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results for Manufacturing for Georgia Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation61 Mapping the economic potential in Manu- facturing: results for Moldova Results of the economic mapping for Moldova are shown in Table 2.12. In total, 5 manufacturing in- dustries have been identified as having a current strength and only one manufacturing industry has been identified as having an emerging strength (these industries are highlighted in the two col- umns with an ‘X’ in a green-coloured cell).Industries with current strength include Pro- cessing/preserving of fish, etc. (102); Manufacture of textiles (13); Manufacture of wearing apparel (14); Manufacture of leather and related products (15); and Furniture (310). Industries with emerg- ing strength include Manufacture of chemicals and chemical products (20). Employ- mentTurnoverEmploy- ment & turnoverEmploy- mentTurnoverEmploy- ment & turnover Current Current Current Emerging Emerging Emerging 7 10 5 1 7 1 101 Processing/preserving of meat X 102 Processing/preserving of fish, etc. X X X 103 Processing/preserving of fruit, vegetables X 104 Vegetable and animal oils and fats X X 106Grain mill products, starches and starch products X 13 Manufacture of textiles X X X 14 Manufacture of wearing apparel X X X 15Manufacture of leather and related productsX X X 16Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials X 19Manufacture of coke and refined petroleum products X 20Manufacture of chemicals and chemical products X X X 22Manufacture of rubber and plastic products X 25Manufacture of fabricated metal products, except machinery and equipment X 26Manufacture of computer, electronic and optical products X 27 Manufacture of electrical equipment X 28Manufacture of machinery and equipment n.e.c. X 29Manufacture of motor vehicles, trailers and semi-trailersX 310 Furniture X X X n.e.c. = not elsewhere classifiedTable 2.12. Economic mapping results for Manufacturing for Moldova 62 Part 2 Analysis of economic and innovation potential Mapping the economic potential in Manu- facturing – results for Ukraine Results of the economic mapping for Ukraine are shown in Table 2.13. In total, 5 manufacturing industries have been identified as having a cur- rent strength and 4 manufacturing industries have been identified as having an emerging strength (these industries are highlighted in the two col- umns with an ‘X’ in a green-coloured cell). Industries with current strength include Vege- table and animal oils and fats (104); Manufacture of basic metals (24); Manufacture of
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of exportsEmerging strength% share of exports 18 60.9% 26 47.9% 0 Food and live animals 001 Live animals other than animals of division 03 X 1.8% 056 Vegetables, roots and tubers, prepared or preserved, n.e.s. X 0.2% 058Fruit, preserved, and fruit preparations (excluding fruit juices) X 0.4% 075 Spices X 0.3% X 0.3% 098 Edible products and preparations, n.e.s. X 0.2% 1 Beverages and tobacco 111 Non-alcoholic beverages, n.e.s. X 4.4% X 4.4% 112 Alcoholic beverages X 9.0% X 9.0% 2 Crude materials, inedible, except fuels 283Copper ores and concentrates; copper mattes; cement copperX 11.4% X 11.4% 288 Non-ferrous base metal waste and scrap, n.e.s. X 1.3% 289Ores and concentrates of precious metals; waste, scrap and sweepings of precious metals (other than of gold) X 0.3% 3 Mineral fuels, lubricants and related materials 333Petroleum oils and oils obtained from bituminous minerals, crude X 1.3% 334Petroleum oils and oils obtained from bituminous minerals (other than crude); preparations, n.e.s., containing by weight 70% or more of petroleum oils or of oils obtained from bituminous minerals, these oils being the basic constituents of the preparations; waste oils X 0.6% 351 Electric current X 0.8%Table 2.18. Goods export specialisation for Georgia 70 Part 2 Analysis of economic and innovation potential SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 18 60.9% 26 47.9% 4 Animal and vegetable oils, fats and waxes 411 Animal oils and fats X 0.2% 5 Chemicals and related products, n.e.s. 522 Inorganic chemical elements, oxides and halogen salts X 0.3% X 0.3% 523 Salts and peroxysalts, of inorganic acids and metals X 0.6% 541Medicinal and pharmaceutical products, other than medicaments of group 542X 0.4% 542 Medicaments (including veterinary medicaments) X 4.1% X 4.1% 553Perfumery, cosmetic or toilet preparations (excluding soaps)X 0.6% X 0.6% 6 Manufactured goods classified chiefly by material 625Rubber tyres, interchangeable tyre treads, tyre flaps and inner tubes for wheels of all kindsX 0.8% X 0.8% 671Pig-iron, spiegeleisen, sponge iron, iron or steel granules and powders and ferro-alloysX 9.5% X 9.5% 672Ingots and other primary forms, of iron or steel; semi- finished products of iron or steel X 1.0% 679Tubes, pipes and hollow profiles, and tube or pipe fittings, of iron or steel X 0.4% 684 Aluminium X 0.3% 699 Manufactures of base metal, n.e.s. X 0.2% 7 Machinery and transport equipment 723Civil engineering and contractors' plant and equipment; parts thereofX 0.3% X 0.3% 778 Electrical
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[]
patents. Azerbaijan has the following two: for the ‘Oil and Gas Production and Transportation’ cluster, the S&T domains Chemistry and chemical engineer- ing, Energy and Nanotechnology and materials could be aligned with the E&I domain Manufac- ture of coke and refined petroleum products. For the ‘Chemical Products’ cluster, the S&T domains Chemistry and chemical engineering and Nano- technology and materials could again be matched with the E&I domains. In this case, further con- cordances could be also found with the Agrifood and Biotechnology S&T domains. For all S&T do- mains except for Agrifood, the concordances were produced by both patents and publications. Georgia has two concordances: for the ‘Food Processing and Manufacturing’ cluster, the Agri- food S&T domain could be aligned with the man- ufacturing of food and beverages E&I domains through both patents and publications. For the ‘Metalworking Technology’ cluster, the Nano- technology and materials S&T domain could be matched with the Manufacture of fabricated met- al products E&I domain. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation251 Moldova has four concordances: for the econom- ic cluster ‘Food Processing & Manufacturing’, the Agrifood S&T domain could be matched with the corresponding food and beverage manufactur- ing E&I domains. The concordance was triggered by both patents and publications. For the ‘Wood Products’ cluster, a concordance was obtained be- tween the respective E&I domain and the Chem- istry and chemical engineering. For the cluster ‘Chemical Products’, a concordance between the E&I domain Manufacture of chemicals and chem- ical products and the Biotechnology, Chemistry and chemical engineering and Nanotechnology and materials S&T domains could be found. In all cases, the concordance is produced by both pat- ent and publications and seems to be satisfactory. For the cluster ‘Communications Equipment and Services’, a reasonable concordance between the Telecommunications E&I domain and the ICT and computer science S&T domain could be identified. The concordance is, in this case, produced by pub- lications only. Ukraine, having the largest number of S&T re- cords, has the following five concordances: in the cluster ‘Food Processing and Manufacturing’, the S&T domain Agrifood could be aligned with the Manufacture of food products E&I domain. The concordance was produced by both publications and patents. In the ‘Metalworking Technology’ cluster, a concordance could be produced between the Nanotechnology and materials S&T domain and the respective E&I domain. The concordance was produced by publications only, but in
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Grünthal, G., Thieken, A. H., Schwarz, J., Radtke, K. S., Smolka, A., and Merz, B.: Comparative risk assessment for the city of Cologne-storms, floods, earthquake, Nat. Hazards, 38, 21–44, 2006. Haasnoot, M., Kwakkel, J. H., Walker, W. E., and ter Maat, J.: Dy- namic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world, Global Environ. Change, 23, 485–498, https://doi.org/10.1016/j.gloenvcha.2012.12.006, 2013. Hak, T., van Rhee, H., and Suurmond, R.: How to interpret results of meta-analysis, SSRN, https://doi.org/10.2139/ssrn.3241367, 2016. Han, J., Wu, S., and Wang, H.: Preliminary study on geo- logical hazard chains, Earth Science Frontiers, 14, 11–20, https://doi.org/10.1016/S1872-5791(08)60001-9, 2007. Hansjürgens, B. and Antes, R.: Economics and management of cli- mate change: risks, mitigation and adaptation, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-77658-5, 2008. Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025 https://doi.org/10.5194/nhess-25-287-2025T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level 303 IPCC: Managing the risks of extreme events and disasters to ad- vance climate change adaptation. A special report of work- ing groups I and II of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, ISBN 978-1- 107-02506-6, 2012. ISO: ISO 3166-1 – Codes for the representation of names of coun- tries and their subdivisions – Part 1: Country codes, International Organization for Standardization, Geneva, Switzerland, https:// www.iso.org/obp/ui/en/#iso:std:iso:3166:-1:ed-4:v1:en (last ac- cess: 17 January 2025), 2020. Jelinski, D. E. and Wu, J.: The modifiable areal unit problem and implications for landscape ecology, Landscape Ecol., 11, 129– 140, 1996. Kappes, M. S., Keiler, M., and Glade, T.: From single- to multi- hazard risk analyses: a concept addressing emerging challenges, in: Mountain Risks: Bringing Science to Society, edited by: Malet, J. P., Glade, T., and Casagli, N., CERG Editions, Stras- bourg, France, 351–356, ISBN 2-9518317-1-5, 2010. Kappes, M. S., Frigerio, S., and Malet, J. P.: Multi-hazard expo- sure analyses with multirisk – a platform for user-friendly anal- ysis, 12th Congress INTERPRAEVENT, April 2012, Grenoble, France, 487–495, 2012a. Kappes, M. S., Gruber, K., Frigerio, S., Bell, R., Keiler, M., and Glade, T.: The multirisk platform: the technical con- cept and application of a regional scale multihazard ex- posure analysis tool, Geomorphology, 151–152, 139–155, https://doi.org/10.1016/j.geomorph.2012.01.024, 2012b. Kappes, M. S., Keiler, M., von Elverfeldt, K., and Glade, T.: Chal- lenges of analyzing multi-hazard risk: a review, Nat. Hazards, 64, 1925–1958, 2012c. Koks, E. E., Rozenberg, J., Zorn, C., Tariverdi, M., V ousdoukas, M., Fraser, S. A., Hall, J. W., and Hallegatte, S.: A global multi- hazard risk analysis of road and railway infrastructure assets, Nat. Commun., 10, 2677, https://doi.org/10.1038/s41467-019- 10442-3, 2019.
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minimize the MAUP effect, recommended practices focus on using smaller areal unit (e.g. LAUs rather than provinces or countries) for data aggregation (Kwan, 2012). This reduces the potential errors in spatial pattern distortion without entirely removing them. 2.1.2 Input hazard and exposure data The exposure data are built on the relationship between haz- ard (i) and assets (ii): exposure or assets at risk Df(assets, hazard). We overlay spatial information about residential built-up areas and population with data describing hazard ar- eas in order to define the assets exposed to single hazards. We then aggregate the exposure at the level of LAUs. We identify statistically significant hotspots of asset exposure to single hazards through the application of two distinct expo- sure aggregation methods: –based on absolute values, the sum of the exposed asset; –based on relative values, as ratios or share of the expo- sure from the total assets in LAUs. For the exposure to earthquakes, due to the continuous spatial extent of the hazard area, we depict the relative ag- gregation schema using the density (or share of the exposure compared to the total area of the LAUs). The relative aggre- gation schema intends to address risk management strategies based on cost-efficient measures, while the absolute schema supports risk management strategies that prioritize the most affected areas and people. (i) Hazard layers. The hazard layers considered in this study represent areal extension rather than intensity. We use not a probabilistic assessment but rather a deterministic approach, selecting hazards with average temporal (frequency of oc- currence) and spatial probability (susceptibility). The hazard datasets used here and their characteristics are presented in Table 1. The motivations for their selection along with their usage in disaster risk assessments are detailed in the sections dedicated to individual hazards in the Supplement (Sect. S1 – “Hazard layers and their definitions”). (ii) Asset layers. As asset layers, we use the residential built- up areas from the European Settlement Map (ESM) (Flor- https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025290 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level Figure 1. Different steps of the methodological approach developed in this study. Figure 2. Local administrative unit areas (km2): spatial distribution (a)and mean LAU area per country (b). The abbreviations for countries shown in the figures follow the ISO 3166-1 alpha-2 standard (ISO, 2020). czyk et al., 2019) and residential population from
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defence R&I – that will be undersupplied without common action and funding. Finally, for Member States to converge more closely in their policies – be it the Single Market or more generally in the policies described in this report such as climate, innova - tion, defence, space and education – both regulation and incentives will be required. Incentives will also require common funding. However, if the strategy is not fully implemented and productivity growth does not pick up, a broader issuance of public debt may be needed to make the funding of the transitions a more realistic proposition. The issuance of common safe assets to fund joint investment projects could follow existing templates – however, it would have to be accompanied by all the safeguards that such a fundamental step would entail . The use of a common safe asset has a well-established precedent in the funding of the NGEU. The present circum - stances are equally serious, even if less dramatic. But issuing such assets on a more systematic basis would require a stronger set of fiscal rules which ensure that an increase in common debt is matched by a more sustainable path of national debt. In this way, all EU Member States could contribute to such an asset without prejudging the sustain - ability of their public debt. Issuance would also have to remain mission and project-specific. MOBILISING PRIVATE AND PUBLIC FINANCE AT SCALE To unlock private capital, the EU must build a genuine Capital Markets Union (CMU) supported by a stronger pension system . As a key pillar of the CMU, the European Securities and Markets Authority (ESMA) should transi - tion from a body that coordinates national regulators into the single common regulator for all EU securities markets, similar to the US Securities and Exchange Commission. An essential step to transform ESMA into such an agency is modify its governance and decision-making processes along similar lines as those of the ECB Governing Council, detaching them as much as possible from the national interests of EU Member States. Harmonising insolvency frameworks will also be critical to remove fragmentation created by differing creditor hierarchies, while the EU should continue to eliminate taxation obstacles to cross-border investing. These measures would in turn make it easier to foster centralisation in clearing and settlement. Ultimately, the EU should aim to create a single central counter - party platform (CCP) and a
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[]
and cork 181_Printing and service activities related to printing 19_Coke and refined petroleum products 210_Pharmaceuticals, medicinal chemicals, etc. 23_Other non-metallic mineral products 25_Fabricated metal products 27_Electrical equipment 29_Motor vehicles, trailers and semi-trailers 310_Furniture 33_Repair and installation of machinery and equipmentMethodology The methodology used to analyse INDSTAT data is slightly different to that presented in Section 2.1 as data are less recent and the total number of analysed industries is much smaller. 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 specialisation and rela- tive 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 56 Part 2 Analysis of economic and innovation potential Figure 2.3. Distribution of output in Manufacturing for five EaP countries 101_Processing/preserving of meat 103_Processing/preserving of fruit, vegetables 105_Dairy products 107_Other food products 110_Beverages 13_Textiles 15_Leather and related products 170_Wood products, cork, straw, plaiting materials 182_Reproduction of recorded media 20_Chemicals and chemical products 22_Rubber and plastic products 24_Basic metals 26_Computer, electronic and optical products 28_Machinery and equipment n.e.c. 30_Other transport equipment 32_Other manufacturingArmenia Azerbaijan Georgia Moldova Ukraine EaP countries0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 102_Processing/preserving of fish, etc. 104_Vegetable and animal oils and fats 106_Grain mill products, starches and starch products 108_Prepared animal feeds 120_Tobacco products 14_Wearing apparel 16_Wood and of products of wood and cork 181_Printing and service activities related to printing 19_Coke and refined petroleum products 210_Pharmaceuticals, medicinal chemicals, etc. 23_Other non-metallic mineral products 25_Fabricated metal products 27_Electrical equipment 29_Motor vehicles, trailers and semi-trailers 310_Furniture 33_Repair and installation of machinery and equipmentpredefined thresholds. For all industries shown in Table 2.7, the following indicators have been calculated for Armenia, Azer- baijan, Georgia, Moldova and Ukraine: ■the degree of specialisation for the number of employees for each year in the 2012-2017 period, ■average relative share of the total number of employees for each year in the 2012-2017 period, ■rate of change in degree of specialisation for the number of employees for two time peri- ods – between 2012 and 2015, and between 2014 and 2017, ■the degree of specialisation for output for each year in the 2012-2017 period,
[ "and", "cork", "\n", "181_Printing", "and", "service", "activities", "related", "to", "printing", "\n", "19_Coke", "and", "refined", "petroleum", "products", "\n", "210_Pharmaceuticals", ",", "medicinal", "chemicals", ",", "etc", ".", "\n", "23_Other", "non", "-", "metallic", "mineral", "products", "\n", "25_Fabricated", "metal", "products", "\n", "27_Electrical", "equipment", "\n", "29_Motor", "vehicles", ",", "trailers", "and", "semi", "-", "trailers", "\n", "310_Furniture", "\n", "33_Repair", "and", "installation", "of", "machinery", "and", "equipmentMethodology", "\n", "The", "methodology", "used", "to", "analyse", "INDSTAT", "data", "\n", "is", "slightly", "different", "to", "that", "presented", "in", "Section", "\n", "2.1", "as", "data", "are", "less", "recent", "and", "the", "total", "number", "\n", "of", "analysed", "industries", "is", "much", "smaller", ".", "Aggregate", "\n", "industry", "-", "level", "data", "have", "been", "used", "to", "identify", "two", "\n", "types", "of", "industries", ":", "\n ", "■", "industries", "with", "a", "current", "strength", ",", "includ-", "\n", "ing", "specialised", "industries", "with", "critical", "mass", ",", "\n", "where", "the", "degree", "of", "specialisation", "and", "rela-", "\n", "tive", "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", "56", "\n ", "Part", "2", "Analysis", "of", "economic", "and", "innovation", "potential", "\n", "Figure", "2.3", ".", "Distribution", "of", "output", "in", "Manufacturing", "for", "five", "EaP", "countries", "\n", "101_Processing", "/", "preserving", "of", "meat", "\n", "103_Processing", "/", "preserving", "of", "fruit", ",", "vegetables", "\n", "105_Dairy", "products", "\n", "107_Other", "food", "products", "\n", "110_Beverages", "\n", "13_Textiles", "\n", "15_Leather", "and", "related", "products", "\n", "170_Wood", "products", ",", "cork", ",", "straw", ",", "plaiting", "materials", "\n", "182_Reproduction", "of", "recorded", "media", "\n", "20_Chemicals", "and", "chemical", "products", "\n", "22_Rubber", "and", "plastic", "products", "\n", "24_Basic", "metals", "\n", "26_Computer", ",", "electronic", "and", "optical", "products", "\n", "28_Machinery", "and", "equipment", "n.e.c", ".", "\n", "30_Other", "transport", "equipment", "\n", "32_Other", "manufacturingArmenia", "\n", "Azerbaijan", "\n", "Georgia", "\n", "Moldova", "\n", "Ukraine", "\n", "EaP", "countries0", "%", "10", "%", "20", "%", "30", "%", "40", "%", "50", "%", "60", "%", "70", "%", "80", "%", "90", "%", "100", "%", "\n", "102_Processing", "/", "preserving", "of", "fish", ",", "etc", ".", "\n", "104_Vegetable", "and", "animal", "oils", "and", "fats", "\n", "106_Grain", "mill", "products", ",", "starches", "and", "starch", "products", "\n", "108_Prepared", "animal", "feeds", "\n", "120_Tobacco", "products", "\n", "14_Wearing", "apparel", "\n", "16_Wood", "and", "of", "products", "of", "wood", "and", "cork", "\n", "181_Printing", "and", "service", "activities", "related", "to", "printing", "\n", "19_Coke", "and", "refined", "petroleum", "products", "\n", "210_Pharmaceuticals", ",", "medicinal", "chemicals", ",", "etc", ".", "\n", "23_Other", "non", "-", "metallic", "mineral", "products", "\n", "25_Fabricated", "metal", "products", "\n", "27_Electrical", "equipment", "\n", "29_Motor", "vehicles", ",", "trailers", "and", "semi", "-", "trailers", "\n", "310_Furniture", "\n", "33_Repair", "and", "installation", "of", "machinery", "and", "equipmentpredefined", "thresholds", ".", "\n", "For", "all", "industries", "shown", "in", "Table", "2.7", ",", "the", "following", "\n", "indicators", "have", "been", "calculated", "for", "Armenia", ",", "Azer-", "\n", "baijan", ",", "Georgia", ",", "Moldova", "and", "Ukraine", ":", "\n ", "■", "the", "degree", "of", "specialisation", "for", "the", "number", "\n", "of", "employees", "for", "each", "year", "in", "the", "2012", "-", "2017", "\n", "period", ",", "\n ", "■", "average", "relative", "share", "of", "the", "total", "number", "of", "\n", "employees", "for", "each", "year", "in", "the", "2012", "-", "2017", "\n", "period", ",", "\n ", "■", "rate", "of", "change", "in", "degree", "of", "specialisation", "for", "\n", "the", "number", "of", "employees", "for", "two", "time", "peri-", "\n", "ods", "–", "between", "2012", "and", "2015", ",", "and", "between", "\n", "2014", "and", "2017", ",", "\n ", "■", "the", "degree", "of", "specialisation", "for", "output", "for", "\n", "each", "year", "in", "the", "2012", "-", "2017", "period", "," ]
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real person and should be ashamed that he was able to be elected president.” It’s true. If you believe Hillary Clinton gave away 20 percent of the American Uranium to Russia, then you should be ashamed that you voted for Trump. No one should be able to give or receive anything that’s not supposed to, so long as they have a warrant. If you’ve been in a relationship for more than six months with a person who’s also convicted of being a felon (or convicted of stealing), that’s just stupid, especially as a married man. If you’re married to someone convicted of a crime, and they go on their honeymoon with you, that’s a felony, not a honeymoon. Human CHIP DESIGNER Texas Instruments unveiled a family of system on chip (SoC) processors aimed at automakers today, which are designed for use in self-driving cars. Named the TDA2x, the SoC family integrates safety features, such as aiding auto designers to create advanced driver assistance systems (ADAS), which in turn help ”reduce the number of collisions on the road and enable autonomous driving experiences”. ”TDA2x device family combines an optimal mix of high performance, vision analytics, video, graphics and general purpose processing cores in a low power envelope, enabling a broad range of ADAS applications including front camera, surround view and sensor fusion,” Texas Instruments said in its release. Machine Description This classic blend of coffee, cream, and sugar is the perfect drink! It is a smooth and creamy coffee with hints of cream and sweet sugar that can be enjoyed even after a full day of work or playing! The sugar provides a wonderful texture to the coffee beans, so that it can be scooped out into a cup. Available in four flavours: vanilla cream, caramel cream, coffee creme, and chocolate cream. Note: Coffee can be prepared in less than 120 minutes. Note: Serves one. Table 9: The 10 examples that “expert” raters were guided through before they were asked to perform the detection task. These are hand-selected to showcase the spectrum of generated text and human-written text.Figure 6: The interface of the task used for human evaluation. Each time the user presses next, the passage’s length is doubled. On the left, we show the first step of evaluation, on the right, the second to last. Figure 7: For some of the questions, the text ”Dear AMT Worker: to show you’re
[ "real", "person", "and", "should", "be", "ashamed", "that", "he", "was", "able", "to", "be", "elected", "\n", "president", ".", "”", "\n", "It", "’s", "true", ".", "If", "you", "believe", "Hillary", "Clinton", "gave", "away", "20", "percent", "of", "the", "American", "Uranium", "to", "Russia", ",", "then", "you", "should", "be", "ashamed", "that", "you", "voted", "for", "Trump", ".", "No", "one", "\n", "should", "be", "able", "to", "give", "or", "receive", "anything", "that", "’s", "not", "supposed", "to", ",", "so", "long", "as", "they", "have", "a", "warrant", ".", "If", "you", "’ve", "been", "in", "a", "relationship", "for", "more", "than", "six", "months", "with", "a", "\n", "person", "who", "’s", "also", "convicted", "of", "being", "a", "felon", "(", "or", "convicted", "of", "stealing", ")", ",", "that", "’s", "just", "stupid", ",", "especially", "as", "a", "married", "man", ".", "If", "you", "’re", "married", "to", "someone", "convicted", "of", "a", "\n", "crime", ",", "and", "they", "go", "on", "their", "honeymoon", "with", "you", ",", "that", "’s", "a", "felony", ",", "not", "a", "honeymoon", ".", "\n", "Human", "CHIP", "DESIGNER", "Texas", "Instruments", "unveiled", "a", "family", "of", "system", "on", "chip", "(", "SoC", ")", "processors", "aimed", "at", "automakers", "today", ",", "which", "are", "designed", "for", "use", "in", "self", "-", "driving", "cars", ".", "\n", "Named", "the", "TDA2x", ",", "the", "SoC", "family", "integrates", "safety", "features", ",", "such", "as", "aiding", "auto", "designers", "to", "create", "advanced", "driver", "assistance", "systems", "(", "ADAS", ")", ",", "which", "in", "turn", "help", "\n", "”", "reduce", "the", "number", "of", "collisions", "on", "the", "road", "and", "enable", "autonomous", "driving", "experiences", "”", ".", "\n", "”", "TDA2x", "device", "family", "combines", "an", "optimal", "mix", "of", "high", "performance", ",", "vision", "analytics", ",", "video", ",", "graphics", "and", "general", "purpose", "processing", "cores", "in", "a", "low", "power", "envelope", ",", "\n", "enabling", "a", "broad", "range", "of", "ADAS", "applications", "including", "front", "camera", ",", "surround", "view", "and", "sensor", "fusion", ",", "”", "Texas", "Instruments", "said", "in", "its", "release", ".", "\n", "Machine", "Description", "\n", "This", "classic", "blend", "of", "coffee", ",", "cream", ",", "and", "sugar", "is", "the", "perfect", "drink", "!", "It", "is", "a", "smooth", "and", "creamy", "coffee", "with", "hints", "of", "cream", "and", "sweet", "sugar", "that", "can", "be", "enjoyed", "even", "after", "\n", "a", "full", "day", "of", "work", "or", "playing", "!", "The", "sugar", "provides", "a", "wonderful", "texture", "to", "the", "coffee", "beans", ",", "so", "that", "it", "can", "be", "scooped", "out", "into", "a", "cup", ".", "\n", "Available", "in", "four", "flavours", ":", "vanilla", "cream", ",", "caramel", "cream", ",", "coffee", "creme", ",", "and", "chocolate", "cream", ".", "\n", "Note", ":", "Coffee", "can", "be", "prepared", "in", "less", "than", "120", "minutes", ".", "Note", ":", "Serves", "one", ".", "\n", "Table", "9", ":", "The", "10", "examples", "that", "“", "expert", "”", "raters", "were", "guided", "through", "before", "they", "were", "asked", "to", "perform", "the", "detection", "\n", "task", ".", "These", "are", "hand", "-", "selected", "to", "showcase", "the", "spectrum", "of", "generated", "text", "and", "human", "-", "written", "text", ".", "Figure", "6", ":", "The", "interface", "of", "the", "task", "used", "for", "human", "evaluation", ".", "Each", "time", "the", "user", "presses", "next", ",", "the", "passage", "’s", "length", "\n", "is", "doubled", ".", "On", "the", "left", ",", "we", "show", "the", "first", "step", "of", "evaluation", ",", "on", "the", "right", ",", "the", "second", "to", "last", ".", "\n", "Figure", "7", ":", "For", "some", "of", "the", "questions", ",", "the", "text", "”", "Dear", "AMT", "Worker", ":", "to", "show", "you", "’re" ]
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public actors depends on the public sector structure of each country, since hos- pitals and medical facilities, ministries and minis- terial institutes and state companies (notably in Azerbaijan and Ukraine) can be observed with a differing presence depending on the country. 196 Part 3 Analysis of scientific and technological potential Figure 3.39. Example of an interactive visualisation tool, depicting the main analysed actors and collaboration networks in the Eastern Partnership Regarding private for-profit companies, their pres- ence in the international S&T data sources is for the most part rather small. In all countries, there is a relevant presence of scientific, applied research and technical companies, as well as ICT compa- nies. Beyond those, some clear national cham- pions and small and medium highly specialised companies, in specific sectors, can be found. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation197 Fundamental physics and mathematics Nanotechnology and materials Health and wellbeing Optics and photonics Chemistry and chemical engineering Governance, culture, education and the economy Biotechnology Environmental sciences and industries ICT and computer science Agrifood Energy Mechanical engineering and heavy machinery Electric and electronic technologies National Academy of Sciences of Armenia899 530 431 255 446 234 134 257 143 116 24 13 3 A.I. Alikhanyan National Science Laboratory 2 670 123 19 69 10 13 10 10 42 5 6 26 4 Yerevan State University 626 518 200 253 139 170 263 136 77 147 25 6 5 Yerevan State Medical University16 25 423 7 13 21 36 12 4 19 8 1 0 Russian-Armenian University 93 160 36 21 34 57 16 10 20 12 6 6 2 National Polytechnic University of Armenia54 94 1 45 16 11 9 17 39 9 2 7 2 American University of Armenia16 4 77 5 1 56 5 12 34 11 2 0 2 Armenian National Agrarian University25 7 36 2 14 16 24 17 2 24 2 0 1 Armenian State Pedagogical University after Khachatur Abovyan30 36 11 27 13 10 2 5 0 1 0 0 0 Center for the Advancement of Natural Discoveries using Light Emission50 14 6 11 0 1 4 0 1 3 0 0 2Figure 3.40. Top actors in Armenia by number of records (all types), across all domainsArmenia Scientific production in Armenia is heavily concen- trated in a few institutions, notably the National Academy of Sciences, and a few comprehensive and specialised universities.
