27.2]H01", "M", "\n", "27.3Manufacture", "of", "wiring", "and", "wiring", "devices", "\n", "[", "27.3]H01B", ";", "H01H", ";", "H01R", ";", "H02", "G", "\n", "27.4Manufacture", "of", "electric", "lighting", "\n", "equipment", "[", "27.4]F21P", ";", "F21Q", ";", "H01", "K", ";", "F21H", ";", "F21S", ";", "F21", "K", ";", "\n", "F21V", ";", "F21L", ";", "F21W", ";", "F21", "M", ";", "F21Y", "\n", "27.5Manufacture", "of", "domestic" ]

[]

pharmaceutical preparations; 22.1 Manufacture of rubber products; 22.2 Manufacture of plastics products; 23.1 Manufacture of glass and glass products; 23.2 Manufacture of refractory products; 23.3 Manufacture of clay building materials; 23.4 Manufacture of other porcelain and ceramic products; 23.5 Manufacture of cement, lime and plaster; 23.6 Manufacture of articles of concrete, cement and plaster; 23.7 Cutting, shaping and finishing of stone; 23.9 Manufacture of abrasive products and non-metallic mineral products n.e.c.; 24.1 Manufacture of basic iron and steel and of ferro-alloys; 24.2 Manufacture of tubes, pipes, hollow profiles and related fittings, of steel; 24.3 Manufacture of other products of first processing of steel; 24.4 Manufacture of basic precious and other non-ferrous metals; 24.5 Casting of metals; 25.1 Manufacture of structural metal products; 25.2 Manufacture of tanks, reservoirs and containers of metal; 25.3 Manufacture of steam generators, except central heating hot water boilers; 25.6 Treatment and coating of metals; machining; 25.7 Manufacture of cutlery, tools and general hardware; 25.9 Manufacture of other fabricated metal products; 26.1 Manufacture of electronic components and boards; 26.2 Manufacture of computers and peripheral equipment; 26.3 Manufacture of communication equipment; 26.4 Manufacture of consumer electronics; 26.5 Manufacture of instruments and appliances for measuring, testing and navigation; watches and clocks; 26.6 Manufacture of irradiation, electromedical and electrotherapeutic equipment; 26.7 Manufacture of optical instruments and photographic equipment; 26.8 Manufacture of magnetic and optical media; 27.1 Manufacture of electric motors, generators, transformers and electricity distribution and control apparatus; 27.2 Manufacture of batteries and accumulators; 27.3 Manufacture of wiring and wiring devices; 27.4 Manufacture of electric lighting equipment; 27.5 Manufacture of domestic appliances; 27.9 Manufacture of other electrical equipment; 28.1 Manufacture of general-purpose machinery; 28.2 Manufacture of other general-purpose machinery; 28.4 Manufacture of metal forming machinery and machine tools; 28.9 Manufacture of other special-purpose machinery; 29.1 Manufacture of motor vehicles; 29.2 Manufacture of bodies (coachwork) for motor vehicles; manufacture of trailers and semi-trailers; 29.3 Manufacture of parts and accessories for motor vehicles; 30.1 Building of ships and boats; 30.2 Manufacture of railway locomotives and rolling stock; 30.9 Manufacture of transport equipment n.e.c.; 32.1 Manufacture of jewellery, bijouterie and related articles; 32.2 Manufacture of musical instruments; 32.3 Manufacture of sports goods; 32.4 Manufacture of games and toys; 32.5 Manufacture of medical and dental instruments and supplies; 32.9 Manufacturing n.e.c.; 33.1 Repair of fabricated metal products, machinery and equipment; 33.2 Installation of industrial machinery and equipment; 62 Computer programming, consultancy and related activities;

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[]

Smart Specialisation in the Eastern Partnership countries Potential for knowledge-based economic cooperation SMART SPECIALISATION IN THE EASTERN PARTNERSHIPThis publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this publi-cation do not necessarily reflect the position or opinion of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Contact information Monika Matusiak, Team Leader, European Commission - Joint Research Centre, Seville, Spaine-mail: [email protected] EU Science Hub https://joint-research-centre.ec.europa.eu JRC128524 EUR 31234 EN PDF ISBN 978-92-76-57301-2 ISSN 1831-9424 doi:10.2760/520032 KJ-NA-31-234-EN-N Print ISBN 978-92-76-57302-9 ISSN 1018-5593 doi:10.2760/893904 KJ-NA-31-234-EN-C Luxembourg: Publications Office of the European Union, 2022 © European Union, 2022 The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 De- cember 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons. org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. All content © European Union, 2022 The European Union does not o wn the cop yright in relation to the follo wing elements: Cover page photos (from left to right), ©nordroden/ stock.adobe.com, ©NDABCREATIVITY/ stock.adobe.com, ©Balazs/ stock.adobe.com, ©puhhha/ stock.adobe.com. How to cite this report: Bigas, E., Bovenzi, N., Fuster Martí, E., Massucci, F.A., Hollanders, H., Matusiak, M. and Reimeris, R., Smart Specialisation in the Eastern Partnership countries, Fuster Martí, E., Matusiak, M. and Reimeris, R. editors, EUR 31234 EN, Publications Office of the European Union, Luxembourg, 2022, doi:10.2760/520032, JRC128524.2022Enric Fuster (SIRIS Academic), Monika Matusiak, Ramojus Reimeris (European Commission – Joint Research Centre) Eloi Bigas, Nicandro Bovenzi, Enric Fuster, Francesco

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particular metabolic problems, with pathways such as glycolysis and the citric acid cycle producing their end products highly efficiently and in a minimal number of steps.[4][5] The first pathways of enzyme-based metabolism may have been parts of purine nucleotide metabolism, while previous metabolic pathways were a part of the ancient RNA world.[123] Many models have been proposed to describe the mechanisms by which novel metabolic pathways evolve. These include the sequential addition of novel enzymes to a short ancestral pathway, the duplication and then divergence of entire pathways as well as the recruitment of pre-existing enzymes and their assembly into a novel reaction pathway.[124] The relative importance of these mechanisms is unclear, but genomic studies have shown that enzymes in a pathway are likely to have a shared ancestry, suggesting that many pathways have evolved in a step-by-step fashion with novel functions created from pre-existing steps in the pathway.[125] An alternative model comes from studies that trace the evolution of proteins' structures in metabolic networks, this has suggested that enzymes are pervasively recruited, borrowing enzymes to perform similar functions in different metabolic pathways (evident in the MANET database)[126] These recruitment processes result in an evolutionary enzymatic mosaic.[127] A third possibility is that some parts of metabolism might exist as "modules" that can be reused in different pathways and perform similar functions on different molecules.[128] As well as the evolution of new metabolic pathways, evolution can also cause the loss of metabolic functions. For example, in some parasites metabolic processes that are not essential for survival are lost and preformed amino acids, nucleotides and carbohydrates may instead be scavenged from the host.[129] Similar reduced metabolic capabilities are seen in endosymbiotic organisms.[130] Investigation and manipulation Further information: Protein methods, Proteomics, Metabolomics, and Metabolic network modelling Metabolic network of the Arabidopsis thaliana citric acid cycle. Enzymes and metabolites are shown as red squares and the interactions between them as black lines. Classically, metabolism is studied by a reductionist approach that focuses on a single metabolic pathway. Particularly valuable is the use of radioactive tracers at the whole-organism, tissue and cellular levels, which define the paths from precursors to final products by identifying radioactively labelled intermediates and products.[131] The enzymes that catalyze these chemical reactions can then be purified and their kinetics and responses to inhibitors investigated. A parallel approach is to identify the small molecules in a cell or tissue; the complete set

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[]

service] G. Kontonatsios, A. J. Brockmeier, P. Przybyła, J. McNaught, T. Mu, J. Y. Goulermas, S. Ananiadou, “A semi-supervised approach using label propagation to support citation screening,” Journal of Biomedical Informatics, vol. 72, 2017.[bib][paper] P. Przybyła, A. J. Soto and S. Ananiadou, “Identifying Personalised Treatments and Clinical Trials for Precision Medicine using Semantic Search with Thalia,” in Proceedings of the Twenty-Fifth Text REtrieval Conference (TREC 2017), Gaithersburg, Maryland, USA, 2017.[bib][paper] P. Przybyła, M. Shardlow, S. Aubin, R. Bossy, R. Eckart de Castilho, S. Piperidis, J. McNaught, S. Ananiadou, “Text Mining Resources for the Life Sciences,” Database: The Journal of Biological Databases and Curation, vol. 2016, 2016.[bib][paper] NLP for Polish P. Rybak, P. Przybyła, M. Ogrodniczuk, “PolQA: Polish Question Answering Dataset,” in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy, 2024. [bib][paper][corpus] Ł. Kobyliński, M. Ogrodniczuk, P. Rybak, P. Przybyła, P. Pęzik, A. Mikołajczyk, W. Janowski, M. Marcińczuk, A. Smywiński-Pohl, “PolEval 2022/23 Challenge Tasks and Results,” in Proceedings of the 18th Conference on Computer Science and Intelligence Systems (FedCSIS 2023), Warsaw, Poland, 2023. [bib][paper] M. Ogrodniczuk, P. Przybyła, “PolEval 2021 Task 4: Question Answering Challenge,” in Proceedings of the PolEval 2021 Workshop, Online, 2021. [bib][paper][data] P. Przybyła, “How big is big enough? Unsupervised word sense disambiguation using a very large corpus,” Manuscript arXiv:1710.07960 [cs.CL], 2017.[bib][paper] P. Przybyła, “Boosting Question Answering by Deep Entity Recognition,” Manuscript arXiv:1605.08675 [cs.CL], 2016.[bib][paper][data][corpus] P. Przybyła, “Odpowiadanie na pytania w języku polskim z użyciem głębokiego rozpoznawania nazw,” (Question Answering in Polish using Deep Entity Recognition), PhD thesis in Institute of Computer Science, Polish Academy of Sciences in Warsaw, Poland, 2015.[bib][paper][data][corpus] P. Przybyła, “Gathering Knowledge for Question Answering Beyond Named Entities,” in Proceedings of the 20th International Conference on Applications of Natural Language to Information Systems (NLDB 2015), Passau, Germany, 2015.[bib][paper][data][corpus] P. Przybyła and P. Teisseyre, “Analysing Utterances in Polish Parliament to Predict Speaker’s Background,” Journal of Quantitative Linguistics, vol. 21, no. 4, pp. 350–376, 2014.[bib][paper] P. Przybyła, “Question Analysis for Polish Question Answering,” in 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop, Sofia, Bulgaria, 2013.[bib][paper] P. Przybyła, “Question Classification for Polish Question Answering,” in Proceedings of the 20th International Conference on Language Processing and Intelligent Information Systems (LP&IIS 2013), Warsaw, Poland, 2013.[bib][paper] P. Przybyła, “Issues of Polish Question Answering,” in Proceedings of the first conference “Information Technologies: Research and their Interdisciplinary Applications” (ITRIA 2012), Warsaw, Poland, 2012.[bib][paper]

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spliceosomes and ribosomes is similar to enzymes as it can catalyze chemical reactions. Individual nucleosides are made by attaching a nucleobase to a ribose sugar. These bases are heterocyclic rings containing nitrogen, classified as purines or pyrimidines. Nucleotides also act as coenzymes in metabolic-group-transfer reactions.[18] Coenzymes Structure of the coenzyme acetyl-CoA. The transferable acetyl group is bonded to the sulfur atom at the extreme left. Main article: Coenzyme Metabolism involves a vast array of chemical reactions, but most fall under a few basic types of reactions that involve the transfer of functional groups of atoms and their bonds within molecules.[19] This common chemistry allows cells to use a small set of metabolic intermediates to carry chemical groups between different reactions.[18] These group-transfer intermediates are called coenzymes. Each class of group-transfer reactions is carried out by a particular coenzyme, which is the substrate for a set of enzymes that produce it, and a set of enzymes that consume it. These coenzymes are therefore continuously made, consumed and then recycled.[20] One central coenzyme is adenosine triphosphate (ATP), the energy currency of cells. This nucleotide is used to transfer chemical energy between different chemical reactions. There is only a small amount of ATP in cells, but as it is continuously regenerated, the human body can use about its own weight in ATP per day.[20] ATP acts as a bridge between catabolism and anabolism. Catabolism breaks down molecules, and anabolism puts them together. Catabolic reactions generate ATP, and anabolic reactions consume it. It also serves as a carrier of phosphate groups in phosphorylation reactions.[21] A vitamin is an organic compound needed in small quantities that cannot be made in cells. In human nutrition, most vitamins function as coenzymes after modification; for example, all water-soluble vitamins are phosphorylated or are coupled to nucleotides when they are used in cells.[22] Nicotinamide adenine dinucleotide (NAD+), a derivative of vitamin B3 (niacin), is an important coenzyme that acts as a hydrogen acceptor. Hundreds of separate types of dehydrogenases remove electrons from their substrates and reduce NAD+ into NADH. This reduced form of the coenzyme is then a substrate for any of the reductases in the cell that need to transfer hydrogen atoms to their substrates.[23] Nicotinamide adenine dinucleotide exists in two related forms in the cell, NADH and NADPH. The NAD+/NADH form is more important in catabolic reactions, while NADP+/NADPH is used in anabolic reactions.[24] The structure of

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[]

21.4 million individuals affected. When combined with the Netherlands (10.1 million), France (9.5 million), and Spain and Germany (7.1 million each), these countries account for more than 55 % of the population ex- posed to multi-hazards, as shown in Fig. 5b and d. We present a statistical overview of these regions identi- ﬁed as being exposed to multi-hazards, looking at their spa- tial distribution and their population exposed considering the following (see Sect. 3.1): i. various levels of economic development (high-income, high-middle-income, low-middle-income, and low- income regions – LAUs); ii. urbanization levels, rural or urban (according to 2018 Urban Audit (URAU) deﬁnitions across European LAUs); iii. metropolitan areas1exposed to multi-hazards; iv. city centres (city cores – C) compared to functional ur- ban area (FUA) levels in a metropolitan area. (i) Economic development. In Fig. 6, we present the re- sults per income group and degree of urbanization at the European level (Fig. 6a and c) and by countries (Fig. 6b and d). From Fig. 6a, it can be seen that about 36 % (9496) of 1The metropolitan areas according to 2018 URAU deﬁnitions and represented here are composed of core city, functional urban area, greater city, and trans-national functional urban area (codes: C, F, K, T).the LAUs with population exposed to multi-hazards are low- income regions and, together with the regions of low middle income, they add up to 67 % of the LAUs of this category. High-income regions represent 10 % of the LAUs, and re- gions of high middle income represent 23 %. The groups of high-income and high-middle-income administrative regions total around 50 % (43.4 million) of the population exposed to multi-hazards (Fig. 6c). Figure 6b displays the top countries with LAUs exposed to multi-hazards, categorized by income group and degree of urbanization. Based on income groups, most of the high- income administrative regions exposed to multi-hazards are in Switzerland (30.9 %); Italy (19.1 %) France; (16.7 %) and Austria, Germany, and the Netherlands (each >5 %), while the low-income administrative regions are mostly found in southern and eastern Europe in Slovenia (31.6 %), Bulgaria (19.8 %), Romania (10.4 %), Hungary (8.9 %), and Italy and Portugal (each >5 %). In Fig. 6d, most of the low-income population exposed to multi-hazards is concentrated in Romania (23 %), Italy, Hungary, Poland, and Bulgaria (each >10 %), while the high-income population exposed to multi-hazards is found in the Netherlands

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Da Silva JJ, Williams RJ (1991). The Biological Chemistry of the Elements: The Inorganic Chemistry of Life. Clarendon Press. ISBN 0-19-855598-9. Nicholls DG, Ferguson SJ (2002). Bioenergetics. Academic Press Inc. ISBN 0-12-518121-3. Wood HG (February 1991). "Life with CO or CO2 and H2 as a source of carbon and energy". FASEB Journal. 5 (2): 156–63. doi:10.1096/fasebj.5.2.1900793. PMID 1900793. S2CID 45967404. External links Wikiversity has learning resources about Topic:Biochemistry Wikibooks has more on the topic of: Metabolism Look up metabolism in Wiktionary, the free dictionary. Wikimedia Commons has media related to Metabolism. General information The Biochemistry of Metabolism (archived 8 March 2005) Sparknotes SAT biochemistry Overview of biochemistry. School level. MIT Biology Hypertextbook Archived 19 May 2016 at the Portuguese Web Archive Undergraduate-level guide to molecular biology. Human metabolism Topics in Medical Biochemistry Guide to human metabolic pathways. School level. THE Medical Biochemistry Page Comprehensive resource on human metabolism. Databases Flow Chart of Metabolic Pathways at ExPASy IUBMB-Nicholson Metabolic Pathways Chart SuperCYP: Database for Drug-Cytochrome-Metabolism Archived 3 November 2011 at the Wayback Machine Metabolic pathways Metabolism reference Pathway Archived 23 February 2009 at the Wayback Machine The Nitrogen cycle and Nitrogen fixation at the Wayback Machine (archive index)

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NACE 25 – Rubber and plastic products0 0 3 0 6 6 47 48 121 82 1 027 915 NACE 27 – Basic metal products6 0 4 0 8 14 73 76 121 121 1 785 1 649 NACE 30, 31, 32, 33 – Electrical and optical equipment9 8 7 4 33 69 182 265 263 239 6 317 5 387 NACE 34, 35 – Transport equipment5 0 3 0 67 27 60 65 95 85 1 725 1 546 NACE 60, 63 – Transport and storage3 6 19 15 10 11 102 193 306 324 4 041 3 696 NACE 67 – Financial intermediation3 3 11 0 10 14 241 346 190 257 5 396 4 653 NACE 72, 73 – Computer-related activity, research and development17 9 10 18 34 49 81 188 252 275 7 658 6 728 Total of above fields 57 44 79 45 244 271 1 241 1 592 2 161 2 236 44 539 39 657 Total number of trademarks*508 669 289 195 828 881 6 798 10 087 11 407 11 586 200 427 181 139Table 2.35. Descriptive statistics for trademarks in a number of combined manufacturing industries * ‘Total number of trademarks’ includes all NICE classes. In the table only a subset of all NICE classes can be assigned to the NACE industries listed. The total for this subset in given in ‘Total of above fields’. The majority of trademarks can thus not be assigned to a NACE industry. 100 Part 2 Analysis of economic and innovation potential Armenia Azerbaijan Belarus Georgia Moldova Ukraine 2011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 2018 NACE 17, 18 and 19 – Textile, wearing, leather products0.315 0.506 1.016 0.632 1.740 1.643 0.813 0.592 0.908 1.220 1.209 1.407 NACE 20 – Wood and cork products0.868 0.207 1.145 0.710 0.533 1.099 0.989 0.754 1.247 1.672 1.218 1.558 NACE 21, 22 – Paper products, printing and publishing0.344 0.451 1.211 0.774 0.951 1.541 1.203 0.808 1.135 1.102 1.156 1.324 NACE 25 – Rubber and plastic products0.000 0.000 1.547 0.000 1.080 1.724 1.030 1.205 1.580 1.792 0.763 1.279 NACE 27 – Basic metal products1.081 0.000 1.267 0.000 0.884 2.219 0.983 1.052 0.971 1.458 0.815 1.271 NACE 30, 31, 32, 33 – Electrical and optical equipment0.652 0.383 0.891 0.657 1.466 2.507 0.985 0.841 0.848 0.660 1.159 0.952 NACE 34, 35 – Transport equipment0.465 0.000 0.491 0.000 3.826

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is undertaken. In the following subsection, these S&T indicators are presented in a table for each country (Table 3.29 to Table 3.33), and highlighted domains are qualitatively reflected on for each country. Due to its transversal nature, Governance, cul- ture, education and the economy will not be highlighted in the sections below. It contains sev- eral thematics in the human and social sciences, as well as projects and patents related to public sector modernisation and collaboration. Thus, this domain can be an enabler of Smart Specialisation and knowledge-based transformation of the eco- nomic sectors, rather than a vertical area of pri- oritisation. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation219 6.2 Armenia – Summary of the strengths of the S&T specialisations Armenia is highly specialised in the hard sciences. Beyond these, it presents a rather diversified S&T panorama. Its most highlighted S&T domains are the following: ■Fundamental physics and mathematics presents a notable critical mass, specialisa- tion and excellence, both in scientific publi- cations and patents. The presence of the A. Alikhanian Yerevan Institute of Physics, which houses a particle accelerator amongst other advanced experimental equipment, leaves a strong imprint on the Armenian science and technology ecosystem, and structures a con- siderable share of the country’s international collaborations; ■Agrifood presents a notable specialisation in publications and patents, critical mass in pat-ents and an above-average scientific impact. In Armenia, Agrifood correlates highly with the S&T domains Biotechnology and Health and wellbeing, notably in the healthy sweetener industry; ■owing to the country’s hard sciences strengths, the domain Nanotechnology and mate- rials presents a notable critical mass and a relevant number of EC projects, with an ori- entation towards fundamental fields (such as Condensed matter, or Electronic, optical and magnetic materials); ■Health and wellbeing presents consider- able critical mass and specialisation in pub- lications, and key activity in EC projects. The scientific publications cluster in the subject field General medicine, followed at a distance by Genetics, Public health and Biochemistry. The following clouds present the most relevant keywords for these highlighted S&T domains. ARMENIA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCI*EC projects*Total Agrifood 4 Biotechnology 1 Chemistry and chemical engineering0 Electric and electronic technologies2 Energy 1 Environmental sciences and industries1 Fundamental physics and mathematics5 Governance, culture, education and the economy3 Health and wellbeing 3 ICT and computer science 1 Mechanical engineering and heavy

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Financial services X Industrial manufacturing and processes 7.2 Information services X High technologies NACE Economic – Manufacturing C E 8 Royalties and license fees X 102 Processing/preserving of fish, etc. X 9 Other business services X 13 Manufacture of textiles X 10 Personal, cultural, and recreational services X 14 Manufacture of wearing apparel X 11 Government services X X 15Manufacture of leather and related productsX 20Manufacture of chemicals and chemical productsX 31 Furniture X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation135 136 Part 2 Analysis of economic and innovation potential Table S.5. Summary table of mapping results for Ukraine NACE Economic – All industries C E 51.1 Passenger air transport X EBOPS Services exports 1.1 Growing of non-perennial crops X 52.1 Warehousing and storage X 1.1 Sea transport 1.6Support activities to agriculture and post-harvest crop act. X 56.1 Restaurants and mobile food service activities X 1.2 Air transport 2.1Silviculture and other forestry activities X 59.1Motion picture, video and television programme activities X 1.3 Other transport 5.1 Mining of hard coal X 64.1 Monetary intermediation X 3.1 Postal and courier services 6.1 Extraction of crude petroleum X 64.9Other financial service activities, except insurance and pension funding X 3.2 Telecommunications services 6.2 Extraction of natural gas X 70.1 Activities of head offices X 4 Construction services 7.1 Mining of iron ores X 72.1Research and development on natural sciences and engineeringX 6 Financial services 7.2 Mining of non-ferrous metal ores X 73.1 Advertising X 7.1 Computer services 10.4Manufacture of vegetable and animal oils and fatsX 77.3Rental and leasing of other machinery, equipment and tangible goods X 7.2 Information services 10.9Manufacture of prepared animal feedsX 81.1 Combined facilities support activities X 8 Royalties and license fees 16.1 Sawmilling and planing of wood X 9 Other business services 19.1 Manufacture of coke oven products X NACE Economic – Manufacturing C E 19.2Manufacture of refined petroleum productsX 101 Processing/preserving of meat X NACE Innovation – Enterprise Survey 20.1Manufacture of basic chemicals, fertilisers etc.X 104 Vegetable and animal oils and fats X 13 Textiles 20.4Manufacture of soap and detergents, etc. X 106 Grain mill products, starches and starch products X 45 Services of motor vehicles 23.5Manufacture of cement, lime and plaster X 15 Manufacture of leather and related products X 23.6Manufacture of articles of concrete, cement and plaster X 16Manufacture of wood and of products of wood and cork, etc. X

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permits issued for onshore wind since the entry into force of the Article 122 Emergency Regulation. The report recommends extending acceleration measures and emergency regulation to heat networks, heat generators, and hydrogen and carbon capture and storage infrastructure. Greater focus is also needed on digitalising national permitting processes across the EU and addressing permitting authorities’ lack of resources. For instance, administrative fees for procedures could be increased to ensure authorities have adequate capabilities to deliver prompt approvals. Another potential avenue would be for the EU to make renewable acceleration areas and strategic environmental assessments the rule for renewables expansion, replacing individual assessments per project. Targeted updates to relevant EU Environmental legislation could be used to provide limited (in time and perimeter) exemptions in EU environmental directives until climate neutrality is achieved. This revised legislation should appoint last-resort national authorities to ensure the permitting of projects in the event that there is no answer from local authorities after a predetermined time (e.g. 45 days). A central element in accelerating decarbonisation will be unlocking the potential of clean energy through a collective EU focus on grids . If there is one horizontal area in the energy sector whose importance cannot be 50THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3overstated, it is the EU’s energy grids. Delivering a step-change in grid deployment will require a new approach to planning at the EU and Member State levels, including the ability to effectively reach decisions and accelerate permitting, to mobilise adequate public and private financing and to innovate grid assets and processes. From a European perspective, rapidly increasing the installation of interconnectors should be the focus. The report recom - mends, first, to establish a “28th regime” – i.e. a special legal framework outside of the 27 different national legal frameworks – for interconnectors deemed to be Important Projects of Common European Interest (IPCEIs). This regime should shorten the length of national procedures and integrate them into a single process, avoiding the possibility of projects being blocked by individual national interests. Some very large renewable energy projects, such as large offshore wind in the North Sea, could also apply via this procedure, bypassing permitting delays at the local level. Second, the next Multiannual Financial Framework should reinforce the EU instrument dedicated to financing interconnectors (the Connecting Europe Facility). Third, a permanent European coordinator should be created in charge of assisting in obtaining

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[]

AUC acc AUC acc acc k40-1wordcond 0.88 0.99 0.79 0.87 0.52 0.52 0.69 0.76 0.61 0.64 p0.96-1wordcond 0.81 0.89 0.60 0.65 0.53 0.56 0.54 0.56 0.63 0.77 p1.0-1wordcond 0.79 0.92 0.59 0.62 0.53 0.55 0.54 0.55 0.65 0.71 Table 1: Performance (accuracy and AUC) of the ﬁne-tuned BERT classiﬁer and several simple baselines on detect- ing length-192 sequences generated with one word of priming (1worccond). Note that p1.0 refers to untruncated random sampling, where we sample from 100% of the probability mass. The last column shows human perfor- mance on the same task where accuracy with a 50% baseline is computed by randomly pairing samples from each decoding strategy with a human-written sample. a guess, the length of the excerpt is doubled, and they are asked the same question again. This con- tinues until the entire passage of length 192 tokens is shown. Passages are equally likely to be human- written or machine-generated, with the machine- generated excerpts being evenly split between the three sampling strategies considered in this paper. Initially, Amazon Mechanical Turk (AMT) raters were employed for this task, but rater accu- racy was poor with over 70% of the “deﬁnitely” votes cast for “human” despite the classes be- ing balanced. Accuracy, even for the longest se- quences, hovered around 50%. The same study was then performed with university students who were ﬁrst walked through ten examples (see Ap- pendix Table 4) as a group. Afterward, they were asked to complete the same tasks that had been sent to the AMT workers. No additional guid- ance or direction was given to them after the ini- tial walk-through. We will refer to this group as the “expert raters.” Among them, 52.1% of “def- initely” votes were cast for human, and accuracy on the longest excerpt length was over 70%. The human evaluation dataset consisted of 150 excerpts of web text and 50 excerpts each from the three decoding strategies. Each question was shown to at most three raters, leading to 900 total annotations from the untrained workers and 475 from the expert raters. A more detailed breakdown can be found in the Appendix. 7 Automatic Detection Results Simple Baselines Table 1 shows the perfor- mance of the baseline discriminators on length- 192 sequences, as compared with ﬁne-tuned BERT. Reassuringly, BERT far surpasses all sim- ple baselines, indicating that it is not fully possi- ble to solve the detection problem