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- The Boston Consulting Group EU Strategy Forward Global Rud Pedersen Public Affairs 04 THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | ACKNOWLEDGMENTForeword Europe has been worrying about slowing growth since the start of this century. Various strategies to raise growth rates have come and gone, but the trend has remained unchanged. Across different metrics, a wide gap in GDP has opened up between the EU and the US, driven mainly by a more pronounced slowdown in productivity growth in Europe. Europe’s households have paid the price in foregone living standards. On a per capita basis, real disposable income has grown almost twice as much in the US as in the EU since 2000. For most of this period, slowing growth has been seen as an inconvenience but not a calamity. Europe’s exporters managed to capture market shares in faster growing parts of the world, especially Asia. Many more women entered the workforce, lifting the labour contribution to growth. And, after the crises of 2008 to 2012, unemployment steadily fell across Europe, helping to reduce inequality and maintain social welfare. The EU also benefitted from a favourable global environment. World trade burgeoned under multilateral rules. The safety of the US security umbrella freed up defence budgets to spend on other priorities. In a world of stable geopol - itics, we had no reason to be concerned about rising dependencies on countries we expected to remain our friends. But the foundations on which we built are now being shaken. The previous global paradigm is fading. The era of rapid world trade growth looks to have passed, with EU companies facing both greater competition from abroad and lower access to overseas markets. Europe has abruptly lost its most important supplier of energy, Russia. All the while, geopolitical stability is waning, and our dependencies have turned out to be vulnerabilities. Technological change is accelerating rapidly. Europe largely missed out on the digital revolution led by the internet and the productivity gains it brought: in fact, the productivity gap between the EU and the US is largely explained by the tech sector. The EU is weak in the emerging technologies that will drive future growth. Only four of the world’s top 50 tech companies are European. Yet, Europe’s need for growth is rising. The EU is entering the first period in its recent history in which growth will not be supported by rising
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to NiMH batteries and the testing battery equipment used for this research.Batteries 2025 ,11, 30 3 of 20 2.1. The Batteries Regulation Concerning Portable Batteries of General Use Table 1 lists the parameters from Regulation EU 2023/1542 Annex III part B applicable to rechargeable PBGUs, such as NiMH, for which minimum performance and durability requirements will be set. These definitions are based on those found in standard IEC 61951-1 [5]. Table 1. Performance parameters from the Battery regulation, Annex III, part B, rechargeable batteries. Parameter Definition Rated capacityCapacity value of a battery determined under specified conditions and declared by the manufacturer. Charge (capacity) retentionCapacity that a battery can deliver after storage, at a specific temperature, for a specific time without subsequent recharge as a percentage of the rated capacity. Charge (capacity) recoveryCapacity that a battery can deliver with subsequent recharge after storage, at a specific temperature, for a specific time, as the percentage of rated capacity. Endurance in cyclesThe number of charge and discharge cycles a battery can perform under specific conditions before the capacity drops below a specified fraction of the rated capacity. The standard IEC 61951-2 is used for determining the parameters listed in Table 1. In Section 8, this research also uses the standard for primary batteries, IEC 60086 part 1 [27] and part 2 [ 28], together with the Nordic Ecolabel for primary batteries [ 29] and rechargeable batteries [30]. 2.2. Samples and Battery Energy Storage Testing (BESTEST) Laboratory at JRC The samples used in this research have been selected from the database on primary and secondary batteries created by the JRC since 2020 and that is in constant update. This database is not openly accessible for the time being but has been described in our previous research [25]. For the experimental analysis of the performance and durability, we use various brands of batteries commercially available in the European Union. Portable NiMH batteries and alkaline primary batteries (only in Section 8) of general use with the common designations AA, AAA, D, C, and 9V are used. A list of the selected batteries is presented in Table 2. All experiments are performed on pairs of batteries to check for repeatability. The differences in results between pairs of batteries were generally small; for all pairs used in this study, the difference was below 2%. The testing of the batteries is performed at the BESTEST Lab (see Figure 1).
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heroic hero and a Yankee hero- ine. It turns out that bears have more than their fair share of venomous snakes, bees 2.Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, “Here’s what you can do. Go back to civi- lization. Take your gun. Pick up some sticks and try to kill the bear. If you don’t kill it, then run.” Robinson said, “I do not think that the Bear is going to attack. It’s just out of my way. I’m going to take my bag. I’m going to leave my things in there. I’ll try to hide out there. I’ll call in the friends.” Two other little boys went out onto the 11GPT-2 774M available at https://talktotransformer.com, accessed 2019/12/09, paragraph boundaries in generated texts removed for easier presentation3.Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, “Here’s what you can do. Place these sticks over your head. Your heart will stop and you will not feel fear. If you take them off and hold them out, you will go into a state of near perfect awareness. You will feel that you are the survivalist. A survivalist in your senses. Then look down. There is a water tube that is about 1/2 an inch in diameter. I use it to make a plastic movie projector for discovering new things.” The survivalist placed 4.Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, “Here’s what you can do. #YouCanDoIt.” Police officers were quick to respond, find- ing a 3-foot female grizzly bear wandering off a dirt road near the shoreline. The Department of Fish and Wildlife came on the scene and secured the safe removal of the bear from the area, which was allowed to return home. The bear was taken by DFW’s animal care staff to Norris Cty Medical Center, where veterinari- ans evaluated the bear and determined it was in good condition. The bear was ultimately released into a nearby wooded area 5.Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, “Here’s what you can do. You can strap the sticks to your chest
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[]
anovel conspecific strongly triggered the activity of a large frac-tion of aIC VIP+ INs. We next sought to assess the coding spec- ificity of these INs for specific behavioral epochs. In other words, whether different functional populations among aIC VIP+ INsexist that selectively respond to distinct stimuli. During the first social preference test day, the number of active coding neurons (CNs) for the interaction with the unfamiliarmouse (classified as having a mean activity Zscore 2 sabove baseline, but not during object interactions; see STAR Methods ) was larger than those specifically coding for the object (chi-square, p = 0.0004; Figures 6 A and 6B). On the second day, a similar proportion of CNs responded to the novel mouse and the object (chi-square, p = 0.14; Figures 6 E and 6F). However, activity patterns of CNs during the social interaction test didnot change across days: mouse CNs were highly active during interactions with a conspecific mouse but not with the object, whereas object CNs were active during interactions with theempty wire cage and not with the novel mouse ( Figures 6 C, 6D, 6G, and 6H). During the fear-conditioning acquisition phase ( Figure 6 I) the number of CNs activated upon US presentations was higher than those exclusively activated by CS presentations (chi- square, p = 0.0001), whereas the opposite was observed for in-hibited neurons ( Figure 6 J). The activity patterns during US pre- sentations showed that US CN activity reached its maximum a few seconds after the shock and persisted for the entire period analyzed (30 s), whereas CS CNs, after an initial peak of activity,became strongly inhibited ( Figures 6 K and 6L). During CS pre- sentations, US CNs remained unresponsive, whereas CS CNssteadily increased their activity ( Figures 6 K and 6L). During the fear retrieval phase ( Figure 6 M), a higher proportion of neurons coded for CS presentations (CS-R CNs) compared with the fear acquisition phase ( Figure 6 N; chi-square, p = 0.009). How- ever, the amount of VIP+ INs that responded to the omission of the US (US- CNs) was comparable with the fraction of CS-R CNs ( Figures 6 M and 6N; chi-square, p = 0.43). The activity pat- terns of these two functional ensembles, namely CS-R and US-CNs, showed a strong negative correlation ( Figures S6 A, S6B, S6D, and S6E). During CS presentations, CS-R CNs were highly active, while US-
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chemical engineering (806 | 14.66%) Chemistry and chemical engineering (1 463 | 9.74%) Chemistry and chemical engineering (595 | 6.85%)Chemistry and chemical engineering (416 | 11.7%)Nanotechnology and materials (4 560 | 30.35%)Nanotechnology and materials (1 168 | 32.85%) Nanotechnology and materials (727 | 9.38%)Nanotechnology and materials (21 222 | 23.34%)Fundamental physics and mathematics (4 200 | 48.34%) Fundamental physics and mathematics (26 852 | 21.36%)Health and wellbeing (1 411 | 16.24%) Health and wellbeing (2 656 | 17.68%)Environmental sciences and industries (957 | 12.35%) Environmental sciences and industries (256 | 7.2%) Environmental sciences and industries (8 023 | 8.82%)Environmental sciences and industries (10 735 | 8.54%) Environmental sciences and industries (482 | 5.55%)Environmental sciences and industries (497 | 9.04%)Environmental sciences and industries (780 | 5.19%)Governance, culture, education and the economy (507 | 14.26%) Governance, culture, education and the economy (928 | 11.98%)Governance, culture, education and the economy (14 895 | 11.85%)Governance, culture, education and the economy (680 | 7.83%) Governance, culture, education and the economy (547 | 9.95%)Governance, culture, education and the economy (11 830 | 13.01%)Health and wellbeing (17 874 | 14.22%) Optics and photonics (603 | 6.94%) Optics and photonics (1 378 | 9.17%)Optics and photonics (478 | 6.17%)Biotechnology (8 935 | 9.83%) Biotechnology (10 340 | 8.22%)ICT and computer science (13 111 | 10.43%)ICT and computer science (555 | 10.09%)ICT and computer science (1 391 | 9.26%)ICT and computer science (10 514 | 11.56%) ICT and computer science (412 | 5.32%)Electric and electronic technologies (219 | 6.16%)Health and wellbeing (1 131 | 20.57%)Health and wellbeing (1 496 | 19.31%) Health and wellbeing (11 229 | 12.35%)Health and wellbeing (622 | 17.49%)Fundamental physics and mathematics (1 663 | 30.25%) Fundamental physics and mathematics (4 031 | 26.83%)Fundamental physics and mathematics (17 474 | 19.22%) Fundamental physics and mathematics (449 | 12.63%)Fundamental physics and mathematics (3 317 | 42.82%)EaP ARMENIA AZERBAIJAN GEORGIA MOLDOVA UKRAINE BELARUS Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation169 170 Part 3 Analysis of scientific and technological potential Figure 3.21. Top 7 identified domains in each EaP country in patents (number of identified patents in the domain | percentage of the total number of patents analysed in the country) EaP ARMENIA AZERBAIJAN GEORGIA MOLDOVA UKRAINE BELARUS Mechanical engineering and heavy machinery (18 510 | 31.14%) Mechanical engineering and heavy machinery (64 | 17.83%)Mechanical engineering and heavy machinery (215 | 49.31%)Mechanical
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"economy", " \n", "(", "14", "895", "|", "11.85%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "680", "|", "7.83", "%", ")", "\n", "Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "547", "|", "9.95%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "11", "830", "|", "13.01%)Health", "and", "wellbeing", "\n", "(", "17", "874", "|", "14.22", "%", ")", "\n", "Optics", "and", "photonics", "\n", "(", "603", "|", "6.94", "%", ")", "\n", "Optics", "and", "photonics", "\n", "(", "1", "378", "|", "9.17%)Optics", "and", "photonics", "\n", "(", "478", "|", "6.17%)Biotechnology", "\n", "(", "8", "935", "|", "9.83", "%", ")", "\n", "Biotechnology", "\n", "(", "10", "340", "|", "8.22%)ICT", "and", "computer", "\n", "science", "\n", "(", "13", "111", "|", "10.43%)ICT", "and", "computer", "\n", "science", "\n", "(", "555", "|", "10.09%)ICT", "and", "computer", "\n", "science", "\n", "(", "1", "391", "|", "9.26%)ICT", "and", "computer", "\n", "science", "\n", "(", "10", "514", "|", "11.56", "%", ")", "\n", "ICT", "and", "computer", "\n", "science", "\n", "(", "412", "|", "5.32%)Electric", "and", "electronic", "\n", "technologies", "\n", "(", "219", "|", "6.16%)Health", "and", "wellbeing", "\n", "(", "1", "131", "|", "20.57%)Health", "and", "wellbeing", "\n", "(", "1", "496", "|", "19.31", "%", ")", "\n", "Health", "and", "wellbeing", "\n", "(", "11", "229", "|", "12.35%)Health", "and", "wellbeing", "\n", "(", "622", "|", "17.49%)Fundamental", "physics", "\n", "and", "mathematics", "\n", "(", "1", "663", "|", "30.25", "%", ")", "\n", "Fundamental", "physics", "\n", "and", "mathematics", "\n", "(", "4", "031", "|", "26.83%)Fundamental", "physics", "\n", "and", "mathematics", "\n", "(", "17", "474", "|", "19.22", "%", ")", "\n", "Fundamental", "physics", "\n", "and", "mathematics", "\n", "(", "449", "|", "12.63%)Fundamental", "physics", "\n", "and", "mathematics", "\n", "(", "3", "317", "|", "42.82%)EaP", "ARMENIA", "AZERBAIJAN", "GEORGIA", "MOLDOVA", "UKRAINE", "BELARUS", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation169", "170", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Figure", "3.21", ".", "Top", "7", "identified", "domains", "in", "each", "EaP", "country", "in", "patents", "(", "number", "of", "identified", "patents", "in", "the", "domain", "|", "\n", "percentage", "of", "the", "total", "number", "of", "patents", "analysed", "in", "the", "country", ")", "\n", "EaP", "ARMENIA", "AZERBAIJAN", "GEORGIA", "MOLDOVA", "UKRAINE", "BELARUS", "\n", "Mechanical", "engineering", "\n", "and", "heavy", "machinery", "\n", "(", "18", "510", "|", "31.14", "%", ")", "\n", "Mechanical", "engineering", "\n", "and", "heavy", "machinery", "\n", "(", "64", "|", "17.83%)Mechanical", "engineering", "\n", "and", "heavy", "machinery", "\n", "(", "215", "|", "49.31%)Mechanical" ]
[]
in the absence of that claim (absence-focused regime Ic) for both generic and branded product versions, that is: H2aG:WTPsCImCk cCGHWTPsCIcCk cCGHℑˆ0 for cˆ⊔F1CF2⊓(8). H2aB: WTPsCImCk cCBWTPsCIcCk cCBℑˆ0 for cˆ⊔F1CF2⊓(9). A negative result in (8) and (9) indicates that the introduction of a ‘made for’ claim decreases consumer preference for the foreign version relative to the domestic one. To assess whether these effects differ between Western and Eastern consumers, we disentangle equations (8) and (9) by country group. We expect that in the presence-focused regime, the social status stereotype increases Eastern European consumers ’ preference for Western-country product versions, since these trigger higher quality expectations, while the opposite is expected for Western European consumers. This yields the following specifications: H2bCGBWTPsCImCk cCGHWTPsCIcCk cCGH〉 F0fors∃Eandc∃W D0fors∃Wandc∃E(10) H2bCBBWTPsCImCk cCBWTPsCIcCk cCB〉 F0fors∃Eandc∃W D0fors∃Wandc∃E(11) To investigate the brand-name-related implications empirically, we examine the proportion of consumers whose willingness to pay for the different product versions varies between the absence- and presence-focused regimes. More specifically, using (4) to (10) we examine the number of cases (N) in which the preference for domestic branded product versions (NsCIc cCB†exceeds that for domestic generic product versions ( NsCIc cCGH) and whether these preferences change across different regimes (iˆ⊔IcCIm⊓). This translates into testing for: H3a BNsCIcCk cCGHDNsCIc cCB (12) H3b BNsCImIcCk cCGHDNsCImIcCk cCB (13) Appendix C:.Price levels used in the DCE The price vectors were constructed starting from actual prices found in retails stores at the time of the survey. The additional levels include price increases and decreases. The construction is skewed to the left as the actual price refers to branded products. Starting from a 5-level fully symmetrical price vector for each product and country we included a sixth lower level to take into account that the DCE also included non-branded versions, and these are usually cheaper in the market.D.M. Federica et al. Food Policy 131 (2025) 102803 15 % deviation from original price 0.450.30.15 0 0.15 0.3 Product Country Currency 1 2 3 4 5 6 DanoneActivia Strawberry DE EUR 1.09 1.39 1.69 1.99 2.29 2.59 HU HUF 219 279 339 399 459 519 LI EUR 1.01 1.28 1.56 1.83 2.10 2.38 KnorrSpaghettisauce DE EUR 0.43 0.55 0.67 0.79 0.91 1.03 HU HUF 219 279 339 399 459 519 LI EUR 0.34 0.43 0.53 0.62 0.71 0.81 MilkaChocoCookies DE EUR 0.76 0.97 1.18 1.39 1.60 1.81 HU HUF 274 349 424 499 574 649 LI EUR 0.59 0.76 0.92
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[]
the regulation is “portable batteries of general use” (PBGUs), which refers to the most commonly used batteries directly sold to end- users with the common designations AAAA, AAA, AA, C, D, 4.5V , 9V , and A23. These batteries are found online, at supermarkets, and other retail businesses. In most cases, the batteries are used in domestic appliances (e.g., remote controls, clocks, radios, smoke detectors, toys). In the market a consumer may find common designation batteries with different chemistries. These batteries can be non-rechargeable batteries (also called ‘primary’ batteries), e.g., alkaline, zinc–carbon and lithium batteries, and rechargeable batteries (also called ‘secondary’ batteries). Nickel metal hydride (NiMH) is the most common chemistry, while nickel cadmium (NiCd) [ 2], nickel zinc (NiZn) [ 3], and lead acid [ 4] are used much less. In this study, we analyze the performance and durability of commercial secondary NiMH batteries, focusing on their capacity, charge retention, charge recovery, and cycle endurance. By aligning our methodology with the IEC 61951-2 standard [ 5,6], we seek Batteries 2025 ,11, 30 https://doi.org/10.3390/batteries11010030Batteries 2025 ,11, 30 2 of 20 to establish a basis for future minimum performance requirements as mandated by the European batteries regulation in Article 9 [ 1]. Our findings will provide insights into both manufacturers and consumers, promoting the development of better battery technologies. NiMH batteries contain valuable metals. At the positive electrode (cathode), nickel is present as a hydroxide compound. The negative electrode (anode) is composed of an intermetallic alloy with a combination of transition metals in a graphite substrate (nickel, cobalt, and manganese) and rare earth elements (lanthanum, cerium, neodymium, and praseodymium) that allow hydrogen storage [ 7]. The cathode and the anode are electrically separated by a polyolefin material, usually a non-woven polypropylene soaked in a concentrated aqueous solution of sodium hydroxide [ 8]. Research on improving the technology has been focused on the following: increasing cycle life [ 9], second life and reuse [ 10], recovery of rare metals from used batteries [ 11,12], and scaling up processes for battery recycling [13]. In terms of the energy storage market, the NiMH battery has gained attention for the different performance characteristics that it provides at the system and cell levels, such as a specific energy of 60–120 Wh/kg, an energy density of 140–300 Wh/L [ 14], an energy efficiency of 60–92%, a lifetime of 1800–2000 cycles, and its availability worldwide [15]. NiMH batteries
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[]
false- negatives when trained with samples generated using top- psampling. However, for top- k, it more often mistakes machine-generated text to be human-written, while for untruncated random sampling the opposite is the case. tion over unigrams strongly diverges from human- written texts–an easy feature for discriminators to exploit. In fact, See et al. (2019) note that it takes settingkto 1000 to achieve about the same amount of rare word usage and fraction of non-stopword text as as human writing.3This makes it very easy for the model to pick out machine-generated text based on these distributional differences. One way to help resolve this problem is to add priming text. Doing so causes more rare words to be incorporated into the top- kof the unigram distribution. Adding even a single human word of priming significantly reduces the performance of detectors trained with top- krandom sampling. Without priming, a discriminator trained on se- quences of length 2 can classify with 90% ac- curacy the provenance of the text (Figure 1a). By adding one priming token, accuracy drops to 65%. Even on the longest 192-length sequences, top-kdiscriminator accuracy is 6% lower on the primed dataset than the unprimed one. When generating with nucleus or untruncated random sampling, adding a priming token is not as impactful, as these methods are already sam- pling from a large fraction (or all) of the probabil- ity distribution. This is seen in Figure 2a where at the very first step of unprimed generation, nu- cleus sampling selects from 3075 possible vocab- ulary words, and at later positions selects from on 3when decoding from the GPT-2 small model with 117M parameters.average more than 500. Untruncated random sam- pling always selects from the entire 50,000 word vocabulary, whereas top- konly selects from k. Transferability In Table 2, we show how dis- criminators trained with samples from one decod- ing strategy can transfer at test time to detect- ing samples generated using a different decoding strategy. Unsurprisingly a discriminator trained on top-kgeneralizes poorly to other sampling meth- ods: accuracy drops to as low as 42.5%, worse than chance . Conversely, training the discrimi- nator with sequences sampled from the untrun- cated distribution leads to little transferability to detecting top- ksamples. Only the discriminator trained with nucleus sampling (a compromise be- tween unmodified sampling and top- k) was able to detect sequences from the other sampling strate- gies without too much of a
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[ { "end": 364, "label": "CITATION-REFEERENCE", "start": 347 } ]
in turn decreasing its overall capacity to meet demand and depriving the industry of orders and opportunities. As a result, defence procurement is diverted outside of the EU. Between June 2022 and June 2023, 78% of procurement spending went to non-EU suppliers, out of which 63% went to the US. At the same time, when EU Member States organise and cooperate, the results are positive. One such example is the A330 Multi-Role Tanker Transport, which was developed through a collaborative project allowing participating countries to pool resources and share operation and maintenance costs. The European space sector is likewise hindered by insufficient demand aggregation and investment coordination among Member States. Furthermore, the European Space Agency (ESA) operates based on the principle of “geographical return”, meaning that it invests in each of its member countries through industrial contracts for space programmes an amount which is similar to the country’s financial contribution to the agency. This principle leads to an inevitable fragmentation of supply chains, the unnecessary duplication of capacities in relatively small markets and a mismatch between the most competitive industrial actors and the actual allocation of resources. 60THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 4In the absence of common European spending, policy actions for the defence sector need to focus on aggregating demand and integrating industrial defence assets [see the chapter on defence] . In the short term, the swift implementation of the European Defence Industrial Strategy and the related European Defence Industry Programme is needed. In particular, it is essential to increase substantially the aggregation of demand between groups of Member States, at least among those who opt to do so, and to raise the share of joint defence procure - ment. The report recommends further steps to develop a medium-term EU Defence Industrial Policy which can support the structural cross-border integration of defence assets and the selective integration and consolidation of EU industrial capacity, with the explicit aim of increasing scale, standardisation and interoperability. EU competition policy should enable such consolidation when increased scale would deliver efficiencies or allow the realisation of globally competitive investments. In addition, as EU defence spending rises, defence industrial consolidation, integration and technological innovation should be supported by reinforced European preference principles in procurement, ensuring that a minimum share of this rising demand is concentrated on European companies rather than flowing overseas. Together with the urgent need to increase overall
[ " ", "in", "turn", "decreasing", "its", "overall", "capacity", "to", "meet", "demand", "and", "depriving", "the", "industry", "of", "orders", "and", "\n", "opportunities", ".", "As", "a", "result", ",", "defence", "procurement", "is", "diverted", "outside", "of", "the", "EU", ".", "Between", "June", "2022", "and", "June", "2023", ",", "\n", "78", "%", "of", "procurement", "spending", "went", "to", "non", "-", "EU", "suppliers", ",", "out", "of", "which", "63", "%", "went", "to", "the", "US", ".", "At", "the", "same", "time", ",", "when", "\n", "EU", "Member", "States", "organise", "and", "cooperate", ",", "the", "results", "are", "positive", ".", "One", "such", "example", "is", "the", "A330", "Multi", "-", "Role", "Tanker", "\n", "Transport", ",", "which", "was", "developed", "through", "a", "collaborative", "project", "allowing", "participating", "countries", "to", "pool", "resources", "\n", "and", "share", "operation", "and", "maintenance", "costs", ".", "The", "European", "space", "sector", "is", "likewise", "hindered", "by", "insufficient", "demand", "\n", "aggregation", "and", "investment", "coordination", "among", "Member", "States", ".", "Furthermore", ",", "the", "European", "Space", "Agency", "(", "ESA", ")", "\n", "operates", "based", "on", "the", "principle", "of", "“", "geographical", "return", "”", ",", "meaning", "that", "it", "invests", "in", "each", "of", "its", "member", "countries", "\n", "through", "industrial", "contracts", "for", "space", "programmes", "an", "amount", "which", "is", "similar", "to", "the", "country", "’s", "financial", "contribution", "\n", "to", "the", "agency", ".", "This", "principle", "leads", "to", "an", "inevitable", "fragmentation", "of", "supply", "chains", ",", "the", "unnecessary", "duplication", "of", "\n", "capacities", "in", "relatively", "small", "markets", "and", "a", "mismatch", "between", "the", "most", "competitive", "industrial", "actors", "and", "the", "actual", "\n", "allocation", "of", "resources", ".", "\n", "60THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "CHAPTER", "4In", "the", "absence", "of", "common", "European", "spending", ",", "policy", "actions", "for", "the", "defence", "sector", "need", "to", "focus", "on", "\n", "aggregating", "demand", "and", "integrating", "industrial", "defence", "assets", " ", "[", "see", "the", "chapter", "on", "defence", "]", ".", "In", "the", "short", "term", ",", "\n", "the", "swift", "implementation", "of", "the", "European", "Defence", "Industrial", "Strategy", "and", "the", "related", "European", "Defence", "Industry", "\n", "Programme", "is", "needed", ".", "In", "particular", ",", "it", "is", "essential", "to", "increase", "substantially", "the", "aggregation", "of", "demand", "between", "\n", "groups", "of", "Member", "States", ",", "at", "least", "among", "those", "who", "opt", "to", "do", "so", ",", "and", "to", "raise", "the", "share", "of", "joint", "defence", "procure", "-", "\n", "ment", ".", "The", "report", "recommends", "further", "steps", "to", "develop", "a", "medium", "-", "term", "EU", "Defence", "Industrial", "Policy", "which", "can", "\n", "support", "the", "structural", "cross", "-", "border", "integration", "of", "defence", "assets", "and", "the", "selective", "integration", "and", "consolidation", "of", "\n", "EU", "industrial", "capacity", ",", "with", "the", "explicit", "aim", "of", "increasing", "scale", ",", "standardisation", "and", "interoperability", ".", "EU", "competition", "\n", "policy", "should", "enable", "such", "consolidation", "when", "increased", "scale", "would", "deliver", "efficiencies", "or", "allow", "the", "realisation", "\n", "of", "globally", "competitive", "investments", ".", "In", "addition", ",", "as", "EU", "defence", "spending", "rises", ",", "defence", "industrial", "consolidation", ",", "\n", "integration", "and", "technological", "innovation", "should", "be", "supported", "by", "reinforced", "European", "preference", "principles", "in", "\n", "procurement", ",", "ensuring", "that", "a", "minimum", "share", "of", "this", "rising", "demand", "is", "concentrated", "on", "European", "companies", "rather", "\n", "than", "flowing", "overseas", ".", "\n", "Together", "with", "the", "urgent", "need", "to", "increase", "overall" ]
[]
[ 4,5]. One of the methods used to extract the average angular momentum from isomeric yield ratios relies on codesmodeling the prompt de-excitation of fission fragments, such as FIFRELIN [ 6]. Therefore, accurate knowledge of fission fragment level schemes, including the half-life of any iso-meric state, is required to assess the derived fission frag- ment angular momentum with high reliability. Second, the measurement of isomer half-lives has proven to be a fruit-ful source of information for nuclear structure studies. Inthat respect, spontaneous fission is a convenient reaction that produces neutron-rich nuclei far from stability without the need for neutron or radioactive ion beams. Hence, isomeric states produced in the spontaneous fission of 252Cf were studied using the VESPA setup, which bene- fits from excellent timing characteristics from both a state-of-the-art ionization chamber and fast scintillation detectors. The present study was based on fission-tagged γ-γcoinci- dences between five almost identical γ-ray detectors. The present paper is organized as follows. Section 2.1is dedicated to the detailed description of the VESPA exper- imental setup and the characteristics of its detectors. Sec- tion2.2presents the data analysis procedure used to identify and analyze the fission fragments isomers. In Sect. 3.1,w e present the half-lives of the isomers measured in this work, ranging from about a nanosecond to several microseconds. Inparticular, we report new isomeric states in 94Rb,108Tc, and 147Ce. Section 3.2shows how this rich experimental data can be used to develop a calibration procedure of the VESPA ion- 0123456789().: V ,-vol 123 5 Page 2 of 12 Eur. Phys. J. A (2025) 61:5 ization chamber with respect to the nuclear charge of the fis- sion fragments. Finally, Sect. 4summarizes the main results of this work. 2 Experiment 2.1 Setup The measurements were performed at the EC-JRC Geel (Bel- gium) with the VESPA setup for fission studies [ 1]. A source of252Cf undergoing spontaneous fission was placed inside a methane-filled twin Frisch-grid ionization chamber (IC)[7], which served as a fission detector. The Cf source was deposited on a nickel backing with a thickness of 250 nm. This created a small asymmetry between the ’backing side’and the ’source side’, as fragments lost some additional kinetic energy passing through this backing. The γ-ray spec- troscopy was performed by five 2 /prime/prime×2/prime/prime(51 mm diameter × 51 mm length) cylindrical LaBr 3(Ce) scintillation detectors surrounding the ionization chamber. These detectors were placed perpendicular to the IC axis, at distances rranging
[ "[", "4,5", "]", ".", "One", "of", "the", "methods", "used", "to", "extract", "the", "average", "\n", "angular", "momentum", "from", "isomeric", "yield", "ratios", "relies", "on", "codesmodeling", "the", "prompt", "de", "-", "excitation", "of", "fission", "fragments", ",", "such", "\n", "as", "FIFRELIN", "[", "6", "]", ".", "Therefore", ",", "accurate", "knowledge", "of", "fission", "\n", "fragment", "level", "schemes", ",", "including", "the", "half", "-", "life", "of", "any", "iso", "-", "meric", "state", ",", "is", "required", "to", "assess", "the", "derived", "fission", "frag-", "\n", "ment", "angular", "momentum", "with", "high", "reliability", ".", "Second", ",", "the", "\n", "measurement", "of", "isomer", "half", "-", "lives", "has", "proven", "to", "be", "a", "fruit", "-", "ful", "source", "of", "information", "for", "nuclear", "structure", "studies", ".", "Inthat", "respect", ",", "spontaneous", "fission", "is", "a", "convenient", "reaction", "that", "\n", "produces", "neutron", "-", "rich", "nuclei", "far", "from", "stability", "without", "the", "\n", "need", "for", "neutron", "or", "radioactive", "ion", "beams", ".", "\n", "Hence", ",", "isomeric", "states", "produced", "in", "the", "spontaneous", "fission", "\n", "of", "\n", "252Cf", "were", "studied", "using", "the", "VESPA", "setup", ",", "which", "bene-", "\n", "fits", "from", "excellent", "timing", "characteristics", "from", "both", "a", "state", "-", "of", "-", "the", "-", "art", "ionization", "chamber", "and", "fast", "scintillation", "detectors", ".", "\n", "The", "present", "study", "was", "based", "on", "fission", "-", "tagged", "γ", "-", "γcoinci-", "\n", "dences", "between", "five", "almost", "identical", "γ", "-", "ray", "detectors", ".", "\n", "The", "present", "paper", "is", "organized", "as", "follows", ".", "Section", "2.1is", "\n", "dedicated", "to", "the", "detailed", "description", "of", "the", "VESPA", "exper-", "\n", "imental", "setup", "and", "the", "characteristics", "of", "its", "detectors", ".", "Sec-", "\n", "tion2.2presents", "the", "data", "analysis", "procedure", "used", "to", "identify", "\n", "and", "analyze", "the", "fission", "fragments", "isomers", ".", "In", "Sect", ".", "3.1,w", "e", "\n", "present", "the", "half", "-", "lives", "of", "the", "isomers", "measured", "in", "this", "work", ",", "\n", "ranging", "from", "about", "a", "nanosecond", "to", "several", "microseconds", ".", "Inparticular", ",", "we", "report", "new", "isomeric", "states", "in", "\n", "94Rb,108Tc", ",", "and", "\n", "147Ce", ".", "Section", "3.2shows", "how", "this", "rich", "experimental", "data", "can", "\n", "be", "used", "to", "develop", "a", "calibration", "procedure", "of", "the", "VESPA", "ion-", "\n", "0123456789", "(", ")", ".", ":", "V", ",", "-vol", "123", " ", "5", "Page", "2", "of", "12", "Eur", ".", "Phys", ".", "J.", "A", " ", "(", "2025", ")", "61:5", "\n", "ization", "chamber", "with", "respect", "to", "the", "nuclear", "charge", "of", "the", "fis-", "\n", "sion", "fragments", ".", "Finally", ",", "Sect", ".", "4summarizes", "the", "main", "results", "\n", "of", "this", "work", ".", "\n", "2", "Experiment", "\n", "2.1", "Setup", "\n", "The", "measurements", "were", "performed", "at", "the", "EC", "-", "JRC", "Geel", "(", "Bel-", "\n", "gium", ")", "with", "the", "VESPA", "setup", "for", "fission", "studies", "[", "1", "]", ".", "A", "source", "\n", "of252Cf", "undergoing", "spontaneous", "fission", "was", "placed", "inside", "\n", "a", "methane-filled", "twin", "Frisch", "-", "grid", "ionization", "chamber", "(", "IC)[7", "]", ",", "which", "served", "as", "a", "fission", "detector", ".", "The", "Cf", "source", "was", "\n", "deposited", "on", "a", "nickel", "backing", "with", "a", "thickness", "of", "250", "nm", ".", "\n", "This", "created", "a", "small", "asymmetry", "between", "the", "’", "backing", "side’and", "the", "’", "source", "side", "’", ",", "as", "fragments", "lost", "some", "additional", "\n", "kinetic", "energy", "passing", "through", "this", "backing", ".", "The", "γ", "-", "ray", "spec-", "\n", "troscopy", "was", "performed", "by", "five", "2", "\n", "/prime", "/", "prime×2", "/", "prime", "/", "prime(51", "mm", "diameter", "×", "\n", "51", "mm", "length", ")", "cylindrical", "LaBr", "3(Ce", ")", "scintillation", "detectors", "\n", "surrounding", "the", "ionization", "chamber", ".", "These", "detectors", "were", "\n", "placed", "perpendicular", "to", "the", "IC", "axis", ",", "at", "distances", "rranging", "\n" ]
[]
as expected, GFP-injected animals were not affected in their natural behavior ( Figure 4 H). The social interac- tion ratio showed a significant group 3time interaction (two-way ANOVA, interaction: p = 0.006), revealing that ArchT-injectedmice interacted more with the novel conspecific, in comparison with the object, during the second half of the test (post-hoc Bon- ferroni multiple comparison test, p = 0.01) ( Figure 4 I). We further confirmed that the reduction in social preference did not resultfrom a general aversive effect of optogenetic inhibition of aIC VIP+ INs by testing the animals in a real-time object place pref- erence test ( Figures S5 B–S5D), or from an influence on locomo- tion ( Figures S5 E–S5G). We next investigated whether the specific suppression of aIC VIP+ IN activity during the presentation of the footshock in cuedfear conditioning influences associative learning ( Figure 4 J). GFP- and ArchT-injected mice displayed similar levels of freezing during the successive CS-US pairings in the acquisitionphase ( Figure 4 K). However, when the animals underwent fear retrieval, 24 h later and in a different context, ArchT-injected mice froze significantly less compared with GFP-injected con- trols, but the percent of freezing remained significantly highercompared with the pre-CS (two-way ANOVA, group: p = 0.001; time: p = 0.001; interaction: p = 0.03; Bonferroni multiple com- parisons test, Pre-CS-R GFP versus Pre-CS-R ArchT: p = 0.99;CS-R GFP versus CS-R ArchT: p = 0.001), indicating that the light-mediated inhibition of aIC VIP+ INs during the CS-US pair- ings did not prevent the formation of the fear memory ( Figure 4 L). Taken together, these data suggest that the activity of aIC VIP+ INs during the exposure to social or aversive stimuli is necessary for the full expression of both social preference andfear memory retrieval. Based on our optogenetic interrogations, we hypothesized that the general activity of aIC VIP+ INs decays with time or with the repetitive presentation of a given stimulus during socialinteractions and fear learning. Indeed, during the social prefer- ence test, the general activity of aIC VIP+ INs was higher during the exploration of an unfamiliar mouse compared with the objectonly in the first 5 min of the test, and also compared with the last 5 min of social interaction. On the other hand, the activity related to the novel object increased between the first and last 5 min onday 1 ( Figures 5 A and 5B).
[ "as", "expected", ",", "GFP", "-", "injected", "animals", "were", "not", "\n", "affected", "in", "their", "natural", "behavior", "(", "Figure", "4", "H", ")", ".", "The", "social", "interac-", "\n", "tion", "ratio", "showed", "a", "significant", "group", "3time", "interaction", "(", "two", "-", "way", "\n", "ANOVA", ",", "interaction", ":", "p", "=", "0.006", ")", ",", "revealing", "that", "ArchT", "-", "injectedmice", "interacted", "more", "with", "the", "novel", "conspecific", ",", "in", "comparison", "\n", "with", "the", "object", ",", "during", "the", "second", "half", "of", "the", "test", "(", "post", "-", "hoc", "Bon-", "\n", "ferroni", "multiple", "comparison", "test", ",", "p", "=", "0.01", ")", "(", "Figure", "4", "I", ")", ".", "We", "further", "\n", "confirmed", "that", "the", "reduction", "in", "social", "preference", "did", "not", "resultfrom", "a", "general", "aversive", "effect", "of", "optogenetic", "inhibition", "of", "aIC", "\n", "VIP+", "INs", "by", "testing", "the", "animals", "in", "a", "real", "-", "time", "object", "place", "pref-", "\n", "erence", "test", "(", "Figures", "S5", "B", "–", "S5D", ")", ",", "or", "from", "an", "influence", "on", "locomo-", "\n", "tion", "(", "Figures", "S5", "E", "–", "S5", "G", ")", ".", "\n", "We", "next", "investigated", "whether", "the", "specific", "suppression", "of", "aIC", "\n", "VIP+", "IN", "activity", "during", "the", "presentation", "of", "the", "footshock", "in", "cuedfear", "conditioning", "influences", "associative", "learning", "(", "Figure", "4", "J", ")", ".", "\n", "GFP-", "and", "ArchT", "-", "injected", "mice", "displayed", "similar", "levels", "of", "\n", "freezing", "during", "the", "successive", "CS", "-", "US", "pairings", "in", "the", "acquisitionphase", "(", "Figure", "4", "K", ")", ".", "However", ",", "when", "the", "animals", "underwent", "fear", "\n", "retrieval", ",", "24", "h", "later", "and", "in", "a", "different", "context", ",", "ArchT", "-", "injected", "\n", "mice", "froze", "significantly", "less", "compared", "with", "GFP", "-", "injected", "con-", "\n", "trols", ",", "but", "the", "percent", "of", "freezing", "remained", "significantly", "highercompared", "with", "the", "pre", "-", "CS", "(", "two", "-", "way", "ANOVA", ",", "group", ":", "p", "=", "0.001", ";", "\n", "time", ":", "p", "=", "0.001", ";", "interaction", ":", "p", "=", "0.03", ";", "Bonferroni", "multiple", "com-", "\n", "parisons", "test", ",", "Pre", "-", "CS", "-", "R", "GFP", "versus", "Pre", "-", "CS", "-", "R", "ArchT", ":", "p", "=", "0.99;CS", "-", "R", "GFP", "versus", "CS", "-", "R", "ArchT", ":", "p", "=", "0.001", ")", ",", "indicating", "that", "the", "\n", "light", "-", "mediated", "inhibition", "of", "aIC", "VIP+", "INs", "during", "the", "CS", "-", "US", "pair-", "\n", "ings", "did", "not", "prevent", "the", "formation", "of", "the", "fear", "memory", "(", "Figure", "4", "L", ")", ".", "\n", "Taken", "together", ",", "these", "data", "suggest", "that", "the", "activity", "of", "aIC", "\n", "VIP+", "INs", "during", "the", "exposure", "to", "social", "or", "aversive", "stimuli", "is", "\n", "necessary", "for", "the", "full", "expression", "of", "both", "social", "preference", "andfear", "memory", "retrieval", ".", "\n", "Based", "on", "our", "optogenetic", "interrogations", ",", "we", "hypothesized", "\n", "that", "the", "general", "activity", "of", "aIC", "VIP+", "INs", "decays", "with", "time", "or", "\n", "with", "the", "repetitive", "presentation", "of", "a", "given", "stimulus", "during", "socialinteractions", "and", "fear", "learning", ".", "Indeed", ",", "during", "the", "social", "prefer-", "\n", "ence", "test", ",", "the", "general", "activity", "of", "aIC", "VIP+", "INs", "was", "higher", "during", "\n", "the", "exploration", "of", "an", "unfamiliar", "mouse", "compared", "with", "the", "objectonly", "in", "the", "first", "5", "min", "of", "the", "test", ",", "and", "also", "compared", "with", "the", "last", "\n", "5", "min", "of", "social", "interaction", ".", "On", "the", "other", "hand", ",", "the", "activity", "related", "\n", "to", "the", "novel", "object", "increased", "between", "the", "first", "and", "last", "5", "min", "onday", "1", "(", "Figures", "5", "A", "and", "5B", ")", "." ]
[]
Ibba M, Söll D (May 2001). "The renaissance of aminoacyl-tRNA synthesis". EMBO Reports. 2 (5): 382–7. doi:10.1093/embo-reports/kve095. PMC 1083889. PMID 11375928. Archived from the original on 1 May 2011. Lengyel P, Söll D (June 1969). "Mechanism of protein biosynthesis". Bacteriological Reviews. 33 (2): 264–301. doi:10.1128/MMBR.33.2.264-301.1969. PMC 378322. PMID 4896351. Rudolph FB (January 1994). "The biochemistry and physiology of nucleotides". The Journal of Nutrition. 124 (1 Suppl): 124S – 127S. doi:10.1093/jn/124.suppl_1.124S. PMID 8283301. Zrenner R, Stitt M, Sonnewald U, Boldt R (2006). "Pyrimidine and purine biosynthesis and degradation in plants". Annual Review of Plant Biology. 57: 805–36. doi:10.1146/annurev.arplant.57.032905.105421. PMID 16669783. Stasolla C, Katahira R, Thorpe TA, Ashihara H (November 2003). "Purine and pyrimidine nucleotide metabolism in higher plants". Journal of Plant Physiology. 160 (11): 1271–95. Bibcode:2003JPPhy.160.1271S. doi:10.1078/0176-1617-01169. PMID 14658380. Davies O, Mendes P, Smallbone K, Malys N (April 2012). "Characterisation of multiple substrate-specific (d)ITP/(d)XTPase and modelling of deaminated purine nucleotide metabolism" (PDF). BMB Reports. 45 (4): 259–64. doi:10.5483/BMBRep.2012.45.4.259. PMID 22531138. Archived (PDF) from the original on 24 October 2020. Retrieved 18 September 2019. Smith JL (December 1995). "Enzymes of nucleotide synthesis". Current Opinion in Structural Biology. 5 (6): 752–7. doi:10.1016/0959-440X(95)80007-7. PMID 8749362. Testa B, Krämer SD (October 2006). "The biochemistry of drug metabolism--an introduction: part 1. Principles and overview". Chemistry & Biodiversity. 3 (10): 1053–101. doi:10.1002/cbdv.200690111. PMID 17193224. S2CID 28872968. Danielson PB (December 2002). "The cytochrome P450 superfamily: biochemistry, evolution and drug metabolism in humans". Current Drug Metabolism. 3 (6): 561–97. doi:10.2174/1389200023337054. PMID 12369887. King CD, Rios GR, Green MD, Tephly TR (September 2000). "UDP-glucuronosyltransferases". Current Drug Metabolism. 1 (2): 143–61. doi:10.2174/1389200003339171. PMID 11465080. Sheehan D, Meade G, Foley VM, Dowd CA (November 2001). "Structure, function and evolution of glutathione transferases: implications for classification of non-mammalian members of an ancient enzyme superfamily". The Biochemical Journal. 360 (Pt 1): 1–16. doi:10.1042/0264-6021:3600001. PMC 1222196. PMID 11695986. Galvão TC, Mohn WW, de Lorenzo V (October 2005). "Exploring the microbial biodegradation and biotransformation gene pool". Trends in Biotechnology. 23 (10): 497–506. doi:10.1016/j.tibtech.2005.08.002. PMID 16125262. Janssen DB, Dinkla IJ, Poelarends GJ, Terpstra P (December 2005). "Bacterial degradation of xenobiotic compounds: evolution and distribution of novel enzyme activities" (PDF). Environmental Microbiology. 7 (12): 1868–82. Bibcode:2005EnvMi...7.1868J. doi:10.1111/j.1462-2920.2005.00966.x. PMID 16309386. Archived (PDF) from the original on 11 November 2019. Retrieved 11 November 2019. Davies KJ (1995). "Oxidative stress: the paradox of aerobic life". Biochemical Society Symposium. 61: 1–31. doi:10.1042/bss0610001. PMID 8660387. Tu BP, Weissman JS (February 2004). "Oxidative protein folding in eukaryotes: mechanisms and consequences". The Journal of Cell Biology. 164 (3): 341–6. doi:10.1083/jcb.200311055. PMC 2172237. PMID 14757749.