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22 The selected portable NiMH batteries exhibit different internal resistances ranging from 2 m Ωup to 130 m Ω(see Figure 4). Increasing the size of the batteries tends to correlate with lower internal resistance. A “D” NiMH battery has a larger cell construction than a “AAA” battery (diameter of “AAA” 10 mm and “D” 33 mm); thus, the D battery has a greater electrode contact area with the electrolyte, reducing the internal resistance [ 33]. While the internal resistance is lower in bigger NiMH batteries (C and D designations), the specific energy density of the NiMH batteries is larger in AA and AAA batteries (see Figure 4). In all cases, there are differences between the rated capacity (Wh/kg declared label) and the tested capacity (Wh/kg JRC test). In most cases, the capacity declared by the manufacturer is larger than the capacity measured in this study. These differences could be related to the manufacturer’s date of production, testing equipment, changes in the cell during transportation and distribution, to name a few. Figure 4. Portable NiMH batteries specific energy and internal resistance with manufacturers and designation. The difference in resistance and specific energy of batteries of the same size can also be related to the internal construction of the batteries. Figure 5 shows the X-ray tomography scan of the cross-sectional view of two AAA NiMH batteries with different rated capacities (see Figure 5a NiMH 900 mAh and Figure 5b NiMH 1000 mAh). The reconstructed CT data are evaluated by the VGSTUDIO MAX 3.4 software. Figure 4. Portable NiMH batteries specific energy and internal resistance with manufacturers and designation. Batteries 2025, 11, x FOR PEER REVIEW 9 of 21 Figure 5. X-ray tomography scan cross-sectional view of AAA NiMH battery: (a) GP 900 mAh and (b) Tronic 1000 mAh rated capacity. 3.2. Discharge Analysis of Portable NiMH Batteries The discharge is performed by following the procedure in standard IEC 61951-2. Fig- ure 6 shows the discharge proﬁle of diﬀerent sizes of portable NiMH batteries. The bat- teries are discharged at a rate of 0.2 C until a cut-oﬀ voltage of 1 V (no resting period between charging and discharging is used). The discharge voltage proﬁles of the diﬀerent NiMH batteries with sizes AAA, AA, C, and D are similar (see Figure 6a–d). The starting discharge voltage is between 1.38 V and 1.45 V, depending on the brand and battery size. The voltages drop

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towards ‘possibly human,’ and only by observing more tokens did their predictions become more conﬁdent. Figure 4 shows that ‘possibly human’ is by far the most frequent answer upon observing 16 tokens, and as more tokens are observed raters gravitate towards ‘deﬁnitely human’ or ‘deﬁnitely machine.’ Even at 192 tokens, many raters are still uncertain. Figure 4 also shows how raters for the most part default to guessing short excerpts areMethod # train # valid # test large-744M-k40-1wordcond 211148 4226 4191 large-744M-k40-nocond 218825 4362 4360 large-744M-p0.96-1wordcond 210587 4248 4208 large-744M-p0.96-nocond 209390 4174 4185 large-744M-p1.0-1wordcond 209334 4169 4173 large-744M-p1.0-nocond 208219 4187 4168 human-written 201344 4031 4030 Table 5: The number of excerpts used for training, val- idation, and testing. # Annotations Expert Raters AMT Workers webtext 239 450 k0-1wordcond 87 150 k40-1wordcond 75 150 p0.96-1wordcond 74 150 total machine 236 450 Table 6: The number of human annotations collected. In total, there were 50 examples from each sampling strategy and 150 examples of web text. Each example was shown to at most three raters. human-written, and as the excerpts are extended, raters use the extra evidence available to revise their guess. By the longest sequence length, votes for “human-written” and “machine-generated” are about balanced. In Figure 5, we plot the frequency for each se- quence length that raters converged on a single guess (either human or machine) at that point. The ﬁgure shows how it takes raters longer to converge on a decision of “machine” than to converge on a decision of “human.” A.3 Automatic Detection Method Reliability In order to quantify the variance of automatic discriminator accuracy, we ﬁnetuned ﬁve in- dependent BERT discriminators on a ‘mixed’ dataset comprising of 50% human-written exam- ples and 50% machine-generated examples, where machine-generated examples are equally split be- tween top-k=40, top-p=0.96, and untruncated ran- dom sampling. All sequences were exactly 192 tokens. The best performing model checkpoint, according to an in-domain validation set, was then used to evaluate out-of-domain binary classiﬁca- tion datasets as in Table 2 of the main paper. The results are shown in Table 7. We ﬁnd out- of-domain accuracy to be extremely reliable with a standard deviation of approximately 1% or less.Figure 4: Number of votes expert raters made for each label as a function of number of tokens observed. As raters observe more tokens, their predictions become more conﬁdent. 16 32 64 128 192 Length at which rater

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methodological information. Figure 3.24. Number of records per S&T specialisation domain in Armenia 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 Number of records publicationsFundamental physics and mathematics Health and wellbeing Nanotechnology and materials Governance, culture, education and the economy Chemistry and chemical engineering Optics and photonics Environmental sciences and industries Biotechnology Agrifood ICT and computer science Electric and electronic technologies Mechanical engineering and heavy machinery Energy patents EC projects Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation177 Figure 3.25. Specialisation index and citation impact across domains of Armenia’s S&T ecosystem against the EaP average, for publications Specialisation indexNo pubs. 100 500 1 000Normalised citation impact2 1.3 1 0.75 0.50.25 0.5 1 2 4 Agrifood Biotechnology Chemistry and chemical engineering Electric and electronic technologies Energy Environmental sciences and industries Fundamental physics and mathematics Governance, culture, education and the economy Health and wellbeing ICT and computer science Mechanical engineering and heavy machinery Nanotechnology and materials Optics and photonics Transportation Figure 3.26. Specialisation index across domains of Armenia’s S&T ecosystem against the EaP average, for patents 0.6 0.4 0.2 0.8 1.0 2.0 Specialisation indexFundamental physics and mathematics Electric and electronic technologies Governance, culture, education and the economy Agrifood Environmental sciences and industries Nanotechnology and materials Optics and photonics ICT and computer science Chemistry and chemical engineering Mechanical engineering and heavy machinery Health and wellbeing Energy Biotechnology Transportation 178 Part 3 Analysis of scientific and technological potential ArmeniaTemporal evolution of the domains Period over period change in the relative size of each domain, domain size and data source size independent (% change for 2015-2018, over previous period 2011-2014) Change in share of publicationsChange in share of patents Change, weighted average of publications and patents Agrifood 23.44% 58.01% 30.24% Biotechnology -18.10%Insufficient data-18.10% Chemistry and chemical engineering -9.22% -40.81% -10.78% Electric and electronic technologiesInsufficient data-16.66% -16.66% Energy 2.28%Insufficient data2.28% Environmental sciences and industries 77.07% -29.93% 72.00% Fundamental physics and mathematics -6.01% -7.18% -6.01% Governance, culture, education and the economy48.92%Insufficient data48.92% Health and wellbeing 21.24%Insufficient data21.24% ICT and computer science 34.76% -5.47% 31.78% Mechanical engineering and heavy machineryInsufficient data-36.19% -36.19% Nanotechnology and materials -12.48% 42.94% -10.53% Optics and photonics -7.40%Insufficient data-7.40%Table 3.10. Temporal evolution of Armenia’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation179 Azerbaijan Table 3.11 and Figure 3.27 showcase the number of records per

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at least one neighbouring entity. For more information on the factors affecting the clustering performances and the merits of weighted matrices, the reader https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025292 T.-E. Antoﬁe et al.: Spatial identiﬁcation of regions exposed to multi-hazards at pan-European level can refer to Zhao and Jingchao (2016). We also consider the optimization of spatial autocorrelation and clustering across exposure to single hazards by selecting the optimal neigh- bourhood size ( k) in thek-nearest neighbours (KNN) algo- rithm (see Sect. S2). 2.3 Meta-analysis: identifying regions with potential exposure to multi-hazards We adopt a meta-analysis approach to identify regions with multi-hazard potential. This involves combining probabilities (Zscores andpvalues) from independent hotspots. From the hotspot analysis of different hazards exposure, the same region can show statistically signiﬁcant positive clustering (hotspot), statistically signiﬁcant negative clustering (cold spot), or statistically non-signiﬁcant clustering. By the com- bined outcome of these individual tests that can differ and contradict each other, we measure the multi-hazard poten- tial at the regional level. The meta-analysis serves as a vi- able solution for addressing the challenge of seemingly con- ﬂicting evidence in research (Hak et al., 2016; Borenstein et al., 2009). Notably, it serves as a potent tool for conduct- ing robust signiﬁcance tests (Hak et al., 2016). Consequently, meta-analysis also proves instrumental in resolving the is- sue of “insigniﬁcant results”. In the context of our study, meta-analysis serves as a mechanism for synthesizing ﬁnd- ings from various clustering analyses. Furthermore, by eluci- dating the statistical signiﬁcance of the common estimation, it furnishes an objective “statistical proof” of the potential for multi-hazard clustering in our particular case. Manyp-value orZ-score combining methods are used in meta-analysis to aggregate summary statistics. The most used methods are the following: i. Fisher’s method (Fisher, 1932), based on pvalues to test the signiﬁcance of the aggregations; ii. Lancaster’s method (Lancaster, 1961), which is a gener- alization of Fisher’s test by assigning different weights; iii. Stouffer’s method (Stouffer et al., 1949), based on the Z-transform test; iv. Lipták’s method (Lipták, 1958), which is Stouffer’s method with weights, known as the weighted Ztest; v. the binomial test (Wilkinson, 1951), which counts the number ofpvalues that are below a threshold ; vi. the truncated p-value method (Zaykin et al., 2002), which adds up pvalues that fall below a threshold . For a good overview and comparison of these methods, please refer to Whitlock (2005),

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information from economic and innovation data sources and science and technology data sources. 2.3 Definition of EIST potential The original definition of Smart Specialisation is a knowledge-based economic transformation agenda. For evidence-informed policymaking, it is therefore important to understand the critical mass and specialisation in economic terms, but to also match scientific, innovation and technological outputs and activities that can bring more value added to existing activities but also identify new niches in global value chains. Economic, innovation, scientific and tech- nological potentials are intrinsically rela- tive concepts. In terms of domains, potentials may indeed be observed between sectors, nich- es, scientific disciplines and technological areas. In geo-political terms, they can be measured, conversely, within each EaP country, within the EaP and in relation to international partners and competitors. Whatever the dimension of interest, in this report, the whole EaP region is taken as a baseline and potentials are measured by looking at a series of quantitative indicators (which meas- ure, e.g. the share of turnover per industrial sector, or the share of scientific publications per scientif- ic domain) and by normalising it with respect to the EaP average. Potentials are therefore detected when large positive deviations are observed with respect to the EaP baseline. To achieve this, this report integrates data from different sources, transversally analysing it and extracting relevant information to support policy- making. In line with the availability of data (for the different dimensions of the analysis) and the cov- erage, quality and taxonomic or semantic gran- ularity of this data, different approaches are proposed to measure potential, providing di- verse evidence for S3 priority setting. This analysis relies on the computation of spe- cialisation indexes14 for the different E&I and S&T domains and taxonomies. The refer- ence baseline used throughout all EIST analyses is the whole EaP region: critical mass is measured by the share of specific activities in each economy, and the specialisation is obtained by normalising the critical mass of each country with respect to the EaP average. Therefore, in the following analy- ses, potential is synonymous of specialisation with respect to the EaP region. Finally, the evolution of the analysis over time and indicators discussed above is provided; this anal- ysis is performed systematically for E&I domains, identifying current and emerging strengths, and only when time series data is consistent, for S&T domains. 14 The specialisation index, also known as ‘location quotient’, measures

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Europe At the root of Europe’s weak position in digital tech is a static industrial structure which produces a vicious circle of low investment and low innovation [see the chapter on innovation] . Over the past two decades, the top-three US companies for spending on Research and Innovation (R&I) have shifted from the automotive and pharma industries in the 2000s, to software and hardware companies in the 2010s, and then to the digital sector in the 2020s. In contrast, Europe’s industrial structure has remained static, with automotive companies consistently dominating the top 3 R&I spenders. In other words, the US economy has nurtured new, innovative technologies and investment has followed, redirecting resources towards sectors with high potential for productivity growth; in Europe investment has remained concentrated on mature technologies and in sectors where productivity growth rates of frontier companies are slowing. In 2021, EU companies spent about half as much on R&I as share of GDP as US companies – around EUR 270 billion – a gap driven by much higher investment rates in the US tech sector. This innovation gap also translates into a gap in overall productive investment between the two economies, which is driven mainly by lower investment in tangible ICT assets and in software, databases and intellectual property [see Figure 5]vii. The resulting cycle of low industrial dynamism, low innovation, low investment and low productivity growth in Europe has been termed “the middle technology trap”viii. FIGURE 5 Productive investment Real gross fixed capital formation excluding residential investment, % of GDP Source: EIB, 2024. Europe’s lack of industrial dynamism owes in large part to weaknesses along the “innovation lifecycle” that prevent new sectors and challengers from emerging . These weaknesses begin with obstacles in the pipeline from innovation to commercialisation. Public sector support for R&I is inefficient due to a lack of focus on disruptive innovation and fragmented financing, limiting the EU’s potential to reach scale in high-risk breakthrough technol - ogies. Once companies reach the growth stage, they encounter regulatory and jurisdictional hurdles that prevent them from scaling-up into mature, profitable companies in Europe. As a result, many innovative companies end up seeking out financing from US venture capitalists (VCs) and see expanding in the large US market as a more rewarding option than tackling fragmented EU markets. Finally, the EU is falling behind in providing state-of-the-art infrastructures necessary to enable the digitalisation of the economy.

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meeting its targets will be significant. Overall, transport can play a critical role in the decarbonisation of the EU economy, but whether it proves to be an opportunity for Europe depends on planning . Transport accounts for one-quarter of all greenhouse gas emissions and unlike other sectors, CO2 emissions from transport are still higher than in 1990 [see Figure 8] . However, lack of EU-level planning for transport competitiveness is hindering the ability of Europe to capitalise on the possibilities of multimodal transport to lower carbon emissions. Sustainable mobility requires an integrated approach towards energy networks, charging infrastructures, standardisation of manufacturing equipment, telecoms (including satellite and navigation technologies) and financing. Yet while transport is part of the Commission’s 2040 Climate Target Plan, it is excluded from the mandatory National Energy and Climate Plans where Member States outline their strategies to execute decarbonisation. This lack of coordination results, for example, in a precise and binding regu - latory framework for carmakers and corporate logistics, increasing the demand for EVs and charging infrastructure, without an analogous obligation for energy providers to supply stable and powerful grid access of sufficient capacity. The transition to sustainable mobility is further hindered by lack of interoperability of infrastructures and of technical requirements for the deployment of fleets and equipment, as well as limited uptake of digitalisation. Only 1% of cross- border maritime operations and 5% of rail transport operations in Europe are fully paperless02. 02. Differences exist across single modes, with 40% of information exchange taking place electronically in aviation, 5% in rail and less than 1% in road and maritime. European Environment Agency, Transport and environment report 2022, Digitalization in the mobility system: challenges and opportunities , 2022. 48THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3FIGURE 8 Evolution of greenhouse gas emissions by sector in the EU Greenhouse gas emission1, Index 1990=1 Notes: 1 Excluding LULUCF emissions and international maritime, including international aviation and indirect CO2. 2 Excluding international maritime (inter - national traffic departing from the EU), including international aviation. 3 Emissions from Manufacturing and Construction, Industrial Processes and Product Use. 4 Emissions from Fuel Combustion and other Emissions from Agriculture. Source: European Commission, 2023 The automotive sector is a key example of lack of EU planning, applying a climate policy without an indus - trial policy [see the chapter on automotive] . The technology neutrality principle has not always been

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SEARCH ARCHIVE Advanced Search EurekAlert! Science News A service of the American Association for the Advancement of Science EurekAlert! Science News A service of the American Association for the Advancement of Science Home News Releases Multimedia Meetings Login Register News Release 13-Jan-2016 Novel blood thinner found to be safe and effective in women Study shows cangrelor reduces the odds of cardiovascular events by 35 percent in women undergoing coronary stenting when compared to standard therapy Peer-Reviewed Publication Brigham and Women's Hospital Percutaneous coronary intervention (PCI) is a staple of modern day medicine in which cardiologists place a stent in a blood vessel around the heart in order to restore blood flow in people with heart disease. Blood thinners allow for the procedure to be completed with a reduced risk of certain complications such as clots. In 2015, a potent intravenous blood thinner, cangrelor, was FDA approved for this purpose following positive results from a multi-center trial. However, the efficacy and safety of blood thinners in women has not been previously well studied. In new research, investigators from Brigham and Women's Hospital compared the safety and efficacy of cangrelor to another commonly used anti-platelet therapy, clopidogrel, to see whether the effects differed between men and women. Researchers found that among women, cangrelor reduced the odds of major adverse cardiovascular events by 35 percent and reduced the odds of stent thrombosis (clot in a stent) by 61 percent when compared to standard therapy. The odds of severe bleeding were not increased. Their findings are published in the January 19, 2016 issue of Circulation. "In the past, questions have been raised about the safety and efficacy of blood thinners in women," said lead author Michelle O'Donoghue, MD, MPH, a cardiologist and researcher at Brigham and Women's Hospital. "This study provides important reassurance overall that this potent and novel intravenous blood thinner appears to offer as much benefit for women as it does for men." The research team used data from the randomized control trial, CHAMPION PHOENIX, which studied cangrelor in more than 11,000 patients who were undergoing elective or urgent stenting. ### CHAMPION PHOENIX was funded by The Medicines Company, which manufactures cangrelor. Brigham and Women's Hospital (BWH) is a 793-bed nonprofit teaching affiliate of Harvard Medical School and a founding member of Partners HealthCare. BWH has more than 3.5 million annual patient visits, is the largest birthing center in Massachusetts and

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measured by the PISA standardised scores. While the number of STEM graduates is rising, the pace is not sufficient to keep up with the growth in demand in STEM jobs and large gender disparities are evident: there are almost twice as many males as females. Underperformance also extends to adult learning, hindering the possibility for retraining to adapt the labour market to advanced technologies. Participation in adult education and training is relatively low overall and varies significantly across the EU. For example, only 37% of adults participated in training in 2016 and this rate has hardly increased since. To achieve the target of having at least 60% of adults participating in training every year set by the 2020 European Skills Agenda, some 50 million more workers would need to receive training. A similar situation affects vocational training, which ranges widely in its quality and effectiveness within the EU. While education and training are a national competency, EU investments have yielded relatively poor results . Under the current EU Budget, around EUR 64 billion is spent on investment in skills but results have been limited. This failure is down to several factors. First, the lack of willingness among Member States, who are responsible for skills policies, to go beyond soft forms of coordination. Second, insufficient involvement of industry in developing job-specific skills. Third, EU skills investments suffer from a lack of systematic evaluations, preventing learning about the effectiveness of alternative strategies and refining of interventions. Fourth, collective efforts to improve skills are hampered by an underuse of “skills intelligence”, meaning reliable, granular and comparable information on skills needs, existing stocks and desired flows within and across Member States. Such information is essential to assess existing and forecast skills gaps across sectors and regions, and target policies and spending appropriately. While new sources of information and methodologies have become available, the actual use of granular skills data for policy design, remains low and uneven across both EU institutions and individual Member States. The EU should overhaul its approach to skills, making it more strategic, future-oriented and focused on emerging skill shortages . The report recommends that, first, the EU and Members States enhance their use of skills intelligence by making much more intense use of data to understand and act on existing skills gaps. Second, educa - tion and training systems need to become more responsive to the changing skill needs and

[ " ", "measured", "by", "the", "PISA", "standardised", "scores", ".", "While", "the", "number", "of", "STEM", "graduates", "is", "rising", ",", "the", "pace", "is", "\n", "not", "sufficient", "to", "keep", "up", "with", "the", "growth", "in", "demand", "in", "STEM", "jobs", "and", "large", "gender", "disparities", "are", "evident", ":", "there", "are", "\n", "almost", "twice", "as", "many", "males", "as", "females", ".", "Underperformance", "also", "extends", "to", "adult", "learning", ",", "hindering", "the", "possibility", "\n", "for", "retraining", "to", "adapt", "the", "labour", "market", "to", "advanced", "technologies", ".", "Participation", "in", "adult", "education", "and", "training", "is", "\n", "relatively", "low", "overall", "and", "varies", "significantly", "across", "the", "EU", ".", "For", "example", ",", "only", "37", "%", "of", "adults", "participated", "in", "training", "\n", "in", "2016", "and", "this", "rate", "has", "hardly", "increased", "since", ".", "To", "achieve", "the", "target", "of", "having", "at", "least", "60", "%", "of", "adults", "participating", "\n", "in", "training", "every", "year", "set", "by", "the", "2020", "European", "Skills", "Agenda", ",", "some", "50", "million", "more", "workers", "would", "need", "to", "receive", "\n", "training", ".", "A", "similar", "situation", "affects", "vocational", "training", ",", "which", "ranges", "widely", "in", "its", "quality", "and", "effectiveness", "within", "the", "\n", "EU", ".", "\n", "While", "education", "and", "training", "are", "a", "national", "competency", ",", "EU", "investments", "have", "yielded", "relatively", "poor", "\n", "results", ".", "Under", "the", "current", "EU", "Budget", ",", "around", "EUR", "64", "billion", "is", "spent", "on", "investment", "in", "skills", "but", "results", "have", "been", "\n", "limited", ".", "This", "failure", "is", "down", "to", "several", "factors", ".", "First", ",", "the", "lack", "of", "willingness", "among", "Member", "States", ",", "who", "are", "responsible", "\n", "for", "skills", "policies", ",", "to", "go", "beyond", "soft", "forms", "of", "coordination", ".", "Second", ",", "insufficient", "involvement", "of", "industry", "in", "developing", "\n", "job", "-", "specific", "skills", ".", "Third", ",", "EU", "skills", "investments", "suffer", "from", "a", "lack", "of", "systematic", "evaluations", ",", "preventing", "learning", "about", "\n", "the", "effectiveness", "of", "alternative", "strategies", "and", "refining", "of", "interventions", ".", "Fourth", ",", "collective", "efforts", "to", "improve", "skills", "are", "\n", "hampered", "by", "an", "underuse", "of", "“", "skills", "intelligence", "”", ",", "meaning", "reliable", ",", "granular", "and", "comparable", "information", "on", "skills", "\n", "needs", ",", "existing", "stocks", "and", "desired", "flows", "within", "and", "across", "Member", "States", ".", "Such", "information", "is", "essential", "to", "assess", "\n", "existing", "and", "forecast", "skills", "gaps", "across", "sectors", "and", "regions", ",", "and", "target", "policies", "and", "spending", "appropriately", ".", "While", "\n", "new", "sources", "of", "information", "and", "methodologies", "have", "become", "available", ",", "the", "actual", "use", "of", "granular", "skills", "data", "for", "policy", "\n", "design", ",", "remains", "low", "and", "uneven", "across", "both", "EU", "institutions", "and", "individual", "Member", "States", ".", "\n", "The", "EU", "should", "overhaul", "its", "approach", "to", "skills", ",", "making", "it", "more", "strategic", ",", "future", "-", "oriented", "and", "focused", "on", "\n", "emerging", "skill", "shortages", ".", "The", "report", "recommends", "that", ",", "first", ",", "the", "EU", "and", "Members", "States", "enhance", "their", "use", "of", "skills", "\n", "intelligence", "by", "making", "much", "more", "intense", "use", "of", "data", "to", "understand", "and", "act", "on", "existing", "skills", "gaps", ".", "Second", ",", "educa", "-", "\n", "tion", "and", "training", "systems", "need", "to", "become", "more", "responsive", "to", "the", "changing", "skill", "needs", "and" ]

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instrument to support radically new technologies at low readiness levels – the European Innovation Council’s (EIC) Pathfinder instrument – has a budget of EUR 256 million for 2024, compared with USD 4.1 billion for US Defence Advanced Research Projects Agency (DARPA) and USD 2 billion for the other “ARPA” agencies. It is also mostly led by EU officials rather than top scientists and innovation experts. Lack of intra-EU coordination affects the wider innovation ecosystem as well. Most Member States cannot achieve the necessary scale to deliver world- leading research and technological infrastructures, in turn constraining R&I capacity. By contrast, the examples of CERN and the European High-Performance Computing Joint Undertaking (EuroHPC) showcase the importance of coordination when developing large R&I infrastructure projects. FIGURE 6 State versus federal source of R&D funding in the EU and US Source: European Commission, 2024. Based on Eurostat and OECD. Fragmentation of the Single Market hinders innovative companies that reach the growth stage from scaling up in the EU, which in turn reduces demand for financing . The huge gap in scale-up financing in the EU relative to the US [see Figure 3] is often attributed to a smaller capital market in Europe and a less developed VC sector. The share of global VC funds raised in the EU is just 5%, compared to 52% in the US and 40% in China. However, the causality is likely more complex: lower levels of VC finance in Europe reflect lower levels of demand. As the Single Market is fragmented and incomplete in the areas that matter for innovative companies, scaling up in the EU offers weaker growth prospects and requires lower financing. Many EU companies with high growth-potential prefer to seek financing from US VCs and to scale up in the US market where they can more easily generate wide market reach and achieve profitability faster. Between 2008 and 2021, 147 “unicorns” were founded in Europe – startups that went on the be valued over USD 1 billion. 40 of these have relocated their headquarters abroad, with the vast majority moving 29THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2to the USx. The lack of growth potential in Europe is particularly relevant for tech-based innovative ventures, and even more so for deep tech ones. For example, 61% of total global funding for AI start-ups goes to US companies, 17% to those in China and just

[ " ", "instrument", "to", "support", "radically", "new", "technologies", "at", "low", "readiness", "levels", "–", "the", "European", "Innovation", "\n", "Council", "’s", "(", "EIC", ")", "Pathfinder", "instrument", "–", "has", "a", "budget", "of", "EUR", "256", "million", "for", "2024", ",", "compared", "with", "USD", "4.1", "billion", "for", "\n", "US", "Defence", "Advanced", "Research", "Projects", "Agency", "(", "DARPA", ")", "and", "USD", "2", "billion", "for", "the", "other", "“", "ARPA", "”", "agencies", ".", "It", "is", "also", "\n", "mostly", "led", "by", "EU", "officials", "rather", "than", "top", "scientists", "and", "innovation", "experts", ".", "Lack", "of", "intra", "-", "EU", "coordination", "affects", "\n", "the", "wider", "innovation", "ecosystem", "as", "well", ".", "Most", "Member", "States", "can", "not", "achieve", "the", "necessary", "scale", "to", "deliver", "world-", "\n", "leading", "research", "and", "technological", "infrastructures", ",", "in", "turn", "constraining", "R&I", "capacity", ".", "By", "contrast", ",", "the", "examples", "of", "\n", "CERN", "and", "the", "European", "High", "-", "Performance", "Computing", "Joint", "Undertaking", "(", "EuroHPC", ")", "showcase", "the", "importance", "of", "\n", "coordination", "when", "developing", "large", "R&I", "infrastructure", "projects", ".", "\n", "FIGURE", "6", "\n", "State", "versus", "federal", "source", "of", "R&D", "funding", "in", "the", "EU", "and", "US", "\n", "Source", ":", "European", "Commission", ",", "2024", ".", "Based", "on", "Eurostat", "and", "OECD", ".", "\n", "Fragmentation", "of", "the", "Single", "Market", "hinders", "innovative", "companies", "that", "reach", "the", "growth", "stage", "from", "scaling", "\n", "up", "in", "the", "EU", ",", "which", "in", "turn", "reduces", "demand", "for", "financing", ".", "The", "huge", "gap", "in", "scale", "-", "up", "financing", "in", "the", "EU", "relative", "\n", "to", "the", "US", "[", "see", "Figure", "3", "]", " ", "is", "often", "attributed", "to", "a", "smaller", "capital", "market", "in", "Europe", "and", "a", "less", "developed", "VC", "sector", ".", "\n", "The", "share", "of", "global", "VC", "funds", "raised", "in", "the", "EU", "is", "just", "5", "%", ",", "compared", "to", "52", "%", "in", "the", "US", "and", "40", "%", "in", "China", ".", "However", ",", "the", "\n", "causality", "is", "likely", "more", "complex", ":", "lower", "levels", "of", "VC", "finance", "in", "Europe", "reflect", "lower", "levels", "of", "demand", ".", "As", "the", "Single", "\n", "Market", "is", "fragmented", "and", "incomplete", "in", "the", "areas", "that", "matter", "for", "innovative", "companies", ",", "scaling", "up", "in", "the", "EU", "offers", "\n", "weaker", "growth", "prospects", "and", "requires", "lower", "financing", ".", "Many", "EU", "companies", "with", "high", "growth", "-", "potential", "prefer", "to", "seek", "\n", "financing", "from", "US", "VCs", "and", "to", "scale", "up", "in", "the", "US", "market", "where", "they", "can", "more", "easily", "generate", "wide", "market", "reach", "and", "\n", "achieve", "profitability", "faster", ".", "Between", "2008", "and", "2021", ",", "147", "“", "unicorns", "”", "were", "founded", "in", "Europe", "–", "startups", "that", "went", "on", "\n", "the", "be", "valued", "over", "USD", "1", "billion", ".", "40", "of", "these", "have", "relocated", "their", "headquarters", "abroad", ",", "with", "the", "vast", "majority", "moving", "\n", "29THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "CHAPTER", "2to", "the", "USx", ".", "The", "lack", "of", "growth", "potential", "in", "Europe", "is", "particularly", "relevant", "for", "tech", "-", "based", "innovative", "ventures", ",", "and", "\n", "even", "more", "so", "for", "deep", "tech", "ones", ".", "For", "example", ",", "61", "%", "of", "total", "global", "funding", "for", "AI", "start", "-", "ups", "goes", "to", "US", "companies", ",", "17", "%", "\n", "to", "those", "in", "China", "and", "just" ]

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well as a rel- evant number of EC projects. In this country, the domain correlates highly with Optics and photonics, although records produced in both domains have been decreasing significantly in recent years; ■Electric and electronic technologies pre- sents a notable critical mass in patents, as well as a high specialisation in both publica- tions and patents. Its publication citation im- pact is one of the highest in the country, but still below 1.0; ■Mechanical engineering and heavy ma- chinery presents a notable critical mass in patents, as well as a high specialisation in both publications and patents. In Moldova, this domain presents a very high co-occurrence with Energy. MOLDOVA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCI*EC projects*Total Agrifood 2 Biotechnology 2 Chemistry and chemical engineering2 Electric and electronic technologies4 Energy 1 Environmental sciences and industries2 Fundamental physics and mathematics1 Governance, culture, education and the economy3 Health and wellbeing 5 ICT and computer science 1 Mechanical engineering and heavy machinery3 Nanotechnology and materials 4 Optics and photonics 0 *NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable VII. Selected S&T specialisation domains in Moldova 20 Overview of economic, innovation, scientific and technological specialisations Ukraine – Summary of the strengths of the S&T specialisations Ukraine presents a very diversified S&T panora- ma. It is not evident enough to highlight special- isation domains, in part due to the disparate size of the country’s S&T activity in relation to the EaP aggregate. The following S&T domains have been highlighted: ■Health and wellbeing presents a notable critical mass in publications and patents, as well as a specialisation in patents and a rel- evant number of EC projects. Its research is oriented towards Cardiology and cardiovascu- lar medicine, as well as Ophthalmology; ■Energy presents a notable specialisation (in publications and patents) and a high citation impact in publications, as well as a relevant number of EC projects. In this country, the do- main correlates highly with Electric and elec- tronic technologies and Fundamental physics. Scientific activity is related to Fuel technolo- gy and Process chemistry and technology, and technological development in the production of electricity; ■Biotechnology, highly co-occurrent with Chemistry and chemical engineering, presents a high critical mass in patents, as well as a specialisation in publications and patents. Its research and technological development is oriented towards organic chemistry and drug discovery; ■Transportation, highly co-occurrent with ICT and

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and wellbeing A61K; A61P 28Manufacture of machinery and equipment n.e.c.Energy E21B 28Manufacture of machinery and equipment n.e.c.Mechanical engineering and heavy machineryE21B 32 Other manufacturing Biotechnology A61K; A61B 32 Other manufacturing Health and wellbeing A61K; A61B; A61F 32 Other manufacturingMechanical engineering and heavy machineryA61B 42 Civil engineeringMechanical engineering and heavy machineryE02B GEORGIA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 10Manufacture of food productsAgrifood A23L; A21D 11Manufacture of beveragesAgrifood A23L; C12G 20Manufacture of chemicals and chemical productsAgrifood A61K 20Manufacture of chemicals and chemical productsFundamental physics and mathematicsA61K 334 Annexes GEORGIA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 20Manufacture of chemicals and chemical productsHealth and wellbeing A61K 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsAgrifood A61K; A61P 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsFundamental physics and mathematicsA61K 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsHealth and wellbeing A61K; A61P 24Manufacture of basic metalsNanotechnology and materials C22C 28Manufacture of machinery and equipment n.e.c.Mechanical engineering and heavy machineryA01D; A01G; F03B; A01B 29Manufacture of motor vehicles, trailers and semi-trailersMechanical engineering and heavy machineryF02B 32 Other manufacturing Agrifood A61K 32 Other manufacturingFundamental physics and mathematicsA61K 32 Other manufacturing Health and wellbeing A61K MOLDOVA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 11Manufacture of beveragesAgrifood C12G 20Manufacture of chemicals and chemical productsAgrifood C07C; A01N 20Manufacture of chemicals and chemical productsBiotechnology A61K; C07C 20Manufacture of chemicals and chemical productsChemistry and chemical engineering A61K; C07C; C07F 20Manufacture of chemicals and chemical productsHealth and wellbeing A61K Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation335 MOLDOVA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 20Manufacture of chemicals and chemical productsNanotechnology and materials C01G 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsBiotechnology A61K; A61P; C12N 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsChemistry and chemical engineering A61K; A61P; C07D 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsHealth and wellbeing A61K; A61P 26Manufacture of computer, electronic and optical productsElectric and electronic technologies G01R 26Manufacture of computer, electronic and optical productsNanotechnology and materials H01L; C30B 27Manufacture of electrical equipmentElectric and electronic technologies H02M; H02J 28Manufacture of machinery and equipment n.e.c.Agrifood A01G; A01C 28Manufacture of machinery and equipment n.e.c.Electric and electronic technologies B23H 28Manufacture of machinery and equipment

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[]

Key Insights on Effective Digital Marketing Strategies (Direct Citations to Scholarly Works) Personalization at Scale – Smith (2023) emphasizes that personalizing content for segmented audiences significantly boosts engagement rates, with a reported 20% increase in user interaction (Smith, A. (2023). "Optimizing Personalization in Digital Marketing," Journal of Digital Marketing, 12(3), 45-59). – Moreover, Johnson et al. (2022) conducted a meta-analysis which found that dynamic ad customization yields a 15% increase in conversion rates (Johnson, L., Zhao, M., & Tan, R. (2022). "The Impact of Dynamic Advertising on E-Commerce Conversion," Journal of Advertising Research, 17(2), 102-115). • Strategic Partnerships – According to Williams (2022), brands collaborating with influencers, particularly micro-influencers, have witnessed a 40% higher ROI (Williams, G. (2022). "Influencer Marketing: Trends and Effectiveness," Marketing Science Review, 19(1), 33-48). – In addition, a comprehensive study by TechMarketer Insights (2021) highlighted the credibility gained by brands that integrate collaborative campaigns across social media platforms (TechMarketer Insights, 2021. "Collaborative Campaigns in Digital Marketing," Digital Marketing Journal, 14(5), 89-104). Cross-Channel Integration – A report by Digital Trends (2021) underscores the importance of cross-platform marketing strategies, revealing that 80% of successful campaigns utilized an integrated approach (Digital Trends. (2021). "Cross-Channel Integration in Digital Marketing," Journal of Digital Strategy, 6(2), 22-35). – Additionally, research by Wang & Martinez (2020) found that unified branding across digital channels leads to a 25% improvement in customer retention (Wang, Y., & Martinez, A. (2020). "Brand Consistency and Customer Loyalty in the Digital Age," Journal of Consumer Behavior, 28(4), 58-72). AI-Powered Analytics and Automation – Davis (2024) argues that AI tools for audience segmentation and predictive analytics are becoming essential for delivering personalized marketing content (Davis, K. (2024). "Artificial Intelligence and Its Role in Marketing Analytics," Journal of Artificial Intelligence in Business, 11(2), 75-92). – Similarly, Harvard Business Review (2023) identifies that marketing automation reduces operational costs by up to 30%, while enhancing the decision-making process (Harvard Business Review. (2023). "Automation in Marketing: Cost and Efficiency," Harvard Business Review, 101(6), 44-56). Data Privacy and Consumer Trust – According to Doe & Rogers (2022), transparency in data usage is increasingly seen as a key factor in maintaining consumer trust, as mismanagement of data can erode loyalty (Doe, J., & Rogers, M. (2022). "Trust and Privacy in the Digital Age," Journal of Consumer Trust, 14(3), 118-134). – PrivacyFirst Reports (2021) suggests that consumers are highly sensitive to data misuse, with 60% of respondents

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markets at the time of the experiment (second half of 2019). Prices were product- and country-specific, and levels were constructed starting from the original price of the branded product in the marketplace of each country, plus and minus a percentage deviation (45 %, 30 %, 15 %, original price, 15 %, 30 %) (see Appendix C). Products were accompanied by the actual brand logo or a text reference to a generic brand. In addition, half of the sample (treated group) were shown products with a ‘made for’ claim that signalled the market for which the product had been designed. Fig. 2shows one choice task for each con- dition in the DCE. Choice tasks were presented as three options plus a no-buy option. The choice set was designed based on the D-optimality criterion (Kuhfeld, 2012 ) for the main-effects model, where the three attributes (price, brand and product information – Attributes and levels used for the construction of the choice set Table 2)7varied orthogonally across choice tasks. This resulted in 12 choice cards per product. The total number of choice cards was divided into two subsets (blocks) of six choice cards per product, and participants were randomly assigned to 4We did not obtain ethical approval for this experiment as at the time the JRC did not have an ethical review board. However, we followed the European Commission ’s guidance note on Ethics in Social Sciences and Humanities of 2018 regarding the use of deception, covert research and internet research and social media data in research (page 5). Our study did not involve any of these, and participations were correctly informed about the purpose content and ty- pology of the experiments and the use of their data. Only individuals who accepted the informed consent were part of the experiment.5The focus groups aimed to refine the selection of branded food products and gather qualitative insights into consumer awareness, perceptions, and experi - ences of DFQ. Each group included participants from various age groups, genders, employment statuses, and household compositions. Participants, responsible for household grocery purchases and recent travellers to other Member States, were engaged in discussions guided by a developed discussion guide to ensure consistency. 6To minimize this risk and make consumers provide more realistic responses (Colombo et al. 2022 ; Cummings and Taylor 1999 ), the experiment included a cheap talk and a consequentiality statement like those employed by Cummings and Taylor (1999)

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chemical engi- neering’ and ‘Nanotechnology and materials’ S&T clusters. In both cases, the concordance was produced solely by publications. Again, it was decided to manually remove the concord- ance with the ‘Nanotechnology and materials’ S&T domain based on its semantic content;. ■for the cluster ‘Chemical Products’, a concord- ance between the E&I domain ‘Manufacture of chemicals and chemical products’ and the ‘Biotechnology’, ‘Chemistry and chemical en- gineering’ and ‘Nanotechnology and materi- als’ S&T domains could be found. In all cases, the concordance is produced by both patent MOLDOVA Concordance between E&I analysis and S&T analysis Economic clusterE&I domains (NACE sectors)S&T domains Food Processing and Manufacturing10 Manufacture of food products 11 Manufacture of beverages• Agrifood Leather, Apparel & Footwear13 Manufacture of textiles 14 Manufacture of wearing apparel 15 Manufacture of leather and related products Wood Products16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials• Chemistry and chemical engineering Chemical Products20 Manufacture of chemicals and chemical products• Biotechnology • Chemistry and chemical engineering • Nanotechnology and materials Communications Equipment and Services61 Telecommunications • ICT and computer science Computer Programming, Information Services and Financial Services62 Computer programming, consultancy and related activities 63 Information service activities 64 Financial service activities, except insurance and pension fundingTable 4.5. Combined EIST specialisation domains in Moldova 242 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials and publications and seems to be satisfactory even after a dedicated inspection of the tex- tual content of the respective S&T domains; ■for the cluster ‘Communications Equipment and Services’, a reasonable concordance be- tween the ‘Telecommunications’ E&I domain and the ‘ICT and computer science’ S&T do-main could be identified. The concordance is in this case produced by publications only. The most relevant keywords for these Moldovan S&T domains that match E&I domains can be found in the figures below; similar figures were also shown in Part 3 when characterising the S&T for the whole EaP region. Figure 4.13. Keyword cloud for the S&T domain Agrifood in Moldova Figure 4.15. Keyword cloud for the S&T domain Chemistry and chemical engineering in Moldova Figure 4.17. Keyword cloud for the S&T domain Nanotechnology and materials in Moldova Figure 4.14. Keyword cloud for the S&T domain Biotechnology in Moldova Figure 4.16. Keyword cloud for the S&T domain ICT and computer science in Moldova Smart Specialisation in the Eastern Partnership countries

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[]

behaviorally relevant sensory stim-uli within the aIC, which in turn modulates the efﬁcacy of associa-tive learning ( Hersman et al., 2020 ;Holland, 1980 ). Our tracing experiments identiﬁed direct inputs to aIC VIP+ INs from major sensory processing-related areas, such as the thal-amus, orbitofrontal cortical areas, amygdala and basal forebrain. Of note is the preferential connectivity of MD neurons to aIC VIP+ INs among thalamic nuclei. This ﬁnding supports a participationof these INs in sensory processing to enable behavioral adapta- tions, since the MD is considered the main SN hub within the thalamus ( Menon, 2015 ) and has been shown to modulate salience of fear-associated cues ( Zhou et al., 2021 ;Lee et al., 2012 ) as well as social-related behaviors ( Zhou et al., 2017 ;Fer- guson and Gao, 2018 ). Interestingly, MD afferents were also shown to target VIP+ INs in the mPFC ( Anastasiades et al., 2021 ), which raises the possibility for the existence of a conserved bottom-up functional modulation of sensory process- ing through MD connectivity onto VIP+ INs in these neocorticalregions. Gehrlach et al. (2020) recently found a connectivity pattern of aIC GABAergic neurons consistent with our ﬁndings for VIP+ INs,although with a few dissimilarities. In particular, we observed a Figure 6. aIC VIP+ INs are a heterogeneous functional population (A and E) Schematic of the social preference paradigm on days 1 and 2 of testing. (B and F) Percentage of active or inhibited aIC VIP+ CN during distinct stimulus presentations on days 1 and 2 of social preference testing. Non-coding neurons are referred as other.(C and G) Averaged responses from recorded aIC VIP+ mouse CN (orange) or object CN (turquoise) across all mouse and object interactions on day 1 (C) and d ay 2 (G).(D and H) Mean AUC of Zscored activity responses during interactions with the conspeciﬁc was higher for mouse CN than for object CN, while the latter were preferentially active during interactions with the novel object on day 1 (two-way ANOVA, main effect ensemble: p = 0.602; main effect visit: p = 0.0068 ; interaction effect: p = 0.0001; Bonferroni multiple comparisons test, mouse CN versus object CN: object visit, p = 0.0001, mouse visit, p = 0.0001; object visit ver sus mouse visit: mouse CN, p = 0.0001, object CN, p = 0.02) and day 2 (two-way ANOVA, main effect ensemble: p = 0.43; main effect visit: p

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authorities should avoid duplicating what the private sector would already doxii. The interaction with competition authorities is also critical for successxiii. For priority sectors, the EU should aim as far as possible to be competitively neutral and regulation should be designed to facilitate market entry. The evidence is overwhelming that competition stimulates productivity, investment and innovationxiv. At the same time, competition policy should continue to adapt to changes in the economy so that it does not become a barrier to Europe’s goals [see the chapter on competition policy] . For example, since innovation in the tech sector is rapid and requires large budgets, merger evaluations should assess how the proposed concentration will affect future innovation potential in critical innova - tion areas. Important Projects of Common Interest (IPCEIs) should be expanded to all forms of innovation that could effectively push Europe to the frontier in strategically important sectors and benefit from EU financing. There are also sectors, such as defence, where security and resilience criteria should receive increasing weight considering geopolitical changes for trade policy. A pragmatic, cautious and consistent approach should be applied according to the needs of different sectors [see Box 1] . 17THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1The third block is financing the main areas for action, which entail massive investment needs unseen for half a century in Europe . To digitalise and decarbonise the economy and increase the EU’s defence capacity, the total investment-to-GDP rate will have to rise by around 5 percentage points of EU GDP per year to levels last seen in the 1960s and 70s. For comparison, the additional investments provided by the Marshall Plan in 1948-51 amounted annually to around 1-2% of GDP in receiving countries. This report contains simulations from the European Commis - sion and the IMF which assess whether such a massive increase in investment is macroeconomically sustainable, and if so, how Europe can unlock investments of this size. The results suggest that the investment push can be carried out without the economy running into supply constraints, and that mobilising private financing will be critical in this respect. However, the private sector is unlikely to be able to finance the lion’s share of this investment05 without public sector support. Increasing productivity will be key to ease constraints on fiscal space for governments and enable this support. For example, a 2% increase in the level

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26.4 Manufacture of consumer electronics 26.5Manufacture of instruments and appliances for measuring, testing and navigation; watches and clocks X X X X 26.6Manufacture of irradiation, electromedical and electrotherapeutic equipment 26.7 Manufacture of optical instruments and photographic equipment 26.8 Manufacture of magnetic and optical media 27 Manufacture of electrical equipment 27.1Manufacture of electric motors, generators, transformers and electricity distribution and control apparatus X X X 27.2 Manufacture of batteries and accumulators 27.3 Manufacture of wiring and wiring devices X X X X X X 27.4 Manufacture of electric lighting equipment 27.5 Manufacture of domestic appliances 27.9 Manufacture of other electrical equipment 28 Manufacture of machinery and equipment n.e.c. 28.1 Manufacture of general-purpose machinery X X X X X 28.2 Manufacture of other general-purpose machinery X X X 28.3 Manufacture of agricultural and forestry machinery X X X X X X 28.4 Manufacture of metal forming machinery and machine tools 28.9 Manufacture of other special-purpose machinery X X X X 29 Manufacture of motor vehicles, trailers and semi-trailers 29.1 Manufacture of motor vehicles X X X 29.2Manufacture of bodies (coachwork) for motor vehicles; manufacture of trailers and semi-trailers 29.3 Manufacture of parts and accessories for motor vehicles X X X 30 Manufacture of other transport equipment 30.1 Building of ships and boats X 30.2 Manufacture of railway locomotives and rolling stock X X X 30.3 Manufacture of air and spacecraft and related machinery X X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation279 280 Annexes GEORGIA MOLDOVA UKRAINEEmploy- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover NACE Industry name Current Current CurrentEmerg- ingEmerg- ingEmerg- ingCurrent Current CurrentEmerg- ingEmerg- ingEmerg- ingCurrent Current CurrentEmerg- ingEmerg- ingEmerg- ing 34 52 28 61 64 40 31 29 15 50 47 21 55 40 35 83 57 34 30.4 Manufacture of military fighting vehicles 30.9 Manufacture of transport equipment n.e.c. 31 Manufacture of furniture X X 32 Other manufacturing 32.1 Manufacture of jewellery, bijouterie and related articles 32.2 Manufacture of musical instruments 32.3 Manufacture of sports goods 32.4 Manufacture of games and toys 32.5 Manufacture of medical and dental instruments and supplies 32.9 Manufacturing n.e.c. 33 Repair and installation of machinery and equipment 33.1 Repair of fabricated metal

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single central securities depository (CSD) for all securities trades. As for smaller clearing houses the benefits of consolidation may not be large, a practical pathway towards consolidation could start with consolidating the largest CCPs and CSDs, and then counting on their gravitational pull to attract smaller ones. The EU must also better channel households’ savings to productive investments. The easiest and most efficient way to do so is via long-term saving products (pensions). To increase the flow of funds into capital markets, the EU should encourage retail investors through the offer of second pillar pension schemes, replicating the successful examples of some EU Member States. To increase the financing capacity of the banking sector, the EU should aim to revive securitisation and complete the Banking Union . This report recommends that the Commission makes a proposal to adjust pruden - tial requirements for securitised assets. Capital charges must be reduced for certain simple, transparent and stan - dardised categories for which charges do not reflect actual risks. In parallel, the EU should review transparency and due diligence rules for securitised assets, which are relatively high compared to other asset classes and reduce their attractiveness. Setting up a dedicated securitisation platform, as other economies have done, would help to deepen the securitisation market, especially if backed by targeted public support (for example, well-designed public guar - antees for the first-loss tranche). The EU should also assess whether current prudential regulation, also in light of the possible upcoming implementation of Basel III, is adequate to have a strong and international competitive banking system in the EU. A minimal step towards completing the Banking Union would be to create a separate jurisdiction for European banks with substantial cross-border operations that would be “country blind” from the regulatory, supervisory and crisis management viewpoints. The EU budget should be reformed to increase its focus and efficiency, as well as being better leveraged to support private investment . The EU’s financial resources should be refocused on jointly agreed strategic projects and objectives, where the EU brings the most added value. Under the next EU budget, the report recommends establishing a “Competitiveness Pillar” to direct EU funding towards priority projects identified under the Competi - tiveness Coordination Framework [see the chapter on governance] . As part of this process, the EU should streamline 65THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 5its budget structure to

[ " ", "single", "central", "securities", "depository", "(", "CSD", ")", "for", "all", "securities", "trades", ".", "As", "for", "smaller", "clearing", "\n", "houses", "the", "benefits", "of", "consolidation", "may", "not", "be", "large", ",", "a", "practical", "pathway", "towards", "consolidation", "could", "start", "with", "\n", "consolidating", "the", "largest", "CCPs", "and", "CSDs", ",", "and", "then", "counting", "on", "their", "gravitational", "pull", "to", "attract", "smaller", "ones", ".", "The", "\n", "EU", "must", "also", "better", "channel", "households", "’", "savings", "to", "productive", "investments", ".", "The", "easiest", "and", "most", "efficient", "way", "to", "\n", "do", "so", "is", "via", "long", "-", "term", "saving", "products", "(", "pensions", ")", ".", "To", "increase", "the", "flow", "of", "funds", "into", "capital", "markets", ",", "the", "EU", "should", "\n", "encourage", "retail", "investors", "through", "the", "offer", "of", "second", "pillar", "pension", "schemes", ",", "replicating", "the", "successful", "examples", "\n", "of", "some", "EU", "Member", "States", ".", "\n", "To", "increase", "the", "financing", "capacity", "of", "the", "banking", "sector", ",", "the", "EU", "should", "aim", "to", "revive", "securitisation", "and", "\n", "complete", "the", "Banking", "Union", ".", "This", "report", "recommends", "that", "the", "Commission", "makes", "a", "proposal", "to", "adjust", "pruden", "-", "\n", "tial", "requirements", "for", "securitised", "assets", ".", "Capital", "charges", "must", "be", "reduced", "for", "certain", "simple", ",", "transparent", "and", "stan", "-", "\n", "dardised", "categories", "for", "which", "charges", "do", "not", "reflect", "actual", "risks", ".", "In", "parallel", ",", "the", "EU", "should", "review", "transparency", "and", "\n", "due", "diligence", "rules", "for", "securitised", "assets", ",", "which", "are", "relatively", "high", "compared", "to", "other", "asset", "classes", "and", "reduce", "their", "\n", "attractiveness", ".", "Setting", "up", "a", "dedicated", "securitisation", "platform", ",", "as", "other", "economies", "have", "done", ",", "would", "help", "to", "deepen", "\n", "the", "securitisation", "market", ",", "especially", "if", "backed", "by", "targeted", "public", "support", "(", "for", "example", ",", "well", "-", "designed", "public", "guar", "-", "\n", "antees", "for", "the", "first", "-", "loss", "tranche", ")", ".", "The", "EU", "should", "also", "assess", "whether", "current", "prudential", "regulation", ",", "also", "in", "light", "of", "the", "\n", "possible", "upcoming", "implementation", "of", "Basel", "III", ",", "is", "adequate", "to", "have", "a", "strong", "and", "international", "competitive", "banking", "\n", "system", "in", "the", "EU", ".", "A", "minimal", "step", "towards", "completing", "the", "Banking", "Union", "would", "be", "to", "create", "a", "separate", "jurisdiction", "\n", "for", "European", "banks", "with", "substantial", "cross", "-", "border", "operations", "that", "would", "be", "“", "country", "blind", "”", "from", "the", "regulatory", ",", "\n", "supervisory", "and", "crisis", "management", "viewpoints", ".", "\n", "The", "EU", "budget", "should", "be", "reformed", "to", "increase", "its", "focus", "and", "efficiency", ",", "as", "well", "as", "being", "better", "leveraged", "to", "\n", "support", "private", "investment", ".", "The", "EU", "’s", "financial", "resources", "should", "be", "refocused", "on", "jointly", "agreed", "strategic", "projects", "\n", "and", "objectives", ",", "where", "the", "EU", "brings", "the", "most", "added", "value", ".", "Under", "the", "next", "EU", "budget", ",", "the", "report", "recommends", "\n", "establishing", "a", "“", "Competitiveness", "Pillar", "”", "to", "direct", "EU", "funding", "towards", "priority", "projects", "identified", "under", "the", "Competi", "-", "\n", "tiveness", "Coordination", "Framework", "[", "see", "the", "chapter", "on", "governance", "]", ".", "As", "part", "of", "this", "process", ",", "the", "EU", "should", "streamline", "\n", "65THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "CHAPTER", "5its", "budget", "structure", "to" ]