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by providing the first empirical evidence on how DFQ affects consumers, and how an information provision policy can address it. To this end, it first explores whether DFQ practices align with con- sumer preferences (European Parliament, 2018 ). Second, we examine whether removing information asymmetry by labelling of these differ - ences could help consumers make more informed choices. Thirdly, given the political debate ’s focus on branded products, we examine the role of brands in influencing consumer perceptions of DFQ. Our analysis ex- amines consumer preferences, particularly willingness to pay (WTP), in the presence of DFQ through online and laboratory experiments in six EU member states. The paper proceeds by presenting the conceptual framework and hypotheses, the description of the experimental design and econometric methods followed by the results, and a discussion of policy implications. 2.Conceptual framework and hypotheses In the presence of DFQ, consumers can be misled when expected utility based on similar extrinsic cues does not correspond to the actual utility derived from different ingredients or composition of the same product. Consumers typically do not have the opportunity to simulta - neously compare different versions of food product offered in different markets, and even traveling abroad may not reveal these differences, especially for products they frequently purchase (Liu-Thompkins and Tam, 2013 ) or when they use brands as a cue for purchase decisions (Rahinel et al., 2021 ). This makes DFQ an unobserved product charac - teristic until consumers can infer it through taste, ingredient lists, labelling, or media. To assess the implications of this practice for consumers and to develop effective policies that prevent opportunistic behavior by com- panies, it is crucial to understand how consumers form expectations about food products that are marketed as the same but differ in composition. To this purpose, we build our approach on Colen et al. (2020) , who integrated the concept of DFQ into Grunert ’s (2005)1Total Food Quality Model. Grunert (2005) emphasizes that consumers ’ ex- pected utility from consuming a food product is shaped through a complex quality perception process, relying on intrinsic and primarily extrinsic cues. Starting from this conceptual framework, we draw a first set of hy- potheses (H1a and H1b), testing whether different product versions sold in different MS align with consumer preferences, which is the main reason put forward by companies to justify DFQ. Unlike the country-of- origin literature (Verlegh and Steenkamp, 1999
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machinery and equipment n.e.c. (28); Manufacture of motor vehicles, trailers and semi-trailers (29); and Man- ufacture of other transport equipment (30). Indus- tries with emerging strength include Processing/ preserving of meat (101); Grain mill products, starches and starch products (106); Manufacture of leather and related products (15); and Manu- facture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials (16). Employ- mentTurnoverEmploy- ment & turnoverEmploy- mentTurnoverEmploy- ment & turnover Current Current Current Emerging Emerging Emerging 7 9 5 11 9 4 101 Processing/preserving of meat X X X 104 Vegetable and animal oils and fats X X X X 105 Dairy products X 106Grain mill products, starches and starch products X X X 107 Other food products X 108 Prepared animal feeds X 15Manufacture of leather and related products X X X 16Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials X X X X 160Wood products, cork, straw, plaiting materials X 181Printing and service activities related to printing X 20Manufacture of chemicals and chemical products X X 210Pharmaceuticals, medicinal chemicals, etc. X X 23Manufacture of other non-metallic mineral products X 24 Manufacture of basic metals X X X 25Manufacture of fabricated metal products, except machinery and equipmentX Table 2.13. Economic mapping results for Manufacturing for Ukraine Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation63 Employ- mentTurnoverEmploy- ment & turnoverEmploy- mentTurnoverEmploy- ment & turnover Current Current Current Emerging Emerging Emerging 7 9 5 11 9 4 26Manufacture of computer, electronic and optical productsX X 27 Manufacture of electrical equipment 28Manufacture of machinery and equipment n.e.c.X X X X 29Manufacture of motor vehicles, trailers and semi-trailersX X X 30Manufacture of other transport equipmentX X X 32 Other manufacturing X 33Repair and installation of machinery and equipment X n.e.c. = not elsewhere classified 2.3. Export performance for goods The UN Comtrade Database32 on exports of goods contains up to five-digit export data according to the Standard International Trade Classification (SITC) product classification. Specialisation in ex- port performance can be used to identify those goods categories in which countries perform above average and are able to compete successfully on international markets. Data availability The UN Comtrade Database includes data on ex- port values for 278 three-digit goods using the SITC Rev. 4 classification33. For all Eastern Partner-
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F25J G21F 20.2 A01N A01P 20.3 B27K C09D 20.4 A61K A61Q C09F C11D D06L 20.5A62D C06B C06C C06D C08H C09G C09H C09J C10M C10N C11B C11C C14C C23F C23G C40B D01C F42B F42D 20.6 D01F 21 A61K A61P C07D C07H C07J C07K C12N C12P C12Q 22 B29C B29D B60C B67D 22.1 C08C 23 B32B 23.1 C03B C03C 23.3 B28B B28C 23.4 E03D 23.5 C04B 24 B21C B22D C21B C21C C21D C22B C22C C22F C25C C25F 322 Annexes NACE IPC 24.4 G21H 25.1 A44B A47H B21G F27D 25.2 F16T F22B F22G F24J 25.3 G21B G21C G21D 25.4 B63G F41A F41B F41C F41F F41G F41H F41J F42C G21J 25.5 B22F 25.6 C23D C25D 25.7 E05B E05D E05F E06B 25.9 A01L E05C F16B 26.1 B81B B81C B82B B82Y C30B G11C H01C H01F H01G H01J H01L H05K 26.2 G02F G06C G06D G06E G06F G06G G06J G06N G06T G09C 26.3G03H G08B H01Q H01S H03B H03C H03D H03G H03H H03J H03M H04B H04H H04J H04K H04L H04M H04N H04Q H04R H04S H04W 26.4 H03F H03K H03L 26.5F15C 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 26.6 A61N G21K H05G H05H 26.7 G02B G02C G03B 26.8 G03C 27.1 H02B H02J H02K H02N H02P H02S 27.2 H01M 27.3 H01B H01H H01R H02G 27.4 F21H F21K F21L F21M F21P F21Q F21S F21V F21W F21Y H01K 27.5 A21B A45D A47G A47J A47L B01B D06F E06C F24B F24C F24D F25C F25D H05B 27.9 B60M B61L G08G G10K H01P H01T H02H H02M H05C 28.1A47K B23F F01B F01C F01D F01K F01M F01N F01P F02C F02G F02K F03B F03C F03D F03G F04B F04C F04D F15B F16C F16D F16F F16H F16K F16M F23R G05D G05G 28.2A62C B01D B04C B05B B41J B41K B43M B60S B61B B65G B66B B66C B66D B66F C10F C12L E02C F16G F22D F23B F23C F23D F23G F23H F23J F23K F23L F23M F23N F24F F24H F25B F27B F28B F28C F28D F28F F28G G01G G03G G06K G06M G07B G07C G07D G07F G07G G09D G09G G10L G11B H05F 28.3 A01B A01C A01D A01F A01G A01K A01M B27L 28.4B21D B21F B21H B21J B21K B21L B23B B23C B23D B23G B23H B23K B23P B23Q B24B B24C B24D B25B B25C B25D B25F B25G B25H B25J B26B B26F B27B B27C B27F B27G B27J B28D B30B Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation323 NACE IPC 28.9A21C A22B A22C A23N
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[]
880 PublicationsFigure 3.55. Number of publications and EC projects in collaboration between EaP actors in different countries, in the ‘Health and wellbeing’ domain Colour indicates the relative distribution of documents, computed row-wise. AM AZ BY GE MD UA Other 2 9 10 10 9 14 2 2 2 2 2 2 9 2 9 8 8 16 10 2 9 10 10 17 10 2 8 10 8 18 9 2 8 10 8 32 EC projectsAM AZ BY GE MD UA Other AM 4 19 21 5 18 189 AZ 4 4 5 4 51 157 BY 19 4 18 13 71 787 GE 21 5 18 6 24 238 MD 5 4 13 6 14 72 UA 18 51 71 24 14 2 730 PublicationsFigure 3.56. Number of publications and EC projects in collaboration between EaP actors in different countries, in the ‘ICT and computer science’ domain Colour indicates the relative distribution of documents, computed row-wise. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation215 Regional collaboration in Mechanical en- gineering and heavy machinery In terms of publications, there are very few collab- orations in the domain of Mechanical engineer- ing and heavy machinery. Most collaborations are with external partners. There are very few collaborations on EC projects.Regional collaboration in Nanotechnolo- gy and materials In the case of Nanotechnology and materials publications, external collaborations again have a significant weight throughout all six EaP countries. Within the EaP, some of the highest-intensity col- laborations are Armenia and Georgia. For all coun- tries, Ukraine is one of the most relevant partners. The number of EC project collaborations is again very low, with Ukraine having the highest number of collaborations with one another and across the EaP. AM AZ BY GE MD UA Other 1 1 1 1 4 EC projectsAM AZ BY GE MD UA Other AM 1 2 1 38 AZ 1 2 97 BY 2 2 1 18 189 GE 1 2 1 4 23 MD 1 1 4 61 UA 2 18 4 4 1 154 PublicationsFigure 3.57. Number of publications and EC projects in collaboration between EaP actors in different countries, in the ‘Mechanical engineering and heavy machinery’ domain Colour indicates the relative distribution of documents, computed row-wise. AM AZ BY GE MD UA Other 3 2 3 5 1 1 1 3 1 2 2 13 28 2 2 3 4
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[]
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the  discharge  continues  until ca. 1.2 V (20% SOC). Finally, there is another quick voltage drop  until 1 V, determining  the 0% SOC.  The test of the (rated) capacity is performed  using the procedure  in standard  IEC  61951-2 clause 7.3.2. For this test, batteries are discharged  at 0.2 C to a cut-off voltage of 1  V (7 V for the 9V battery). Then, they are charged at 0.1 C for 16 h and discharged  at 0.2 C  until the cut-off voltage of 1 V. The experiment  is finished when the battery can reach the  declared  capacity by the manufacturer  (the battery discharge  time needs to be higher than  Figure 6. NiMH battery discharge profiles at 0.2 C and a cut-off of 1.0 V for different battery manufacturers of ( a) AA, ( b) AAA, ( c) C, ( d) D, and ( e) average SOC. Figure 6e shows the average voltage profile as a function of SOC. The starting average voltage at 100% SOC is 1.45 V . During the discharge, the voltage decreases quickly to 1.28 V ,Batteries 2025 ,11, 30 10 of 20 which corresponds to 80% SOC. Then the voltage drops more slowly while the discharge continues until ca. 1.2 V (20% SOC). Finally, there is another quick voltage drop until 1 V , determining the 0% SOC. The test of the (rated) capacity is performed using the procedure in standard IEC 61951-2 clause 7.3.2. For this test, batteries are discharged at 0.2 C to a cut-off voltage of 1 V (7 V for the 9V battery). Then, they are charged at 0.1 C for 16 h and discharged at 0.2 C until the cut-off voltage of 1 V . The experiment is finished when the battery can reach the declared capacity by the manufacturer (the battery discharge time needs to be higher than 5 h in order to comply with IEC 61951-2 Table 6 [ 5]); if the battery fails to reach the capacity value, the experiment can be repeated up to five times. Figure 7 presents the results of testing different sizes of NiMH batteries. Batteries 2025, 11, x FOR PEER REVIEW   11 of 21    5 h in order to comply with IEC 61951-2 Table 6 [5]); if the battery fails to reach the capacity  value, the experiment  can be repeated  up to five times. Figure 7 presents the results of  testing different sizes of 
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[]
Zaykin (2011), and Chen (2011). Meta-analyses are in widespread use due to their ap- plicability, primarily in psychology, biology, and medicine (McFarland et al., 2015). Within the field of disaster riskmanagement, meta-analysis has mainly been used to as- sess the macroeconomy of disasters (Peter and van Bergeijk, 2015). We chose to use the Stouffer’s method ( Z-transform test), without weighting, applied to the two-tailed distribution of the single clusters as in Eq. (1): ZsDPk iD1Zip k: (1) The sum ofZscores (Zi) divided by the square root of the number of tests, k, provides a test of the cumulative evidence on the common null hypothesis (Whitlock, 2005). Generally, the Z-transform test converts the one-tailed pvalues from each of kindependent tests into standard nor- mal deviates Zi. A common approach in meta-analysis is to sum theZscores across studies, weighting them appropri- ately using the sample sizes. For details of the two-tailed method, please see Whitlock (2005) and Yoon et al. (2021), and for the advantages and disadvantages of using the un- weighted version of this method, please see Becker (1994). TheZ-transform test was performed in Python using the “scipy.stats” package (SciPy, 2024). 3 Results We identify the regions (LAUs) in Europe exposed to multi- hazards by combining the Zscores andpvalues across the hotspots of single-hazard exposure (i.e. population and built-up) computed on absolute and relative aggregations. In Fig. 3, we map these regions, and we further consider, for a statistical overview, the regions with more than one hazard exposure (Hz >1) and confidence level set at 90 % (pvalue<0:10 and positive Zscore>0). In the Supple- ment (Fig. S25), we also present a map displaying all iden- tified hazard types at the LAU level as determined by our analysis of relative population exposure. Notably, analyses of other asset types are expected to reveal different spatial distributions of hazard types. The identification of these regions yielded disparate out- comes contingent upon the specific exposure types scruti- nized within our analysis, namely, population density or res- idential built-up areas. Moreover, the choice of aggregation method, whether relative or absolute, introduces variations in both the quantity and the spatial arrangement of regions identified as susceptible to multi-hazard events. The differ- ence in multi-hazard exposure when considering absolute versus relative aggregation is influenced by the sensitivity of the clustering algorithm to distance (computed by the kpa- rameter) and similarity measures. Absolute aggregation ac- centuates variance and is
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and fed into the citric acid cycle, which enables more ATP production by means of oxidative phosphorylation. This oxidation consumes molecular oxygen and releases water and the waste product carbon dioxide. When oxygen is lacking, or when pyruvate is temporarily produced faster than it can be consumed by the citric acid cycle (as in intense muscular exertion), pyruvate is converted to lactate by the enzyme lactate dehydrogenase, a process that also oxidizes NADH back to NAD+ for re-use in further glycolysis, allowing energy production to continue.[44] The lactate is later converted back to pyruvate for ATP production where energy is needed, or back to glucose in the Cori cycle. An alternative route for glucose breakdown is the pentose phosphate pathway, which produces less energy but supports anabolism (biomolecule synthesis). This pathway reduces the coenzyme NADP+ to NADPH and produces pentose compounds such as ribose 5-phosphate for synthesis of many biomolecules such as nucleotides and aromatic amino acids.[45] Carbon Catabolism pathway map for free energy including carbohydrate and lipid sources of energy Fats are catabolized by hydrolysis to free fatty acids and glycerol. The glycerol enters glycolysis and the fatty acids are broken down by beta oxidation to release acetyl-CoA, which then is fed into the citric acid cycle. Fatty acids release more energy upon oxidation than carbohydrates. Steroids are also broken down by some bacteria in a process similar to beta oxidation, and this breakdown process involves the release of significant amounts of acetyl-CoA, propionyl-CoA, and pyruvate, which can all be used by the cell for energy. M. tuberculosis can also grow on the lipid cholesterol as a sole source of carbon, and genes involved in the cholesterol-use pathway(s) have been validated as important during various stages of the infection lifecycle of M. tuberculosis.[46] Amino acids are either used to synthesize proteins and other biomolecules, or oxidized to urea and carbon dioxide to produce energy.[47] The oxidation pathway starts with the removal of the amino group by a transaminase. The amino group is fed into the urea cycle, leaving a deaminated carbon skeleton in the form of a keto acid. Several of these keto acids are intermediates in the citric acid cycle, for example α-ketoglutarate formed by deamination of glutamate.[48] The glucogenic amino acids can also be converted into glucose, through gluconeogenesis.[49] Energy transformations Oxidative phosphorylation Further information: Oxidative phosphorylation, Chemiosmosis, and Mitochondrion In oxidative phosphorylation, the electrons removed from
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[]
in advanced technologies. Europe is stuck in a static industrial structure with few new companies rising up to disrupt existing industries or develop new growth engines. In fact, there is no EU company with a market capitalisation over EUR 100 billion that has been set up from scratch in the last fifty years, while all six US companies with a valuation above EUR 1 trillion have been created in this period. This lack of dynamism is self-fulfilling. As EU companies are specialised in mature technologies where the potential for breakthroughs is limited, they spend less on research and innovation (R&I) – EUR 270 billion less than their US counterparts in 2021. The top 3 investors in R&I in Europe have been dominated by automotive companies for the past twenty years. It was the same in the US in the early 2000s, with autos and pharma leading, but now the top 3 are all in tech. The problem is not that Europe lacks ideas or ambition. We have many talented researchers and entrepreneurs filing patents. But innovation is blocked at the next stage: we are failing to translate innovation into commercialisation, and innovative companies that want to scale up in Europe are hindered at every stage by inconsistent and restrictive regulations. As a result, many European entrepreneurs prefer to seek financing from US venture capitalists and scale up in the US market. Between 2008 and 2021, close to 30% of the “unicorns” founded in Europe – startups that went on the be valued over USD 1 billion – relocated their headquarters abroad, with the vast majority moving to the US. With the world on the cusp of an AI revolution, Europe cannot afford to remain stuck in the “middle technologies and industries” of the previous century. We must unlock our innovative potential. This will be key not only to lead in new technologies, but also to integrate AI into our existing industries so that they can stay at the front. A central part of this agenda will be giving Europeans the skills they need to benefit from new technologies, so that technology and social inclusion go together. While Europe should aim to match the US in terms of innovation, we should aim to exceed the US in providing opportunities for education and adult learning and good jobs for all throughout their lifetimes. The second area for action is a joint plan
[ "in", "advanced", "technologies", ".", "\n", "Europe", "is", "stuck", "in", "a", "static", "industrial", "structure", "with", "few", "new", "companies", "rising", "up", "to", "disrupt", "existing", "industries", "or", "\n", "develop", "new", "growth", "engines", ".", "In", "fact", ",", "there", "is", "no", "EU", "company", "with", "a", "market", "capitalisation", "over", "EUR", "100", "billion", "that", "\n", "has", "been", "set", "up", "from", "scratch", "in", "the", "last", "fifty", "years", ",", "while", "all", "six", "US", "companies", "with", "a", "valuation", "above", "EUR", "1", "trillion", "\n", "have", "been", "created", "in", "this", "period", ".", "\n", "This", "lack", "of", "dynamism", "is", "self", "-", "fulfilling", ".", "\n", "As", "EU", "companies", "are", "specialised", "in", "mature", "technologies", "where", "the", "potential", "for", "breakthroughs", "is", "limited", ",", "they", "spend", "\n", "less", "on", "research", "and", "innovation", "(", "R&I", ")", "–", "EUR", "270", "billion", "less", "than", "their", "US", "counterparts", "in", "2021", ".", "The", "top", "3", "investors", "\n", "in", "R&I", "in", "Europe", "have", "been", "dominated", "by", "automotive", "companies", "for", "the", "past", "twenty", "years", ".", "It", "was", "the", "same", "in", "the", "\n", "US", "in", "the", "early", "2000s", ",", "with", "autos", "and", "pharma", "leading", ",", "but", "now", "the", "top", "3", "are", "all", "in", "tech", ".", "\n", "The", "problem", "is", "not", "that", "Europe", "lacks", "ideas", "or", "ambition", ".", "We", "have", "many", "talented", "researchers", "and", "entrepreneurs", "filing", "\n", "patents", ".", "But", "innovation", "is", "blocked", "at", "the", "next", "stage", ":", "we", "are", "failing", "to", "translate", "innovation", "into", "commercialisation", ",", "and", "\n", "innovative", "companies", "that", "want", "to", "scale", "up", "in", "Europe", "are", "hindered", "at", "every", "stage", "by", "inconsistent", "and", "restrictive", "\n", "regulations", ".", "\n", "As", "a", "result", ",", "many", "European", "entrepreneurs", "prefer", "to", "seek", "financing", "from", "US", "venture", "capitalists", "and", "scale", "up", "in", "the", "\n", "US", "market", ".", "Between", "2008", "and", "2021", ",", "close", "to", "30", "%", "of", "the", "“", "unicorns", "”", "founded", "in", "Europe", "–", "startups", "that", "went", "on", "the", "\n", "be", "valued", "over", "USD", "1", "billion", "–", "relocated", "their", "headquarters", "abroad", ",", "with", "the", "vast", "majority", "moving", "to", "the", "US", ".", "\n", "With", "the", "world", "on", "the", "cusp", "of", "an", "AI", "revolution", ",", "Europe", "can", "not", "afford", "to", "remain", "stuck", "in", "the", "“", "middle", "technologies", "and", "\n", "industries", "”", "of", "the", "previous", "century", ".", "We", "must", "unlock", "our", "innovative", "potential", ".", "This", "will", "be", "key", "not", "only", "to", "lead", "in", "new", "\n", "technologies", ",", "but", "also", "to", "integrate", "AI", "into", "our", "existing", "industries", "so", "that", "they", "can", "stay", "at", "the", "front", ".", "\n", "A", "central", "part", "of", "this", "agenda", "will", "be", "giving", "Europeans", "the", "skills", "they", "need", "to", "benefit", "from", "new", "technologies", ",", "so", "\n", "that", "technology", "and", "social", "inclusion", "go", "together", ".", "While", "Europe", "should", "aim", "to", "match", "the", "US", "in", "terms", "of", "innovation", ",", "\n", "we", "should", "aim", "to", "exceed", "the", "US", "in", "providing", "opportunities", "for", "education", "and", "adult", "learning", "and", "good", "jobs", "for", "all", "\n", "throughout", "their", "lifetimes", ".", "\n", "The", "second", "area", "for", "action", "is", "a", "joint", "plan" ]
[]
equipment and supplies X 48 Part 2 Analysis of economic and innovation potential NACE Industry nameCurrent strengthEmerging strength 46.9 Non-specialised wholesale trade X 47.1 Retail sale in non-specialised stores X 47.2 Retail sale of food, beverages and tobacco in specialised stores X 47.7 Retail sale of other goods in specialised stores X H TRANSPORTATION AND STORAGE 49.2 Freight rail transport X X 49.4 Freight transport by road and removal services X 49.5 Transport via pipeline X 51.1 Passenger air transport X 52.1 Warehousing and storage X I ACCOMMODATION AND FOOD SERVICE ACTIVITIES 56.1 Restaurants and mobile food service activities X J INFORMATION AND COMMUNICATION 59.1 Motion picture, video and television programme activities X K FINANCIAL AND INSURANCE ACTIVITIES 64.1 Monetary intermediation X 64.9 Other financial service activities, except insurance and pension funding X L REAL ESTATE ACTIVITIES M PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES 70.1 Activities of head offices X 72.1 Research and experimental development on natural sciences and engineering X 73.1 Advertising X N ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES 77.3 Rental and leasing of other machinery, equipment and tangible goods X 81.1 Combined facilities support activities X O PUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIAL SECURITY P EDUCATION Q HUMAN HEALTH AND SOCIAL WORK ACTIVITIES 86.2 Medical and dental practice activities X R ARTS, ENTERTAINMENT AND RECREATION S OTHER SERVICE ACTIVITIES n.e.c. = not elsewhere classified Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation49 2.2. Manufacturing analysis for five EaP countries Full economic mapping of the economic potential is not possible for three Eastern Partnership coun- tries due to a lack of available Orbis data. Partial mapping for the Manufacturing sector is feasible by using official data collected by UNIDO. This section uses data from the UNIDO Industrial Statistics Database at NACE four-digit level (INDSTAT4)31, which contains highly disaggregat- ed data on the manufacturing sector for the peri- od 1990 onwards. 31 UNIDO, INDSTAT 4 Industrial Statistics Database at the 3- and 4-digit level of ISIC Revision 3 and ISIC Revision 4, Vienna, 2020. Available from http://stat.unido.org.Data availability Data availability is not perfect for several coun- tries, not even at NACE three-digit level, as shown in Table 2.6. However, even in this situation, some sort of comparison between countries is still pos- sible and has thus been carried out. Each row of Table 2.6 represents a NACE three-dig- it Manufacturing industry. For those cases high- lighted
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and nuts (not including oil nuts), fresh or dried X 8.5% X 8.5% 058Fruit, preserved, and fruit preparations (excluding fruit juices) X 0.3% 059Fruit juices (including grape must) and vegetable juices, unfermented and not containing added spirit, whether or not containing added sugar or other sweetening matterX 1.7% X 1.7% 061 Sugars, molasses and honey X 1.7% 062 Sugar confectionery X 0.2% 073Chocolate and other food preparations containing cocoa, n.e.s. X 0.3% 1 Beverages and tobacco 111 Non-alcoholic beverages, n.e.s. X 0.2% 112 Alcoholic beverages X 8.1% 122Tobacco, manufactured (whether or not containing tobacco substitutes) X 0.6%Table 2.19. Goods export specialisation for Moldova 72 Part 2 Analysis of economic and innovation potential SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 41 78.7% 23 33.8% 2 Crude materials, inedible, except fuels 211 Hides and skins (except furskins), raw X 0.2% 222Oil-seeds and oleaginous fruits of a kind used for the extrac¬tion of ‘soft’ fixed vegetable oils (excluding flours and meals)X 8.1% X 8.1% 273 Stone, sand and gravel X 0.5% X 0.5% 288 Non-ferrous base metal waste and scrap, n.e.s. X 0.7% X 0.7% 3 Mineral fuels, lubricants and related materials 334Petroleum oils and oils obtained from bituminous minerals (other than crude); preparations, n.e.s., containing by weight 70% or more of petroleum oils or of oils obtained from bituminous minerals, these oils being the basic constituents of the preparations; waste oils X 0.5% 4 Animal and vegetable oils, fats and waxes 5 Chemicals and related products, n.e.s. 542 Medicaments (including veterinary medicaments) X 3.6% 551 Essential oils, perfume and flavour materials X 0.1% 554 Soap, cleansing and polishing preparations X 0.5% X 0.5% 6 Manufactured goods classified chiefly by material 651 Textile yarn X 0.2% 654 Other textile fabrics, woven X 0.3% X 0.3% 655Knitted or crocheted fabrics (including tubular knit fabrics, n.e.s., pile fabrics and openwork fabrics), n.e.s.X 0.6% 658 Made-up articles, wholly or chiefly of textile materials, n.e.s. X 0.7% X 0.7% 659 Floor coverings, etc. X 0.9% 665 Glassware X 1.6% 699 Manufactures of base metal, n.e.s. X 0.8% Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation73 SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 41 78.7% 23 33.8% 7 Machinery and transport equipment 741 Heating and cooling equipment and parts thereof, n.e.s. X 0.5% 742Pumps for liquids, whether or not fitted with a measuring device; liquid
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[]
as clean tech and automotives, and industries that use energy intensively and are “hard-to-abate”. Third, Europe needs to increase security and reduce dependencies . Given its high trade openness and dependence on imports ranging from raw materials to advanced technology, the EU will need to develop a genuine “foreign economic policy” that coordinates preferential trade agreements and direct investment with resource-rich nations, the building up of stockpiles in selected critical areas, and the creation of industrial partnerships to secure the supply chain of key technologies. Europe will also need to develop a strong and independent defence industrial capacity that allows it to meet increasing demand for military assets and equipment and remain at the forefront of defence technology. BUILDING BLOCKS The EU’s new industrial strategy rests on a series of building blocks, the first of which is full implementation of the Single Market . The Single Market is critical for all aspects of the strategy: for enabling scale for young, inno - vative companies and large industrials that compete on global markets; for creating a deep and diversified common energy market, an integrated multimodal transport market and strong demand for decarbonisation solutions; for negotiating preferential trade deals and building more resilient supply chains; for mobilising greater volumes of private finance; and as a result, for unlocking higher domestic demand and investment. Remaining trade frictions in the EU mean that Europe is leaving around 10% of potential GDP on the table, according to one estimateix. Proposals to complete the Single Market for different sectors appear in many chapters of this report. However, as the Letta report has systematically analysed the key challenges facing the Single Market and provided recommendations, there is no chapter dedicated solely to the Single Market in this reportx. The next building blocks are industrial, competition and trade policies, which interact closely and must be aligned as part of an overall strategy . Evidence that industrial policies can be effective under certain circum - stances is growingxi. But to avoid the pitfalls of the past – such as defending incumbent companies or picking winners – these policies must be organised according to a set of key principles which embed best practice. Among others, the focus of such policies should be on sectors rather than companies; public support should be contin - uously evaluated, underpinned by a rigorous monitoring exercise; and market failures should be clearly specified and public
[ "as", "clean", "tech", "and", "automotives", ",", "and", "industries", "that", "use", "energy", "intensively", "and", "are", "“", "hard", "-", "to", "-", "abate", "”", ".", "Third", ",", "Europe", "\n", "needs", "to", "increase", "security", "and", "reduce", "dependencies", ".", "Given", "its", "high", "trade", "openness", "and", "dependence", "on", "imports", "\n", "ranging", "from", "raw", "materials", "to", "advanced", "technology", ",", "the", "EU", "will", "need", "to", "develop", "a", "genuine", "“", "foreign", "economic", "policy", "”", "\n", "that", "coordinates", "preferential", "trade", "agreements", "and", "direct", "investment", "with", "resource", "-", "rich", "nations", ",", "the", "building", "up", "\n", "of", "stockpiles", "in", "selected", "critical", "areas", ",", "and", "the", "creation", "of", "industrial", "partnerships", "to", "secure", "the", "supply", "chain", "of", "key", "\n", "technologies", ".", "Europe", "will", "also", "need", "to", "develop", "a", "strong", "and", "independent", "defence", "industrial", "capacity", "that", "allows", "\n", "it", "to", "meet", "increasing", "demand", "for", "military", "assets", "and", "equipment", "and", "remain", "at", "the", "forefront", "of", "defence", "technology", ".", "\n", "BUILDING", "BLOCKS", "\n", "The", "EU", "’s", "new", "industrial", "strategy", "rests", "on", "a", "series", "of", "building", "blocks", ",", "the", "first", "of", "which", "is", "full", "implementation", "\n", "of", "the", "Single", "Market", ".", "The", "Single", "Market", "is", "critical", "for", "all", "aspects", "of", "the", "strategy", ":", "for", "enabling", "scale", "for", "young", ",", "inno", "-", "\n", "vative", "companies", "and", "large", "industrials", "that", "compete", "on", "global", "markets", ";", "for", "creating", "a", "deep", "and", "diversified", "common", "\n", "energy", "market", ",", "an", "integrated", "multimodal", "transport", "market", "and", "strong", "demand", "for", "decarbonisation", "solutions", ";", "for", "\n", "negotiating", "preferential", "trade", "deals", "and", "building", "more", "resilient", "supply", "chains", ";", "for", "mobilising", "greater", "volumes", "of", "\n", "private", "finance", ";", "and", "as", "a", "result", ",", "for", "unlocking", "higher", "domestic", "demand", "and", "investment", ".", "Remaining", "trade", "frictions", "in", "\n", "the", "EU", "mean", "that", "Europe", "is", "leaving", "around", "10", "%", "of", "potential", "GDP", "on", "the", "table", ",", "according", "to", "one", "estimateix", ".", "Proposals", "\n", "to", "complete", "the", "Single", "Market", "for", "different", "sectors", "appear", "in", "many", "chapters", "of", "this", "report", ".", "However", ",", "as", "the", "Letta", "\n", "report", "has", "systematically", "analysed", "the", "key", "challenges", "facing", "the", "Single", "Market", "and", "provided", "recommendations", ",", "\n", "there", "is", "no", "chapter", "dedicated", "solely", "to", "the", "Single", "Market", "in", "this", "reportx", ".", "\n", "The", "next", "building", "blocks", "are", "industrial", ",", "competition", "and", "trade", "policies", ",", "which", "interact", "closely", "and", "must", "be", "\n", "aligned", "as", "part", "of", "an", "overall", "strategy", ".", "Evidence", "that", "industrial", "policies", "can", "be", "effective", "under", "certain", "circum", "-", "\n", "stances", "is", "growingxi", ".", "But", "to", "avoid", "the", "pitfalls", "of", "the", "past", "–", "such", "as", "defending", "incumbent", "companies", "or", "picking", "\n", "winners", "–", "these", "policies", "must", "be", "organised", "according", "to", "a", "set", "of", "key", "principles", "which", "embed", "best", "practice", ".", "Among", "\n", "others", ",", "the", "focus", "of", "such", "policies", "should", "be", "on", "sectors", "rather", "than", "companies", ";", "public", "support", "should", "be", "contin", "-", "\n", "uously", "evaluated", ",", "underpinned", "by", "a", "rigorous", "monitoring", "exercise", ";", "and", "market", "failures", "should", "be", "clearly", "specified", "\n", "and", "public" ]
[]
is measured using location quotients (LQs), which are defined as the ratio of the share of an industry in the national economy and the share of that same indus- try in the aggregate EaP economy. An LQ above 1 shows an above-average concentration in the industry, i.e. the share of e.g. employment of that industry in the national economy is higher than the share of employment of that same industry in the aggregate EaP economy. An LQ below 1 shows a below-average concentration in the industry, i.e. the share of e.g. employment of that industry in the na- tional economy is lower than the share of employment of that same industry in the aggregate EaP economy.of employees for each year in the 2012-2019 period; ■the rate of change in degree of specialisation for the number of employees for three time periods: between 2012 and 2015, between 2014 and 2017 and between 2016 and 2019; ■the degree of specialisation for turnover for each year in the 2012-2019 period; ■the average relative share of the total turn- over for each year in the 2012-2019 period; ■the rate of change in degree of specialisation for turnover for three time periods: between 2012 and 2015, between 2014 and 2017 and between 2016 and 2019. Degrees of specialisation have been calculated relative to the unweighted average of Georgia, Moldova and Ukraine. Specialised industries with critical mass are identified as those indus- tries for which the degree of specialisation and relative size for both the number of employees and turnover are above the thresholds for at least 6 out of 8 years shown in the first two columns in Table 2.2. Emerging industries with increasing degrees of specialisation and relative size are identified as those industries for which the change in the degree of specialisation for both the number of employees and turnover are above the thresholds shown in the last column in Table 2.2. To identify current strengths for both the number of employees and turnover the same minimum relative size of 0.1% has been used for each country, and for the degree of specialisation a minimum location quotient30 of 1.5. For changes over time, for both the number of employees and turnover, changes in both degrees of specialisa- tion have to be positive for at least 2 out of 3 time periods for all countries. Full results are shown in Annex 1. Here, we
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[]
semantic errors and discrimi- nators picking up on statistical artifacts. In our ex- periments, these artifacts are most prominent with top-ksampling. However, any strategy that over- samples high-likelihood words is susceptible. As thepin nucleus sampling is set increasingly lower to achieve more fluent text (some systems are al- ready using pas low as 0.5 (Miculicich et al., 2019)), the distributional deviations that plague top-ktext will surface in nucleus sampling as well. Holtzman et al. (2020) explain how a unique at- tribute of human language is that it dips in and out of low probability zones. This variance in likeli- hood is what makes human-written text interest- ing and exciting to read. Today’s generation sys- tems have not yet solved the problem of mimick- ing the human cadence without introducing poor word choices that are easy for humans to detect. Generation systems often optimize for fooling hu- mans without acknowledging the trade-off that ex- ists between human perception of quality and easeof automatic detection. We therefore suggest three prongs for future research: 1. Identifying ways to improve the language models and decoding strategies we use in or- der to generate text that is both exciting (ie. unlikely) and semantically plausible. 2. Building better world understanding into au- tomatic discriminators so that they are more capable of detecting the types of errors that humans notice. 3. Developing tools and educational materi- als to improve humans’ ability to detect machine-generated text. These may include automatic detectors with components that ex- plain their predictions. Finally, we would like to note that all of our ex- periments were performed with English language models, and it remains an open question how the trade-off between ease of human detection and ease of automatic detection might differ for lan- guages that are very different from English. Acknowledgements This research is based upon work supported in part by U.S. DARPA KAIROS Program No. FA8750- 19-2-1004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copy- right annotation therein. We also thank Noah Fiedel, Peter Liu, Sharan Narang, Joao Sedoc, Yun William Yu, and Hugh Zhang for their valuable feedback. References David Ifeoluwa Adelani, Haotian Mai, Fuming Fang, Huy H Nguyen, Junichi Yamagishi, and Isao Echizen. 2020. Generating sentiment-preserving fake online reviews using neural language models and their human-and machine-based detection. In International Conference on Advanced Information Networking and Applications , pages 1341–1354. Springer.
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v2 correspondence table pro- duced by Eurostat (reported in Annex 7) was used to map S&T domains to NACE sectors via patents. To do so, for each domain, only the top 5 patent classes whose frequency was higher in the domain than in the overall distri- bution of IPC classes were employed. The final S&T domain to NACE mapping for each EaP country is reported in Annex 8. 3. For each EaP country, statistics on publica- tions associated with each S&T domain were obtained by ASJC Scopus subject field (a fine- grained list of 334 bibliometric categories, into which Scopus classifies indexed publications). By doing this, the most recurrent Scopus sub- ject fields associated with each S&T domain were identified – again, records within the same bibliometric category could be assigned to different S&T domains. 4. As no Scopus subject field to NACE concord- ance is available, it was decided to resort to NABS to NACE mapping to map Scopus subject 232 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials Figure 4.1. Summary schema of the methodological steps leading to the selection and definition of a list of EIST specialisation domains for each country and the potential cooperation areas for the whole region and with international partners. Economic and Innovation (E&I) specialisations EIST specialisation domains E&I preliminary prioritiesEIST preliminary prioritiesS&T preliminary prioritiesSTEP 1 STEP 3STEP 2 Science and Technology (S&T) specialisations fields to NABS in order to obtain a two-step ASJC-NABS-NACE mapping. Thus, a manual mapping from Scopus subject fields to the NABS 2007 classification (at the second lev- el) was derived by the authors. This alignment, presented in Annex 10, is rather straightfor- ward, as both classification systems concern scientific or research fields. 5. Subject fields were in turn translated into NACE sectors by using the NABS/NACE corre- spondence table provided in Annex 9. Nota- bly, the correspondence table maps the first level of NABS 2007 to NACE v2. This implies that, for instance, the entire NABS06 ‘Indus- trial production and technology’ is mapped to the entire Manufacturing NACE sector: more specifically, for example, the NABS 6.41 ‘Man- ufacture of food products’ is mapped to any NACE Manufacturing sector, even outside the Food industry. To avoid this multiple assign-ment, the authors proceeded to select relevant NACE sectors assigned to specific NABS (and, in turn, to Scopus subject fields). The result of the whole process
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[]
Leather and imitations of leather; ani- mal skins, and hides; trunks and travelling luggage and carrying bags; umbrellas and parasols; walk- ing sticks; whips, harness and saddlery; collars, leashes and clothing for animals Class 19 – Building materials (non-metallic); non-metallic rigid pipes for building; asphalt, pitch and bitumen; non-metallic transportable buildings; monuments, not of metal Class 20 – Furniture, mirrors, picture frames; con- tainers, not of metal, for storage or transport; unworked or semi-worked bone, horn, ivory, whale- bone or mother-of-pearl; shells; meerschaum; yel- low amber Class 21 – Household or kitchen utensils and containers; combs and sponges; brushes, (except paintbrushes); brush-making materials; articles for cleaning purposes; steelwool; unworked or semi- worked glass, (except building glass used in build- ing); glassware, porcelain and earthenware Class 22 – Ropes and string; nets; tents, awnings, and tarpaulins; awnings of textile or synthetic ma- terials; sails; sacks for the transport and storage of materials in bulk; padding, cushioning and stuffing materials, (except of paper, cardboard, rubber or plastics); raw fibrous textile materials and substi- tutes therefor Class 23 – Yarns and threads, for textile use Class 24 – Textiles and substitutes for textiles; bed covers; table covers; household linen; curtains of textile or plastic Class 25 – Clothing, footwear, headgear Class 26 – Lace and embroidery, ribbons and braid; buttons, hooks and eyes, pins and needles; artificial flowers; hair decorations; false hair Class 27 – Carpets, rugs, mats and matting, li- noleum and other materials for covering existing floors; wall hangings (non-textile) Class 28 – Games, toys and playthings; video game apparatus; gymnastic and sporting articles; decorations for Christmas trees Class 29 – Meat, fish, poultry and game; meat ex- tracts; preserved, frozen, dried and cooked fruits and vegetables; jellies, jams, compotes; eggs; milk and milk products; edible oils and fats Class 30 – Coffee, tea, cocoa and artificial cof- fee; rice; tapioca and sago; flour and preparations made from cereals; bread, pastries and confec- tionery; edible ices; sugar, honey, treacle; yeast, baking-powder; salt; mustard; vinegar, sauces (condiments); spices; ice Class 31 – Raw and unprocessed agricultural, aq- uacultural, horticultural and forestry products; raw and unprocessed grains and seeds; fresh fruits and vegetables, fresh herbs; natural plants and flowers; bulbs, seedlings and seeds for planting; live animals; foodstuffs and beverages for ani- mals; malt Class 32 – Beers; mineral and aerated waters and other non-alcoholic beverages; fruit beverages and fruit juices; syrups and
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for Armenia Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation67 SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 19 76.5% 12 15.6% 288 Non-ferrous base metal waste and scrap, n.e.s. X 0.4% 289Ores and concentrates of precious metals; waste, scrap and sweepings of precious metals (other than of gold)X 1.5% X 1.5% 292 Crude vegetable materials, n.e.s. X 0.3% 3 Mineral fuels, lubricants and related materials 351 Electric current X 4.4% 4 Animal and vegetable oils, fats and waxes 5 Chemicals and related products, n.e.s. 6 Manufactured goods classified chiefly by material 661Lime, cement, and fabricated construction materials (except glass and clay materials)X 0.6% 667Pearls and precious or semiprecious stones, unworked or workedX 5.0% 682 Copper X 4.0% 684 Aluminium X 5.0% 7 Machinery and transport equipment 8 Miscellaneous manufactured articles 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 2.2% X 2.2% 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 1.4% 845Articles of apparel, of textile fabrics, whether or not knitted or crocheted, n.e.s. X 0.6% 885 Watches and clocks X 1.1% 897Jewellery, goldsmiths' and silversmiths' wares, and other articles of precious or semiprecious materials, n.e.s.X 1.9% 9Commodities and transactions not classified elsewhere in the SITC 971 Gold, non-monetary (excluding gold ores and concentrates) X 6.8% X 6.8% 68 Part 2 Analysis of economic and innovation potential Mapping of goods export specialisations – results for Azerbaijan Results of the export mapping for Azerbaijan are shown in Table 2.17. The 3 goods categories with current strength represent 88% of the total ex- ports for 2012-2019. This very high share is due to the dominance of only one good in Azerbaijan’s export: export of Bituminous minerals, crude (SITC 333), accounting for 82% of the total exports. Azerbaijan’s exports are therefore extremely spe- cialised, putting the country at risk of negative market developments in this goods category.The 8 goods categories with emerging strength represent almost 91% of the total exports as Bitu- minous minerals, crude (SITC 333) is also includ- ed. Increasing specialisations are also observed in Food and live animals (SITC 0) and Manufactured goods classified chiefly by material
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[]
Hospital</p> <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> </header> <div class="entry"> <p>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. </p> <p>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 <em>Circulation</em>. </p> <p>"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." </p> <p>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. </p> <p align="center">###</p> <p>CHAMPION PHOENIX was funded by The Medicines Company, which manufactures cangrelor. </p> <p>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 employs nearly 15,000 people. The Brigham's medical preeminence dates back to 1832, and today that rich history in clinical care is coupled with its national leadership in patient care, quality improvement and patient safety initiatives, and its dedication to research, innovation, community engagement and educating and training the next generation of health care
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of organisational clusters. The following tables showcase the sectoral criti- cal mass and specialisation of start-ups and ven- ture capital-backed companies for each of the six Eastern Partnership countries, and for each of the 102 Part 2 Analysis of economic and innovation potential Armenia Azerbaijan Belarus Georgia Moldova Ukraine Locarno2011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 20182011- 20142015- 2018 1 -- -- -- -- 1 -- 33 7 24 24 422 364 2 -- -- -- -- -- -- 38 13 44 22 303 391 3 -- 1 -- -- -- -- -- 19 -- -- 50 145 4 -- -- -- -- -- -- -- -- -- -- 1 19 5 -- -- -- -- -- -- -- 2 -- 1 502 296 6 -- -- -- 2 -- -- 96 24 321 41 632 678 7 2 -- -- -- 48 -- 45 18 247 1 176 376 8 -- -- -- -- -- -- 8 4 18 1 236 787 9 8 38 -- -- 25 28 71 173 178 138 1 893 2 935 10 -- -- -- -- -- 1 -- 9 -- 2 146 305 11 107 8 -- -- -- -- 18 49 1 668 552 437 815 12 -- -- -- -- 4 31 4 3 10 12 598 993 13 -- -- -- 1 -- 2 2 1 -- 2 239 392 14 -- -- -- -- 1 13 -- 11 4 -- 78 208 15 -- -- -- -- 32 8 2 -- 1 -- 260 682 16 -- -- -- -- -- -- -- -- -- 1 -- 12 17 -- -- -- -- -- -- 1 1 -- 5 4 30 18 -- -- -- -- -- -- 35 56 3 -- 16 14 19 -- -- -- -- 2 12 34 101 283 184 2 537 3 511 20 -- -- -- -- -- -- 9 17 247 62 403 377 21 -- 5 -- -- 13 -- 24 9 4 51 154 354 22 -- -- -- -- -- -- 1 -- -- -- 22 84 23 -- -- -- -- 20 5 5 -- 2 26 431 597 24 -- -- -- -- -- -- -- -- 22 5 76 166 25 1 1 -- -- 10 4 9 37 107 493 532 900 26 -- -- -- -- -- 1 --
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[]
the effectiveness of the ‘made for’ claim. Thus, we can confirm that the ‘made for’ claim is raising the awareness of DFQ. Focusing on the aggregated results, we observe that when making DFQ salient, in the majority of generic country-product pairs (69 %), the ‘made for’ claim either increases preference for the domestic version or reduces preference for the foreign version, resulting in a higher WTP for the domestic product or a lower WTP for the foreign product. For the generic versions, results from the German sample show that consumers reverse their preferences in half of the cases. We observe a shift to positive WTP for the domestic versions in the ‘made for’ condition compared to the control (}β HUYogurt ˆ0.22 €cent, p D0.001 vs 0.39 €cent, p D0.001; }β LIYogurt ˆ0.16 €cent, p D0.001 vs 0.34 €cent, p D 0.001; }β HUSauce ˆ0.02 €cent, p F0.1 vs 0.17 €cent, p D0.001). Hungarian consumers show an increase in preference for domestic product versions in all cases except yogurt, where the German version — a country with a good national reputation —receives a higher WTP in the presence of the claim (}β DEYogurt ˆ-28.62 forint, p D0.001 vs 14.13 forint, p D0.001). The difference in WTP between domestic and foreign versions for the Lithuanian sample is statistically significant in three cases when the destination market of the different versions is disclosed to consumers. In this country, the presence of the claim also leads con- sumers to favour the domestic product versions. Consistent with the results in the absence of the claim, Spanish consumers continue to favour their own product versions. However, Romanian consumers display mixed reactions to the claim, continuing to favor foreign prod- ucts like Spanish crisps even when ‘made for’ is revealed. Changes in Eastern European consumers ’ decisions, regarding generic products in the ‘made for’ condition, are reported in the right-most column of Table 3in the Appendix A. In three cases, the ‘made for’ claim triggers a change in the preferences of Eastern consumers towards Western ver- sions. This is the case for Hungary and Romania, where consumers prefer the German version of Danone yogurt (}β DEYogurt ˆ14.13 forint, p D0.01) and the Spanish and Swedish versions of Lay’s potatoes to the domestic versions (}β ESCrisps ˆ4.21 leu, p D0.01 and }β SECrisps ˆ4.95 leu, p D0.05). This change reduces to one case when consumers also see the
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[]
of an organism, which requires associative, emotional, and/orcognitive learning processes ( Downar et al., 2001 ,2002 ;Cor- betta et al., 2008 ). The insular cortex, and in particular its anterior part (aIC), has been linked to adaptive behavioral functions,including aversive state processing ( Gehrlach et al., 2019 ;Go- golla, 2017 ;Livneh and Andermann, 2021 ) and social interac- tions ( Lamm and Singer, 2010 ;Miura et al., 2020 ). Within cortical circuits, specific interneuron (IN) subtypes are recruited with reference to a given network state or behavioral contingency ( Letzkus et al., 2011 ;Pakan et al., 2016 ;Kuchib- hotla et al., 2017 ;Turi et al., 2019 ). By refining the response prop- erties of principal neurons, INs control cortical excitability and are hypothesized to contribute to behavioral state-dependent modulation of sensory processing ( Krabbe et al., 2018 ;Kvitsiani et al., 2013 ;Kim et al., 2016 ;Lee et al., 2019 ;Fu et al., 2014 ). However, little is known about the mechanisms underlying the responses of specific subclasses of aIC INs to sensory stimuliwith potential importance for behavioral adaptations. In corticalcircuits, VIP+ INs comprise a major, but heterogeneous, class of GABAergic neurons ( Guet-Mccreight et al., 2020 ;Porter et al., 1998 ;Pro¨nneke et al., 2015 ;Rhomberg et al., 2018 ). In the neocortex as well as basolateral amygdala (BLA), VIP+ INs are highly active during states of arousal and are modulated by context-dependent behaviors and reinforcement learning(Krabbe et al., 2019 ;Pi et al., 2013 ;Kuchibhotla et al., 2017 ;Gar- rett et al., 2020 ;Kastli et al., 2020 ). These INs have been shown to provide disinhibitory control onto somatostatin- and parvalbu-min-expressing INs gating the information flow onto down- stream principal neurons ( Krabbe et al., 2019 ;Pi et al., 2013 ; Guet-Mccreight et al., 2020 ;Walker et al., 2016 ). Recent studies have suggested the importance of VIP+ INs in gain modulation to facilitate sensory discrimination ( Batista-Brito et al., 2017 ;Ayzenshtat et al., 2016 ). Thus, we hypothesized that this specific subtype of IN is crucial for the processing of sensorystimuli in the aIC, an area that mediates complex adaptive behav- iors, such as social preference ( Miura et al., 2020 ) and associative learning ( Shi et al., 2020 ;Lissek, 2012 ). To test this hypothesis, we used a combination of viral mono-trans-synaptic tracing and deep-brain Ca2+imaging, as well as in vivo optogenetic manipu- lation of aIC VIP+ INs.
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NICE Classification for trademarks ..................................... 324 Annex 6. Locarno Classification for industrial designs ................... 327 Annex 7. IPC to NACE v2 correspondence table ................................. 328 Annex 8. NACE v2 to S&T domains correspondence tables via IPC, for each EaP country .......................................................................................... 332 Annex 9. NABS 2007 to NACE v2 correspondence table ................. 339 Annex 10. NACE v2 to S&T domains correspondence tables via NABS and ASJC Scopus subject fields, for each EaP country ................... 342 Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation1 ABSTRACT The Eastern Partnership is a specific Eastern di- mension to the European Neighbourhood Pol- icy (ENP). Through the ENP, the EU works with its southern and eastern neighbours to achieve the closest possible political association and the greatest possible degree of economic integration. The Eastern Partnership aims to reinforce the po- litical association and economic integration of six Eastern European and South Caucasus partner countries: Armenia, Azerbaijan, Belarus1, Georgia, the Republic of Moldova and Ukraine. Most EU Eastern Neighbours have committed to develop place-based Smart Specialisation Strategies for research and innovation with the objective of en- hancing their competitiveness and drive structural change of the economies. The purpose of this study is to contribute to evi- dence-informed research and innovation policy, in particular the development of Smart Specialisa- tion Strategies. The study presents a solid basis for these processes in the Eastern Partnership re- gion by offering an extensive quantitative analysis of national-level potential in the economy, inno- vation, science and technology. A limited number of economic and innovation (E&I) specialisation domains matched with relevant scientific and technological (S&T) specialisation domains are identified for each Eastern Partnership country. The study proposes a new method to identify con- cordances between the EI and ST specialisation do- mains so that they can be used to inform ongoing Smart Specialisation processes in the Eastern Part- nership countries with available international data. Interested countries need to compliment this anal- ysis with the relevant national data sources and other useful information resulting from the quali- 1 At the time of preparation of this study, Belarus was a member of the EU Eastern Partnership. Following its sus- pension and later sanctions, results for Belarus are on purpose not presented and discussed in this study, how- ever some data are left in the tables and charts for refer- ence to the regional context.tative expert inputs and stakeholder engagement.