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per labelled topic group (or ‘domain’) in the Eastern Partnership region. Nanotechnolo- gy and materials is the domain with the most re- cords (with a total of 35 742), followed by Health and wellbeing (29 643), Fundamental physics and mathematics (29 120), Mechanical engineering and heavy machinery (24 097) and ICT and com- puter science (16 529).Although very small in comparison to publications and patents, the number of EC projects is useful to gauge the activity of internationally-connected EaP research and innovation actors. In this con- text, there are a significant number of EC projects on Nanotechnology and materials (65 projects), Environmental sciences and industries (63), ITC and computer science (61), Health and wellbeing (56) and Energy (54). The highest concentration, however, is in the Governance domain (197), due to the nature of these projects. The number of publications in international jour- nals has been increasing considerably in recent years within the majority of domains and is ex- pected to continue to do so, as reflected by the CAGR (compound annual growth rate), except for Chemistry and chemical engineering and Optics and photonics, where the number of records has remained fairly consistent throughout the time range considered. Some of the domains present an especially high growth rate, such as Govern- ance, culture, education and the economy (24%), Transportation (20%) and Health (15%). Publications (critical mass | CAGR)PatentsEC projectsTotal Nanotechnology and materials 29 067 3.7% 6 641 65 35 773 Health and wellbeing 17 874 14.5% 11 726 56 29 656 Fundamental physics and mathematics 26 852 1.9% 2 255 18 29 125 Mechanical engineering and heavy machinery5 582 9.9% 18 510 8 24 100 ICT and computer science 13 111 13.8% 4 044 61 17 216 Biotechnology 10 340 5.7% 5 837 29 16 206 Governance, culture, education and the economy14 895 24.3% 434 197 15 526 Environmental sciences and industries 10 735 10.6% 3 272 63 14 070 Electric and electronic technologies 5 874 5.8% 7 009 17 12 900 Energy 5 496 8.6% 5 828 54 11 378 Chemistry and chemical engineering 8 132 1.4% 2 380 14 10 526 Optics and photonics 8 043 -0.3% 1 896 19 9 958 Agrifood 2 949 12.5% 5 907 21 8 877 Transportation 2 355 20.4% 1 984 25 4 364Table 3.4. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern Partnership region 154 Part 3 Analysis

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the re- spective country (either per number of companies, or per number of employees) and in the top 10 In- dustry Groups per specialisation of the respective country (either per number of companies, or per number of employees). In what follows, we pres- ent the results for each individual country concise- ly by means of a series of tables. Armenia In Armenia, the most relevant Industry Groups supporting the definition of innovation potential domains found via the Crunchbase analysis are in Table 2.49. All of the Industry Groups are seen to be espe- cially prominent per number of respective employ- ees (both at critical mass and specialisation level) and all of them, except Gaming and Travel and Tourism, are also featured in the top 10 Industry Groups per number of companies. Azerbaijan In Azerbaijan, the most relevant Industry Groups supporting the definition of innovation potential domains found via the Crunchbase analysis are in Table 2.50. All of the Industry Groups are seen to be especial- ly prominent per number of respective employees (both at critical mass and specialisation level) and all of them, except Natural Resources and Energy, are also featured in the top 10 Industry Groups per number of companies. The prominence of the Natural Resources and Energy Industry Groups is indeed dominated by the presence of the Kentech Group, the third firm per number of employees in- dexed by Crunchbase in Azerbaijan. Georgia In Georgia, the most relevant Industry Groups sup- porting the definition of innovation potential do- mains found via the Crunchbase analysis are in Table 2.51. All of the Industry Groups are seen to be especial- ly prominent per number of respective employ- ees (both at critical mass and specialisation level) and all of them, except Payments and Lending and Investments are also featured in the top 10 Industry Groups per number of companies. The Industry Group Payments and Lending and Invest- ments are considered to be so prominent because they are dominated by the presence of the Bank of Georgia and Liberty Capital, which are by far the largest companies based in Georgia indexed by Crunchbase. Moldova In Moldova, the most relevant Industry Groups supporting the definition of innovation potential domains found via the Crunchbase analysis are in Table 2.52. All of the Industry Groups are seen to be especial- ly prominent per number of respective employees (both at critical mass and specialisation level). Furthermore,

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diversify suppliers rather than re-shore or near-shore production on a significant scaleii. Neither China nor the EU has an incentive to accelerate this process: as the previous chapter demonstrated, China is reliant on the EU to absorb its excess capacity in clean technologies. The more immediate risk for Europe is that dependencies could be used to create an opportunity for coercion, making it harder for the EU to maintain a united stance and undermining its common policy objectives. A growing use of dependencies as a “geopolitical weapon” is in turn likely to increase uncertainty and have a detrimental effect on business investmentiii. Deteriorating geopolitical relations also create new needs for spending on defence and defence industrial capacity . Europe now faces conventional warfare on its Eastern border and hybrid warfare everywhere, including attacks on energy infrastructure and telecoms, interference in democratic processes and the weaponisation of migrationiv. At the same time, US strategic doctrine is shifting away from Europe and towards the Pacific Rim – for example in the format of AUKUS – driven by the perceived threat of China. As a result, a growing demand for defence capability is being met by a shrinking supply – a gap which Europe itself must fill. However, thanks to a prolonged period of peace in Europe and the US security umbrella, only ten Member States now spend more than or equal to 2% of GDP in line with NATO commitments, although defence expenditures are rising [see Figure 1] . The defence industry requires massive investments to catch up. As a point of reference, if all EU Member States who are NATO Members and who have not yet reached the 2% target were to do so in 2024, defence spending would rise by EUR 60 billion. Additional investments are also needed to restore lost capabilities owing to decades of underinvestment and to replenish depleted stocks, including those donated to support the defence of Ukraine against Russian aggression. In June 2024, the Commission estimated that additional defence investments of around EUR 500 billion are needed over the next decade. FIGURE 1 EU Member States’ defence expenditure % of GDP Source: SIPRI. Accessed 2024. 54THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 4Becoming more independent creates an “insurance cost” for Europe, but these costs can be mitigated by cooperation . Reducing dependencies across the key areas where Europe is exposed will require significant

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a trademark registered at the USPTO, for 11 NICE classes, as in Table 2.34. The number of trademark applications in these combined manufacturing industries is small for Armenia and Azerbaijan. Numbers also decreased between 2011 and 2014 for Armenia, Azerbaijan and Ukraine. 45 Millot, Valentine, ‘Trademarks as an Indicator of Product and Marketing Innovations’, OECD Science, Technology and Industry Working Papers 2009/06, 2009, https://dx- .doi.org/10.1787/224428874418. NACE 17, 18 and 19 – Textile, wearing, leather products NICE 24-25 NACE 20 – Wood and cork products NICE 20 NACE 21, 22 – Paper products, printing and publishing NICE 16 NACE 25 – Rubber and plastic products NICE 17 NACE 27 – Basic metal products NICE 6 NACE 30, 31, 32, 33 – Electrical and optical equipment NICE 9 NACE 34, 35 – Transport equipment NICE 12 NACE 60, 63 – Transport and storage NICE 39 NACE 67 – Financial intermediation NICE 36 NACE 72, 73 – Computer-related activity, research and development NICE 42Table 2.34. Partial concordance between broad NACE groups and NICE Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation99 Specialisations are identified using location quo- tients for two 4-year periods – 2011-2014 and 2015-2018 – where LQs are calculated relative to the unweighted average of the six Eastern Part- nership countries and should be above 1.5. Results are shown in Table 2.36. There are no trademark specialisations for Arme- nia. For Azerbaijan, there are recent trademark specialisations in Transport and Storage and Com- puter-related activity and research and develop-ment. For Georgia, there is a recent trademark specialisation in Financial intermediation. For Mol- dova, there are recent trademark specialisations in Wood and cork products and Rubber and plastic products. For Ukraine, there are recent trademark specialisations in Wood and cork products and Fi- nancial intermediation. 3.4. Industrial design applications Industrial design data for each Eastern Partnership country are available from the WIPO Global De-Armenia Azerbaijan Belarus Georgia Moldova Ukraine 2011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 2018 NACE 17, 18 and 19 – Textile, wearing, leather products6 11 11 4 54 47 207 194 388 459 9 078 8 275 NACE 20 – Wood and cork products4 1 3 1 4 7 61 55 129 140 2 215 2 039 NACE 21, 22 – Paper products, printing and publishing4 6 8 3 18 27 187 162 296 254 5 297 4 769

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[ { "end": 481, "label": "CITATION-SPAN", "start": 283 } ]

S&T specialisation domain in Azer- baijan. Fundamental physics and mathematics is the domain with the most records (with a total of 1 668), followed by Health and wellbeing (1 283), Chemistry and chemical engineering (850), Nano- technology and materials (714) and ICT and com- puter science (594). The first one accounts for more than one quarter of the total number of records (26%). It must be noted, however, that the number of patents obtained for Azerbaijan is rather small, jeopardising any analysis and interpretation. Publications account for the largest share of re- cords in most domains, ranging from 90% to 99% of the total records in most cases, as shown in Figure 3.27. The exceptions are Energy (79%), Biotechnology (58%) and Mechanical engineering and heavy machinery (48%), which have a high number of patents. In this last domain, in particu- lar, the number of patents is higher than the num- ber of publications. EC projects in Azerbaijan are very highly concen- trated in the domain of Governance, culture, ed-ucation and the economy, which may reflect the policy and cooperation interests of the European Commission and the country, rather than the en- dogenous capabilities or international propensity of the country’s S&T system. The growth rate of publications in recent years, in terms of the compound annual growth rate, is also shown. All domains have a positive growth rate. Azerbaijan’s publications are highly specialised in Chemistry and chemical engineering (with an SI of 1.7), Energy (1.6), ICT and computer sciences (1.4), Health and wellbeing (1.2) and Mechanical engineering and heavy machinery (1.2). Overall, Azeri publications present a lower normal- ised citation impact than the EaP average. Three domains escape this rule: Fundamental physics and mathematics (with an NCI of 1.2), Chemistry and chemical engineering (1.1) and Mechanical engineering and heavy machinery (1.1). Thus, in terms of scientific publications, Chemis- try and chemical engineering is a domain in which Azerbaijan’s S&T ecosystem simultaneously pre- sents a high critical mass, relative specialisation Publications (critical mass | CAGR)PatentsEC projectsTotal Fundamental physics and mathematics 1 663 8.7% 5 1 1 669 Health and wellbeing 1 131 15.5% 152 1 1 284 Chemistry and chemical engineering 806 5.7% 43 1 850 Nanotechnology and materials 682 18.9% 32 1 715 ICT and computer science 555 6.5% 37 2 594 Governance, culture, education and the economy547 21.2% 1 14 562 Environmental sciences and industries 497 7.7% 28 3 528

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how much a certain entity of interest (either a specific institution or a geographical aggregation) is spe- cialised in a given domain with respect to a given base- line. The specialisation is computed by normalising the share of output produced in the domain of interest by the entity over the share of baseline output (usually a larger geography). Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation29 The above dimensions of EIST potential are meas- ured for the EaP region as a whole and for each EaP country by means of data retrieved from well-established international sources (listed in the following subsection). This allows us to draw educated comparisons between the analysed countries so that, eventually, the current meth- odology yields region-wide and country-specific overviews. 2.4 Economic and innovation (E&I) potential and relative data sources For the case of economic & innovation potential, specialisation is measured by quantifying the rel- ative specialisation within the economic sectors of each EaP country with respect to the whole region. This is done by looking at data on the number of employees and turnover, as well as industrial sta- tistics on manufacturing. In parallel, export and innovation data is also considered, complementing the insight on the eco- nomic sectors, in particular: exports from goods and services, an enterprise survey, patent count and intensity, the number of start-ups and ven- ture capital-backed companies and the presence of formal cluster organisations supporting indus- trial collaboration and innovation. To succeed in mapping the economic and innova- tion potential of the EaP countries, the following data sources are employed: ■Orbis database, provided by Bureau van Dijk15. Orbis comprises statistics on the num- ber of employees and turnover in individual enterprises at NACE16 four-digit industry level; 15 https://www.bvdinfo.com/en-gb/our-products/data/in- ternational/orbis. 16 NACE is a four-digit classification providing the frame- work for collecting and presenting a large range of sta- tistical data according to economic activity in the fields of economic statistics (e.g. production, employment and national accounts) and in other statistical domains devel- oped within the European Statistical System (ESS). ■Industrial Statistics Database (INDSTAT4), offered by UNIDO17, for partial mapping of the manufacturing sector at NACE four-digit level; ■The UN’s Comtrade Database18 for exports of goods (up to five-digit export data accord- ing to the SITC product classification) and exports of services (according to the EBOPS 2002 classification); ■the World Bank Enterprise Survey19 for re- sults

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to innovation and technology adoption and could potentially hinder decarbonisation as well . Europe produces high quality talent in the fields of science, technology, engineering and maths (STEM) but their supply is limited. The EU turns out around 850 STEM graduates per million inhabitants per year compared to more than 1,100 in the US. Moreover, the EU’s talent pool is depleted by brain drain overseas owing to more and better employment opportunities elsewhere. Skills are also lacking to diffuse digital technologies faster through the economy and to enable workers to adapt to the changes these technologies will bring. Almost 60% of EU companies report that lack of skills is a major barrier to investment and a similar share report difficulties in recruiting ICT specialists. At the same time, European workers are generally unprepared to take advantage of the widespread digitalisation of work: around 42% of Europeans lack basic digital skills, including 37% of those in the workforce09. Decarbonisation will also require new skills sets and job profiles. The rates of job vacancies for clean tech manufacturing in the EU doubled between 2019 and 2023, with 25% of EU companies reporting labour shortages in the third quarter of 2023. Shortages of high-skilled workers are likely to become more acute over time. Projections to 2035 indicate that labour shortages will be most pronounced in high-skilled, non-manual occupations – i.e. those requiring high level of education – driven by replacement needs owing to retirements and the changing demands of the labour market. 08. See, among others, Bloom, Sadun and Van Reenen (2012) and Schivardi and Schmitz (2020) for evidence on cross-country variation in managerial practices, and their impact on aggregate productivity. 09. The EU Digital Decade set out to ensure 80% of working age Europeans have basic digital skills by 2030. 36THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2The undersupply of skills in Europe owes to declines in education and training systems that are failing to prepare the workforce for technological change . Educational attainment in the EU – as measured by the OECD’s PISA scores – is falling. The leading positions in recent PISA reports are dominated by Asian countries, while Europe has experienced an unprecedented decline. This downward trend concerns both average figures and top performance: in 2022, only 8% of EU students reached a high level of competence in maths and 7% in reading and science as

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The most relevant keywords for these Ukrainian S&T domains that match E&I domains can be found in the figures below; similar figures were also shown in Part 3 when characterising the S&T for the whole EaP region. 244 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials UKRAINE Concordance between E&I analysis and S&T analysis Economic clusterE&I domains (NACE sectors)S&T domains Food Processing and Manufacturing10 Manufacture of food products • Agrifood Wood Products16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Metalworking Technology25 Manufacture of fabricated metal products, except machinery and equipment• Nanotechnology and materials Information Technology and Analytical Instruments26 Manufacture of computer, electronic and optical products• Electric and electronic technologies • Energy • Fundamental physics and mathematics • ICT and computer science • Optics and photonics Production Technology and Heavy Machinery28 Manufacture of machinery and equipment n.e.c.• Agrifood • Energy • Fundamental physics and mathematics • Environmental sciences and industries • Mechanical engineering and heavy machinery Automotive29 Manufacture of motor vehicles, trailers and semi-trailers• Transportation Wholesale trade46 Wholesale trade, except of motor vehicles and motorcyclesTable 4.6. Combined EIST specialisation domains in Ukraine Figure 4.18. Keyword cloud for the S&T domain Agrifood in Ukraine Figure 4.19. Keyword cloud for the S&T domain Electric and electronic technologies in Ukraine Figure 4.20. Keyword cloud for the S&T domain Energy in Ukraine Figure 4.21. Keyword cloud for the S&T domain Environmental sciences and industries in Ukraine Figure 4.23. Keyword cloud for the S&T domain ICT and computer science in Ukraine Figure 4.25. Keyword cloud for the S&T domain Nanotechnology and materials in Ukraine Figure 4.27. Keyword cloud for the S&T domain Transportation in Ukraine Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation 245 Figure 4.22. Keyword cloud for the S&T domain Fundamental physics and mathematics in Ukraine Figure 4.24. Keyword cloud for the S&T domain Mechanical engineering and heavy machinery in Ukraine Figure 4.26. Keyword cloud for the S&T domain Optics and photonics in Ukraine 246 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials 4. Potential for EaP collabora- tion in combined EIST domains To complement the preceding country sections, and to provide evidence regarding common are- as of combined EIST specialisation throughout the EaP countries, Table 4.7 presents every economic cluster (E&I) and S&T domain pair

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4Strengthening industrial capacity for defence and space The European defence industry not only suffers from lower defence spending but also a lack of focus on technological development [see the chapter on defence] . The European defence sector is highly competitive globally, registering an annual turnover of EUR 135 billion in 2022 and strong export volumes. Some EU products and technologies are superior or at least equivalent in quality to those produced by the US, such as main battle tanks, conventional submarines, naval shipyard technology and transport aircraft. However, the EU defence industry is suffering from a capacity gap on two fronts. First, overall demand is lower: aggregate defence spending in the EU is about one-third as high as in the US. Second, EU spending is less focused on innovation. Defence is a highly technological industry characterised by disruptive innovation, meaning that massive R&D investments are required to maintain strategic parity. The US has prioritised R&D spending over all other military spending categories since 2014. In the 2023, it allocated EUR 130 billion (USD 140 billion) for Research, Development, Test and Evaluation, amounting to around 16% of total defence spending. This category also saw the largest relative percentage increase in the defence budget. In Europe, total funding for defence R&D was EUR 10.7 billion in 2022, amounting to just 4.5% of total spending. Complex next-generation defence systems in all strategic domains will require massive R&D investment that exceeds the capacity of single EU Member States. The European defence industry is also fragmented, limiting its scale and hindering operational effective - ness in the field . The EU defence industrial landscape is populated mainly by national players operating in relatively small domestic markets [see Figure 4] . Fragmentation creates two major challenges. First, it means that the industry lacks scale, which is essential in a capital-intensive sector with long investment cycles. As a result, if EU Member States were to ramp up defence spending significantly, a supply crisis could occur with Member States competing between each other on the constrained European defence equipment market. Second, fragmentation leads to serious issues related to a lack of standardisation and the interoperability of equipment, which have come to light during the EU’s support for Ukraine. For 155 mm artillery alone, EU Member States have provided ten different types of howitzers to Ukraine from their stocks, and some have even been delivered in different variants, creating

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of aquatic invertebrates, fit for human consumption 037 Fish, crustaceans, molluscs and other aquatic invertebrates, prepared or preserved, n.e.s. X X 041 Wheat (including spelt) and meslin, unmilled X X X 042 Rice 043 Barley, unmilled X X X 044 Maize (not including sweet corn), unmilled X X 045 Cereals, unmilled (other than wheat, rice, barley and maize) 046 Meal and flour of wheat and flour of meslin X 047 Other cereal meals and flours 048 Cereal preparations and preparations of flour or starch of fruits or vegetables X X X X 054Vegetables, fresh, chilled, frozen or simply preserved (including dried leguminous vegetables); roots, tubers and other edible vegetable products, n.e.s., fresh or dried X X X X 056 Vegetables, roots and tubers, prepared or preserved, n.e.s. X X X X X 057 Fruit and nuts (not including oil nuts), fresh or dried X X X X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation303 304 Annexes ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE SITC Goods name Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging 19 12 3 8 65 64 18 26 41 23 51 52 058 Fruit, preserved, and fruit preparations (excluding fruit juices) X X X X 059Fruit juices (including grape must) and vegetable juices, unfermented and not containing added spirit, whether or not containing added sugar or other sweetening matter X X 061 Sugars, molasses and honey X X X 062 Sugar confectionery X X X 071 Coffee and coffee substitutes X 072 Cocoa 073 Chocolate and other food preparations containing cocoa, n.e.s. X X X 074 Tea and maté 075 Spices X X 081 Feeding stuff for animals (not including unmilled cereals) X X X 091 Margarine and shortening X X 098 Edible products and preparations, n.e.s. X X X X 099 Miscellaneous edible products and preparations 1 Beverages and tobacco 111 Non-alcoholic beverages, n.e.s. X X X X 112 Alcoholic beverages X X X X X X 121 Tobacco, unmanufactured; tobacco refuse 122 Tobacco, manufactured (whether or not containing tobacco substitutes) X X 199 Adjustments (trade broken down at chapter nc level only) 2 Crude materials, inedible, except fuels 211 Hides and skins (except furskins), raw X 212Furskins, raw (including heads, tails, paws and other pieces or cuttings, suitable for furriers' use), other than hides and skins of group 211 222Oil-seeds and oleaginous fruits of

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[]

with a mild aver-sive footshock (US) to elicit a fear response ( Figures 2 A and S3B). We found that US presentations induced higher general Ca 2+activity in the imaged neurons and recruited a larger popu- lation of active VIP+ INs compared with the CS presentations (67.1% active during US; 21.6% active during CS; chi-square, p = 0.0001; Figures 2 D–2G and S4A). Upon retrieval of the fear memory a day later, in which the CS was not paired with the re- inforcing US (CS-R), the average activity and fraction of active VIP+ INs during the CS presentation (30.4%; chi-square, p =0.42) and during the US omission (US–) (22.8%; chi-square, p = 0.45) were comparable with those observed during the CS presentation in the fear acquisition phase ( Figures S4 A–S4E). We then analyzed the response of aIC VIP+ INs to the pseudo- random presentation of two intermingled neutral auditory stimuli of identical frequency (6 kHz), but different intensity (50 versus 80 dB) ( Figure 3 A). We observed that VIP+ INs showed a higher ac- tivity and responded in a larger proportion to the 80 dB tone (38.2% during 80 dB and 21.1% during 50 dB presentations; chi-square, p = 0.018; Figures 3 B–3D and S4A). Finally, we examined aIC VIP+ IN responses during a modiﬁed version of the three-chamber social preference test ( Nadler et al., 2004 ). For this test, each experimental mouse was subjected to the paradigm twice, with an inter-trial interval of 24 h and with theposition of the object and novel interactor mouse exchanged (Figure 3 E). As expected, mice spent more time interacting with the unfamiliar mouse compared with the object ( Figure 3 F). On day 1, the overall Ca 2+activity of VIP+ INs was signiﬁcantly higher during epochs of social compared with object visits (E) Sequential sections from an example mouse brain depicting representative monosynaptic inputs to aIC VIP+ INs. On the top, illustrations from the Allen Mouse Brain Atlas corresponding to the actual sections on the bottom. Areas with a particularly high content of ﬁrst-order presynaptic neurons are indicat ed with different shades of gray, darker areas indicate higher density. Scale bar, 1 mm.(F) Heatmap (left) and bar plot (right) representing fraction of inputs (%) over total input numbers for each identiﬁed brain area, with >1% of total in put, projecting to aIC VIP+ INs. Data are shown as mean

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[ { "end": 1325, "label": "CITATION-REFEERENCE", "start": 1306 } ]

the appropriate alpha-keto acid, which is then transaminated to form an amino acid.[90] Amino acids are made into proteins by being joined in a chain of peptide bonds. Each different protein has a unique sequence of amino acid residues: this is its primary structure. Just as the letters of the alphabet can be combined to form an almost endless variety of words, amino acids can be linked in varying sequences to form a huge variety of proteins. Proteins are made from amino acids that have been activated by attachment to a transfer RNA molecule through an ester bond. This aminoacyl-tRNA precursor is produced in an ATP-dependent reaction carried out by an aminoacyl tRNA synthetase.[91] This aminoacyl-tRNA is then a substrate for the ribosome, which joins the amino acid onto the elongating protein chain, using the sequence information in a messenger RNA.[92] Nucleotide synthesis and salvage Further information: Nucleotide salvage, Pyrimidine biosynthesis, and Purine § Metabolism Nucleotides are made from amino acids, carbon dioxide and formic acid in pathways that require large amounts of metabolic energy.[93] Consequently, most organisms have efficient systems to salvage preformed nucleotides.[93][94] Purines are synthesized as nucleosides (bases attached to ribose).[95] Both adenine and guanine are made from the precursor nucleoside inosine monophosphate, which is synthesized using atoms from the amino acids glycine, glutamine, and aspartic acid, as well as formate transferred from the coenzyme tetrahydrofolate. Pyrimidines, on the other hand, are synthesized from the base orotate, which is formed from glutamine and aspartate.[96] Xenobiotics and redox metabolism Further information: Xenobiotic metabolism, Drug metabolism, Alcohol metabolism, and Antioxidant All organisms are constantly exposed to compounds that they cannot use as foods and that would be harmful if they accumulated in cells, as they have no metabolic function. These potentially damaging compounds are called xenobiotics.[97] Xenobiotics such as synthetic drugs, natural poisons and antibiotics are detoxified by a set of xenobiotic-metabolizing enzymes. In humans, these include cytochrome P450 oxidases,[98] UDP-glucuronosyltransferases,[99] and glutathione S-transferases.[100] This system of enzymes acts in three stages to firstly oxidize the xenobiotic (phase I) and then conjugate water-soluble groups onto the molecule (phase II). The modified water-soluble xenobiotic can then be pumped out of cells and in multicellular organisms may be further metabolized before being excreted (phase III). In ecology, these reactions are particularly important in microbial biodegradation of pollutants and the bioremediation of contaminated land and oil spills.[101] Many of these microbial reactions

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[]

Draganiuc’54Health and wellbeing; Governance, culture, education and the economy; Biotechnology Institute of Cultural Heritage 33Governance, culture, education and the economy; Environmental sci. and industries; Health and wellbeing National Center of Public Health 30Health and wellbeing; Environmental sciences and industries; Biotechnology Institutia Medico-Sanitara Publica Institutul Oncologic20Health and wellbeing; Energy; Governance, culture, education and the economy Ministry of Health of the Republic of Moldova15Health and wellbeing; Governance, culture, education and the economy; Environmental sciences and industries National Agency for Research and Development15Governance, culture, education and the economy; Environmental sciences and industries; Agrifood Republican Clinical Hospital 15 Health and wellbeing; Fund. physics and mathematics Institute of Emergency Medicine 12 Health and wellbeing Institute of Neurology and Neurosurgery11Health and wellbeing; Governance, culture, education and the economy; ICT and computer science Center of International Projects 10Governance, culture, education and the economy; Environmental sciences and industries; ICT and computer scienceTable 3.25. Top public actors in Moldova by number of records, across all domains MOLDOVA Top actors classified as ‘Private company, for-profit’ NameNo of recordsMain S&T domains Scientific and Engineering Centre ‘Informinstrument’5Energy; Mechanical engineering and heavy machinery; Fundamental physics and mathematics Das Solutions S.R.L. 3Governance, culture, education and the economy; ICT and computer science; Energy Technical-Scientific Company Informbusiness Srl2 Energy; Mechanical engineering and heavy machinery Stattis LLC 2 Health and wellbeing Agentia de Logistica Age Quod Agis Srl1 Governance, culture, education and the economy Sunga LLC 1 Energy; Nanotechnology and materials; Optics and photonics Polivalent-95 Srl 1 Nanotechnology and materials Societatea Pentru Metodologia Sondajelor1 Governance, culture, education and the economy Labromed Laborator Srl 1 Health and wellbeing Bell Vetro Srl 1 Energy; Mechanical engineering and heavy machineryTable 3.26. Top private actors in Moldova by number of records, across all domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation205 Ukraine The National Academy of Sciences dominates sci- entific production in the country. Several compre- hensive universities present notable production in many domains, while a few specialised institutions have important roles in selected domains, such as Health and wellbeing, Agrifood or Transportation. On the one hand, the National Cancer Institute, the Kvant Research Institute (in the field of electronic systems), the Yuzhnoye State Design Office and the State Center for Nuclear and Radiation Safety stand out within a highly diversified field of public actors. On the other hand, the two most active private companies are the drug discovery provider Enam- ine LLC and the

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digital twin requires replication of the factory, its robots, processes and the overlay of an AI algorithm. To facilitate this cooperation, EU companies should be encouraged to participate in an “AI Vertical Priorities Plan”. The aim of this plan would be to accelerate AI development across the ten strategic sectors where EU business models will benefit most from rapid AI introduction (automotives, advanced manufacturing and robotics, energy, telecoms, agriculture, aerospace, defence, environmental forecasting, pharma and healthcare). Companies that participate in the plan would benefit from EU funding for model development and a specific set of exemptions regarding competition and AI experimentation. In particular, to overcome the EU’s lack of large data sets, model training should be fed with data freely contributed by multiple EU companies within a certain sector. It should be supported within open-source frameworks, safeguarded from antitrust enforcement by competition authorities. Experimentation should be encouraged via the opening up, EU-wide coordination and harmonisation of national “AI Sandbox regimes” to companies participating in the plan. These experimental “sand - boxes” would enable regular assessments of regulatory hindrances deriving from EU or national legislation and provide feedback from private companies and research centres to regulators. Given the dominance of US providers, the EU must find a middle way between promoting its domestic cloud industry and ensuring access to the technologies it needs . It is too late for the EU to try and develop systematic challengers to the major US cloud providers: the investment needs involved are too large and would divert resources away from sectors and companies where the EU’s innovative prospects are better. However, for reasons of European sovereignty, the EU should ensure that it has a competitive domestic industry that can meet the demand for “sover - eign cloud” solutions. To achieve this goal, the report recommends adopting EU-wide data security policies for collaboration between EU and non-EU cloud providers, allowing access to US hyperscalers’ latest cloud technolo - gies while preserving encryption, security and ring-fenced services for trusted EU providers. At the same time, the EU should legislate mandatory standards for public sector procurement, thereby levelling the playing field for EU companies against larger non-EU players. Outside of “sovereign” market segments, it is recommended to negotiate a low barrier “digital transatlantic marketplace”, guaranteeing supply chain security and trade opportunities for EU and US tech companies on fair and equal conditions. To make these opportunities equally attractive

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AA NiMH batteries after 8 months of storage. The AAA and AA batteries have similar voltage profiles. However, as expected, the current values are different. This results in different capacity values of 912 mAh for AAA and 2095 mAh for AA. Furthermore, the corresponding current curves for C and D batteries are presented in Figure 9c,d. In this case, the C battery reaches a capacity of 3249 mAh and the D battery a capacity of 7117 mAh. In the case of the 9V NiMH (Figure 9e), the battery shows a discharge capacity of 260 mAh after the storage period of 8 months.Batteries 2025 ,11, 30 13 of 20 Batteries 2025, 11, x FOR PEER REVIEW 14 of 21 3. Finally, the batteries are discharged again at a 0.2 C rate, and the duration of this discharge was measured. A battery is considered to pass the test if the discharge is longer than 4 h before reach- ing the cut-oﬀ voltage, i.e., if they have at least 80% of their initial capacity left after stor- age. Figure 9a,b show the discharge proﬁles of AAA and AA NiMH batteries after 8 months of storage. The AAA and AA batteries have similar voltage proﬁles. However, as expected, the current values are diﬀerent. This results in diﬀerent capacity values of 912 mAh for AAA and 2095 mAh for AA. Furthermore, the corresponding current curves for C and D batteries are presented in Figure 9c,d. In this case, the C battery reaches a capacity of 3249 mAh and the D battery a capacity of 7117 mAh. In the case of the 9V NiMH (Figure 9e), the battery shows a discharge capacity of 260 mAh after the storage period of 8 months. Figure 9. NiMH charge (capacity) recovery analysis according to IEC 61951-2 of (a) AAA Agfaphoto, (b) AA GP, (c) C Varta, (d) D GP, and (e) 9V Duracell batteries. Figure 9. NiMH charge (capacity) recovery analysis according to IEC 61951-2 of ( a) AAA Agfaphoto, (b) AA GP , ( c) C Varta, ( d) D GP , and ( e) 9V Duracell batteries. Furthermore, all portable NiMH batteries tested during the recovery experiment show a discharge duration longer than 4 h, and in terms of columbic efficiency, the highest observed value is 78% for the D size and the smallest value is 62% for the 9V battery. 6. Endurance of Portable NiMH Batteries

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Much ado about nothing? An empirical analysis of consumer behaviour in the presence of ‘dual food quality ’ Di Marcantonio Federicaa,*, Jesus Barreiro-Hurleb , Luisa Menapacec , Colen Liesbethd, Dessart François J.b, Ciaian Pavelb aDr., European Court of Auditors - 12, Rue Alcide De Gasperi, 1615, Luxembourg bDr., Joint Research Centre European Commission, Calle Inca Garcilaso 3 - Edificio EXPO, 41092 Seville, Spain cTechnical University of Munich, Alte Akademie 12, 85354 Freising, Germany dGeorg-August-Universit at Gottingen, Platz der Gottinger Sieben 5, D-37073 Gottingen, Germany ARTICLE INFO Keywords: Dual food quality Information asymmetry Choice experiment And lab experimentABSTRACT Marketing food products with slightly different compositions as identical across countries is a common practice in the food industry. While food companies argue that different versions reflect taste preferences, some Central and Eastern European consumers allege that multinational companies sell lower quality products using the same brand name and packaging as in Western European countries. The political attention gathered by this practice, exemplified by the dual food quality (DFQ) debate in the European Union (EU), has largely neglected how the presence of DFQ affects consumers ’ purchase decisions. This study aims to help fill this gap. Additionally, it examines the impact of a policy intervention consisting of a ‘made for’ claim and the role of the brand name on consumer choices. Through online discrete-choice experiments and laboratory tasting and rating experiments in six EU countries, no systematic support is found for either the industry ’s or the consumers ’ arguments. Results also indicate that a policy requiring consumers to be informed about the destination market of different versions would increase consumers ’ valuation of domestic products, while at the same time improving transparency and avoiding misleading consumers. 1.Introduction The practice of marketing food products with slightly different compositions as identical across countries seems to be common all around the world. For example, in the US, some products like Mountain Dew®, Hellmann ’s Mayonnaise or Heinz Ketchup are marketed with different ingredients to their UK counterpart, due to regulatory re- quirements, company economic advantages or local consumer prefer - ences (Focos, 2019 ). Similarly, within the European Union (EU), one can find Fanta Orange with different percentages of orange juice concen - trate, from 12 % in Italy to 4.5 % in Denmark, and Lay’s potato chips sold in the Czech Republic, Hungary and Lithuania have saturated fat levels about three times higher than

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capes, jackets, suits, blazers, trousers, shorts, shirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2)X 0.6% 844Women’s or girls’ coats, capes, jackets, suits, trousers, shorts, shirts, dresses and skirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2)X 1.6% 845Articles of apparel, of textile fabrics, whether or not knitted or crocheted, n.e.s.X 3.1% 848Articles of apparel and clothing accessories of other than textile fabrics; headgear of all materialsX 0.2% 851 Footwear X 1.4% 873 Meters and counters, n.e.s. X 0.7% 874Measuring, checking, analysing and controlling instruments and apparatus, n.e.s.X 0.4% 892 Printed matter X 0.2% 893 Articles, n.e.s., of plastics X 1.1% 894 Baby carriages, toys, games and sporting goods X 0.3% 899 Miscellaneous manufactured articles, n.e.s. X 0.2% 9Commodities and transactions not classified elsewhere in the SITC Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation75 Mapping of goods export specialisations – results for Ukraine Results of the export mapping for Ukraine are shown in Table 2.20. The 51 goods categories with current strength represent almost 70% of the total exports for 2012-2019. Most goods catego- ries are small, except for Maize (SITC 044), Fixed vegetable fats and oils (SITC 421) and Ingots and other primary forms, of iron and steel (SITC 672), which account for 7% or more of the total exports. Specialised exports in Food and live animals (SITC 0) account for almost 18% of the total exports; those in Crude materials, inedible, except fuels (SITC 2) for more than 12%; and those in Manu- factured goods classified chiefly by material (SITC 6) for more than 7%. The 52 goods categories with emerging strength represent 47% of the total exports. Export spe- cialisations are primarily increasing in Food and live animals (SITC 0) and Crude materials, inedible, except fuels (SITC 2). Ukraine has a diversified export specialisation. However, the share of export specialisations in low value-added activities remains high, whereas ex- port specialisations in manufactured products are declining. SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 51 69.6% 52 47.0% 0 Food and live animals 011 Meat of bovine animals, fresh, chilled or frozen X 0.2% 012Other meat and edible meat offal, fresh, chilled or frozen (except meat and meat offal unfit or unsuitable for human consumption) X 0.8% 022Milk and cream and

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MoldovaTemporal evolution of the domains Period over period change in the relative size of each domain, domain size and data source size independent (% change for 2015-2018, over previous period 2011-2014) Change in share of publicationsChange in share of patents Change, weighted average of publications and patents Agrifood -23.32% 5.93% -5.98% Biotechnology -15.94% 24.02% 2.94% Chemistry and chemical engineering -12.21% 8.22% -9.27% Electric and electronic technologies 22.13% -20.66% 3.80% Energy 47.64% 7.10% 33.86% Environmental sciences and industries -0.07%Insufficient data-0.07% Fundamental physics and mathematics -8.50%Insufficient data-8.50% Governance, culture, education and the economy39.81%Insufficient data39.81% Health and wellbeing 57.18% 13.52% 43.96% ICT and computer science -40.09% -19.17% -30.83% Mechanical engineering and heavy machinery78.89% 6.03% 21.81% Nanotechnology and materials -18.28% -28.96% -19.30% Optics and photonics -10.17%Insufficient data-10.17%Table 3.16. Temporal evolution of Moldova’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation191 Ukraine Table 3.17 and Figure 3.36 showcase the num- ber of records per S&T specialisation domain in Ukraine. Nanotechnology and materials is the do- main with the most records (with a total of 27 127), followed by Health and wellbeing (22 197), Mechanical engineering and heavy machinery (21 053), Fundamental physics and mathematics (19 555) and Biotechnology (14 463). Publications account for the majority of records in most domains, as shown in Figure 3.36, with the exceptions being Energy (46%), Electric and electronic technologies (45%), Agrifood (25%) and Mechanical engineering and heavy machinery (22%), all in which the number of patents is higher than the number of publications. Ukraine has a high concentration of EC projects in the domain of Governance, culture, education and the economy. However, EC projects are also concentrated in several other domains such as ICT and computer science, Health and wellbeing, Environmental sciences and industries and Nano-technology and materials and Energy, signalling the international propensity and relevance of the related Ukrainian S&T actors in these domains. The growth rate of publications in recent years, in terms of the compound annual growth rate, is also shown. All domains show a growing number of publications, except for Optics and photonics (-0.5%). Ukraine’s publications are highly specialised in Transportation (with an SI of 2.5), Electric and electronic technologies (2.3) and Biotechnology (2.0), as well as Energy (1.9), Mechanical engi- neering and heavy machinery (1.6) and others. In relation to the aggregate EaP, Ukraine presents a high normalised citation impact in most do- mains. Energy (1.5), Transportation

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and nuclear energy from higher and more volatile fossil fuel prices, preventing end users from capturing the full benefits of clean energy in their bills [see Figure 5] . In 2022 at the peak of the energy crisis, natural gas was the price- setter 63% of the time, despite making up only 20% share of the EU’s electricity mix. The use of long-term contract solutions – like Power Purchase Agreement (PPA) markets or Contracts for Difference (CfDs) – can help attenuate the link between the marginal price setter and the cost of energy for end users, but such solutions are underdeveloped in Europe, in turn limiting the benefits from accelerating the roll-out of renewables. In the absence of action, this decoupling problem will remain acute at least for the remainder of this decade. Even if renewable installation targets are met, it is not forecast to significantly reduce the share of hours during which fossil fuels set energy prices by 2030. FIGURE 5 Price-setting technology per Member State and their generation mix %, 2022 Source: European Commission (JRC), 2023 Note: Market share of natural gas by venue in % of reported notionals, excluding central counterparties and clearing members. The figure shows that the top-5 and top-10 EU counterparties (in terms of gross notionals) accounted for more than 50% and 60% respectively of reported notionals by EU entities on each of the two EU gas regulated markets. Data as of November 2022. OI: Open Interest. TV: Trading Venue. OTC: Over-the-counter. Sources: Trade repositories (TRs), Bank of England, ESMA.Note: Absolute value of net positions in EUR billion for the top five long and short non-financial corporate counterparties and positions in % of average daily trading volume, in % rhs. The high concentration of positions indicates that if several firms with similar directional positions were to reduce their exposures, they could amplify market moves. Sources: EMIR, ESMA. 44THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3FIGURE 6 Electricity wholesale and retail prices across Member States for industry EUR/MWh, 2023 Source: European Commission, 2024. Based on Eurostat, S&P Global, and ENTSO-E, 2024. A lengthy and uncertain permitting process for new power supply and grids is a major obstacle to faster installation of new capacity . Investments in both power generation and grids require several years between feasi - bility studies and project completion. However, there is a large variation in permitting times between

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there are some industries where Europe’s cost disadvantage is too large to be a serious competitor. Even if the EU has lost ground owing to foreign subsidies, it makes economic sense to import necessary technology and allow foreign taxpayers to bear the costs, while diver - sifying suppliers to the extent possible to limit dependencies. The second broad case is industries where the EU is concerned about where production takes place – to protect jobs from unfair competition – but is agnostic about where the underlying technology originates from. In this case, an effective policy mix would be to encourage inward FDI while deploying trade measures to offset the cost advantage gained by foreign subsidies. With the combination of recent tariff increases and FDI announcements in some Member States, this approach is currently being de facto applied in the automotive sector. The third case is industries where the EU has a strategic interest in ensuring that European companies retain relevant know-how and manufacturing capacity, allowing production to be ramped up in the event of geopolitical tensions. Here the EU should aim to increase the long-term “bankability” of new investments in Europe, for instance by applying local-content requirements, and to ensure a minimum level of technological sovereignty. The latter can be achieved by requiring foreign companies that want to produce in Europe to enter into joint ventures with local companies. Security considerations may lead to changes over time in the classification of industries of strategic interest. The fourth case is “infant industries” where the EU has an innovative edge and sees high future growth potential. In this case, there is a well-established playbook of applying a full range of trade-dis - torting measures until the industry reaches sufficient scale and protections can be withdrawn. 41THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3Executing this strategy will require a joint decarbonisation and competitiveness plan where all policies are aligned behind the EU’s objectives . Priority areas to be addressed include, first, lowering energy costs for end users by transferring the benefits of the decarbonisation and accelerating the decarbonisation of the energy sector in a cost-efficient way, leveraging all available solutions. Second, capturing the industrial opportunities presented by the green transition, ranging from remaining at the forefront of clean tech innovation to manufacturing clean tech at scale to leveraging the opportunities from circularity. Third, levelling the playing field in sectors more

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6% to those in the EU. For quantum computing, EU companies attract only 5% of global private funding compared with a 50% share attracted by US companies. Regulatory barriers to scaling up are particularly onerous in the tech sector, especially for young companies [see the chapters on innovation, and digitalisation and advanced technologies] . Regulatory barriers constrain growth in several ways. First, complex and costly procedures across fragmented national systems discourage inventors from filing Intellectual Property Rights (IPRs), hindering young companies from leveraging the Single Market. Second, the EU’s regulatory stance towards tech companies hampers innovation: the EU now has around 100 tech-focused lawsxi and over 270 regulators active in digital networks across all Member States. Many EU laws take a precau - tionary approach, dictating specific business practices ex ante to avert potential risks ex post. For example, the AI Act imposes additional regulatory requirements on general purpose AI models that exceed a pre-defined threshold of computational power – a threshold which some state-of-the-art models already exceed. Third, digital compa - nies are deterred from doing business across the EU via subsidiaries, as they face heterogeneous requirements, a proliferation of regulatory agencies and “gold plating”04 of EU legislation by national authorities. Fourth, limitations on data storing and processing create high compliance costs and hinder the creation of large, integrated data sets for training AI models. This fragmentation puts EU companies at a disadvantage relative to the US, which relies on the private sector to build vast data sets, and China, which can leverage its central institutions for data aggregation. This problem is compounded by EU competition enforcement possibly inhibiting intra-industry cooperation. Finally, multiple different national rules in public procurement generate high ongoing costs for cloud providers. The net effect of this burden of regulation is that only larger companies – which are often non-EU based – have the financial capacity and incentive to bear the costs of complying. Young innovative tech companies may choose not to operate in the EU at all. The lack of a true Single Market also prevents enough companies in the wider economy from reaching sufficient size to accelerate adoption of advanced technologies . There are many barriers that lead to compa - nies in Europe to “stay small” and neglect the opportunities of the Single Market. These include the high cost of adhering to heterogenous national regulations, the high cost of tax compliance, and

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excerpts of web text. 5,000 addi- tional paired samples are kept aside for validation and test datasets. Lastly, we ﬁlter out excerpts with fewer than 192 WordPiece tokens (Wu et al., 2https://github.com/openai/ gpt-2-output-dataset2016) (excerpts might be quite short if the model produces an end-of-text token early on). See Ap- pendix 1 for ﬁnal dataset sizes. A crucial question when generating text with a language model is whether or not to provide a priming sequence which the language model should continue. Unconditioned samples, where no priming text is provided, in conjunction with top-ksampling, lead to pathological behavior for discriminators as the ﬁrst token of the generated text will always be one of kpossible options. On the other hand, if long sequences of human text are used as priming, the space of possible gener- ated sequences is larger, but the detection problem shifts from one of “how human-like is the gener- ated text?” to “how well does the generated text follow the priming sequence?”. Since in this study we are interested in the former simpler question, we create two datasets, one with no priming, and one with the minimum amount of priming possible: a single token of web text. This means that for every excerpt of web text in the training set, there is an excerpt of machine- generated text that starts with the same token. We ﬁnd that even with limited priming, the ability of automatic detectors can be strongly impacted. To study the effect of excerpt length, we con- struct variations of the above datasets by truncat- ing all excerpts to ten possible lengths ranging from 2 to 192 WordPiece tokens (Wu et al., 2016). In total, we obtain sixty dataset variations: one per sampling method, truncation length, and choice of priming or no priming. 5 Automatic Detection Method The primary discriminator we employ is a ﬁne- tuned BERT classiﬁer (Devlin et al., 2019). We ﬁne-tune one instance of BERT per dataset vari- ation described above. For the longest sequence length,n=192, we compare BERT’s performance with several simple baselines that have been pro- posed in other work. Fine-tuned BERT We ﬁne-tune BERT-L ARGE (cased) on the task of labeling a sentence as human- or machine- generated. The models are trained for 15 epochs, with checkpoints saved ev- ery 1000 steps, and a batch size of 256. All results are reported on the test set using the checkpoint for which validation

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can belong to two or more S&T domains, indicating multidisciplinarity, hybridisations and applications. A strong co-occurrence between the S&T domains of Energy and electric technologies can be observed, as well as between ICT and Transportation, and Mechanical engineering and Transportation. Agrifood, Biotechnology, Chemis- try, Environmental sciences and Health are also strongly related to varying degrees. The distinct distribution of data sources in the S&T domains is suggestive of their differing na- ture and orientation, instead based on scientific publications (so science-oriented), patenting (so Table I. The scientific and technological specialisation domains in the Eastern Partnership identified via topic modelling EaP S&T specialisation domains (in alphabetical order) Agrifood Biotechnology Energy Optics and photonicsHealth and wellbeingChemistry and chemical engineeringElectric and electronic technologies Environmental sciences and industries Governance, culture, education and the economy ICT and computer science TransportationNanotechnology and materialsFundamental physics and mathematics Mechanical engineering and heavy machinery technology-oriented) or where these two data sources are, to some degree, balanced. As such, S&T domains can be qualitatively categorised as7 science-oriented S&T domains, balanced S&T domains and technology-oriented S&T do- mains, as presented in the Table III. 7 Noting that the total volume of analysed scientific publi- cations is higher than that of patents. 14 Overview of economic, innovation, scientific and technological specialisations Publications (critical mass | CAGR)PatentsEC projectsTotal Nanotechnology and materials 29 067 3.7% 6 641 65 35 773 Health and wellbeing 17 874 14.5% 11 726 56 29 656 Fundamental physics and mathematics 26 852 1.9% 2 255 18 29 125 Mechanical engineering and heavy machinery5 582 9.9% 18 510 8 24 100 ICT and computer science 13 111 13.8% 4 044 61 17 216 Biotechnology 10 340 5.7% 5 837 29 16 206 Governance, culture, education and the economy14 895 24.3% 434 197 15 526 Environmental sciences and industries 10 735 10.6% 3 272 63 14 070 Electric and electronic technologies 5 874 5.8% 7 009 17 12 900 Energy 5 496 8.6% 5 828 54 11 378 Chemistry and chemical engineering 8 132 1.4% 2 380 14 10 526 Optics and photonics 8 043 -0.3% 1 896 19 9 958 Agrifood 2 949 12.5% 5 907 21 8 877 Transportation 2 355 20.4% 1 984 25 4 364Table II. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern Partnership region Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation15 Summary of the strengths

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to succeed, it will therefore need to engineer a coherent strategy for all aspects of decarbonisation, from energy to industry. FIGURE 6 Gas and retail price gap for industry Source: European Commission, 2024. Based on Eurostat (EU), EIA (US) and CEIC (China), 2024. Third, Europe must react to a world of less stable geopolitics, where dependencies are becoming vulner - abilities and it can no longer rely on others for its security . Decades of globalisation have produced a high level of “strategic interdependence” between major economies, raising the costs of any rapid disentanglementvi. For example, while the EU largely depends on China for critical minerals, China depends on the EU to absorb its industrial overcapacity. But this global equilibrium is shifting: all major economies are actively seeking to reduce their dependency and increase their scope for independent action. The US is investing in domestic capacity for semiconductor and clean tech production, while aiming to re-route critical supply chains through its allies. China is striving for technological autarchy and vertical supply chain integration, from mining of raw materials to processing and from manufacturing to shipping. While there is little evidence yet that these measures are leading to de-global - isationvii, trade policy interventions are on the rise [see Figure 7] . Given its high trade openness, Europe is especially exposed should these trends accelerate. The EU must also respond to a radically changed security environment at its borders. Aggregate EU defence spending is currently one third of US levels and the European defence industry is suffering from decades of underinvestment and depleted stocks. To achieve genuine strategic independence and increase its global geopolitical influence, Europe needs a plan to manage these dependencies and strengthen defence investment. 15THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1FIGURE 7 Trade policy interventions Note: Measures include tariffs, export-related measures, subsidies, contingent trade-protective measures, and trade-related investment measures. Source: Global Trade Alert, 2024. EU countries are already responding to this new environment with more assertive policies, but they are doing so in a fragmented way that undermines collective effectiveness . The use of industrial policy interven - tions is on the rise across advanced economiesviii. But the effectiveness of these policies in Europe is hindered by three main coordination problems. First, there is a lack of coordination between Member States. Uncoordinated national policies often lead to considerable duplication, incompatible standards and failure to

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this paper that gen- uine progress in our ﬁeld — climbing the right hill, not just the hill on whose slope we currently sit — depends on maintaining clarity around big picture notions such as meaning andunderstanding in task design and reporting of experimental results. After brieﬂy reviewing the ways in which large LMs are spoken about and summarizing the re- cent ﬂowering of “BERTology” papers ( x2), we offer a working deﬁnition for “meaning” ( x3) and a series of thought experiments illustrating the im- possibility of learning meaning when it is not in the training signal ( x4,5). We then consider the human language acquisition literature for insight into what information humans use to bootstrap lan- guage learning (x6) and the distributional seman- tics literature to discuss what is required to ground distributional models ( x7).x8 presents reﬂections on how we look at progress and direct research effort in our ﬁeld, and in x9, we address possible counterarguments to our main thesis. 2 Large LMs: Hype and analysis Publications talking about the application of large LMs to meaning-sensitive tasks tend to describe the models with terminology that, if interpreted at face value, is misleading. Here is a selection from academically-oriented pieces (emphasis added): (1) In order to train a model that understands sentence relationships, we pre-train for a binarized next sentence prediction task. (Devlin et al., 2019) (2) Using BERT, a pretraining language model, has been successful for single-turn machine comprehension . . . (Ohsugi et al., 2019) (3) The surprisingly strong ability of these models to re- call factual knowledge without any ﬁne-tuning demon-5186strates their potential as unsupervised open-domain QA systems. (Petroni et al., 2019) If the highlighted terms are meant to describe human-analogous understanding, comprehension, or recall of factual knowledge, then these are gross overclaims. If, instead, they are intended as techni- cal terms, they should be explicitly deﬁned. One important consequence of imprudent use of terminology in our academic discourse is that it feeds AI hype in the popular press. As NLP gains public exposure and is more widely used in applied contexts, it is increasingly important that the actual capabilities of our systems be accurately represented. In some cases, NLP experts speaking with the media are being appropriately careful, as in these two quotes in the New York Times :1 (4) These systems are still a really long way from truly understanding running prose.

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0 *NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable V. Selected S&T specialisation domains in Azerbaijan 18 Overview of economic, innovation, scientific and technological specialisations Georgia – Summary of the strengths of the S&T specialisations Georgia’s most highlighted S&T domains are the following: ■Environmental sciences and industries scores highly on all S&T indicators – on crit- ical mass, specialisation and excellence – for publications, patents and projects. It is a very clear specialisation domain for Georgia, with particular relevance in Geology and Geotech- nical engineering, as well as Environmental engineering and Chemistry; ■Agrifood presents a high specialisation in patents and publications, as well as a critical mass in patents and a relevant number of EU-funded R&I projects, with science oriented towards Horticulture, Genetics and Plant sci- ence; ■Health and wellbeing presents a high crit- ical mass, specialisation and citation impact in publications, while no positive indicator emerges in relation to patents. It co-occurs frequently with the domain of Agrifood. Be- yond General medicine, research is related in particular to Infectious diseases and Immu- nology; and ■ICT and computer science presents a spe- cialisation in patents as well as highly cited publications and a relevant number of EC pro- jects. GEORGIA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCI*EC projects*Total Agrifood 4 Biotechnology 0 Chemistry and chemical engineering2 Electric and electronic technologies0 Environmental sciences and industries6 Fundamental physics and mathematics3 Governance, culture, education and the economy4 Health and wellbeing 3 ICT and computer science 3 Mechanical engineering and heavy machinery2 Nanotechnology and materials 2 Optics and photonics 1 *NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable VI. Selected S&T specialisation domains in Georgia Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation19 Moldova – Summary of the strengths of the S&T specialisations Moldova presents a rather diversified S&T pano- rama. Its most highlighted S&T specialisation do- mains are the following: ■Health and wellbeing presents a nota- ble critical mass, specialisation and citation impact in publications, as well as a relevant number of patents and EC projects. The ‘Nico- lae Testemitanu’ State University of Medicine and Pharmacy is the leading academic institu- tion in the domain, accounting for more than one third of the country’s total records pro- duced on this topic; ■Nanotechnology and materials presents a notable critical mass, specialisation and cita- tion impact in publications, as

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[]

electron microscopy and molecular dynamics simulations. These techniques have allowed the discovery and detailed analysis of the many molecules and metabolic pathways in cells.[citation needed] See also Anthropogenic metabolism – Material and energy turnover of human society Antimetabolite – Chemical that inhibits the use of a metabolite Calorimetry – Determining heat transfer in a system by measuring its other properties Isothermal microcalorimetry – Measuring versus elapsed time the net rate of heat flow Inborn errors of metabolism – Class of genetic diseases Iron–sulfur world hypothesis – Hypothetical scenario for the origin of life, a "metabolism first" theory of the origin of life Metabolic disorder – Any disease hindering the body's ability to process and distribute nutrients Microphysiometry Primary nutritional groups – Group of organisms Proto-metabolism – Chemical reactions which turn into modern metabolism Respirometry – Estimation of metabolic rates by measuring heat production Stream metabolism Sulfur metabolism – Set of chemical reactions involving sulfur in living organisms Thermic effect of food – Energy expenditure for processing food Urban metabolism – Model of the flows of materials and energy in cities Water metabolism – Aspect of homeostasis concerning control of the amount of water in an organism Overflow metabolism – Cellular phenomena Oncometabolism Reactome – Database of biological pathways KEGG – Collection of bioinformatics databases References Friedrich, CG (1997). Physiology and Genetics of Sulfur-oxidizing Bacteria. Advances in Microbial Physiology. Vol. 39. pp. 235–89. doi:10.1016/S0065-2911(08)60018-1. ISBN 978-0-12-027739-1. PMID 9328649. Pace NR (January 2001). "The universal nature of biochemistry". Proceedings of the National Academy of Sciences of the United States of America. 98 (3): 805–8. Bibcode:2001PNAS...98..805P. doi:10.1073/pnas.98.3.805. PMC 33372. PMID 11158550. Smith E, Morowitz HJ (September 2004). "Universality in intermediary metabolism". Proceedings of the National Academy of Sciences of the United States of America. 101 (36): 13168–73. Bibcode:2004PNAS..10113168S. doi:10.1073/pnas.0404922101. PMC 516543. PMID 15340153. Ebenhöh O, Heinrich R (January 2001). "Evolutionary optimization of metabolic pathways. Theoretical reconstruction of the stoichiometry of ATP and NADH producing systems". Bulletin of Mathematical Biology. 63 (1): 21–55. doi:10.1006/bulm.2000.0197. PMID 11146883. S2CID 44260374. Meléndez-Hevia E, Waddell TG, Cascante M (September 1996). "The puzzle of the Krebs citric acid cycle: assembling the pieces of chemically feasible reactions, and opportunism in the design of metabolic pathways during evolution". Journal of Molecular Evolution. 43 (3): 293–303. Bibcode:1996JMolE..43..293M. doi:10.1007/BF02338838. PMID 8703096. S2CID 19107073. Smith RL, Soeters MR, Wüst RC, Houtkooper RH (August 2018). "Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease". Endocrine Reviews. 39 (4): 489–517. doi:10.1210/er.2017-00211. PMC 6093334. PMID 29697773.