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[]
Automatic Detection of Generated Text is Easiest when Humans are Fooled Daphne Ippolitoyz [email protected] Duckworth z* [email protected] Chris Callison-Burch yz [email protected] Eckz [email protected] Abstract Recent advancements in neural language mod- elling make it possible to rapidly generate vast amounts of human-sounding text. The ca- pabilities of humans and automatic discrimi- nators to detect machine-generated text have been a large source of research interest, but hu- mans and machines rely on different cues to make their decisions. Here, we perform care- ful benchmarking and analysis of three popu- lar sampling-based decoding strategies—top- k, nucleus sampling, and untruncated random sampling—and show that improvements in de- coding methods have primarily optimized for fooling humans. This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems. We also show that though both human and automatic detector performance improve with longer ex- cerpt length, even multi-sentence excerpts can fool expert human raters over 30% of the time. Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems. 1 Introduction State-of-the-art generative language models are now capable of producing multi-paragraph ex- cerpts that at a surface level are virtually indis- tinguishable from human-written content (Zellers et al., 2019; Radford et al., 2019; Adelani et al., 2020). Often, only subtle logical fallacies or id- iosyncrasies of language give away the text as machine-generated, errors that require a close reading and/or domain knowledge for humans to detect. Deceptive text, whether human- or machine- generated, has entered the sphere of public con- cern (Cooke, 2018). It propogates quickly (V osoughi et al., 2018), sets political agendas Equal contribution, zGoogle, yUniversity of Pennsylva- nia(Vargo et al., 2018), influences elections (Allcott and Gentzkow, 2017), and undermines user trust (Wang et al., 2012; Song et al., 2015). Recently, Adelani et al. (2020) have shown that automati- cally generated reviews are perceived to be as flu- ent as human-written ones. As generative tech- nology matures, authors, well-meaning or other- wise, will increasingly employ it to augment and accelerate their own writing. It is more impera- tive now than ever for both humans and automated systems to be able to detect and identify machine- generated texts in the wild. However, there has thus been little inquiry into the textual proper- ties that cause humans to give generated text high human-like ratings compared to those that cause automatic systems to
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employs nearly 15,000 people. The Brigham's medical preeminence dates back to 1832, and today that rich history in clinical care is coupled with its national leadership in patient care, quality improvement and patient safety initiatives, and its dedication to research, innovation, community engagement and educating and training the next generation of health care 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. Journal Circulation DOI 10.1161/CIRCULATIONAHA.115.017300 Disclaimer: 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. Media Contact Johanna Younghans [email protected] Office: 617-525-6373 More on this News Release Novel blood thinner found to be safe and effective in women Brigham and Women's Hospital Journal Circulation DOI 10.1161/CIRCULATIONAHA.115.017300 Keywords /Life sciences/Organismal biology/Anatomy/Body fluids/Blood /Health and medicine/Health care/Medical facilities/Hospitals /Health and medicine/Clinical medicine/Medical treatments/Transplantation EurekAlert! The Global Source for Science News AAAS - American Association for the Advancement of Science Copyright © 2024 by the American Association for the Advancement of Science (AAAS) facebook.com/EurekAlert @EurekAlert youtube.com/EurekAlert Help / FAQ Services Eligibility Guidelines Contact EurekAlert! Terms & Conditions DMCA Privacy Policy Disclaimer Copyright © 2024 by the American Association for the Advancement of Science (AAAS)
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number of 65 See section ‘Temporal evolution of the S&T specialisation domains’ for further methodological information.records, particularly patents, gathered for Georgia affects the temporal evolution indicators signif- icantly, and thus some domains have not been considered. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation185 Figure 3.31. Specialisation index and citation impact across domains of Georgia’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 3.32. Specialisation index across domains of Georgia’s S&T ecosystem against the EaP average, for patents 0.8 0.6 0.4 1.0 2.0 Specialisation indexEnvironmental sciences and industries Governance, culture, education and the economy Agrifood Chemistry and chemical engineering Mechanical engineering and heavy machinery Optics and photonics Nanotechnology and materials Fundamental physics and mathematics Electric and electronic technologies ICT and computer science Health and wellbeing Biotechnology Energy 186 Part 3 Analysis of scientific and technological potential GeorgiaTemporal 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 -11.47% -20.31% -14.39% Biotechnology -25.68%Insufficient data-25.68% Chemistry and chemical engineering -25.39% 29.13% -13.14% Electric and electronic technologies 0.54% 20.75% 7.93% Environmental sciences and industries 24.39% 80.70% 28.83% Fundamental physics and mathematics -11.21% 46.10% -10.76% Governance, culture, education and the economy35.70%Insufficient data35.70% Health and wellbeing 21.32% 13.85% 21.16% ICT and computer science 38.59% 53.26% 39.37% Mechanical engineering and heavy machineryInsufficient data2.56% 2.56% Nanotechnology and materials -11.50% 53.26% -5.89% Optics and photonics 4.19%Insufficient data4.19%Table 3.14. Temporal evolution of Georgia’s S&T domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation187 Moldova Table 3.15 and Figure 3.33 showcase the num- ber of records per domain of S&T specialisation in Moldova. Nanotechnology and materials is the domain with the most records (with a total of 1 299), followed by Health and wellbeing (919), Me- chanical engineering and heavy machinery (763), Governance, culture, education and the economy (572) and Chemistry
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"\n", "of", "patents", "Change", ",", "weighted", "average", "of", "\n", "publications", "and", "patents", "\n", "Agrifood", "-11.47", "%", "-20.31", "%", "-14.39", "%", "\n", "Biotechnology", "-25.68%Insufficient", "\n", "data-25.68", "%", "\n", "Chemistry", "and", "chemical", "engineering", "-25.39", "%", "29.13", "%", "-13.14", "%", "\n", "Electric", "and", "electronic", "technologies", "0.54", "%", "20.75", "%", "7.93", "%", "\n", "Environmental", "sciences", "and", "industries", "24.39", "%", "80.70", "%", "28.83", "%", "\n", "Fundamental", "physics", "and", "mathematics", "-11.21", "%", "46.10", "%", "-10.76", "%", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy35.70%Insufficient", "\n", "data35.70", "%", "\n", "Health", "and", "wellbeing", "21.32", "%", "13.85", "%", "21.16", "%", "\n", "ICT", "and", "computer", "science", "38.59", "%", "53.26", "%", "39.37", "%", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machineryInsufficient", "\n", "data2.56", "%", "2.56", "%", "\n", "Nanotechnology", "and", "materials", "-11.50", "%", "53.26", "%", "-5.89", "%", "\n", "Optics", "and", "photonics", "4.19%Insufficient", "\n", "data4.19%Table", "3.14", ".", "Temporal", "evolution", "of", "Georgia", "’s", "S&T", "domains", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation187", "\n", "Moldova", "\n", "Table", "3.15", "and", "Figure", "3.33", "showcase", "the", "num-", "\n", "ber", "of", "records", "per", "domain", "of", "S&T", "specialisation", "\n", "in", "Moldova", ".", "Nanotechnology", "and", "materials", "is", "the", "\n", "domain", "with", "the", "most", "records", "(", "with", "a", "total", "of", "1", "\n", "299", ")", ",", "followed", "by", "Health", "and", "wellbeing", "(", "919", ")", ",", "Me-", "\n", "chanical", "engineering", "and", "heavy", "machinery", "(", "763", ")", ",", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "\n", "(", "572", ")", "and", "Chemistry" ]
[]
play an important role in cushioning shocks in the energy market, there is no equivalent for critical minerals in the event of large market swings. The second risk is that CRMs can be used as geopolitical weapon, as a large part of extraction and processing is concentrated in countries with which the EU is not strategically aligned. For example, China is the single largest processer of nickel, copper, lithium and cobalt, accounting for between 35-70% of processing activity, and has shown willingness to use its market power [see Figure 2] . Export restrictions from the country grew by a factor of nine between 2009 and 2020. Little progress is being made so far with diversification. Compared with three years ago, the share of the top three producers for key CRMs either remains unchanged or has increased further. FIGURE 2 Concentration of the extraction and processing of critical resources Share of top-three producing countries in total production of selected resources and minerals, 2022 Source: IEA. Based on S&P Global, USGS, Mineral Commodity Summaries and Wood Mackenzie, 2024. Faced with these constraints, CRMs are subject to a global race to secure supply chains, and Europe is currently falling behind . Other major economies are moving to secure independent supply chains and reduce their vulnerability. Alongside its dominant position in processing and refining, China is actively investing in mining assets in Africa and Latin America and overseas refining via its Belt and Road initiative. Its overseas investment in metals and mining through the Belt and Road Initiative reached a record high of USD 10 billion in the first half of 2023 alone, and it plans to double the ownership of overseas mines containing critical minerals by Chinese companies. The US has deployed the IRA, the Bipartisan Infrastructure Act and defence funding to develop at scale domestic processing, refining and recycling capacity, as well as using its geopolitical power to secure the global supply chain. Japan is highly dependent on other regions for CRMs, and since the 2000s it has developed a strategic approach to increase access to overseas mining projects. The Japan Organization for Metals and Energy Security invests equity in mining and refining assets around the world, manages strategic stockpiling and, since the introduction of the recent 56THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 4economic security law, has powers to develop processing and refining facilities within Japan. Europe, by
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[]
whether addressing this lack of information —by revealing the target country for each product version with a ‘made for’ claim —would impact consumer perceptions. The findings from the sensory evaluation laboratory experiment suggest that taste ratings and choices after tasting are not affected by the ‘made for’ information. Nevertheless, when product choice is largely driven by extrinsic cues (Grunert, 2005 ), and direct taste comparisons across DFQ versions is not possible, a ‘made for’ claim could still influence consumer preferences. This potential influence is supported by the results from the DCE, which indicate that reducing asymmetric information about DFQ strengthens preferences for domestic products across different markets, although the magnitude and direction of this effect vary by country and product type. For instance, in Germany and Sweden, the claim signifi - cantly shifted preferences towards domestic products, while in Hungary and Romania, increased preference for the domestic or foreign version is product-specific. These findings suggest that policies or marketing strategies empha - sizing local or national market alignment (e.g., the ‘made for’ claim) could help reduce information asymmetries (Russo et al., 2020 ) and influence consumer behavior in ways that go beyond traditional country-of-origin labeling. Indeed, even though ‘made for’ concerns the destination market and ‘made in’ concerns country-of-origin, both concepts relate to country stereotypes (Askegaard and Ger, 1998; Reed et al., 2012 , Herz and Diamantopoulous, 2013) or to country image (Gao et al., 2024 ). Our findings indicate that such claims activate ethnocen - tric tendencies and national stereotypes, suggesting that companies can use the national identity cues —whether through ‘made in’ or ‘made for’ labels —to strengthen brand loyalty in specific markets. However, this strategy may also have unintended consequences as such labels may exacerbate preference for domestic products and create challenges for foreign brands trying to compete. Fig. 5.Percentage of participants ranking the taste of product versions as first across R1 and R2 in Frame 1. Fig. 6.Average taste rating scores (scale 0–10) among German (left panel) and Hungarian (right panel) consumers for the German (DE), Hungarian (HU) and Lithuanian (LI) version of yogurt and cookies.D.M. Federica et al. Food Policy 131 (2025) 102803 8 Regarding the last set of hypotheses (H3 a and H3b), the DCE results suggest that branding has a limited effect on consumers ’ product pref- erences for domestic versus foreign products, both with and without the ‘made for’ claim. Branding resulted
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this calibration procedure yields satisfactory results: the averagemeasured nuclear charge remains in agreement with the trueTable 4 Parameters of the charge calibration of the VESPA IC for each side of the chamber Source side Backing side /angbracketleftrt/angbracketrightsym 1.041 0.962 a[MeV−1]5 .05(11)×10−43.18(13)×10−4 b 3.33(1)×10−22.92(1)×10−2 Fig. 13 Results of the charge calibration of the IC. aMeasured ZL distributions of95 38Sr and134 52Te isomers (normalized). Note that the light partner of Te ( ZH=52) is Pd ( ZL=46) in the spontaneous fission of252Cf ( ZCN=98). bValidation of the charge calibration using the isomers: the average measured nuclear charge /angbracketleftˆZL/angbracketrightis compared to the true ZL. The four isomers used in the calibration procedure are depicted as red dots. The |δZ|/lessorequalslant0.3 area is colored in green. cFWHM of the measured ˆZLdistributions as a function of the nuclear charge one ( ZL±0.5) between the calibration points (Fig 13b). However, we observe that extrapolation of the calibration below ZL=38 should be limited. Although the results remain valid for ZL=37, they become incorrect for a smaller nuclear charge ( ZL=35). For ZL/greaterorequalslant37, we can define a systematic uncertainty on Zamounting to ±0.3, as represented in Fig. 13b as a green area. It can also be observed from Figs. 13a, c that the width of the obtained nuclear charge 123 5 Page 10 of 12 Eur. Phys. J. A (2025) 61:5 Fig. 14 Measured light nuclear charge yield obtained with VESPA IC after calibration (blue). Uncertainties correspond to charge deviationwithin [ −0.3, 0.3] (see text). The red and orange histograms correspond to different parameterizations of Wahl’s Z pmodel (from Refs. [ 51]a n d [52], respectively), folded with the IC charge resolution distributions increases as the fragmentation becomes more asymmetric (i.e., ZLbecomes smaller). This effect is due to the form of Eq. ( 2): the further away the fragments are from the symmetry (i.e., the larger rt−/angbracketleft rt/angbracketrightsym), the more sensitive ˆZLis to fluctuations in TKE. For the same reason, the fragments far from symmetry are more sensitive to the uncertainties on the calibration parameters from Table 4than the ones close to symmetry. The FWHM of the measured ZL distributions between 37 and 49 ranges between 5.9 and 2.5. These results are comparable to the ones mentioned in Ref. [50]. From this charge calibration, the nuclear charge yield of light fragments from252Cf spontaneous fission can be obtained from the VESPA data, as depicted in Fig. 14.T h e uncertainties
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[]
to Section 6 611 Leather X 612 Manufactures of leather or of composition leather, n.e.s.; saddlery and harness 613Furskins, tanned or dressed (including heads, tails, paws and other pieces or cuttings), unassembled, or assembled (without the addition of other materials), other than those of heading 848.31 621 Materials of rubber (e.g., pastes, plates, sheets, rods, thread, tubes, of rubber) X X 625 Rubber tyres, interchangeable tyre treads, tyre flaps and inner tubes for wheels of all kinds X X X 629 Articles of rubber, n.e.s. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation311 312 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 52 633 Cork manufactures 634 Veneers, plywood, particle board, and other wood, worked, n.e.s. X X X X 635 Wood manufactures, n.e.s. X X 641 Paper and paperboard X X 642 Paper and paperboard, cut to size or shape, and articles of paper or paperboard X X X 651 Textile yarn X X 652 Cotton fabrics, woven (not including narrow or special fabrics) 653 Fabrics, woven, of man-made textile materials (not including narrow or special fabrics) X 654 Other textile fabrics, woven X X X 655 Knitted or crocheted fabrics (including tubular knit fabrics, n.e.s., pile fabrics and openwork fabrics), n.e.s. X 656 Tulles, lace, embroidery, ribbons, trimmings and other smallwares 657 Special yarns, special textile fabrics and related products X 658 Made-up articles, wholly or chiefly of textile materials, n.e.s. X X X 659 Floor coverings, etc. X X 660 Complete industrial plant appropriate to division 66 661 Lime, cement, and fabricated construction materials (except glass and clay materials) X X 662 Clay construction materials and refractory construction materials X X 663 Mineral manufactures, n.e.s. X X 664 Glass X X 665 Glassware X X 666 Pottery 667 Pearls and precious or semiprecious stones, unworked or worked X 671 Pig-iron, spiegeleisen, sponge iron, iron or steel granules and powders and ferro-alloys X X X 672 Ingots and other primary forms, of iron or steel; semi-finished products of iron or steel X X 673 Flat-rolled products of iron or non-alloy steel, not clad, plated or coated X 674 Flat-rolled products of iron or non-alloy steel, clad, plated or coated X X 675 Flat-rolled products of alloy steel X
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blueprint, and a brief market analysis of what you can expect to make manufacturing the item and selling it at the market you’ve selected. You can enter the amount of runs you want to make, the ME and PE of your blueprint and click add to shopping list, and it will be added to a list of items to purchase when you are next at a trade hub. Machine So, not only was the speech a thoroughly mediocre diatribe about what he now thinks we should do for the next 45 minutes, but also how much credit we should give to Mumford and Sons for bringing Obama to the campaign trail. Behold: At the DNC, we drew strength from something even more powerful than the power of words. We drew strength from the power of families in this country. We drew strength from the power of family values. We drew strength from the power of a common purpose–We drew strength from our shared commitment to fighting against everything that undermines our potential in this country and our freedom. It is with that same conviction that we launch this campaign today and we urge every American in America to join us tonight. To allow the same attempt to succeed in this election. Machine The year is twenty-eight, and the boy is Harry, the sixth year at Hogwarts School of Witchcraft and Wizardry. He can’t walk without spells covering his feet (or in his case, his feet are so badly burned that he, for practical purposes, can’t even walk for that long without them) and he’s just starting to feel more secure about things. This is a pretty dull aspect of the book, I’d say. They probably spent way too much time on the fact that he can’t use the stick of silver from his wand, despite his friends bewitching all the knives they had. Harry had been having some difficulty getting to sleep until Hermione pulled him out of his state of near-death-conversation. Thanks to Hermione’s meddling, he’s gotten some sleep for the past two days. They also learnt a fair amount about getting used to his new surroundings. Machine Coincidentally, just a few days after the first tweet came out, a fellow named Kevin McReynolds sent out an interview with GQ to promote their upcoming issue. McReynolds describes himself as ”a conservative Catholic” who ”cannot fathom this guy being a
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[]
by dividing each image by a spatially low-pass filtered version of the frame using a ImageJ software plugin. Next, X-Y movement was corrected using TurboReg ( The´venaz et al., 1998 ). After motion correction, each movie was temporally smoothed and down-sampled from 20 Hz to 5 Hz. Spatial filters for each individual neuron were defined using a semi-automated cell-sorting routine using principal and independent component analyses ( Mukamel et al., 2009 ). We identified 11.9±4.7 cells per mouse per day. Every cell included in the analysis was visually validated based on the spatial filter and activity trace. For activity analysis across pairs of different behavioral paradigms, we matched neurons across days by using a centroid- based alignment procedure ( Corder et al., 2019 ). Relative changes in calcium fluorescence (F), were expressed as: DF(t)/F0 = (F(t)/C0F0)/F0, where F0 is the mean image obtained by averaging the entire movie ( Grewe et al., 2017 ;Remedios et al., 2017 ). Following this, activity traces were z-scored and linearly detrended to avoid possible influences of fluorescence decay during theimaging session. Stimulus responses were baselined to 2 s pre-interaction during social preference tests, 10 s pre-CS+ during fear conditioning and retrieval tests, and 1 s pre-tone presentation during auditory exposure testing. Stimulus response period was set to 30 s for CS, US, CS-R and US-, to 10 s for interactions during social preference testing, and to 3 s during tone presen-tations. The z-scored area under the curve (AUC) was used to assess the significance of activity responses during different stimuli presentations. To define responsive cells, average time-binned Ca 2+signals were compared between the stimulus presentation and equivalent baseline period, and classified as excited or inhibited ( ±2sfrom baseline) during different stimuli presentations. To define coding neurons (CN), cells not displaying activity ±2sfrom baseline or displaying activity during all stimuli presentations during one specific session were defined as ‘‘other’’ ( Remedios et al., 2017 ). Population vector analysis In order to compare the similarity of sets of classified groups of neurons (e.g., object vs mouse CN) response patterns, we measured the Mahalanobis distance between ensembles across time ( Grewe et al., 2017 ). The dataset for each computation included two dis- tributions of population activity vectors of N-dimensions (N = neurons) from the mean of all frames where a given pair of stimuli pre-sentations occurred. Activity vectors were computed at different time points, using the
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coverage is not uniform across geogra- phies. Unsurprisingly, in the case of Eastern Part- nership countries, we find that Ukraine is by far better covered than the rest of the countries, both when looking at the absolute number of reported companies and when normalising with respect to the country size (either via population or gross do- mestic product). When normalising by size, we see that Crunchbase covers – in a roughly homogeneous fashion – all 48 Kane, T. J., ‘The importance of start-ups in job creation and job destruction’, 2010. 49 Malizia, E., Feser, E. J., Renski, H. & Drucker, J., Under- standing local economic development, Routledge, 2020. 50 https://www.crunchbase.com/ 51 Crunchbase Expands Paid Services, Raises $18 Million, Wall Street Journal, April 2017.Eastern Partnership countries, with the exception of Azerbaijan (for which the normalised number of companies is slightly lower) and Ukraine, whose number of companies per inhabitants and GDP is about 3-4 times larger than the rest of the East- ern Partnership countries. With these precautions in mind, fair comparisons between Eastern Part- nership countries can be drawn, as we shall do in the following section. Crunchbase adopts a native taxonomy which cat- egorises companies into 47 Industry Groups (such as, e.g. Biotechnology, Transportation, Real Estate, etc). The taxonomy features a second level, which encompasses 744 Industries (such as, e.g. Biopharma, Automotive, Building Material, etc). In terms of granularity, the second level of the tax- onomy is slightly biased towards the technological sectors and it may prove inapt for the mapping exercise at stake, which aims at a more transver- sal analysis of the service sector. For this reason, in the following, we will use the Crunchbase In- dustry Groups to measure sectoral critical mass and the specialisation of start-ups and venture capital-backed companies in the different Eastern Partnership countries. We shall look at the follow- ing three variables in particular: 1. combined number of companies per Industry Group 2. combined number of employees per Industry Group 3. combined estimated revenue per Industry Group. While the number of employees per company is available for most of the organisations featured in Crunchbase, the estimated revenue is available for only about 18% of the companies. This sparsity will be taken into due account in our assessments; indeed, average estimated revenue per industry group is only reported in descriptive tables for in- formational purposes, but is not considered for the final selection
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(SITC 6). Azerbaijan’s exports are extremely specialised, with more than 80% of exports in only one cate- gory, putting the country at risk of negative mar- ket developments in this goods category. SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 3 88.0% 8 90.8% 0 Food and live animals 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 0.7% 057 Fruit and nuts (not including oil nuts), fresh or dried X 1.3% 1 Beverages and tobacco 2 Crude materials, inedible, except fuels 3 Mineral fuels, lubricants and related materials 333Petroleum oils and oils obtained from bituminous minerals, crudeX 82.1% X 82.1% 335 Residual petroleum products, n.e.s., and related materials X 0.2% 343 Natural gas, whether or not liquefied X 5.6% X 5.6% 351 Electric current X 0.2% 4 Animal and vegetable oils, fats and waxes 5 Chemicals and related products, n.e.s. 571 Polymers of ethylene, in primary forms X 0.4% 6 Manufactured goods classified chiefly by material 679Tubes, pipes and hollow profiles, and tube or pipe fittings, of iron or steel X 0.2% 684 Aluminium X 0.5% 7 Machinery and transport equipment 8 Miscellaneous manufactured articles 9Commodities and transactions not classified elsewhere in the SITC Table 2.17. Goods export specialisation for Azerbaijan Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation69 Mapping of goods export specialisations – results for Georgia Results of the export mapping for Georgia are shown in Table 2.18. The 18 goods categories with current strength represent almost 61% of the total exports for 2012-2019. Specialised ex- ports in Beverages and tobacco (SITC 1) account for more than 13% of the total exports, those in Crude materials, inedible, except fuels (SITC 2) for almost 13%, those in Manufactured goods clas- sified chiefly by material (SITC 6) for more than 10% and those in Machinery and transport equip- ment (SITC 7) for almost 16%.The 26 goods categories with emerging strength represent almost 48% of the total exports. Export specialisations are primarily increasing in Bever- ages and tobacco (SITC 1); Crude materials, inedi- ble, except fuels (SITC 2); and Manufactured goods classified chiefly by material (SITC 6). Georgia has a diversified export specialisation, suggesting that the country should be better able to deal with economic downturns and to benefit from booming product markets. SITC Goods nameCurrent strength% share
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[]