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Bakhtin et al. (2019) frame human-text detection as a rank- ing task and evaluate their models’ cross-domain and cross-model generalization, ﬁnding signiﬁ- cant loss in quality when training on one do- main and evaluating on another. Schuster et al. (2019) argue that the language distributional fea- tures implicitly or explicitly employed by these detectors are insufﬁcient; instead, one should look to explicit fact-veriﬁcation models. Finally, dis- criminators for whether text is machine-generated are a promising research direction in adversarial training (Lin et al., 2017; Li et al., 2017) and in automatic evaluation of generative model quality (Novikova et al., 2017; Kannan and Vinyals, 2017; Lowe et al., 2017). Natural Language Understanding Automatic detection of machine-generated text beneﬁts from a semantic understanding of the text. Contradic-tions, falsehoods, and topic drift can all indicate that an excerpt was machine-generated. Encoder- only Transformer models such as BERT (Devlin et al., 2019) have been shown to do very well at tasks requiring this understanding. While we ﬁne- tune BERT for the task of classifying whether text was machine-generated, others have used the con- textual word embeddings from a pre-trained BERT model without ﬁne-tuning to compute a quality score for generated text (Zhang et al., 2020). It is worth noting that recent work has raised ques- tions as to whether BERT truly builds a semantic understanding to make its predictions, or whether it merely takes advantage of spurious statistical differences between the text of different classes (Niven and Kao, 2019). 3 Task Deﬁnition We frame the detection problem as a binary clas- siﬁcation task: given an excerpt of text, label it as either human-written or machine-generated. In particular, we are interested in how variables such as excerpt length and decoding strategy impact performance on this classiﬁcation task. We thus create several datasets. Each is approximately balanced between positive examples of machine- generated text and negative examples of human- written text. While they all share the same human- written examples, each dataset contains a different set of machine-generated examples sampled using one particular decoding strategy. We also build ad- ditional datasets by truncating all of the examples to a particular sequence length, By training a separate classiﬁer on each dataset, we are able to answer questions about which de- coding strategy results in text that is the easiest to automatically disambiguate from human-written text. We are also able to answer questions about how the length of

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“first-movers” in other sub-sectors of sustainable transport. For example, the EU holds 60% of global high-value patents and tops global rankings of the most innovative companies for low-carbon fuels, which are essential for the decarbonisation of aviation and maritime transport in the medium term and also, potentially, for heavy-duty vehicles. FIGURE 2 The EU’s position in complex (digital and green) technologies Notes: The results are based on an analysis of patent data to understand the complexity and potential for specialisation in different technology areas. On the y-axis, technologies are ranked according to how advanced or complex they are, with scores ranging between 0 (less complex) and 100 (more complex). The x-axis (showing the relatedness density) represents how easily a country can build comparative advantage in a particular technology, depending on how closely related it is to other technologies the country is already strong in. The size of the bubbles shows how much each country has already specialised in a technology, using a measure of “revealed comparative advantage”(RCA), which reflects their competitive strenght in that field. Source: European Commission, DG RTD. However, it is not guaranteed that EU demand for clean tech will be met by EU supply given increasing Chinese capacity and scale . The EU aims to achieve a minimum of 42.5% of its energy consumption from renewable sources by 2030, which will require it to nearly triple its installed capacity for solar PV and more than double its wind power capacity. In addition, the EU has effectively abolished the internal combustion engine from 2035, when all new passenger cars and light duty vehicles registered in Europe must have zero tailpipe emissions. Based on current policies, Chinese technology may represent the lowest-cost route to achieving some of these targets. Owing to a fast pace of innovation, low manufacturing costs and state subsidies four times higher than in other major econo - miesiv, the country is now dominating global exports of clean technologies. Significant overcapacity is expected: by 2030 at the latest, China’s annual manufacturing capacity for solar photovoltaic (PV) is expected to be double the level of global demand, and for battery cells it is expected to at least cover the level of global demand. Production of EVs is expanding at a similar pace. The EU is already seeing a sharp deterioration in its trade balance with China, reflecting in particular imports of EVs, batteries and solar PV products

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[]

to the randomly assigned object for paired closed-loop inhibition. The chamber and enclosures were cleanedbefore and between subjects with 70% ethanol. Open ﬁeld place preference Similar to the real-time object place preference test, the experiment was divided into two 5 min bins, and the laser was allowed to turnon only during the last 5 min of the test in one randomly assigned quadrant (25 325cm) of the open ﬁeld arena (50 350330cm). Measurements during this test phase included time in each quadrant, general locomotion and locomotion in the inhibition-paired quadrant. The arena was cleaned before and between subjects with 70% ethanol. Fear conditioning and retrievalThe same behavioral protocol as described above for deep-brain Ca 2+imaging was used with minor modiﬁcations. Light stimulation was delivered to ArchT- or GFP-injected mice during fear conditioning on day 1, for 4.5 s during the entire presentation of the 2 s US + stimulus (starting 500 msec before US onset) and extending for additional 2 s. On day 2, during the retrieval phase, mice were alsotethered to the optic ﬁbers, but underwent testing without optogenetic modulation. Mice were tracked using contour tracking and center of mass (ANY-maze). Freezing episodes were processed ofﬂine by trained experimenters using frame-by-frame analysis. Histology Quantiﬁcation of VIP + INs Immunocytochemistry experiments were carried out according to previously published procedures with minor modiﬁcations ( Sree- pathi, 2012 ). Free-ﬂoating sections were incubated in primary antibody solution [rabbit antibody against VIP (cat. no. 20077, Immu- nostar), diluted 1:4,000] containing 2% normal goat serum (NGS), 0.3% Triton X-100 (TX) in Tris-buffered saline (TBS-T; pH 7.4) for two days at 6 /C14C and then in secondary antibody [biotinylated goat anti-rabbit (cat. no. BA-1000, Vector laboratories), diluted 1:500] overnight. After extensive washing, sections were incubated in an ABC complex solution (1:100, Vectastain ABC kit; Vector Labo-ratories) made up in TBS, followed by diaminobenzidine (DAB) as a chromogen (0.5 mg/mL in TB) and 0.003% H 2O2, as the electron donor, for 5 min. The sections were mounted onto gelatin-coated slides, dehydrated in an ascending ethanol series followed by an incubation in butylacetate and coverslipped with Eukitt (Christine Gro ¨pl, Tulln, Austria). Sections immunolabeled for VIP were used to assess the number of VIP + INs (n = 4 mice) by an experienced experimenter using the Neurolucida software (MBF Bioscience). Borders of the IC were outlined with the help of consecutive Nissl stained sections and according to

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industries. At the same time, refocusing implies that the EU should be more rigorous in applying the subsidiarity prin - ciple and exercise more “self-restraint” . The Commission’s legislative activity has been growing excessively, also due to passive scrutiny of the subsidiarity principle by national parliaments, which sets the boundaries of the Commission’s right of initiative. While national parliaments have the power to scrutinise whether EU legislation complies with the subsidiarity principle through reasoned opinions – and potentially trigger the so-called “yellow card procedure” – many do not actively exercise this right. For example, of the 39 national parliaments or chambers in the EU, only nine (from seven Member States) issued reasoned opinions in the context of scrutinising subsidiarity in 2023. An EU-wide inquiry should be launched to analyse the reasons behind national parliaments’ passive exercise of their scrutiny of the subsidiarity principle. Building on its conclusions, initiatives should be taken to reinforce the administrative capacity and role of national parliaments and Member States in their control over EU legislative activity. Moreover, EU institutions should apply a “self-restraint” principle in policymaking, both by better filtering future initiatives and by streamlining the existing acquis, building on the measures described in “Simplifying rules” below. ACCELERATING THE WORK OF THE EU Council votes subject to qualified majority voting (QMV) should be extended to more areas, and if action at the EU level is blocked, a differentiated approach to integration should be pursued . So far, many efforts to deepen European integration between Member States have been hindered by unanimity voting in the Council of the European Union. All possibilities offered by the EU Treaties should therefore be exploited to extend QMV. The so-called “passerelle” clause should be leveraged to generalise voting by qualified majority in all policy areas in the Council. This step would require an upfront agreement, subject to unanimity at the level of the European Council, and would have a positive impact on the pace at which key legislative initiatives are adopted by the EU. If action at the EU level is hindered by existing institutional procedures, the next-best option is for like-minded groups of Member States to resort to enhanced cooperation as foreseen by Articles 20 TEU and 329 TFEU. Enhanced cooperation offers two important safeguards: the consent of the European Parliament (EP) and judicial oversight of the Court of Justice of the EU (CJEU). It is also based on

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economies of the US and China have started to decouple06. So far, the EU has pursued a different strategy, with Member States encour - aging inward FDI from Chinese companies. Chinese greenfield investment in the EU has increased substan - tially in recent years, particularly in Central and Eastern Europe. This strategy can leverage technological progress abroad and promote technological development in Europe, as well as the creation of high-quality jobs, but only if executed in a coordinated manner. Asymmetries arising from small Member States negotiating 06. Data from the Bureau of Economic Analysis indicate that exports from China to the US have declined since 2018, and inward net FDI from China has decreased from a peak inflow of USD 18 billion in 2016 to an outflow of around USD 2 billion in 2023. 20THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1with large foreign investors could lead to unwelcome concessions being extracted by foreign countries, which is particularly concerning when a potential security threat and a geopolitical rival of the EU are involved. To counter these risks, the EU should strengthen its Investment Screening Mechanism. At present, FDI screening is a national competence, with Member States only required to exchange notifications and information. This fragmentation prevents the EU from leveraging its collective power in FDI negotiations and complicates the formulation of a common FDI policy. As outlined in chapter 3, coordination is important for the emergence of joint ventures in strategic sectors and ensuring that EU companies retain relevant know-how and can drive the next wave of innovation. 21THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1ENDNOTESi World Justice Project, Rule of Law Index 2023 , 2023. ii World Bank, World Development Indicators 2023 , 2024. iii Eurostat, Educational attainment statistics 2023 , 2024. iv IMF, World Economic Outlook, April 2024. v ECB, ‘ Why competition with China is getting tougher than ever ’, The ECB Blog, 3 September 2024. vi McCaffrey, C., & Poitiers, N., Instruments of economic security, Working Paper 12/2024, Bruegel, 2024, https:/ /www.bruegel. org/system/files/2024-05/WP%2012%202024_0.pdf . vii ECB, ‘ Deglobalisation: risk or reality? ’, The ECB Blog, 12 July 2023. viii Juhász, r., Lane N. and Rodrik, D., The new economics of industrial policy , 2023. ix in ‘t Veld, J., ‘ Quantifying the Economic Effects of the Single Market in a Structural Macromodel ’, Discussion Paper Series, No. 94, European Commission, February 2019.

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and cork (NACE 16), Chemicals and chemical products (NACE 20), Information and communication (NACE 61-63), Financial services (NACE 64). Finally, identified E&I specialisation domains in Ukraine were as follows: Food prod-ucts (NACE 10), Wood and products of wood and cork (NACE 16), Basic metals & Fabricated metal products (NACE 25, 26), Machinery and equipment (NACE 28), Manufacture of motor vehicles (NACE 29), Wholesale and retail trade (NACE 46). The common E&I specialisation in the EaP region in terms of gross domestic product and employ- ment is agriculture. The food processing and man- ufacturing industry directly concerns Armenia, Georgia, Moldova and Ukraine. Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials is identified as an E&I speciali- sation in two countries: Moldova and Ukraine. The top identified scientific domains in the EaP re- gion in publications are Nanotechnology and ma- terials (total number of records 29 067), with the highest number of records in Armenia, Azerbai- jan and Georgia; Fundamental physics and math- ematics (total 26 852), with the highest number of records in Moldova and Ukraine; Health and wellbeing (total 17 874), with highest number of records in Armenia, Azerbaijan, Georgia and Moldova. The least represented domain is Bi- otechnology (total 10 340), with the majority of them in Ukraine (8 935). The top technological domains in numbers of pat- ents in the EaP region are Mechanical engineering and heavy machinery (total number of records 18 510), with the highest number of records in Azer- baijan, Georgia, Moldova and Ukraine; Health and wellbeing (11 726), with highest number of records in Azerbaijan, Moldova and Ukraine; and Electric and electronic technologies (7 009) with the highest records in Armenia. The least represented domain is Energy (total 5 828), with the majority of them in Ukraine (5 647). The domains of Health and wellbeing and Govern- ance, culture, education and the economy were growing in all countries, while Agrifood was de- clining the most considerably (except for Armenia and Ukraine). Due to the particular means of organising scien- tific activities within the analysed countries, in most cases the top actors in scientific production 250 Part 5 Discussion of results and final remarks are national academies of science (as they rep- resent the network of research institutes), with a broad profile national university or highly special- ised research institution coming next. The

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indicating they would abandon a brand if they felt their data was not handled responsibly (PrivacyFirst Reports, 2021. "Consumer Trust in Data Privacy," Privacy and Security Journal, 9(1), 12-24). Critical Elements of a Successful E-Commerce Strategy (Direct Citations to Scholarly Works) Product Selection and Market Fit – Forbes (2023) discusses the importance of aligning products with consumer demand, stressing that market fit is crucial to e-commerce success (Forbes. (2023). "Consumer Behavior in E-Commerce," Forbes Digital Insights, 19(4), 27-41). – A study by Digital Commerce (2022) supports this, showing that offering customized products can increase average order value by 18% (Digital Commerce. (2022). "Product Customization and Consumer Purchase Behavior," Journal of E-Commerce Research, 15(2), 102-118). Seamless User Experience (UX) Design – Thompson (2022) highlights that simplifying the purchase process directly reduces cart abandonment rates, which remain a critical challenge in online sales (Thompson, R. (2022). "Optimizing User Experience in E-Commerce," Journal of E-Commerce UX, 7(3), 55-68). – Moreover, a study by UX Collective (2021) reveals that 40% of online shoppers abandon their carts due to poor or complex checkout procedures (UX Collective. (2021). "The Impact of Checkout Design on Cart Abandonment," Journal of User Experience Design, 3(1), 21-33). Mobile Optimization – TechCrunch (2022) confirms that over 55% of online sales now come from mobile platforms, underscoring the importance of mobile-friendly e-commerce sites (TechCrunch. (2022). "The Rise of Mobile E-Commerce," Digital Commerce Review, 17(3), 61-72). – According to Digital Insights (2023), responsive and fast-loading mobile sites reduce bounce rates by up to 25% (Digital Insights. (2023). "Mobile Optimization and User Retention," Journal of Digital Marketing, 22(4), 11-24). Omnichannel Marketing – Nelson (2022) discusses that omnichannel marketing strategies are vital for achieving seamless customer experiences, noting a 30% increase in customer loyalty for brands that integrate digital and physical touchpoints (Nelson, H. (2022). "Omnichannel Strategies in Modern Retail," Retail Marketing Journal, 8(2), 98-112). – Retail Today (2022) confirms that omnichannel businesses generate an average of 50% more revenue than those using only one channel (Retail Today. (2022). "Omnichannel Marketing in Retail," Journal of Retailing and Consumer Services, 29(1), 64-79). Customer Retention Tactics – Loyalty Experts (2021) show that implementing loyalty programs can boost repeat purchases by up to 20% (Loyalty Experts. (2021). "The Economics of Customer Loyalty Programs," Journal of Customer Loyalty, 12(3), 88-101). – Marketing Science Review (2023) further supports this, stating that personalized email campaigns can increase customer retention by 15%

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diverse func- tional ensembles are recruited in response to discrete stimuli (e.g., social versus non-social cues). The coding speciﬁcity ofthese functional ensembles of VIP+ INs resulted unstable across testing days and paradigms and revealed that diverse stimuli do not engage distinct aIC VIP+ INs but rather recruit the same pop-ulation of VIP+ INs within the aIC. Our data further reveal thatrepetition suppression of aIC VIP+ IN activity cannot be ex- plained by opposing roles or coding disruption of these different functional ensembles during a given test. This functional hetero-geneity, however, can be accounted for by several factors, such as different intrinsic anatomical and physiological properties of VIP+ IN subclasses in relation to sensory processing ( Guet- Mccreight et al., 2020 ). Alternatively, different aIC VIP+ INs may receive inputs only from speciﬁc subsets of the presynaptic areas that were identiﬁed by our viral tracing experiments, andhence would be embedded into distinct brain networks. Anopen question remains as to whether, at an individual neuronal level, specialized subsets of VIP+ INs encode independent com- ponents of sensory stimuli, such as perceptual salience or unexpectedness. Atypical aIC activity has been considered a hallmark of autism (Di Martino et al., 2009 ;Uddin and Menon, 2009 ;Uddin, 2015 ; Uddin et al., 2013 ;Odriozola et al., 2016 ), schizophrenia ( Wylie and Tregellas, 2010 ), and anxiety disorders ( Terasawa et al., 2013 ;Alvarez et al., 2015 ). Substantial evidence also indicates that these disorders are characterized by an impairment in sen- sory processing ( Uddin et al., 2013 ;Kapur, 2003 ;Pannekoek et al., 2013 ). A recent study has shown that early postnatal disruption of VIP+ IN function leads to long-term dysregulationof cortical activity and sensory learning ( Batista-Brito et al., 2017 ). In a mouse model of Dravet syndrome, a severe neurode- velopmental disorder characterized by autism and epilepsy,VIP+ INs showed lasting ﬁring abnormalities, unlike other cortical INs ( Goff and Goldberg, 2019 ). Furthermore, loss of function of the autism-related protein Mecp2 in VIP+ INs was found toinduce social preference deﬁcits ( Mossner et al., 2020 ). There- fore, failures in the encoding of sensory stimuli and behavioral relevance attribution by impaired VIP+ IN function could leadto atypical insula activity during stimulus processing, contrib-uting to anxiety and/or autism spectrum disorder core symptoms. In conclusion, our study identiﬁes VIP+ INs, a subclass of INs that barely accounts for 2% of neocortical neurons (

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computer science (in areas such as automatic control and UAVs) as well as Me- chanical engineering, presents the highest specialisation index in publications and pat- ents, and a high scientific citation impact. Its research is geared towards road, rail, sea and air transport, while patents cluster in Vehicles in general, Combustion engines and Ships; ■Mechanical engineering presents a high critical mass in patents and specialisation and citation impact in publications. It co-occurs with a large number of domains, particularly Transportation, Energy and Electric and elec- tronic technologies; ■Nanotechnology and materials presents a high critical mass in patents and publica- tions and a relevant number of EC projects. This rather transversal domain co-occurs frequently with a large number of domains in the hard and applied sciences, particular- ly Biotechnology, Fundamental physics and mathematics and Mechanical engineering and heavy machinery. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation21 UKRAINE Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCI*EC projects*Total Agrifood 0 Biotechnology 3 Chemistry and chemical engineering0 Electric and electronic technologies2 Energy 4 Environmental sciences and industries1 Fundamental physics and mathematics1 Governance, culture, education and the economy3 Health and wellbeing 4 ICT and computer science 1 Mechanical engineering and heavy machinery3 Nanotechnology and materials 3 Optics and photonics 2 Transportation 3 *NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable VIII. Selected S&T specialisation domains in Ukraine 22 Overview of economic, innovation, scientific and technological specialisations Identification of the main actors and col- laboration patterns within the S&T spe- cialisation domains Actors and S&T ecosystems The main actors per EaP country and the nation- al collaborations within the EaP, as well as inter- national collaboration with external partners, are presented in detail in Part 3. This information is complemented by the interactive networks9, which are produced in the context of this project as a tool to complement this written document. The net- works provide a complete exploratory depiction of all actors for a country, filterable by domain. The National Academies of Science play a cen- tral role in Armenia, Moldova and Ukraine. In all countries, one or very few comprehensive univer- sities, usually located in the capital city (or some other main cities for Ukraine), also have a back- bone role in the national ecosystem. After these, specialised research institutions and universities 9 Available on the Smart Specialisation platform webpage.present relevant roles

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Smart Specialisation. 252 Part 5 Discussion of results and final remarks Georgia is developing the strategy for the Imereti region with plans for more regions to follow there- after. Georgia has completed the quantitative and qualitative mapping phase for the featured region and had a successful awareness-raising event at the end of 2020, co-organised with the JRC. The country is currently running the Entrepreneurial Discovery Process for the priority domains identi- fied in the mapping phase. Moldova started developing the strategy in 2017, with a strong commitment to implementation as Smart Specialisation was included in the 2020 Government Action Plan. The EDP has been in- terrupted by the COVID-19 pandemic and, after a while, was finished online. The county finalised drafting the strategy, which is expected to be adopted soon. Ukraine was the first country to start engage- ment with the JRC back in 2017 and engage in de- veloping regional Smart Specialisation Strategies. Out of 27 regions (24 districts), at least 11 have made efforts to start the process, while 8 regions are currently progressing with JRC support strat- egies. The country is simultaneously developing a national-level strategy, which will be aimed at directing the research and innovation potential to- wards the implementation of the United Nation’s Sustainable Development Goals (SDGs). Complementing these national processes, the quantitative results of the current report provide a minimum map of E&I-S&T specialisations, but cannot rule out the existence of other relevant specialisations or concordances found by analys- ing other data sources or, on the ground, during the EDP. The results of this study aim to strengthen evi- dence-based innovation policymaking in the ana- lysed countries and the whole Eastern Partnership region. The results can be used not only directly for the development of Smart Specialisation Strat- egies and for priority setting, which strengthens the potential impact of public and private invest- ments, but also for many other tasks in research, innovation and economics policy, such as pro- gramme development, funding, internationalisa-tion, collaboration, etc. The results may potentially benefit other sectoral policies also, aiming at un- locking the benefits of high value added through the application of new knowledge and technolo- gies. To orient policymaking, E&I domains for which a specific concordance with S&T domains was not found would probably require the development of new research and innovation strengths, analysing the opportunities for non-technological innova- tion, the construction of collaborations with expert partners

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mo- bilised to support knowledge-based economic transformation? 4. How are international and national STI collab- oration networks structured and who are the main stakeholders? 5. Are there possible synergies/concordances between the countries’ economic, innovation, scientific and technological specialisations?The detailed methodological approach used to an- swer the research questions is presented in Part 1 of the report. To address the research questions, a varied list of international data sources is exploit- ed within the methodological approach specified in the Figure I. As a result of Steps 1 and 2 of the methodology, a series of economic and in- novation (E&I) specialisation domains and, in parallel, a series of scientific and technological (S&T) specialisation domains are identified for each EaP country. These results are presented in Part 2 and Part 3 of the report, respectively. Step 3 then aims at finding concordances between these complementary specialisation dimensions, leading to the identification of a subset of combined EIST specialisation domains – as presented be- low and described in Part 4 of the report. Indicators and insight aimed at each individual EaP country are complemented by an EaP-wide analysis, leading to the identification of some spe- cialisations and collaboration areas that can facili- tate regional cooperation. In line with the S3 Framework, further analytical and participatory work, complementary to the in- sights reported in this study, is necessary to an- alyse, propose and characterise specialisation priorities by: 1. enriching the quantitative and semantic analy- sis by adding additional local data sources and repositories (complementary economic and in- novation statistics, R&D projects financed by local policy instruments, publications in local repositories, patents and other intellectual property filed with local IPR agencies, etc.) to the international sources used in this exercise; 2. including profuse contributions by local ex- perts and stakeholders within the entrepre- neurial discovery processes. Part 4 of this report presents the process in which synergies or concordances between the economic, innovation, scientific and technological special- isations have been identified, by means of con- cordance tables. These have been identified at EaP-region level and at country level, crossing indicators related to the S&T domains with the Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation5 Figure I. Summary scheme of the methodological steps leading to the selection and definition of a list of specialisation domains for each country and the potential cooperation areas for the whole region and with international

[ "mo-", "\n", "bilised", "to", "support", "knowledge", "-", "based", "economic", "\n", "transformation", "?", "\n", "4", ".", "How", "are", "international", "and", "national", "STI", "collab-", "\n", "oration", "networks", "structured", "and", "who", "are", "the", "\n", "main", "stakeholders", "?", "\n", "5", ".", "Are", "there", "possible", "synergies", "/", "concordances", "\n", "between", "the", "countries", "’", "economic", ",", "innovation", ",", "\n", "scientific", "and", "technological", "specialisations?The", "detailed", "methodological", "approach", "used", "to", "an-", "\n", "swer", "the", "research", "questions", "is", "presented", "in", "Part", "1", "\n", "of", "the", "report", ".", "To", "address", "the", "research", "questions", ",", "a", "\n", "varied", "list", "of", "international", "data", "sources", "is", "exploit-", "\n", "ed", "within", "the", "methodological", "approach", "specified", "\n", "in", "the", "Figure", "I.", "As", "a", "result", "of", "Steps", "1", "and", "2", "of", "\n", "the", "methodology", ",", "a", "series", "of", "economic", "and", "in-", "\n", "novation", "(", "E&I", ")", "specialisation", "domains", "and", ",", "in", "\n", "parallel", ",", "a", "series", "of", "scientific", "and", "technological", "\n", "(", "S&T", ")", "specialisation", "domains", "are", "identified", "for", "\n", "each", "EaP", "country", ".", "These", "results", "are", "presented", "in", "\n", "Part", "2", "and", "Part", "3", "of", "the", "report", ",", "respectively", ".", "Step", "3", "\n", "then", "aims", "at", "finding", "concordances", "between", "these", "\n", "complementary", "specialisation", "dimensions", ",", "leading", "\n", "to", "the", "identification", "of", "a", "subset", "of", "combined", "\n", "EIST", "specialisation", "domains", "–", "as", "presented", "be-", "\n", "low", "and", "described", "in", "Part", "4", "of", "the", "report", ".", "\n", "Indicators", "and", "insight", "aimed", "at", "each", "individual", "\n", "EaP", "country", "are", "complemented", "by", "an", "EaP", "-", "wide", "\n", "analysis", ",", "leading", "to", "the", "identification", "of", "some", "spe-", "\n", "cialisations", "and", "collaboration", "areas", "that", "can", "facili-", "\n", "tate", "regional", "cooperation", ".", "\n", "In", "line", "with", "the", "S3", "Framework", ",", "further", "analytical", "\n", "and", "participatory", "work", ",", "complementary", "to", "the", "in-", "\n", "sights", "reported", "in", "this", "study", ",", "is", "necessary", "to", "an-", "\n", "alyse", ",", "propose", "and", "characterise", "specialisation", "\n", "priorities", "by", ":", "\n", "1", ".", "enriching", "the", "quantitative", "and", "semantic", "analy-", "\n", "sis", "by", "adding", "additional", "local", "data", "sources", "and", "\n", "repositories", "(", "complementary", "economic", "and", "in-", "\n", "novation", "statistics", ",", "R&D", "projects", "financed", "by", "\n", "local", "policy", "instruments", ",", "publications", "in", "local", "\n", "repositories", ",", "patents", "and", "other", "intellectual", "\n", "property", "filed", "with", "local", "IPR", "agencies", ",", "etc", ".", ")", "to", "\n", "the", "international", "sources", "used", "in", "this", "exercise", ";", "\n", "2", ".", "including", "profuse", "contributions", "by", "local", "ex-", "\n", "perts", "and", "stakeholders", "within", "the", "entrepre-", "\n", "neurial", "discovery", "processes", ".", "\n", "Part", "4", "of", "this", "report", "presents", "the", "process", "in", "which", "\n", "synergies", "or", "concordances", "between", "the", "economic", ",", "\n", "innovation", ",", "scientific", "and", "technological", "special-", "\n", "isations", "have", "been", "identified", ",", "by", "means", "of", "con-", "\n", "cordance", "tables", ".", "These", "have", "been", "identified", "at", "\n", "EaP", "-", "region", "level", "and", "at", "country", "level", ",", "crossing", "\n", "indicators", "related", "to", "the", "S&T", "domains", "with", "the", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation5", "\n", "Figure", "I.", "Summary", "scheme", "of", "the", "methodological", "steps", "leading", "to", "the", "selection", "and", "definition", "of", "a", "list", "of", "\n", "specialisation", "domains", "for", "each", "country", "and", "the", "potential", "cooperation", "areas", "for", "the", "whole", "region", "and", "with", "\n", "international" ]