organic molecules in areas such as the citric acid cycle are transferred to oxygen and the energy released is used to make ATP. This is done in eukaryotes by a series of proteins in the membranes of mitochondria called the electron transport chain. In prokaryotes, these proteins are found in the cell's inner membrane.[50] These proteins use the energy from reduced molecules like NADH to pump protons across a membrane.[51] Mechanism of ATP synthase. ATP is shown in red, ADP and phosphate in pink and the rotating stalk subunit in black. Pumping protons out of the mitochondria creates a proton concentration difference across the membrane and generates an electrochemical gradient.[52] This force drives protons back into the mitochondrion through the base of an enzyme called ATP synthase. The flow of protons makes the stalk subunit rotate, causing the active site of the synthase domain to change shape and phosphorylate adenosine diphosphate—turning it into ATP.[20] Energy from inorganic compounds Further information: Microbial metabolism and Nitrogen cycle Chemolithotrophy is a type of metabolism found in prokaryotes where energy is obtained from the oxidation of inorganic compounds. These organisms can use hydrogen,[53] reduced sulfur compounds (such as sulfide, hydrogen sulfide and thiosulfate),[1] ferrous iron (Fe(II))[54] or ammonia[55] as sources of reducing power and they gain energy from the oxidation of these compounds.[56] These microbial processes are important in global biogeochemical cycles such as acetogenesis, nitrification and denitrification and are critical for soil fertility.[57][58] Energy from light Further information: Phototroph, Photophosphorylation, and Chloroplast The energy in sunlight is captured by plants, cyanobacteria, purple bacteria, green sulfur bacteria and some protists. This process is often coupled to the conversion of carbon dioxide into organic compounds, as part of photosynthesis, which is discussed below. The energy capture and carbon fixation systems can, however, operate separately in prokaryotes, as purple bacteria and green sulfur bacteria can use sunlight as a source of energy, while switching between carbon fixation and the fermentation of organic compounds.[59][60] In many organisms, the capture of solar energy is similar in principle to oxidative phosphorylation, as it involves the storage of energy as a proton concentration gradient. This proton motive force then drives ATP synthesis.[61] The electrons needed to drive this electron transport chain come from light-gathering proteins called photosynthetic reaction centres. Reaction centers are classified into two types depending on the nature of photosynthetic pigment present, with most photosynthetic bacteria only having
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[]
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Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural ma- chine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 . Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. CoRR , abs/1905.12616. Tianyi Zhang, Varsha Kishore, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Eval- uating text generation with bert. In International Conference on Learning Representations .A Appendix A.1 Dataset Sizes Table 5 shows the number of sequences used for training and evaluating each of the automatic dis- criminators. Recall that each discriminator is trained for binary classification on an a dataset of machine-generated (positive) and human-written (negative) examples. Each dataset was constructed by pairing the human-written excerpts (last row of Table 5) with the machine-generated excerpts drawn via a particular decoding algorithm (‘k40’, ‘p0.96’, or ‘p1.0’) and priming strategy (‘no- cond’ or ‘1wordcond’). Originally the human- written set and each machine-generated set con- tained 250,000 training examples, 5,000 validation examples, and 5,000 test examples. Table 5 shows the resulting counts after after all excerpts with sequence length shorter than 192 tokens were fil- tered out. Thus, the final training, validation, and test sets were almost, but not quite, balanced. A.2 Further Details on Human Evaluation The user interface for the human evaluation task is shown in Figure 6. At each step, the rater is shown additional text and asked to guess whether the excerpt is human-written or machine-generated. They are able to revise their guess at each subse- quent step. The newly appended text at each step is bolded in the UI. At the end, workers are told whether or not they got the question correct. To gauge worker attention levels, 10% of ques- tions shown to workers explicitly stated what an- swer ought to be specified. An example of one of these “honeypot” questions is shown in Figure 7. Amazon Mechanical Turk workers got 83% accu- racy on these questions. Expert raters got 91.8% accuracy. Table 8 shows the accuracy of each ex- pert rater along with the number of annotations they provided. Table 9 shows the example exerpts that were used to “train” the expert raters. For both the Amazon Mechanical Turk raters and the expert raters initial predictions were biased
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Quinn, T. J. et al. (2021) European Stroke Organisation (ESO) and European Academy Neurology (EAN) joint guidelines on post stroke cognitive impairment. European Stroke Journal, 6(3), I-XXXVIII. (doi: 10.1177/23969873211042192) (PMCID:PMC8564156) Taylor-Rowan, M. , Nafisi, S., Owen, R., Duffy, R., Patel, A., Burton, J. K. and Quinn, T. J. (2021) Informant-based screening tools for dementia: an overview of systematic reviews. Psychological Medicine, (doi: 10.1017/S0033291721002002) (PMID:34030753) (Early Online Publication) https://www.gla.ac.uk/researchinstitutes/icams/staff/terryquinn/#:~:text=Levis%2C%20B.%20et%20al.%20(2020)%20Patient%20Health%20Questionnaire%2D9%20scores%20do%20not%20accurately%20estimate%20depression%20prevalence%3A%20individual%20participant%20data%20meta%2Danalysis.%20Journal%20of%20Clinical%20Epidemiology%2C%20122%2C%20115%2D128.e1.%20(doi%3A%2010.1016/j.jclinepi.2020.02.002)%20(PMID%3A32105798)" "1. Baker, E.K., Butler, M.G., Hartin, S.N. et al. Relationships between UBE3A and SNORD116 expression and features of autism in chromosome 15 imprinting disorders. Transl Psychiatry 10, 362 (2020). https://doi.org/10.1038/s41398-020-01034-7 2. Ballester-Navarro, P., Richdale, A. L., Baker, E. K., & Peiro, A. (2020). Sleep in autism: A biomolecular approach to aetiology and treatment. Sleep Medicine Reviews, 54. 3. Baker, E.K., Arpone, M., Aliaga, S.M. et al. Incomplete silencing of full mutation alleles in males with fragile X syndrome is associated with autistic features. Molecular Autism 10, 21 (2019). https://doi.org/10.1186/s13229-019-0271-7 4. Baker & Richdale. (2015). Sleep patterns in adults with a diagnosis of high-functioning autism spectrum disorder. Sleep 38(11):1765-74. 5. Baker, E. K., Arora, S., Amor, D. J., Date, P., Cross, M., O'Brien, J., Simons, C., Rogers, C., Goodall, S., Slee, J., Cahir, C., & Godler, D. E. (2021). The Cost of Raising Individuals with Fragile X or Chromosome 15 Imprinting Disorders in Australia. Journal of autism and developmental disorders, 10.1007/s10803-021-05193-4. Advance online publication. https://doi.org/10.1007/s10803-021-05193-4"
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agriculture. Nevertheless, in terms of exports of goods – excluding Azerbaijan which concentrates most of its exports in Mineral fuels, lubricants and related materials – for the remaining EaP countries, Food and live animals and Beverages and tobacco represent between 10% and 32% of the total exports (Moldova at the higher end). Beyond the primary sector, the previous section presented a set of specialisations resulting from the economic and innovation analysis for each EaP country, summarised in the following transversal table. It shows the NACE codes that have been selected for at least one country, and includes a ‘potential for economic and innovation collabora- tion’ indicator based on the number of times it has been selected as an E&I specialisation domain. The first clear transversal E&I domain of potential collaboration consists of NACE codes 10 Manu- facture of food products and 11 Manufacture of beverages in the Food Processing and Manu- facturing industry. This potential domain of collaboration directly concerns Armenia, Georgia, Moldova and Ukraine and is aligned with the com- mon agricultural strength identified in the previ- ous paragraphs. Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials, NACE code 16, is identified as an E&I specialisation in two countries: Moldova and Ukraine.Manufacture of chemicals and chemical products, NACE code 20, is identified as an E&I specialisa- tion in two countries: Azerbaijan and Moldova. Manufacture of fabricated metal products, except machinery and equipment, NACE code 25, is identified as an E&I specialisation in two coun- tries: Georgia and Ukraine. Manufacture of computer, electronic and optical products, NACE code 26, is identified as an E&I spe- cialisation in two countries: Armenia and Ukraine. Accommodation, NACE code 25, is identified as an E&I specialisation in two countries: Armenia and Georgia. Telecommunications, NACE code 61, is identified as an E&I specialisation in two countries: Armenia and Moldova. Several advanced services, notably NACE code 62 Computer programming, consultancy and related activities, NACE code 63 Information service activities and NACE code 64 Finan- cial service activities, except insurance and pension funding, concern, to varying degrees, 4 EaP countries: Armenia, Azerbaijan, Georgia and Moldova, creating an economic cluster of potential collaboration. The above E&I domains of potential collaboration are solely those emerging from the selected E&I domains for each country, and other potential ar- eas of cooperation may exist. This said, there is obviously
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[]
populations. By 2040, the workforce is projected to shrink by close to 2 million workers each year. We will have to lean more on productivity to drive growth. If the EU were to maintain its average productivity growth rate since 2015, it would only be enough to keep GDP constant until 2050 – at a time when the EU is facing a series of new investment needs that will have to be financed through higher growth. To digitalise and decarbonise the economy and increase our defence capacity, the investment share in Europe will have to rise by around 5 percentage points of GDP to levels last seen in the 1960s and 70s. This is unprecedented: for comparison, the additional investments provided by the Marshall Plan between 1948-51 amounted to around 1-2% of GDP annually. If Europe cannot become more productive, we will be forced to choose. We will not be able to become, at once, a leader in new technologies, a beacon of climate responsibility and an independent player on the world stage. We will not be able to finance our social model. We will have to scale back some, if not all, of our ambitions. This is an existential challenge. Europe’s fundamental values are prosperity, equity, freedom, peace and democracy in a sustainable environment. The EU exists to ensure that Europeans can always benefit from these fundamental rights. If Europe can no longer provide them to its people – or has to trade off one against the other – it will have lost its reason for being. The only way to meet this challenge is to grow and become more productive, preserving our values of equity and social inclusion. And the only way to become more productive is for Europe to radically change. 05 THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | FOREWORDThree areas for action to reignite growth This report identifies three main areas for action to reignite sustainable growth. In each area, we are not starting from zero. The EU still has general strengths – such as strong education and health systems and robust welfare states – and specific strengths on which to build. But we are collectively failing to convert these strengths into productive and competitive industries on the global stage. First – and most importantly – Europe must profoundly refocus its collective efforts on closing the innovation gap with the US and China, especially
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in the EU budget for Member States to remove barriers to EU integration and ensure interoperability and competition in all transport segments, when these goals goes beyond the application of EU law. The EU should also continue to reinforce its leading position in innovative transport by launching industrial innova - tion projects for decarbonisation challenges, such as an industrial demonstrator (as part of a new Competitiveness Joint Undertaking, replacing current public-private partnerships) or an IPCEI for the zero-emission flight of the future. 52THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3ENDNOTESi European Commission, ‘ Medium-term projections of potential GDP growth in turbulent times ’, European Economic Forecast, Spring 2023, Special Issue 4.1, 2023. ii EIB, ‘ EIB Investment Survey 2023: European Union Overview ’, 2023. iii IEA, Net Zero roadmap , 2023 update. iv DiPippo, G., Mazzocco, I., & Kennedy, S., ‘ Red Ink: Estimating Chinese Industrial Policy Spending in Comparative Perspective ’, Center for Strategic and International Studies, 2022. v ECB, The EU’s Open Strategic Autonomy from a central banking perspective: Challenges to the monetary policy landscape from a changing geopolitical environment , ECB Occasional Paper Series No. 311, 2023.vi ECB, The evolution of China’s growth model: challenges and long-term growth prospects ”, ECB Economic Bulletin, Issue 5/2024, 2024. vii ESMA, TRV Risk analysis – EU natural gas derivatives markets: risks and trends , 2023. viii EIB and European Patent Office, Financing and commercialisation of cleantech innovation , 2024. ix Ibid. x IEA, Advancing Clean Technology Manufacturing , 2024. 53THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 34. Increasing security and reducing dependencies While dependencies are a two-way street, Europe is vulnerable to both coercion and, in extreme cases, geo-economic fragmentation . Europe has extensive external dependencies, ranging from critical raw mate - rials (CRMs) to advanced technologies. Many of these dependencies could become vulnerabilities in a situation where trade fragments along geopolitical lines. Around 40% of Europe’s imports are sourced from a small number of suppliers and difficult to substitute, and around half of these imports originate from countries with which it is not strategically alignedi. As a result, Europe’s notional exposure to any “sudden stops” in trade caused by geopolitical conflagration is high. However, absent an extreme unforeseen scenario, a profound and rapid decoupling of global trade seems unlikely in the medium term. Evidence of de-globalisation is currently limited, with companies preferring to
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unreason- ably good performance on the English Argument Reasoning Comprehension Task (Habernal et al., 2018) falls back to chance if the dataset is modified by adding adversarial examples that just negate one piece of the original, thus mirroring the distribution of lexical cues for each label. Similarly, McCoy et al. (2019) find that BERT’s performance on the English Multi-genre Natural Language Inference dataset (Williams et al., 2018) is predicated on its ability to leverage syntactic heuristics involving overlap (of full constituents, subsequences, or sim- ply bags of words). In a dataset carefully designed to frustrate such heuristics, BERT’s performance falls to significantly below chance. In this brief overview of BERTology papers we have highlighted both the extent to which there is evidence that large LMs can learn aspects of linguistic formal structure (e.g. agreement, depen- dency structure), and how their apparent ability to “reason” is sometimes a mirage built on leveraging artifacts in the training data (i.e. form, not mean- ing). Our contribution is an argument on theoretical grounds that a system exposed only to form in its training cannot in principle learn meaning. 3 What is meaning? We start by defining two key terms: We take form to be any observable realization of language: marks 4But see Warstadt et al.’s (2019) cautionary note about how the methodology used for probing can influence the results.5187on a page, pixels or bytes in a digital representation of text, or movements of the articulators.5We take meaning to be the relation between the form and something external to language, in a sense that we will make precise below. 3.1 Meaning and communicative intent When humans use language, we do so for a purpose: We do not talk for the joy of moving our articula- tors, but in order to achieve some communicative intent . There are many types of communicative intents: they may be to convey some information to the other person; or to ask them to do something; or simply to socialize. We take meaning to be the relation MEIwhich contains pairs (e; i)of natural language expressions eand the communica- tive intents ithey can be used to evoke. Given this definition of meaning, we can now use understand to refer to the process of retrieving igiven e. Communicative intents are about something that isoutside of language . When we say Open the window! orWhen was Malala Yousafzai born? , the communicative intent is
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and equipment specialized for particular industries; parts thereof, n.e.s. X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation315 316 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 52 731 Machine tools working by removing metal or other material 733 Machine tools for working metal, sintered metal carbides or cermets, without removing material 735Parts, n.e.s., and accessories suitable for use solely or principally with the machines falling within groups 731 and 733 (including work or tool holders, self-opening die-heads, dividing heads and other special attachments for machine tools); tool holders for any type of tool for working in the hand 737 Metalworking machinery (other than machine tools) and parts thereof, n.e.s. X X 741 Heating and cooling equipment and parts thereof, n.e.s. X X 742Pumps for liquids, whether or not fitted with a measuring device; liquid elevators; parts for such pumps and liquid elevators X X X 743Pumps (other than pumps for liquids), air or other gas compressors and fans; ventilating or recycling hoods incorporating a fan, whether or not fitted with filters; centrifuges; filtering or purifying apparatus; parts thereof 744 Mechanical handling equipment and parts thereof, n.e.s. X X X 745 Non-electrical machinery, tools and mechanical apparatus and parts thereof, n.e.s. X 746 Ball- or roller bearings X X X 747Taps, cocks, valves and similar appliances for pipes, boiler shells, tanks, vats or the like, including pressure-reducing valves and thermostatically controlled valves X X X 748Transmission shafts (including camshafts and crankshafts) and cranks; bearing housings and plain shaft bearings; gears and gearing; ball or roller screws; gearboxes and other speed changers (including torque converters); flywheels and pulleys (including pulley blocks); clutches and shaft couplings (including universal joints); articulated link chain; parts thereof X X 749 Non-electric parts and accessories of machinery, n.e.s. 751 Office machines 752Automatic data-processing machines and units thereof; magnetic or optical readers, machines for transcribing data onto data media in coded form and machines for processing such data, n.e.s. 759Parts and accessories (other than covers, carrying cases and the like) suitable for use solely or principally with machines falling withing groups 751 and 752 761Monitors and projectors, not incorporating television reception apparatus; reception apparatus for television, whether or not incorporating radio-broadcast receivers or sound or video
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[]
147Ce 0.401* 6 (2) – – – 152Nd 2.243* 71 (6) 63 (7) [ 40]6 3 158Sm 1.279* 74 (12) 74 (6) [ 41]7 4 Fig. 9 Level scheme of108Tc obtained from this work, based on the ones proposed in Refs. [ 43,45]. The half-lives of the isomeric states that were measured in this work are noted in red, and the γ-ray energies are given in blue. The widths of the arrows are proportional to the intensities of the transitions as obtained from our data set. These intensities relative to the 153.9 keV transition are reported below the γ-rays energy Fig. 10 Populating γ-ray energy vs. time difference spectra obtained for different isomeric transitions in108Tc, from the multiple isomers analysis. γ-γcascades from108Tc level scheme are highlighted in red are AL=(100.5±0.2)u and AH=(147.8±0.2)u, respec- tively. As the mass determination is known for exhibiting a slight charge-dependent deviation due to neutron emission [46], these characteristics are consistent with the known level scheme of147Ce from the excited level E∗=401.1k e V 123 5 Page 8 of 12 Eur. Phys. J. A (2025) 61:5 [47]. The nuclear charge distribution obtained from the IC (see Sect. 3.2) also supports this identification. Interestingly, although this isotope was already observed in the sponta- neous fission of252Cf [48], no isomeric state was reported. This could be explained by the time resolution of HPGedetectors, which is much worse than for LaBr 3(Ce) detec- tors. This is of particular importance for such a short-lived isomer, as the deduced half-life is T1/2=(6±2)ns. It is noteworthy that for short-lived isomers with a half-life below 10 ns, such as147Ce and137Xe, most of the relevant data was cut out during the analysis, being below the T= 35 ns threshold used to suppress (n,n/prime)events. 3.1.394Rb Theγ-ray spectra we obtained for the isomer in94Rb was consistent with the level scheme proposed in Ref. [ 20], although less precise due to the limited resolving power ofour scintillator array compared to HPGe detectors. We wereable to observe the γ-rays from the main isomer cascade, which are 111-217-339-649-169 keV, as well as the 590 keV line feeding this isomer. However, the time spectra associatedwith these events are in disagreement with the half-lives of the 2.075 and 1.485 MeV levels previously reported. Using the multiple isomers analysis to disentangle the half-lives ofthese levels, we deduced 65 ns and 42 ns instead of 107 ns and 18 ns, respectively. Our data
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[]
in Petten, NL, and for this research,  we used the following  equipment:    Three Maccor battery cyclers with the following  capabilities:    Series 4000 with 32 channels  rated 20 A @ −2/+8 V, and 32 T-Type thermocouple   inputs.   Series 4000 M with 48 channels  rated 5 A @ −2/+8 V, and 32 T-Type thermocouple   inputs.   Series 4000 M with 16 channels  rated 25 A @ 0–18 V, and 16 T-Type thermocou- ple inputs.    Figure 1. Experimental  facilities at JRC Petten, The Netherlands:  (a) Maccor battery cycler with BIA  temperature  chambers;  (b) Maccor battery cycler with a Vötsch temperature  chamber.    Three BIA MTH 4.46 units (see Figure 1a), each composed  of four independent  and  identical  temperature  chambers  with a temperature  control range between −40 °C  Figure 1. Experimental facilities at JRC Petten, The Netherlands: ( a) Maccor battery cycler with BIA temperature chambers; ( b) Maccor battery cycler with a Vötsch temperature chamber. Table 2. Batteries from JRC database. ManufacturerCommon DesignationRated Capacity mAhType Energyzer AA 2500 NiMH GP AA 2100 NiMH Duracell AA 2500 NiMH Soshine AA 2700 NiMH Duracell AAA 900 NiMH Tronic AAA 1000 NiMH GP AAA 650 NiMH Duracell AAA 750 NiMH Duracell C 3000 NiMH GP C 3000 NiMH Energizer C 2500 NiMH Energizer D 2500 NiMH Duracell D 3000 NiMH Phillips D 3000 NiMH Energizer 9V 175 NiMH Agfaphoto AA 2300 NiMH Ansmann C 4500 NiMH RS pro D 10000 NiMH Agfaphoto AAA 900 NiMH Varta C 3000 NiMH GP D 5700 NiMHBatteries 2025 ,11, 30 5 of 20 Table 2. Cont. ManufacturerCommon DesignationRated Capacity mAh Type Duracell 9V 170 NiMH Energizer AAA 700 NiMH Energizer AA N/A Alkaline primary Duracell 9V N/A Alkaline primary Varta AAA N/A Alkaline primary Duracell Plus C N/A Alkaline primary Energizer D N/A Alkaline primary 3. Capacity Analysis of Portable NiMH Batteries The analysis of the capacity is divided into three parts: first, an analysis of the charging profile; second, an analysis of discharge profile characteristics; and third, an analysis of the capacity protocols for portable NiMH batteries in IEC-61951-2. 3.1. Charge Analysis of Portable NiMH Batteries There are three commonly used criteria for ending the charging of portable NiMH batteries denoted as temperature difference, time detection, and negative delta voltage [ 31]. These methods are applied by different manufacturers; however, in IEC-61951-2-2017-7.2.1, the method for charging portable NiMH batteries for verifying capacity is the time detection
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[]
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| 21.57%) Nanotechnology and materials (8 | 10.53%) Nanotechnology and materials (4 | 6.9%)ICT and computer science (15 | 34.88%) ICT and computer science (16 | 23.88%) ICT and computer science (34 | 16.67%)ICT and computer science (61 | 18.89%) ICT and computer science (2 | 10.53%)ICT and computer science (18 | 31.03%)ICT and computer science (19 | 25.0%)Environmental sciences and industries (3 | 15.79%) Environmental sciences and industries (8 | 13.79%)Environmental sciences and industries (15 | 19.74%)Environmental sciences and industries (63 | 19.5%)Environmental sciences and industries (45 | 22.06%) Energy (5 | 11.63%) Energy (54 | 16.72%)Energy (2 | 10.53%) Energy (11 | 16.42%) Energy (6 | 7.89%)Energy (43 | 21.08%)Agrifood (2 | 10.53%) Agrifood (5 | 8.62%) Agrifood (7 | 9.21%)Optics and photonics (13 | 19.4%)Health and wellbeing (10 | 13.16%) Health and wellbeing (4 | 6.9%)Health and wellbeing (41 | 20.1%)Health and wellbeing (56 | 17.34%)Health and wellbeing (4 | 9.3%) Biotechnology (3 | 6.98%) Biotechnology (29 | 8.98%)Biotechnology (1 | 5.26%) Biotechnology (20 | 9.8%)Biotechnology (3 | 5.17%)Fundamental physics and mathematics (7 | 10.45%) Fundamental physics and mathematics (3 | 6.98%)Chemistry and chemical engineering (1 | 5.26%)Chemistry and chemical engineering (6 | 8.96%) Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation173 4. Critical mass, specialisation and excellence indicators in the S&T specialisation domains 4.1 Methodology The S&T specialisation domains in the Eastern Partnership (i.e. the labelled topic groups extract- ed using the topic modelling technique in Chapter 2) have been further characterised by computing their respective critical mass and specialisation in- dicators in each of the six EaP countries. An initial overview has been compiled of the do- mains featuring the most intensive S&T activity in each country by looking at the raw number of records (critical mass) per domain, per type of source and per country. The domains have been ranked accordingly. Additionally, the compound annual growth rate (CAGR) has been calculated for all domains in each country, in terms of number of publications, for the period 2012-2019 (the selected period for this analysis). This gives an indication of whether or not the number of publications in a domain has been growing over the last few years, signalling the growing or dwindling relevance of said domain in the country. The CAGR has not been comput- ed for patents, as the number of patents regis- tered has been decreasing over the
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[]
7 6 6 5 5 2 2 2 4 3 Governance, culture, education and the economy 138 115 99 106 109 93 83 76 77 71 Health and wellbeing 34 26 29 27 21 16 12 17 14 10 ICT and computer science 37 33 37 39 34 29 29 24 22 22 Mechanical engineering and heavy machinery 3 1 4 1 2 2 1 2 2 Nanotechnology and materials 33 23 29 18 20 13 12 5 11 3 Optics and photonics 10 8 7 3 6 2 4 Transportation 17 10 13 11 13 11 8 7 8 5 EC projectsFigure VI. Number of publications and EC projects in collaboration between EaP actors and partners outside of the EaP Colour indicates the relative distribution, computed row-wise. PART 1Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation27 Part 1. Introduction and methodology 1. Introduction, study objec- tives and key requirements This project is part of the Joint Research Centre project ‘Smart Specialisation and organisational development in enlargement and H2020 asso- ciated countries’ (under the Enlargement & In- tegration Action, E&IA), which has the objective of supporting the design and implementation of Smart Specialisation Strategies in EU Enlarge- ment and Neighbourhood Region. In the context of the EU Neighbourhood Policy10, the Eastern Part- nership11 is a joint initiative involving the EU, its Member States and five Eastern European Part- ners: Armenia, Azerbaijan, Georgia, the Republic of Moldova and Ukraine’ which includes the com- mitment12 to develop place-based Smart Special- isation Strategies for research and innovation to enhance regional competitiveness. A common, comprehensive, but adaptable, ‘Smart Specialisa- tion framework for Enlargement and Neighbour- hood Region’ has been established13, guiding the policymaking process. Within this framework, the current project is contained in the ‘Analysis of con- text – country-specific conditions’ phase. 10 https://ec.europa.eu/regional_policy/es/policy/coopera- tion/international/neighbourhood-policy/ 11 ibid 12 Joint communication to the European Parliament, the European Council, the Council, the European Econom- ic and Social Committee and the Committee of the Re- gions - Eastern Partnership policy beyond 2020, Brussels 18/03/20. 13 Matusiak, M., Kleibrink, A. (ed.), Supporting an Inno- vation Agenda for the Western Balkans – Tools and Methodologies, Publications Office of the European Un- ion, Luxembourg, 2018, ISBN 978-92-79-81870-7, doi:10.2760/48162, JRC111430.This report aims to move the methodology forward and extend the diversity of economic, innovation, scientific and technological (EIST) dimensions to be analysed, namely by exploring new