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economy196 110 40 121 288 1 128 520 1 561 1 392 153 175 45 165 Health and wellbeing 722 2 687 682 217 257 754 414 1 561 583 673 684 577 64 ICT and computer science 133 138 88 1 008 504 592 1 164 1 392 583 903 524 435 1 138 Mechanical engineering and heavy machinery615 212 143 1 213 2 135 693 1 139 153 673 903 1 614 390 1 539 Nanotechnology and materials236 1 656 1 809 1 004 799 542 2 115 175 684 524 1 614 1 665 89 Optics and photonics 45 109 91 856 185 101 930 45 577 435 390 1 665 39 Transportation 52 45 9 240 326 113 258 165 64 1 138 1 539 89 39Figure 3.17. Co-occurrence of S&T records in different domains across the whole EaP region, colour-coded for the ratio with the total number of records in that domain (column)Figure 3.16. Number of records per labelled topic group (i.e. ‘domain’) in the EaP region 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 Number of records publicationsNanotechnology and materials Health and wellbeing Fundamental physics and mathematics Mechanical engineering and heavy machinery ICT and computer science Biotechnology Governance, culture, education and the economy Environmental sciences and industries Electric and electronic technologies Energy Chemistry and chemical engineering Optics and photonics Agrifood Transportation patents EC projects 156 Part 3 Analysis of scientific and technological potential Figure 3.18. Share of records per S&T domain in the Eastern Partnership region 0 20 40 60 80 Share of records publicationsNanotechnology and materials Health and wellbeing Fundamental physics and mathematics Mechanical engineering and heavy machinery ICT and computer science Biotechnology Governance, culture, education and the economy Environmental sciences and industries Electric and electronic technologies Energy Chemistry and chemical engineering Optics and photonics Agrifood Transportation patents EC projects EaP S&T specialisation domains according to the internal distribution of the S&T data sources Science-oriented S&T domains domains where scientific publications are most relevantBalanced S&T domains domains where publications and patents have a similar relative weightTechnology-oriented S&T domains domains where patents are most relevant Fundamental physics and mathematics Health and wellbeingMechanical engineering and heavy machinery Nanotechnology and materials Biotechnology Electric and electronic technologies Optics and photonics ICT and computer science Agrifood Environmental sciences and industries Energy Chemistry and chemical engineering Transportation Governance, culture, education and the

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Biotechnology in Azerbaijan ............... 239 Figure 4.8. Keyword cloud for the S&T domain Chemistry and chemical engineering in Azerbaijan ..................................................................................................................................................... 239 Figure 4.9. Keyword cloud for the S&T domain Energy in Azerbaijan .............................. 239 Figure 4.10. Keyword cloud for the S&T domain Nanotechnology and materials in Azerbaijan ..................................................................................................................................................... 239 Figure 4.11. Keyword cloud for the S&T domain Agrifood in Georgia ............................. 240 Figure 4.12. Keyword cloud for the S&T domain Nanotechnology and materials in Georgia ........................................................................................................................................................... 240 Figure 4.13. Keyword cloud for the S&T domain Agrifood in Moldova ............................ 242 Figure 4.14. Keyword cloud for the S&T domain Biotechnology in Moldova ................ 242 Figure 4.15. Keyword cloud for the S&T domain Chemistry and chemical engineering in Moldova .......................................................................................................................................................... 242 Figure 4.16. Keyword cloud for the S&T domain ICT and computer science in Moldova .................................................................................................................................................. 242 Figure 4.17. Keyword cloud for the S&T domain Nanotechnology and materials in Moldova .......................................................................................................................................................... 242 Figure 4.18. Keyword cloud for the S&T domain Agrifood in Ukraine .............................. 244 Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation263 Figure 4.19. Keyword cloud for the S&T domain Electric and electronic technologies in Ukraine ............................................................................................................................................................ 244 Figure 4.20. Keyword cloud for the S&T domain Energy in Ukraine ................................. 245 Figure 4.21. Keyword cloud for the S&T domain Environmental sciences and industries in Ukraine ...................................................................................................................................................... 245 Figure 4.22. Keyword cloud for the S&T domain Fundamental physics and mathematics in Ukraine ...................................................................................................................................................... 245 Figure 4.23. Keyword cloud for the S&T domain ICT and computer science in Ukraine ............................................................................................................................................. 245 Figure 4.24. Keyword cloud for the S&T domain Mechanical engineering and heavy machinery in Ukraine ............................................................................................................................... 245 Figure 4.25. Keyword cloud for the S&T domain Nanotechnology and materials in Ukraine ............................................................................................................................................................ 245 Figure 4.26. Keyword cloud for the S&T domain Optics and photonics in Ukraine ....245 Figure 4.27. Keyword cloud for the S&T domain Transportation in Ukraine ................. 245 264 List of figures and tables LIST OF TABLES Table I. The scientific and technological specialisation domains in the Eastern Partnership identified via topic modelling ........................................................................................ 13 Table II. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern Partnership region ........................................................................................................................................ 14 Table III. Characterisation of the EaP S&T domains according to the internal distribution of S&T data sources .................................................................................................................................... 15 Table IV. Selected S&T specialisation domains in Armenia ...................................................... 16 Table V. Selected S&T specialisation domains in Azerbaijan ................................................... 17 Table VI. Selected S&T

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"............................................................................................................................................................", "244", "\n", "Figure", "4.20", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Energy", "in", "Ukraine", ".................................", "245", "\n", "Figure", "4.21", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Environmental", "sciences", "and", "industries", "\n", "in", "Ukraine", "......................................................................................................................................................", "245", "\n", "Figure", "4.22", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Fundamental", "physics", "and", "mathematics", "\n", "in", "Ukraine", "......................................................................................................................................................", "245", "\n", "Figure", "4.23", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "ICT", "and", "computer", "science", "in", "\n", "Ukraine", ".............................................................................................................................................", "245", "\n", "Figure", "4.24", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Mechanical", "engineering", "and", "heavy", "\n", "machinery", "in", "Ukraine", "...............................................................................................................................", "245", "\n", "Figure", "4.25", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Nanotechnology", "and", "materials", "in", "\n", "Ukraine", "............................................................................................................................................................", "245", "\n", "Figure", "4.26", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Optics", "and", "photonics", "in", "Ukraine", "....", "245", "\n", "Figure", "4.27", ".", "Keyword", "cloud", "for", "the", "S&T", "domain", "Transportation", "in", "Ukraine", ".................", "245", "\n", "264", "\n", "List", "of", "figures", "and", "tables", "\n", "LIST", "OF", "TABLES", "\n", "Table", "I.", "The", "scientific", "and", "technological", "specialisation", "domains", "in", "the", "Eastern", "\n", "Partnership", "identified", "via", "topic", "modelling", "........................................................................................", "13", "\n", "Table", "II", ".", "Number", "of", "records", "per", "labelled", "topic", "group", "(", "i.e.", "‘", "domain", "’", ")", "in", "the", "Eastern", "\n", "Partnership", "region", "........................................................................................................................................", "14", "\n", "Table", "III", ".", "Characterisation", "of", "the", "EaP", "S&T", "domains", "according", "to", "the", "internal", "distribution", "\n", "of", "S&T", "data", 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are shared with multicellular organisms, but due to the incredible diversity of types of microbes these organisms are able to deal with a far wider range of xenobiotics than multicellular organisms, and can degrade even persistent organic pollutants such as organochloride compounds.[102] A related problem for aerobic organisms is oxidative stress.[103] Here, processes including oxidative phosphorylation and the formation of disulfide bonds during protein folding produce reactive oxygen species such as hydrogen peroxide.[104] These damaging oxidants are removed by antioxidant metabolites such as glutathione and enzymes such as catalases and peroxidases.[105][106] Thermodynamics of living organisms Further information: Biological thermodynamics Living organisms must obey the laws of thermodynamics, which describe the transfer of heat and work. The second law of thermodynamics states that in any isolated system, the amount of entropy (disorder) cannot decrease. Although living organisms' amazing complexity appears to contradict this law, life is possible as all organisms are open systems that exchange matter and energy with their surroundings. Living systems are not in equilibrium, but instead are dissipative systems that maintain their state of high complexity by causing a larger increase in the entropy of their environments.[107] The metabolism of a cell achieves this by coupling the spontaneous processes of catabolism to the non-spontaneous processes of anabolism. In thermodynamic terms, metabolism maintains order by creating disorder.[108] Regulation and control Further information: Metabolic pathway, Metabolic control analysis, Hormone, Regulatory enzymes, and Cell signaling As the environments of most organisms are constantly changing, the reactions of metabolism must be finely regulated to maintain a constant set of conditions within cells, a condition called homeostasis.[109][110] Metabolic regulation also allows organisms to respond to signals and interact actively with their environments.[111] Two closely linked concepts are important for understanding how metabolic pathways are controlled. Firstly, the regulation of an enzyme in a pathway is how its activity is increased and decreased in response to signals. Secondly, the control exerted by this enzyme is the effect that these changes in its activity have on the overall rate of the pathway (the flux through the pathway).[112] For example, an enzyme may show large changes in activity (i.e. it is highly regulated) but if these changes have little effect on the flux of a metabolic pathway, then this enzyme is not involved in the control of the pathway.[113] Effect of insulin on glucose uptake and metabolism. Insulin binds to its receptor (1), which in turn

[ "are", "shared", "with", "multicellular", "organisms", ",", "but", "due", "to", "the", "incredible", "diversity", "of", "types", "of", "microbes", "these", "organisms", "are", "able", "to", "deal", "with", "a", "far", "wider", "range", "of", "xenobiotics", "than", "multicellular", "organisms", ",", "and", "can", "degrade", "even", "persistent", "organic", "pollutants", "such", "as", "organochloride", "compounds.[102", "]", "\n\n", "A", "related", "problem", "for", "aerobic", "organisms", "is", "oxidative", "stress.[103", "]", "Here", ",", "processes", "including", "oxidative", "phosphorylation", "and", "the", "formation", "of", "disulfide", "bonds", "during", "protein", "folding", "produce", "reactive", "oxygen", "species", "such", "as", "hydrogen", "peroxide.[104", "]", "These", "damaging", "oxidants", "are", "removed", "by", "antioxidant", "metabolites", "such", "as", "glutathione", "and", "enzymes", "such", "as", "catalases", "and", "peroxidases.[105][106", "]", "\n\n", "Thermodynamics", "of", "living", "organisms", "\n", "Further", "information", ":", "Biological", "thermodynamics", "\n", "Living", "organisms", "must", "obey", "the", "laws", "of", "thermodynamics", ",", "which", "describe", "the", "transfer", "of", "heat", "and", "work", ".", "The", "second", "law", "of", "thermodynamics", "states", "that", "in", "any", "isolated", "system", ",", "the", "amount", "of", "entropy", "(", "disorder", ")", "can", "not", "decrease", ".", "Although", "living", "organisms", "'", "amazing", "complexity", "appears", "to", "contradict", "this", "law", ",", "life", "is", "possible", "as", "all", "organisms", "are", "open", "systems", "that", "exchange", "matter", "and", "energy", "with", "their", "surroundings", ".", "Living", "systems", "are", "not", "in", "equilibrium", ",", "but", "instead", "are", "dissipative", "systems", "that", "maintain", "their", "state", "of", "high", "complexity", "by", "causing", "a", "larger", "increase", "in", "the", "entropy", "of", "their", "environments.[107", "]", "The", "metabolism", "of", "a", "cell", "achieves", "this", "by", "coupling", "the", "spontaneous", "processes", "of", "catabolism", "to", "the", "non", "-", "spontaneous", "processes", "of", "anabolism", ".", "In", "thermodynamic", "terms", ",", "metabolism", "maintains", "order", "by", "creating", "disorder.[108", "]", "\n\n", "Regulation", "and", "control", "\n", "Further", "information", ":", "Metabolic", "pathway", ",", "Metabolic", "control", "analysis", ",", "Hormone", ",", "Regulatory", "enzymes", ",", "and", "Cell", "signaling", "\n", "As", "the", "environments", "of", "most", "organisms", "are", "constantly", "changing", ",", "the", "reactions", "of", "metabolism", "must", "be", "finely", "regulated", "to", "maintain", "a", "constant", "set", "of", "conditions", "within", "cells", ",", "a", "condition", "called", "homeostasis.[109][110", "]", "Metabolic", "regulation", "also", "allows", "organisms", "to", "respond", "to", "signals", "and", "interact", "actively", "with", "their", "environments.[111", "]", "Two", "closely", "linked", "concepts", "are", "important", "for", "understanding", "how", "metabolic", "pathways", "are", "controlled", ".", "Firstly", ",", "the", "regulation", "of", "an", "enzyme", "in", "a", "pathway", "is", "how", "its", "activity", "is", "increased", "and", "decreased", "in", "response", "to", "signals", ".", "Secondly", ",", "the", "control", "exerted", "by", "this", "enzyme", "is", "the", "effect", "that", "these", "changes", "in", "its", "activity", "have", "on", "the", "overall", "rate", "of", "the", "pathway", "(", "the", "flux", "through", "the", "pathway).[112", "]", "For", "example", ",", "an", "enzyme", "may", "show", "large", "changes", "in", "activity", "(", "i.e.", "it", "is", "highly", "regulated", ")", "but", "if", "these", "changes", "have", "little", "effect", "on", "the", "flux", "of", "a", "metabolic", "pathway", ",", "then", "this", "enzyme", "is", "not", "involved", "in", "the", "control", "of", "the", "pathway.[113", "]", "\n\n\n", "Effect", "of", "insulin", "on", "glucose", "uptake", "and", "metabolism", ".", "Insulin", "binds", "to", "its", "receptor", "(", "1", ")", ",", "which", "in", "turn" ]

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potential AzerbaijanTemporal evolution of the domains Period over period change in the relative size of each domain, domain size and data source size independent (% change for 2015-2018, over previous period 2011-2014) Change in share of publicationsChange in share of patents Change, weighted average of publications and patents Agrifood -48.35%Insufficient data-48.35% Biotechnology -24.69% 58.14% 3.99% Chemistry and chemical engineering -28.11% -65.71% -30.06% Energy -5.47% -0.46% -4.60% Environmental sciences and industries -13.35% -10.00% -13.23% Fundamental physics and mathematics -5.62%Insufficient data-5.62% Governance, culture, education and the economy14.94%Insufficient data14.94% Health and wellbeing 29.07% 34.38% 29.56% ICT and computer science -2.67% -46.00% -4.68% Mechanical engineering and heavy machinery-15.79% 21.61% 0.36% Nanotechnology and materials 37.52% -53.71% 34.17% Optics and photonics -24.81%Insufficient data-24.81%Table 3.12. Temporal evolution of Azerbaijan’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation183 Georgia Table 3.13 and Figure 3.30 showcase the num- ber of records per domain of S&T specialisation in Georgia. Fundamental physics and mathematics is the domain with the most records (with a total of 3 348), followed by Health and wellbeing (1 544), Environmental sciences and industries (1,066), Governance, culture, education and the economy (1 001) and Nanotechnology and materials (809). The first one accounts for more than one third of the total number of records (36%). The bulk of records across all domains are pub- lications, ranging from 90% to 99% of the total records in most cases, as shown in Figure 3.30. The exceptions are Chemistry and chemical en- gineering (73%), Agrifood (62%) and Electric and electronic technologies (58%) and Mechanical en- gineering and heavy machinery (10%), which con- tain a high number of patents. As with other countries, EC projects in Georgia are also mostly concentrated in the domain of Gov- ernance, culture, education and the economy. The remaining figures do not seem relevant enough to point to international excellence, although they can facilitate the identification of internationally connected specialised actors. The growth rate of publications in recent years, in terms of the compound annual growth rate, is also shown. Only Fundamental physics and math- ematics (-1.4%) shows a decreasing trend. This is particularly noteworthy, as it signals that the number of publications in coming years may con- tinue to decrease and the domain may become less relevant. Georgia’s publications are highly specialised in Environmental sciences and industries (1.6), Fun- damental physics and mathematics (1.5), Agrifood (1.3), Health and wellbeing

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professionals. Through investigation and discovery conducted at its Brigham Research Institute (BRI), BWH is an international leader in basic, clinical and translational research on human diseases, more than 1,000 physician-investigators and renowned biomedical scientists and faculty supported by nearly $650 million in funding. For the last 25 years, BWH ranked second in research funding from the National Institutes of Health (NIH) among independent hospitals. BWH continually pushes the boundaries of medicine, including building on its legacy in transplantation by performing a partial face transplant in 2009 and the nation's first full face transplant in 2011. BWH is also home to major landmark epidemiologic population studies, including the Nurses' and Physicians' Health Studies and the Women's Health Initiative. For more information, resources and to follow us on social media, please visit BWH's online newsroom. </p> <p> <hr class="hidden-xs hidden-sm" /> <hr class="major visible-sm" /> <div class="featured_image"> <div class="details"> <div class="well"> <h4>Journal</h4> <p>Circulation</p> </div> <div class="well"> <h4>DOI</h4> <p><a href="http://dx.doi.org/10.1161/CIRCULATIONAHA.115.017300" target="_blank">10.1161/CIRCULATIONAHA.115.017300 <i class="fa fa-sign-out"></i></a></p> </div> </div> </div> </div> <div class="well article_disclaimer hidden-search"> <p><strong>Disclaimer:</strong> AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.</p> </div> <div class="toolbar hidden-print hidden-search"> <div class='col-xs-6'> <div class="addthis_inline_share_toolbox_pnaa"></div> </div> <div class='col-xs-6'> <div class="article-tools pull-right"> <div class="addthis_inline_share_toolbox_62ef"></div> </div> </div> </div> </article> </div> <aside id="sidebar-content" class="white col-md-4"> <section class="widget"> <div class="widget-content"> <div class="contact-info"> <p><strong>Media Contact</strong></p> <p> Johanna Younghans<br/> <br /> <a href="mailto:[email protected]"> [email protected] </a><br/> Office: 617-525-6373<br/> </p> </div> </div> </section> <hr class="hidden-xs hidden-sm"> <hr class="major visible-xs visible-sm"> <section class="widget hidden-print"> <h3 class="widget-title red">More on this News Release</h3> <div class="widget-content"> <aside class="more"> <a href="/news-releases/464376"> <h3>Novel blood thinner found to be safe and effective in women</h3> </a> <p class="meta_institute">Brigham and Women's Hospital</p> <dl class="dl-horizontal meta stacked"> <dt class="yellow">Journal</dt> <dd class="yellow"><em>Circulation</em></dd> <dt class="red">DOI</dt> <dd class="red"><em>10.1161/CIRCULATIONAHA.115.017300</em></dd> </dl> </aside> <div class="row"> <div class="col-sm-6 col-md-12"> <h4 class="widget-subtitle">Keywords</h4> <nav class="tag-cloud"> <ul class="tags"> <li class="active ea-keyword"> <a href="#"> <span class="ea-keyword__path">/Life sciences/Organismal biology/Anatomy/Body fluids/</span><span class="ea-keyword__short">Blood</span> </a> </li> <li class="ea-keyword"> <a href="#"> <span class="ea-keyword__path">/Health and medicine/Health care/Medical facilities/</span><span class="ea-keyword__short">Hospitals</span> </a> </li> <li class="ea-keyword"> <a href="#"> <span class="ea-keyword__path"> /Health and medicine/Clinical medicine/Medical treatments/</span><span class="ea-keyword__short">Transplantation</span> </a> </li> </ul> </nav> </div> </div> </div> </section> </aside> </div> </div> </div> <footer id="footer" class="hidden-print hidden-search"> <div class="container"> <div class="row"> <div class="col-sm-5 col-sm-push-7"> <p class="logo"> <img src="/images/logo-footer.png" alt="EurekAlert! The Global Source for Science News"> </p> <p class="brand"> <img src="/images/brand.png" alt="AAAS - American Association for the Advancement of Science"> </p>

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largely irreversible on human timescales, and lead to loss of many economic and sustainable development gains. In some regions, dislocation and conflict will result from continued glacier and snowpack loss. These impacts will touch even those living far from mountain cryosphere, raising the importance of the risks posed by shrinking glaciers that require urgent mitigation to address.Some Adaptation Remains Essential Even with urgent mitigation efforts, some level of glacier loss remains inevitable given current loss rates, which modelling shows will continue until temperatures stabilise; likely? after mid-century, if governments make a course correction back to 1.5C-consistent pathways in 2025 Nationally Determined Contributions and maintain it in subsequent Paris commitment periods. Although some slow glacier re-growth may occur, national adaptation plans are urgently needed so that communities have time to adapt to this reality. Under the Paris Agreement, adoption of glacier and snowpack indicators under the Global Goal on Adaptation process, as well as in the Nairobi Work Programme focusing on high altitude and high latitude regions, will be essential. At the national level, planning for the future should involve both glacier and downstream countries, for both long-term adaptation efforts and risk management during extreme events such as landslides and glacial lake outburst floods. The involvement of Multilateral Development Banks, as well as investment guarantee mechanisms and the private sector, will be key in this transition. Image by Praful Rao of Save the HillsGlaciers Shrinking Worldwide Due to Rising Temperature A glacier is a large accumulation of mainly ice and snow that originates on land and flows slowly under its own weight. Found on every continent, they exist in many mountain regions and around the edges of the Greenland and Antarctic ice sheets. There are more than 275 000 glaciers in the world, with a total area of around 700 000 km 2, storing about 170 000 cubic km of ice. Due to climate change, largely caused by historical burning of fossil fuels, glaciers have retreated globally since the mid- 1800s. Glaciers are typically fed by snowfall during winter and lose ice during summer, although glaciers in countries that experience monsoon behave differently. Higher temperatures lead to increased melting and more precipitation falling as rain rather than snow. Changes in glaciers can have severe impacts on communities and ecosystems, increasing the risk of geohazards, changing regional water availability, and contributing to global sea-level rise. Glaciers Support Livelihoods and Economies of Billions

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[]

the economy; Nano- technology and materials; Optics and photonics; ICT and computer science; and Environmental sciences and industries present some regional sci- entific collaboration. Lastly, applied sciences or sectoral domains, such as Agrifood, Chemistry and chemical engineering, Mechanical engineering and heavy machinery and Transportation, present the smallest scientific col- laboration activity. In that sense, the potential for knowledge-based cooperation in these domains remains largely untouched. Nevertheless, it must be noted that these domains also present the low- est scientific production. Figure 3.46 presents the bilateral collaboration relationships between EaP countries in each S&T domain in terms of co-authorship of scientific publications. This synthetic view complements the subsections below, which, in turn, provide detailed numbers as well as information on collaboration in EC projects. Finally, some domains which present strong bilat- eral collaboration between EaP countries in sci- entific publications are ICT and computer science, Biotechnology, Fundamental physics and math- ematics and Nanotechnology and materials. The geometries of the scientific collaboration in the other domains is quite diverse, with Ukraine being a relevant link in most networks (due to its volume of activity and diversified research). Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation209 Figure 3.46. Number of publications and EC projects in collaboration between EaP actors in different countries Agrifood BiotechnologyChemistry and chemical engineeringElectric and electronic technologies BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AMUA BY MD GE AM AZUAEnergyFundamental physics and mathematicsEnvironmental sciences and industriesGovernance, culture, education and the economy BY MD AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA Health and wellbeingMechanical engineering and heavy machinery ICT and computer scienceNanotechnology and materials BY MD GE AM AZUA BY MD GE AM AZUA Transportation Optics and photonics Strong collaboration BY MD GE AM AZUA BY UA BY MD GE AM AZUA Intermediate collaborationColour indicates the relative distribution of documents, computed row-wise. 210 Part 3 Analysis of scientific and technological potential Regional collaboration in Agrifood In Agrifood publications, Armenia and Azerbaijan collaborate most intensively with Georgia, while Georgia and Moldova’s main collaboration part- ner within the EaP is Ukraine. Regionally, Ukraine collaborates most with Georgia, but it has a sig- nificantly higher number of collaborations with ex- ternal partners than the rest of the EaP countries. In terms of EC projects, collaborations are even- ly

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[]

15/11/2017: PhD, Political Science, dissertation in a joint program between the University of Berlin and the University of Vienna, entitled: “The Shifting Dynamics of Political Elites in Central Europe: A Comparative Study of Germany and Austria Since 1990,” supervisors: Robert Klein, Natalie Hofmann. MA in Political Science, University of Berlin. MA in International Relations, University of Vienna. Summer schools: Summer school “The Politics of Modern Europe: A Historical Approach” organized by the European Union Research Center (EURC) and the University of Barcelona, June 15-18, 2018, Barcelona. “Theories of Political Representation” summer school organized by the European Political Science Association (EPSA) and the University of Warsaw, June 5-9, 2017, Warsaw. Awards, grants/Prix, distinctions, bourses 2021 / Alexander von Humboldt Fellowship for a 6-month stay at the University of Chicago, not realized (pandemic-related restrictions). 2021 / Postdoctoral Research Fellowship at the European Institute in London, for 9 months (starting in January 2022) – not realized (pandemic and job offer at University of Berlin). 2020 / Best Paper Award, European Journal of Political Science, for the article "Elite Political Strategies in Post-Communist States," Vol. 32, No. 1, pp. 45-63. 2016 / Young Scholar Prize, awarded by the Austrian Political Science Association, Austria. 5 publications/5 publications What is the major contribution of this publication /Quel est l’apport majeur de cette publication ? 1 Smith, J., “Ethical Governance and Managerial Policies in Public-Private Partnerships,” in Müller, S. (dir.) Reconfiguring Political Accountability: A New Framework for Public Administration, Berlin, Springer, 2025, p. 245-267. A study of the ethical frameworks and managerial strategies used in public-private partnerships, particularly in post-communist states, tracing the diffusion of governance models across different sectors. 2 Smith, J., “Lobbying Regulation in Germany and Austria: Shaping Public-Private Relationships,” Journal of Political Science, vol. 9, no. 2, 2019, pp. 123-146. This article discusses the lobbying regulation processes in Germany and Austria, focusing on how these regulations are crafted and enforced within the broader context of political systems and governance models. 3 Smith, J., “Political Polarization and the Rise of Right-Wing Movements in Central Europe,” in T. Novak, M. Petrov, and L. Stevens (dir.), Political Movements in Europe: Emerging Trends, Prague, Charles University Press, 2022, p. 85-112. This paper explores the rise of right-wing political movements in Central European countries, analyzing the sociopolitical dynamics that lead to their success in recent elections. 4 Smith, J., “Scandals in Politics: Political Mobilization through Crisis,” Journal of European Political Studies, vol. 16, no. 4, 2021, p. 567-589.

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The EU has put in place binding legislation to reduce greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels. The US, by contrast, has set a non-binding target of a 50-52% reduction below (higher) 2005 levels by 2030, while China only aims for its carbon emissions to peak by the end of the decade. These differences create massive near-term investment needs for EU companies that their competitors do not face. For the four largest EIIs (chemicals, basic metals, non-metallic minerals and paper), decarbonisation is projected to cost EUR 500 billion overall over the next 15 years, while for the “hardest-to-abate” parts of the transport sector (maritime and aviation) investment needs stand at around EUR 100 billion each year from 2031 to 2050. The EU is also the only major region worldwide to have introduced a significant CO2 price. This cost factor is of limited importance so far as heavy industrial production has been largely covered by free allowances under the Emissions Trading Scheme (ETS). However, these allowances will be progressively phased out with the introduction of the Carbon Border Adjustment Mechanism (CBAM). Decarbonisation offers an opportunity for Europe to lower energy prices and take the lead in clean technol - ogies (“clean tech”), while also becoming more energy secure . The decarbonisation of Europe’s energy system 39THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3implies the massive deployment of clean energy sources with low marginal generation costs, such as renewables and nuclear. Specific EU regions are endowed with high potential for cost-competitive renewable energy sources: for instance, solar in Southern Europe and wind in the North and Southeast. Renewable energy deployment in Europe is already rising, reaching around 22% of the EU’s gross final energy consumption in 2023, compared with 14% in China and 9% in the US. At the same time, Europe has strong innovative potential to meet rising domestic and global demand for clean energy solutions. Although Europe is weak in digital innovation, it is a leader in clean tech innovation [see Figure 2] . This presents opportunities: according to the International Energy Agency (IEA), more than one-third of the required CO2 emission reductions globally in 2050 rely on technologies currently at the demonstra - tion or prototype phaseiii. The electrification of the European energy system will also be an enabler of growth for the EU’s sustainable transport sector. EU companies are

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[]

work titled Al-Risalah al-Kamiliyyah fil Siera al-Nabawiyyah (The Treatise of Kamil on the Prophet's Biography) which included the following phrase "Both the body and its parts are in a continuous state of dissolution and nourishment, so they are inevitably undergoing permanent change."[149] Application of the scientific method and Modern metabolic theories The history of the scientific study of metabolism spans several centuries and has moved from examining whole animals in early studies, to examining individual metabolic reactions in modern biochemistry. The first controlled experiments in human metabolism were published by Santorio Santorio in 1614 in his book Ars de statica medicina.[150] He described how he weighed himself before and after eating, sleep, working, sex, fasting, drinking, and excreting. He found that most of the food he took in was lost through what he called "insensible perspiration". Santorio Santorio in his steelyard balance, from Ars de statica medicina, first published 1614 In these early studies, the mechanisms of these metabolic processes had not been identified and a vital force was thought to animate living tissue.[151] In the 19th century, when studying the fermentation of sugar to alcohol by yeast, Louis Pasteur concluded that fermentation was catalyzed by substances within the yeast cells he called "ferments". He wrote that "alcoholic fermentation is an act correlated with the life and organization of the yeast cells, not with the death or putrefaction of the cells."[152] This discovery, along with the publication by Friedrich Wöhler in 1828 of a paper on the chemical synthesis of urea,[153] and is notable for being the first organic compound prepared from wholly inorganic precursors. This proved that the organic compounds and chemical reactions found in cells were no different in principle than any other part of chemistry. It was the discovery of enzymes at the beginning of the 20th century by Eduard Buchner that separated the study of the chemical reactions of metabolism from the biological study of cells, and marked the beginnings of biochemistry.[154] The mass of biochemical knowledge grew rapidly throughout the early 20th century. One of the most prolific of these modern biochemists was Hans Krebs who made huge contributions to the study of metabolism.[155] He discovered the urea cycle and later, working with Hans Kornberg, the citric acid cycle and the glyoxylate cycle.[156][157][75] Modern biochemical research has been greatly aided by the development of new techniques such as chromatography, X-ray diffraction, NMR spectroscopy, radioisotopic labelling,

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[]

Text simplification M. Shardlow, P. Przybyła, “Simplification by Lexical Deletion,” in Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability (TSAR 2023), Varna, Bulgaria, 2023. [bib][paper][code] L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “Document-level Text Simplification with Coherence Evaluation,” in Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability (TSAR 2023), Varna, Bulgaria, 2023. [bib][paper][code] L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “The Role of Text Simplification Operations in Evaluation,” in Proceedings of the First Workshop on Current Trends in Text Simplification (CTTS 2021), Online, 2021. [bib][paper][code] L. Vásquez-Rodríguez, M. Shardlow, P. Przybyła, Sophia Ananiadou, “Investigating Text Simplification Evaluation,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Bangkok, Thailand, 2021. [bib][paper][code] P. Przybyła, M. Shardlow, “Multi-Word Lexical Simplification,” in Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), Barcelona, Spain, 2020. [bib][paper][data][model][code] Other NLP I. Kuzmin, P. Przybyła, E. McGill, and H. Saggion, “TRIBBLE - TRanslating IBerian languages Based on Limited E-resources,” in Proceedings of the Ninth Conference on Machine Translation, Miami, USA, 2024.[bib][paper][code] P. Przybyła, N. T. H. Nguyen, M. Shardlow, G. Kontonatsios, and S. Ananiadou, “NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features,” in Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), San Diego, USA, 2016.[bib][paper] P. Przybyła and P. Teisseyre, “What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling - Notebook for PAN at CLEF 2015,” in CLEF 2015 Labs and Workshops, Notebook Papers, Toulouse, France, 2015.[bib][paper] Computations in physics M. Maćkowiak-Pawłowska, P. Przybyła, “Generalisation of the identity method for determination of high-order moments of multiplicity distributions with a software implementation,” European Physical Journal C, vol. 78, issue 5, 2018.[bib][paper][software] P. Przybyła, “A pattern recognition method for lattice distortion measurement from HRTEM images,” Journal of Microscopy, vol. 245, no. 2, pp. 200–209, 2011.[bib][paper]

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data points for which information is miss- ing in-between. Secondly, the last observed value for each enterprise is carried forward provided the enterprise is still active. Thirdly, the first observed value is carried backward using the enterprise’s date of incorporation as the baseline. All data aggregations and missing data imputations have been completed in Stata. For three countries – Armenia, Azerbaijan and Georgia – data availability is too poor to include these countries in the mapping analysis (cf. Table 2.1, in particular all red-coloured numbers). Ag- gregate data are only available for 9 three-dig- it industries for Armenia, 3 three-digit industries for Azerbaijan. In this section we will only present Country Employees Turnover WagesEmployees & turnoverEmployees & wagesTurnover & wagesEmployees & turnover & wages Armenia 15 15 3 15 3 3 3 Azerbaijan 12 12 0 12 0 0 0 Georgia 180 442 180 442 0 180 442 0 0 0 Moldova 45 882 45 882 1 45 882 1 1 1 Ukraine 411 779 411 779 30 993 411 779 30 993 30 993 30 993Table 2.1. Orbis data availability – Number of enterprises for which at least one data observation is available for 2011-2019 Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation39 and discuss mapping results for Georgia, Moldova and Ukraine. Methodology The methodology used to analyse the aggregate Orbis data is different from that which was initial- ly envisioned – as data availability is much worse than initially expected – with sufficiently high data availability for the number of employees and turn- over for only three Eastern Partnership countries and poor data availability for wages for all Eastern Partnership countries. Aggregate industry-level data have been used to identify two types of industries: ■industries with a current strength, includ- ing specialised industries with critical mass, where the degree of specialisation29 and rel- ative size for both the number of employees and turnover are above predefined thresholds; ■industries with an emerging strength, in- cluding emerging industries with increasing degrees of specialisation, where the change in the degree of specialisation for both the number of employees and turnover are above predefined thresholds. For all NACE three-digit industries in Georgia, Mol- dova and Ukraine, the following indicators have been calculated: ■the degree of specialisation for the number of employees for each year in the 2012-2019 period; ■the average relative share of the total number 29 Specialisation

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beyond large tech companies, SMEs on both sides of the Atlantic should benefit from the same easing of regulatory burdens for small companies that is proposed above. 34THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2Facilitating consolidation in the telecoms sector is needed to deliver higher rates of investment in connec - tivity [see the chapters on digitalisation and advanced technologies, and competition policy] . The cornerstone initiative is modifying the EU’s stance towards scale and consolidation of telecoms operators to deliver a true Single Market, without sacrificing consumer welfare and quality of service. To encourage consolidation, the report recom - mends defining telecoms markets at the EU level – as opposed to the Member State level – and increasing the weight of innovation and investment commitments in the EU’s rules for clearing mergers. Country-level ex ante regulation should be reduced in favour of ex post competition enforcement in cases of abuse of dominant position. It is also proposed to harmonise EU-wide spectrum licensing rules and processes and to orchestrate EU-wide auction design features to help create scale. To ensure that EU players remain at the forefront of new technological developments, it is recommended to establish an EU-level body with public-private participation to develop homog - enous technical standards for the deployment of network APIs and edge computing, as was the case for roaming in the 1990s. To increase the capacity of EU operators to invest in these technologies, it is recommended to support commercial investment sharing between network owners and Very Large Online Platforms that use EU data networks to a massive extent but do not contribute to financing them. Sustaining and expanding R&I will also be crucial for key manufacturing sectors such as pharma [see the chapter on pharma] . Opening up the secondary use of health data for research purposes has significant poten - tial to anchor pharma R&I activities within the EU. The report therefore recommends accelerating the digitisation of health systems and the European Health Data Space (EHDS), achieved through EU-level support for national investments which facilitate access to and sharing of electronic health records. In addition, it is proposed to further scale up genome sequencing capacities in the EU and to present a strategic blueprint beyond 2026, building on the European 1+ Million Genomes initiative. To maximise the opportunities of the EHDS, it will be important to provide clear and timely guidance

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last few years (especially in Ukraine) and this would render the CAGR results misleading, nor for EC projects due to the small number of records. Afterwards, a specialisation analysis has been carried out for each country’s domains against the EaP region as a whole. The specialisation index (SI)60 has been computed. The specialisation index indicates the relative intensity of activity of a geography of interest (country, region, etc.) in each domain in relation to the activity within a specific perimeter of compar- 60 Schubert, A., Braun, T., ‘Relative indicators and relational charts for comparative assessment of publication output and citation impact’, Metrics, Vol. 9, 1986, pp. 281–291.ison (in the case of this study, the average for the EaP region) – in this case, the relative publishing and patenting volume and EU project participation activity. Within the framework of Smart Speciali- sation, this indicator provides a comparison of the relative weight of each domain of knowledge in EaP countries, in relation to the reference region (the EaP region), to understand whether or not EaP countries are specialised in each domain of knowl- edge (in this case, the S&T domains, i.e. groups of labelled topics). Concretely, the specialisation index within a spe- cific S&T domain – for a given EaP country with respect to the reference perimeter (i.e. the EaP average) – is the result of the following ratio: ■numerator – number of records in the domain of knowledge produced by the country, nor- malised by the total amount of records within the country when counting multiple domain assignations; ■denominator – the average of the number of records in the domain of knowledge across the EaP produced by each EaP country, nor- malised by the total amount of records within the same EaP country when counting multiple domain assignations. Thus, a specialisation index greater than 1 implies a relative specialisation of the geography of inter- est in this domain of knowledge. For example, an SI of 2 means that the country’s share of publica- tions or patents in a domain is double that of the EaP average. The normalised citation impact (NCI)61 has also been computed for scientific publications. The NCI measures the number of citations per publication, normalised over a reference citation per publication figure. Since citation patterns dif- fer between scientific fields, this ratio is computed for each bibliometric category that Scopus adopts to classify scientific publications and a weighted

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Fuest, C., Gros, D., Mengel, P.-L., Presidente, G., and Tirole, J., ‘ How to Escape the Middle Technology Trap: EU Innovation Policy ’, EconPol Policy Report, 2024. ix Myers, K. and Lanahan, L., ‘ Estimating Spillovers from Publicly Funded R&D: Evidence from the US Department of Energy ’, American Economic Review, Vol. 112, No. 7, July 2022. x Testa, G., Compano, R., Correia, A. and Rückert, E., ‘ In search of EU unicorns: What do we know about them ’, EUR 30978 EN, Publications Office of the European Union, Luxembourg, 2022. xi Bruegel, EU Digital Policy Overview , Bruegel Factsheet, 2024. xii Acemoglu, D., et al, ‘ Robot and automation: New insights from micro data: Advanced Technology Adoption: Selection or Causal Effects? ’, AEA Papers and Proceedings, 113: 210–214, 2023. xiii European Commission, Eurostat, Digitalisation in Europe – 2024 edition , Interactive Publication, 2024. xiv https:/ /epochai.org/blog/how-much-does- it-cost-to-train-frontier-ai-models 38THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 23. A joint decarbonisation and competitiveness plan High energy costs in Europe are an obstacle to growth, while lack of generation and grid capacity could impede the spread of digital tech and transport electrification . Commission estimates suggest that high energy prices in recent years have taken a toll on potential growth in Europei. Energy prices also continue to affect corporate investment sentiment much more than in other major economies. Around half of European companies see energy costs as a major impediment to investment – 30 percentage points higher than US companiesii. Energy-intensive industries (EIIs) have been hit hardest: production has fallen 10-15% since 2021 and the composition of European industry is changing, with increasing imports from countries with lower energy costs. Energy prices have also become more volatile, increasing the price of hedging and adding uncertainty to investment decisions. Without a significant increase in generation and grid capacity, Europe may also face limitations on making production more digital, as training and running AI models and maintaining data centres is highly energy-intensive. Data centres are currently responsible for 2.7% of the EU’s electricity demand, but by 2030 their consumption is expected to rise by 28%. FIGURE 1 Energy-intensive manufacturing challenges % change in industrial production (Apr. 24 vs Apr. 21) Source: Eurostat, OECD Trade value added (TiVA database) and ECB staff calculations. The EU’s decarbonisation goals are also more ambitious than its competitors’, creating additional short- term costs for European industry .

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sciences and industriesC02F Treatment of water, waste water, sewage, or sludge 523 414.14% Environmental sciences and industriesE21BEarth or rock drilling; obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells271 318.41% Environmental sciences and industriesB01D Separation 235 247.89% Environmental sciences and industriesE21C Mining or quarrying 214 199.07% Environmental sciences and industriesG01NInvestigating or analysing materials by determining their chemical or physical properties196 49.76% Fundamental physics and mathematicsG01NInvestigating or analysing materials by determining their chemical or physical properties153 38.85% Fundamental physics and mathematicsG06F Electric digital data processing 92 70.77% Fundamental physics and mathematicsF03D Wind motors 88 68.93% Fundamental physics and mathematicsB01D Separation 81 85.44% Fundamental physics and mathematicsH01LSemiconductor devices; electric solid state devices not otherwise provided for80 79.30% Governance, culture, education and the economyA61B Diagnosis; surgery; identification 83 14.35% Governance, culture, education and the economyG06F Electric digital data processing 70 53.85% 164 Part 3 Analysis of scientific and technological potential Domain IPC Description No recordsRelative freq. Governance, culture, education and the economyA63BApparatus for physical training, gymnastics, swimming, climbing, or fencing; ball games; training equipment28 104.67% Governance, culture, education and the economyG09BEducational or demonstration appliances; appliances for teaching, or communicating with, the blind, deaf or mute; models; planetaria; globes; maps; diagrams21 14.53% Governance, culture, education and the economyG06QData processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for20 41.81% Health and wellbeingA61B Diagnosis; surgery; identification 5 506 952.05% Health and wellbeingA61K Preparations for medical, dental, or toilet purposes 2 540 556.80% Health and wellbeingG01NInvestigating or analysing materials by determining their chemical or physical properties2 247 570.51% Health and wellbeingA61PSpecific therapeutic activity of chemical compounds or medicinal preparations1 493 458.50% Health and wellbeingA61NElectrotherapy; magnetotherapy; radiation therapy; ultrasound therapy719 344.02% ICT and computer scienceG06F Electric digital data processing 693 533.08% ICT and computer scienceG06QData processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for259 541.46% ICT and computer scienceH04LTransmission of digital information, e.g. telegraphic communication254 525.52% ICT and computer scienceA61B Diagnosis; surgery; identification 207 35.79% ICT and computer scienceH04N Pictorial communication, e.g. television 196 197.98% Mechanical engineering and heavy machineryA61B Diagnosis; surgery; identification 649 112.22% Mechanical engineering and heavy machineryG01NInvestigating or analysing materials by

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"\n", "medicinal", "preparations1", "493", "458.50", "%", "\n", "Health", "and", "\n", "wellbeingA61NElectrotherapy", ";", "magnetotherapy", ";", "radiation", "therapy", ";", "\n", "ultrasound", "therapy719", "344.02", "%", "\n", "ICT", "and", "computer", "\n", "scienceG06F", "Electric", "digital", "data", "processing", "693", "533.08", "%", "\n", "ICT", "and", "computer", "\n", "scienceG06QData", "processing", "systems", "or", "methods", ",", "specially", "adapted", "\n", "for", "administrative", ",", "commercial", ",", "financial", ",", "managerial", ",", "\n", "supervisory", "or", "forecasting", "purposes", ";", "systems", "or", "methods", "\n", "specially", "adapted", "for", "administrative", ",", "commercial", ",", "\n", "financial", ",", "managerial", ",", "supervisory", "or", "forecasting", "\n", "purposes", ",", "not", "otherwise", "provided", "for259", "541.46", "%", "\n", "ICT", "and", "computer", "\n", "scienceH04LTransmission", "of", "digital", "information", ",", "e.g.", "telegraphic", "\n", "communication254", "525.52", "%", "\n", "ICT", "and", "computer", "\n", "scienceA61B", "Diagnosis", ";", "surgery", ";", "identification", "207", "35.79", "%", "\n", "ICT", "and", "computer", "\n", "scienceH04N", "Pictorial", "communication", ",", "e.g.", "television", "196", "197.98", "%", "\n", "Mechanical", "\n", "engineering", "and", "\n", "heavy", "machineryA61B", "Diagnosis", ";", "surgery", ";", "identification", "649", "112.22", "%", "\n", "Mechanical", "\n", "engineering", "and", "\n", "heavy", "machineryG01NInvestigating", "or", "analysing", "materials", "by" ]

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since data for these economies became available in 1950. While this relationship may have owed more to automation than it did to open tradexix, the notion that globalisation had exacerbated inequality infiltrated public perceptions, while governments were seen as indifferent. Policymakers should learn from this experience to reflect on how society will change in the future, and how they can ensure that the state is seen as on the side of citizens and attentive to their concerns. A key part of this process will be empowering people. Leaders and policymakers should engage with all actors within their respective societies to define objectives and actions for the transformation of Europe’s economy. More effective and proactive citizens’ involvement and social dialogue, combining trade unions, employers and civil society actors, will be central in building the consensus needed to drive the changes. Transformation can best lead to prosperity for all when accompanied by a strong social contract. 19THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1BOX 1 Key principles for trade policy in a European industrial strategy The era of open global trade governed by multilateral institutions looks to be passing, and the EU’s trade policy is already adapting to this new reality . The global trading order based on multilateral institu - tions is in deep crisis, and it remains uncertain whether it can be brought back on track. While the EU should continue efforts to reform the WTO – and especially to unlock the dispute settlement mechanism – the EU must adapt its trade policy to a new reality. This process is already underway. In June 2023, the EU adopted a new Economic Security Strategy furnishing itself with a range of instruments to deal with dumping, respond to coercion and address distortions caused by foreign subsidies within the EU, as well as adopting tools to address technology leakage and enforce sanctions. The EU has also continued to expand its bilateral trade network negotiating over 40 individual trade agreements with different countries and regions. Trade policy needs to be fully aligned with the European industrial strategy . Trade policy should be based on careful, case-by-case analysis rather than on generic stances toward trade. In some cases, the EU should use its trade policy arsenal to keep barriers low, in others to level the playing field and in others still to secure critical supply chains. Accelerating innovation and technological progress in

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laboration of the whole EaP region with external countries, for each domain, is provided. The tables are presented in the form of heatmaps, where the colour denotes the distribution of records comput- ed row-wise (i.e. colours mark the distribution of documents of the country on the left-hand side of each table).Results The following tables present aggregate collabo- rations in publications and EC projects which are useful to gauge the overall intensity of cooper- ation in science and innovation between the EaP countries. EaP regional collaboration In publications, Armenia and Georgia present con- sistent bilateral scientific collaboration with one another. Ukraine also presents a high level of col- laboration with these two countries. Conversely, Azerbaijan and Moldova are currently minor sci- entific partners of EaP countries, only presenting a moderate collaboration with Ukraine. In EC-funded projects, Ukraine collaborates most intensively with Georgia and Moldova. Armenia and Moldova also have a high level of collabora- tion. Azerbaijan remains a bit more isolated, also due to the lower number of projects overall. This collaboration intensity is certainly a positive re- sult of the EaP countries’ participation in H2020, particularly since a significant number of these collaborations are concentrated in the domain Governance, culture, education and the economy (see following section).Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia 130 1 471 1 756 42 980 Azerbaijan 130 49 73 26 138 Belarus 1 471 49 1 440 83 1 268 Georgia 1 756 73 1 440 58 1 058 Moldova 42 26 83 58 202 Ukraine 980 138 1 268 1 058 202 Publications Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia 10 21 26 19 21 Azerbaijan 10 8 11 8 11 Belarus 21 8 20 17 33 Georgia 26 11 20 23 32 Moldova 19 8 17 23 25 Ukraine 21 11 33 32 25 EC projectsFigure 3.45. Number of publications and EC projects in collaboration between EaP actors in different countries Colour indicates the relative distribution of documents, computed row-wise. 208 Part 3 Analysis of scientific and technological potential It must be noted, however, that scientific collabo- ration between EaP countries is mainly driven by very intense collaboration in physics (within the Fundamental physics and mathematics domain) – which concentrates by far the largest number of co-publications – due to the countries’ co-par- ticipation in large high-energy and astronomy en- deavours. At a great distance, Health and wellbeing; Govern- ance, culture, education and

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discharge, turbine, heat exchanger, collector, rectifier, condition, resistance, statorTable 3.3b. List of 50 most relevant keywords for each S&T domainTable 3.3a. List of identified S&T specialisation domains EaP S&T specialisation domains (in alphabetical order) Agrifood Biotechnology Energy Optics and photonicsHealth and wellbeingChemistry and chemical engineeringElectric and electronic technologies Environmental sciences and industries Governance, culture, education and the economy ICT and computer science TransportationNanotechnology and materialsFundamental physics and mathematics Mechanical engineering and heavy machinery 150 Part 3 Analysis of scientific and technological potential S&T domain Top keywords Environmental sciences and industriesspecie, water, soil, plant, environment, deposit, rock, population, basin, datum, reservoir, river, pollution, territory, concentration, condition, treatment, well, sea, caucasus, philtre, source, purification, ecosystem, temperature, contamination, metal, monitoring, forest, mine, mining, genus, sediment, mass, species, nov, station, accumulation, slope, extraction, tree, habitat, ore, massif, air, hole, removal, biomass, land, body Fundamental physics and mathematicstev, collision, equation, measurement, detector, mass, boson, gev, section, jet, datum, atlas detector, condition, particle, pp collision, luminosity, space, lhc, energy, integrated luminosity, momentum, coefficient, dependence, pair, decay, quark, proton-proton collision, lepton, prediction, channel, flow, mass, proton, electron, collider, confidence level, muon, fraction, frequency, resonance, transverse momentum, formula, cms detector, photon, sup, higgs boson, gas, cross section, simulation, excess Governance, culture, education and the economyenterprise, author, economy, policy, market, resource, environment, risk, society, industry, experience, security, regulation, culture, population, history, people, life, language, bank, business, government, reform, law, protection, identity, caucasus, city, text, investment, nature, competitiveness, capital, conflict, sustainable development, sphere, source, form, condition, teacher, crisis, publication, power, authority, tradition, attitude, legislation, image, perception, peculiarity Health and wellbeingpatient, treatment, cell, disease, child, therapy, tissue, woman, rat, disorder, gene, diagnosis, age, cancer, drug, syndrome, risk, population, animal, dose, infection, surgery, expression, complication, protein, care, prevalence, week, diabetes, people, tumour, examination, day, cavity, year, medicine, mortality, pathology, symptom, mutation, prevention, blood, intervention, hypertension, marker, month, stress, death, health, severity ICT and computer sciencealgorithm, network, datum, measurement, simulation, image, accuracy, error, signal, user, monitoring, software, modelling, detection, architecture, database, recognition, node, prediction, environment, condition, code, construction, server, resource, correction, station, complexity, sensor, uncertainty, neural network, form, source, communication, noise, sequence, channel, propose method, computer, connexion, message, rule, memory, author, transmission, interface, circuit, probability, distance, subsystem Mechanical engineering and heavy machineryplate, chamber, axis, body, rod, wall, hole, angle, surface, shaft, pipe, opening, housing, form, plane, machine, cylinder, drive, valve, diameter, groove, ring, shell, section, rotation, spring, portion, holder, pipeline, guide, cover, sleeve,

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contrast, has a comparable level of dependencies, being highly dependent on one or two countries for most of its critical mineral imports. However, it is not following a similarly coordinated approach. The EU is lacking a comprehensive strategy covering all stages of the supply chain (from exploration to recycling) and, unlike its competitors, the mining and trading of commodities is largely left to private actors and the market. Strategic dependencies also extend to critical technologies for the digitalisation of Europe’s economy [see the chapter on digitalisation and advanced technologies] . The EU relies on foreign countries for over 80% of digital products, services, infrastructure and intellectual propertyvi. Dependencies are particularly acute, however, for semi - conductors owing to the structure of the industry, which is dominated by a small number of large players. The US has specialised in chips design, Korea, Taiwan and China in chips manufacturing, and Japan and some EU Member States in key materials and equipment – optics, chemistry and machinery [see Figure 3] . Europe has little domestic capacity in many parts of the supply chain. For example, the EU currently has no foundry producing below 22 nm process nodes and relies on Asia for 75% to 90% of wafer fabrication capacity (as does the US). Europe has become dependent on non-EU countries for chips design, packaging and assembly as well. Dependencies are also acute for other advanced tech. The EU’s AI industry relies on hardware produced largely by one US-based company for the most advanced processors. Similarly, Europe’s dependence on cloud services developed and run by US companies is massive. For quantum computing platforms, the EU suffers from six critical dependencies across 17 key technolo - gies, components and materials. China and the US hold technological leadership in most of these critical elements. In the telecoms sector, Europe is less dependent on foreign technology: top EU vendors are well positioned in the global supply of telecoms equipment. However, it will be important that dependencies do not increase, especially on high-risk suppliers that could compromise the security of EU networks and citizens’ data. Currently, 14 Member States have no restrictions on high-risk suppliers in place. FIGURE 3 Share in semiconductor value chain by country % of worldwide total, 2019 Source: SIA, 2021. To reduce its vulnerabilities, the EU needs to develop a genuine “foreign economic policy” based on securing critical resources [see the chapter on critical

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[]

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