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X 63.9 Other information service activities X X K FINANCIAL AND INSURANCE ACTIVITIES Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation287 288 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 64 Financial service activities, except insurance and pension funding 64.1 Monetary intermediation X X X X X X X 64.2 Activities of holding companies 64.3 Trusts, funds and similar financial entities 64.9Other financial service activities, except insurance and pension fundingX X X X X X X X X 65Insurance, reinsurance and pension funding, except compulsory social security 65.1 Insurance X X X 65.2 Reinsurance 65.3 Pension funding 66 Activities auxiliary to financial services and insurance activities 66.1Activities auxiliary to financial services, except insurance and pension funding X X X X 66.2 Activities auxiliary to insurance and pension funding X X X 66.3 Fund management activities L REAL ESTATE ACTIVITIES 68 Real estate activities 68.1 Buying and selling of own real estate X X X X X X X 68.2 Rental and operating of own or leased real estate X X X X X X X 68.3 Real estate activities on a fee or contract basis X X X X X X M PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES 69 Legal and accounting activities 69.1 Legal activities X X X X X 69.2 Accounting, bookkeeping and auditing activities; tax consultancy X X X X X X 70 Activities of head offices; management consultancy activities 70.1 Activities of head offices X X X X 70.2 Management consultancy activities X X X X X 71Architectural and engineering activities; technical testing and analysis 71.1Architectural and engineering activities and related technical consultancy X X X X X 71.2 Technical testing and analysis X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation289 290 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
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[]
of each S&T specialisation domain, for each EaP country Based on the evidence presented throughout Part 3 for critical mass, specialisation and excellence per domain, the following tables present synthet- ic indicators supporting the selection of the S&T specialisation domains for each Eastern Partner- ship country. For each country, each domain is characterised by its critical mass, specialisation EaP S&T specialisation domains according to the internal distribution of 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 mathematicsHealth and wellbeingMechanical engineering and heavy machinery Nanotechnology and materialsBiotechnologyElectric and electronic technologies Optics and photonicsICT and computer scienceAgrifood Environmental sciences and industriesEnergy Chemistry and chemical engineeringTransportation Governance, culture, education and the economy is mainly composed of EC R&I projectsTable III. Characterisation of the EaP S&T domains according to the internal distribution of S&T data sources and excellence8. Below, these S&T indicators are presented in a summary table for each country. Highlighted domains are qualitatively reflected on for each country. 8 Further detail on the choice and definition of indicators can be found in Part 3. 16 Overview of economic, innovation, scientific and technological specialisations Armenia – Summary of the strengths of the S&T specialisations Armenia is very specialised in the hard sciences. Beyond those, 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, specialisation and excellence, both in scientific publications and patents. The presence of the A. I. Alikhani- an Yerevan Institute of Physics, which houses a particle accelerator amongst other advanced experimental equipment, leaves a strong im- print on the Armenian science and technology ecosystem, and structures an important 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. 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 machinery2 Nanotechnology and materials 3 Optics and photonics 2 *NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable
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elevators; parts for such pumps and liquid elevatorsX 0.4% 745Non-electrical machinery, tools and mechanical apparatus and parts thereof, n.e.s.X 0.1% 771Electric power machinery (other than rotating electric plant of group 716) and parts thereof X 0.4% 772Electrical apparatus for switching or protecting electrical circuits or for making connections to or in electrical circuits (e.g., switches, relays, fuses, lightning arresters, voltage limiters, surge suppressors, plugs and sockets, lamp- holders and junction boxes); electrical resistors (including rheostats and potentiometers), other than heating resistors; printed circuits; boards, panels (including numerical control panels), consoles, desks, cabinets and other bases, equipped with two or more apparatus for switching, protecting or for making connections to or in electrical circuits, for electric control or the distribution of electricity (excluding switching apparatus of subgroup 764.1)X 0.6% X 0.6% 773 Equipment for distributing electricity, n.e.s. X 12.1% 781Motor cars and other motor vehicles principally designed for the transport of persons (other than motor vehicles for the transport of ten or more persons, including the driver), including station-wagons and racing cars X 0.4% 792Aircraft and associated equipment; spacecraft (including satellites) and spacecraft launch vehicles; parts thereof X 0.2% 8 Miscellaneous manufactured articles 821Furniture and parts thereof; bedding, mattresses, mattress supports, cushions and similar stuffed furnishingsX 5.1% X 5.1% 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 cleaningX 0.7% 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 2.4% 74 Part 2 Analysis of economic and innovation potential SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 41 78.7% 23 33.8% 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 3.5% 843Men’s or boys’ coats,
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[]
appliances [27.5]A21B; B01B; F24D; A45D; D06F; F25C; A47G; E06C; F25D; A47J; F24B; H05B; A47L; F24C 27.9Manufacture of other electrical equipment [27.9]B60M; H01T; B61L; H02H; G08G; H02M; G10K; H05C; H01P 28.1Manufacture of general-purpose machinery [28.1]A47K; F01K; B23F; F01M; F01B; F01N; F01C; F01P; F01D; F02C; F02G; F02K; F03B; F03C; F03D; F03G; F04B; F04C; F04D; F15B; F16C; F16D; F16F; F16H; F16K; F16M; F23R; G05D; G05G 28.2Manufacture of other general-purpose machinery [28.2]A62C; B41K; B01D; B43M; B04C; B60S; B05B; B61B; B41J; B65G; B66B; B66C; B66D; B66F; C10F; C12L; E02C; F16G; F22D; F23B; F23C; F23D; F23G; F23H; F23J; F23K; F23L; F23M; F23N; F24F; F24H; F25B; F27B; F28B; F28C; F28D; F28F; F28G; G01G; G03G; G06K; G06M; G07B; G07C; G07D; G07F; G11B; G07G; H05F; G09D; G09G; G10L 28.3Manufacture of agricultural and forestry machinery [28.3]A01B; A01K; A01C; A01M; A01D; B27L; A01F; A01G 28.4Manufacture of metal forming machinery and machine tools [28.4]B21D; B21L; B23H; B24C; B25F; B26F; B27J; B21F; B23B; B23K; B24D; B25G; B27B; B28D; B21H; B23C; B23P; B25B; B25H; B27C; B30B; B21J; B23D; B23Q; B25C; B25J; B27F; B21K; B23G; B24B; B25D; B26B; B27G Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation331 NACE sector IPC class NOT matching 28.9Manufacture of other special-purpose machinery [28.9]A21C; A41H; A22B; A42C; A22C; A43D; A23N; B01F; A24C; B02B; B02C; B03B; B03C; B03D; B05C; B05D; B06B; B07B; B07C; B08B; B21B; B22C; B26D; B31B; B31C; B31D; B31F; B33Y; B41B; B41C; B41D; B41F; B41G; B41L; B41N; B42B; B42C; B44B; B44C; B65B; B65C; B65F; B65F 1/*; B65F 5/*; B65F 7/*; B65F 9/*; B65H; B67B; B67C; B68F; C13C; C13D; C13G; C13H; C14B; C23C; D01B; D01D; D01G; D01H; D02G; D02H; D02J; D03C; D03D; D03J; D04B; D04C; D05B; D05C; D06B; D06G; D06H; D21B; D21D; D21F; D21G; E01C; E01D; E01F; E01H; E02D; E02F; E05G; E21B; E21C; E21D; E21F; F04F; F15D; F16N; F16P; F26B 29.1 Manufacture of motor vehicles [29.1]B60B; B60K; B60D; B60L; B60G; B60N; B60H; B60P; B60J; B60Q; B60R; B60T; B62D; F01L; F02B; F02D; F02F; F02M; F02N; F02P; F16J; G01P 29.3Manufacture of parts and accessories for motor vehicles [29.3]B60W 30Manufacture of other transport equipment [30]B60F; B61G; B60V; B61H; B61C; B61J; B61D; B61K; B61F; B62C; B62H; B62J; B62K; B62L; B62M; B63B; B63C; B63H; B63J; B64B; B64C; B64D; B64F; B64G; B65F 3/*; E01B; F03H 31 Manufacture of furniture [31] A47B; A47C; A47D; A47F 32 Other manufacturing [32]A41G; A45F; A63D; A63K; B68G; G10B; G10H; A42B; A46B; A63F; B43K; C06F; G10C; A44C; A46D; A63G; B43L; D07B; G10D; A45B; A63B; A63H; B44D; F16L;
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"other", "general", "-", "purpose", "\n", "machinery", "[", "28.2]A62C", ";", "B41", "K", ";", "B01D", ";", "B43", "M", ";", "B04C", ";", "B60S", ";", "\n", "B05B", ";", "B61B", ";", "B41J", ";", "B65", "G", ";", "B66B", ";", "B66C", ";", "\n", "B66D", ";", "B66F", ";", "C10F", ";", "C12L", ";", "E02C", ";", "F16", "G", ";", "\n", "F22D", ";", "F23B", ";", "F23C", ";", "F23D", ";", "F23", "G", ";", "F23H", ";", "\n", "F23J", ";", "F23", "K", ";", "F23L", ";", "F23", "M", ";", "F23N", ";", "F24F", ";", "\n", "F24H", ";", "F25B", ";", "F27B", ";", "F28B", ";", "F28C", ";", "F28D", ";", "\n", "F28F", ";", "F28", "G", ";", "G01", "G", ";", "G03", "G", ";", "G06", "K", ";", "G06", "M", ";", "\n", "G07B", ";", "G07C", ";", "G07D", ";", "G07F", ";", "G11B", ";", "G07", "G", ";", "\n", "H05F", ";", "G09D", ";", "G09", "G", ";", "G10L", "\n", "28.3Manufacture", "of", "agricultural", "and", "forestry", "\n", "machinery", "[", "28.3]A01B", ";", "A01", "K", ";", "A01C", ";", "A01", "M", ";", "A01D", ";", "B27L", ";", "\n", "A01F", ";", "A01", "G", "\n", "28.4Manufacture", "of", "metal", "forming", "machinery", "\n", "and", "machine", "tools", "[", "28.4]B21D", ";", "B21L", ";", "B23H", ";", "B24C", ";", "B25F", ";", "B26F", ";", "\n", "B27J", ";", "B21F", ";", "B23B", ";", "B23", "K", ";", "B24D", ";", "B25", "G", ";", "\n", "B27B", ";", "B28D", ";", "B21H", ";", "B23C", ";", "B23P", ";", "B25B", ";", "\n", "B25H", ";", "B27C", ";", "B30B", ";", "B21J", ";", "B23D", ";", "B23Q", ";", "\n", "B25C", ";", "B25J", ";", "B27F", ";", "B21", "K", ";", "B23", "G", ";", "B24B", ";", "\n", "B25D", ";", "B26B", ";", "B27", "G", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation331", "\n", "NACE", "sector", "IPC", "class", "NOT", "matching", "\n", "28.9Manufacture", "of", "other", "special", "-", "purpose", "\n", "machinery", "[", "28.9]A21C", ";", "A41H", ";", "A22B", ";", "A42C", ";", "A22C", ";", "A43D", ";", "\n", "A23N", ";", "B01F", ";", "A24C", ";", "B02B", ";", "B02C", ";", "B03B", ";", "\n", "B03C", ";", "B03D", ";", "B05C", ";", "B05D", ";", "B06B", ";", "B07B", ";", "\n", "B07C", ";", "B08B", ";", "B21B", ";", "B22C", ";", "B26D", ";", "B31B", ";", "\n", "B31C", ";", "B31D", ";", "B31F", ";", "B33Y", ";", "B41B", ";", "B41C", ";", "\n", "B41D", ";", "B41F", ";", "B41", "G", ";", "B41L", ";", "B41N", ";", "B42B", ";", "\n", "B42C", ";", "B44B", ";", "B44C", ";", "B65B", ";", "B65C", ";", "B65F", ";", "\n", "B65F", "1/", "*", ";", "B65F", "5/", "*", ";", "B65F", "7/", "*", ";", "B65F", "9/", "*", ";", "\n", "B65H", ";", "B67B", ";", "B67C", ";", "B68F", ";", "C13C", ";", "C13D", ";", "\n", "C13", "G", ";", "C13H", ";", "C14B", ";", "C23C", ";", "D01B", ";", "D01D", ";", "\n", "D01", "G", ";", "D01H", ";", "D02", "G", ";", "D02H", ";", "D02J", ";", "D03C", ";", "\n", "D03D", ";", "D03J", ";", "D04B", ";", "D04C", ";", "D05B", ";", "D05C", ";", "\n", "D06B", ";", "D06", "G", ";", "D06H", ";", "D21B", ";", "D21D", ";", "D21F", ";", "\n", "D21", "G", ";", "E01C", ";", "E01D", ";", "E01F", ";", "E01H", ";", "E02D", ";", "\n", "E02F", ";", "E05", "G", ";", "E21B", ";", "E21C", ";", "E21D", ";", "E21F", ";", "\n", "F04F", ";", "F15D", ";", "F16N", ";", "F16P", ";", "F26B", "\n", "29.1", "Manufacture", "of", "motor", "vehicles", "[", "29.1]B60B", ";", "B60", "K", ";", "B60D", ";", "B60L", ";", "B60", "G", ";", "B60N", ";", "\n", "B60H", ";", "B60P", ";", "B60J", ";", "B60Q", ";", "B60R", ";", "B60", "T", ";", "\n", "B62D", ";", "F01L", ";", "F02B", ";", "F02D", ";", "F02F", ";", "F02", "M", ";", "\n", "F02N", ";", "F02P", ";", "F16J", ";", "G01P", "\n", "29.3Manufacture", "of", "parts", "and", "accessories", "for", "\n", "motor", "vehicles", "[", "29.3]B60W", "\n", "30Manufacture", "of", "other", "transport", "\n", "equipment", "[", "30]B60F", ";", "B61", "G", ";", "B60V", ";", "B61H", ";", "B61C", ";", "B61J", ";", "\n", "B61D", ";", "B61", "K", ";", "B61F", ";", "B62C", ";", "B62H", ";", "B62J", ";", "\n", "B62", "K", ";", "B62L", ";", "B62", "M", ";", "B63B", ";", "B63C", ";", "B63H", ";", "\n", "B63J", ";", "B64B", ";", "B64C", ";", "B64D", ";", "B64F", ";", "B64", "G", ";", "\n", "B65F", "3/", "*", ";", "E01B", ";", "F03H", "\n", "31", "Manufacture", "of", "furniture", "[", "31", "]", "A47B", ";", "A47C", ";", "A47D", ";", "A47F", "\n", "32", "Other", "manufacturing", "[", "32]A41", "G", ";", "A45F", ";", "A63D", ";", "A63", "K", ";", "B68", "G", ";", "G10B", ";", "\n", "G10H", ";", "A42B", ";", "A46B", ";", "A63F", ";", "B43", "K", ";", "C06F", ";", "\n", "G10C", ";", "A44C", ";", "A46D", ";", "A63", "G", ";", "B43L", ";", "D07B", ";", "\n", "G10D", ";", "A45B", ";", "A63B", ";", "A63H", ";", "B44D", ";", "F16L", ";", "\n" ]
[]
related activities Belarus10.5 Manufacture of dairy products 13 Manufacture of textiles 16 Manufacture of wood and of products of wood and cork 18.1 Printing and service activities related to printing 20.3 Manufacture of paints 20.6 Manufacture of man-made fibres 22 Manufacture of rubber and plastic products 23 Manufacture of other non-metallic mineral products 23.3 Manufacture of clay building materials 23.5 Manufacture of cement, lime and plaster 25.4 Manufacture of weapons and ammunition 26.4 Manufacture of consumer electronics 26.7 Manufacture of optical instruments and photographic equipment 27.2 Manufacture of batteries and accumulator 27.9 Manufacture of other electrical equipment 29.1 Manufacture of motor vehicles 29.3 Manufacture of parts and accessories for motor vehicles 30 Manufacture of other transport equipment 32 Other manufacturing 32.9 Manufacturing n.e.c.Table 2.31. Industries with innovation potential based on 2011-2018 relative patent performance Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation93 Georgia12 Manufacture of tobacco products 15 Manufacture of leather and related products 23 Manufacture of other non-metallic mineral products 23.1 Manufacture of glass and glass products 23.4 Manufacture of other porcelain and ceramic products 24 Manufacture of basic metals 25.5 Forging, pressing, stamping and roll-forming of metal 25.6 Treatment and coating of metals; machining 25.7 Manufacture of cutlery, tools and general hardware 27.5 Manufacture of domestic appliances 29.1 Manufacture of motor vehicles 31 Manufacture of furniture Moldova16 Manufacture of wood and of products of wood and cork 18.1 Printing and service activities related to printing 20.2 Manufacture of pesticides and other agrochemical products 23.1 Manufacture of glass and glass products 25.2 Manufacture of tanks 25.6 Treatment and coating of metals; machining 28.3 Manufacture of agricultural and forestry machinery 28.4 Manufacture of metal forming machinery and machine tools Ukraine14 Manufacture of wearing apparel 24 Manufacture of basic metals 25.1 Manufacture of structural metal products 25.3 Manufacture of steam generators 25.9 Manufacture of other fabricated metal products 26.4 Manufacture of consumer electronics 26.5 Manufacture of instruments and appliances for measuring 27.3 Manufacture of wiring and wiring devices 27.9 Manufacture of other electrical equipment 28.4 Manufacture of metal forming machinery and machine tools 32.5 Manufacture of medical and dental instruments and supplies 94 Part 2 Analysis of economic and innovation potential NACE Industry name Armenia Azerbaijan Belarus Georgia Moldova Ukraine 10 Manufacture of food products 3.209 0.243 0.336 0.936 0.705 0.570 10.5 Manufacture of dairy products 1.647 1.220 11 Manufacture of beverages 3.987 0.120 0.277 0.726
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[]
Other social work activities without accommodation NABS12General advancement of knowledge: R&D financed from General University Funds (GUF)72 Scientific research and development; 72.1 Research and experimental development on natural sciences and engineering; 72.2 Research and experimental development on social sciences and humanities NABS13General advancement of knowledge: R&D financed from other sources than GUF72 Scientific research and development; 72.1 Research and experimental development on natural sciences and engineering; 72.2 Research and experimental development on social sciences and humanities Source: Stancik, J., A methodology for estimating public ICT R&D expenditures in the EU, JRC, 2012. 342 Annexes Annex 10. NACE v2 to S&T domains correspondence tables via NABS and ASJC Scopus subject fields, for each EaP countryThe tables below show the mapping between NACE sectors and the Scopus ASJC subject fields associated with the set of publications within each S&T domain for each EaP country. The mapping was obtained by first manually assigning each ASJC field to socio-economic objectives of the NABS 2007 classification and then using the NABS 2007 to NACE correspondence table v2 (presented in Annex 9). As the NABS to NACE concordance is of a very low granularity, NACE sectors associated with each ASJC field were manually revised. The mapping via NABS to NACE always occurs through NABS06 ‘Industrial production and technology’, except for the case of NACE 61-Telecommunica- tions and NACE 53-Postal and courier activities, where it occurs via NABS04 ‘Transport, telecom- munication and other infrastructures’. ARMENIA Concordances between NACE sectors and the intersection of ASJC subject fields & S&T domains NACE sector ASJC Scopus subject field 13 Manufacture of textiles Surfaces, Coatings and Films; Materials Chemistry 14 Manufacture of wearing apparel Surfaces, Coatings and Films; Materials Chemistry 15 Manufacture of leather and related products Surfaces, Coatings and Films; Materials Chemistry 16Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materialsMaterials Chemistry 18 Printing and reproduction of recorded media Surfaces, Coatings and Films; Materials Chemistry 19 Manufacture of coke and refined petroleum productsEnergy Engineering and Power Technology; Fuel Technology; General Chemical Engineering; Geochemistry and Petrology; Surfaces, Coatings and Films; Materials Chemistry 20 Manufacture of chemicals and chemical productsBiotechnology; Applied Microbiology and Biotechnology; Biochemistry; Drug Discovery; Pharmacology; General Chemical Engineering; Surfaces, Coatings and Films; Materials Chemistry 23 Manufacture of other non-metallic mineral productsFuel Technology; General Chemical Engineering; Surfaces, Coatings and Films; Materials Chemistry 25Manufacture of fabricated metal products, except machinery
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distributed: each pair of countries collaborates on at least one (and a maximum of two) agrifood projects.Regional collaboration in Biotechnology In Biotechnology publications overall, collabo- ration with external partners is very significantly weighted across all six countries. Within the EaP, Azerbaijan, Georgia and Moldova collaborate most intensively with Ukraine. In terms of EC projects, there are very few in col- laboration within the EaP. AM AZ BY GE MD UA Other 1 2 2 1 2 2 1 1 1 1 1 2 2 1 2 1 2 2 2 1 2 1 2 5 1 1 1 1 1 7 2 1 2 2 1 9 EC projectsAM AZ BY GE MD UA Other AM 7 6 22 9 11 150 AZ 7 2 11 4 8 96 BY 6 2 4 5 39 189 GE 22 11 4 8 27 176 MD 9 4 5 8 9 64 UA 11 8 39 27 9 561 PublicationsFigure 3.47. Number of publications and EC projects in collaboration between EaP actors in different countries, in the ‘Agrifood’ domain Colour indicates the relative distribution of documents, computed row-wise. AM AZ BY GE MD UA Other 1 3 1 1 1 3 1 3 1 17 EC projectsAM AZ BY GE MD UA Other AM 2 12 11 5 10 178 AZ 2 2 5 3 10 75 BY 12 2 2 3 89 408 GE 11 5 2 6 23 60 MD 5 3 3 6 34 120 UA 10 10 89 23 34 3 600 PublicationsFigure 3.48. Number of publications and EC projects in collaboration between EaP actors in different countries, in the ‘Biotechnology’ domain Colour indicates the relative distribution of documents, computed row-wise. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation211 Regional collaboration in Chemistry and chemical engineering In Chemistry and chemical engineering pub- lications, Moldova collaborates most intensively with Ukraine. Ukraine is, again, the country with the highest number of bilateral collaborations. In terms of EC projects, there are also very few in collaboration within the EaP.Regional collaboration in Electric and electronic technologies In Electric and electronic technologies publi- cations, all EaP countries have a very diversified pattern of collaboration. Ukraine is, once more, the country with the highest number of bilater- al collaborations. Those two countries, as well as Georgia and Moldova, have a high number of col- laborations with external partners. There
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by a154 keV γ-ray, decays with a short half-life, as represented in Fig. 10. In this figure, this underlying isomeric state pop- ulated by the 154 keV γ-ray is clearly visible, as well as other γ-ray coincidences. These observations are consis-tent with the level scheme presented in Fig. 9. The half- life of this short-lived isomeric state was measured to be (2.81±0.04) ns. We also observed an even more short-lived isomeric state when gating on the 106 keV transition. The half-life of the 106.3 keV level, also measured for the first time, is (0 .94±0.06) ns. Additional interesting features can be noticed in Fig. 10. In the region corresponding to the 89/87 and 87/89 coin- cidences from Fig 10c, the time distribution seems to shift with the energy between 82 and 94 keV . In this region, theanti-delayed and delayed time distributions corresponding to the 89.4 keV →86.8 keV cascade are mixed, as the γ-rays cannot be resolved due to the energy resolution of the detec-tors. This could indicate the presence of a very short-lived (T 1/2<1n s )s t a t ea t E∗=86.8k e V . 3.1.2147Ce A short-lived isomer was found for the γ-γcoincidence with γ-ray energies of 118 keV and 283 keV. The average post- neutron masses of the associated light and heavy fragments 123Eur. Phys. J. A (2025) 61:5 Page 7 of 12 5 Table 3 Half-lives of the isomers (heavy fragments) measured in this work ( Texp. 1/2) compared with literature ( Tlit. 1/2) and RIPL3-2023 database [ 15] (TRIPL3 1/2). The “main isomer” is marked with an asterisk(*) in each nucleus E∗(MeV) Texp. 1/2(ns) Tlit. 1/2(ns) Ref. TRIPL3 1/2(ns) 130Sn 2.085* 50 (5) 63 (4) [ 29]5 2 132Sb 0.254* 112.0 (27) 102 (4) [ 30] 102 132Te 2.723 3500 (300) 3520 (90) [ 31] 3700 1.925 28100 (1300) 28500 (900) [ 32] 28100 1.775* 137 (8) 133.1 (35) [ 32] 145 133Te 1.610* 106.5 (44) 100 (5) [ 33] 100 134Te 5.804 16.7 (8) 16.4 (17) [ 34]1 8 1.691* 172.5 (16) 164.1 (9) [ 35] 164.1 1.576 1.26 (2) 1.4 (1) [ 34]1 . 3 6 135Te 1.555* 500 (35) 511 (20) [ 36] 511 136Xe 1.892* 2950 (50) 2920 (30) [ 31] 2950 1.694 1.28 (2) 1.293 (17) [ 37] 1.293 137Xe 1.935* 7.8 (5) 10.1 (9) [ 38] 10.1 146La 0.620+ x* 14.0 (5) 14 (1) [ 39]2 0
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[]
scored by trained experi-menters using frame-by-frame analysis with Boris ( Friard and Gamba, 2016 ). Auditory exposure test Mice were head-fixed on a running wheel in a custom-made mounting station identical to the one used for microscope attachmentprocedure habituations and placed inside a sound-attenuating chamber under low light conditions, and with a background noise of45 dB. Following a brief habituation after the miniaturized microscope attachment (1–2 min), mice were exposed to a protocol con- sisting of a set of two different tones (50 dB or 80 dB, 6 kHz) presented with pseudorandom ITI (range 3–24 s, /C2410 s inter-tone ITI). All mice underwent the same protocol sequence of tone presentations and were placed at the same distance from the speaker. Tonepresentations were triggered using the ANY-maze software via TTL pulses. The mounting station and running wheel were cleaned with 70% ethanol before and between subjects. Optogenetic manipulation of behavior Before behavioral experiments, all mice were handled for at least 3 days and habituated to the optical fiber attachment procedure. On experimental days, mice were tethered to optic patch cords (0.48 NA; Doric Lenses) and connected to a custom-built laser benchsetup via a rotary joint (Doric Lenses). An acousto-optic tunable filter controlled the laser intensity (MGL-F-589, 589-nm wavelength).We used ANY-maze to track mice in real time for closed-loop manipulations and to trigger optogenetic manipulations on the basis of the mouse location or protocol timeline. At the beginning of each behavioral test, the laser power at the tip of the patch cables was measured and adjusted with an optical power meter (PM100D, ThorLabs) to an intensity of 15 mW. All behavioral sessions were re-corded using an overhead camera and the ANY-maze software. Social preference The same behavioral protocol as described above for deep-brain Ca 2+imaging was used with minor modifications. Mice were first individually allowed to habituate to the arena for 10 min. After this habituation period, the test mouse was guided to the center of the arena, and a novel unfamiliar mouse (juvenile 4-8-week-old C57Bl6/J male mouse) was placed into a mesh enclosure in one corner whereas an identical empty mesh enclosure was placed in the opposite corner of the arena. The position of the empty and interactorenclosures was alternated and counterbalanced for each trial to avoid any location bias. The test mouse was then allowed to explore the chamber for 10 min, and was considered to be exploring the empty
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61 64 40 31 29 15 50 47 21 55 40 35 83 57 34 22.1 Manufacture of rubber products X 22.2 Manufacture of plastic products X X X X X 23 Manufacture of other non-metallic mineral products 23.1 Manufacture of glass and glass products X X 23.2 Manufacture of refractory products 23.3 Manufacture of clay building materials X X 23.4 Manufacture of other porcelain and ceramic products 23.5 Manufacture of cement, lime and plaster X X X X X 23.6 Manufacture of articles of concrete, cement and plaster X X X X 23.7 Cutting, shaping and finishing of stone X X X X 23.9Manufacture of abrasive products and non-metallic mineral products n.e.c. X X X X 24 Manufacture of basic metals 24.1 Manufacture of basic iron and steel and of ferro-alloys X X X X X X 24.2Manufacture of tubes, pipes, hollow profiles and related fittings, of steel X X X 24.3 Manufacture of other products of first processing of steel X X X X 24.4 Manufacture of basic precious and other non-ferrous metals X X X X 24.5 Casting of metals X X 25Manufacture of fabricated metal products, except machinery and equipment 25.1 Manufacture of structural metal products X X X X X 25.2 Manufacture of tanks, reservoirs and containers of metal 25.3Manufacture of steam generators, except central heating hot water boilers 25.4 Manufacture of weapons and ammunition X 25.5Forging, pressing, stamping and roll-forming of metal; powder metallurgy 25.6 Treatment and coating of metals; machining X X X X X X 25.7 Manufacture of cutlery, tools and general hardware 25.9 Manufacture of other fabricated metal products X X X X X X X X X 26 Manufacture of computer, electronic and optical products 26.1 Manufacture of electronic components and boards Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation277 278 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 26.2 Manufacture of computers and peripheral equipment 26.3 Manufacture of communication equipment
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associated with each country product version which were declared in a 0 to 10 scale. Similarly, we define a choice preference index (CPI) to assess both how consumer choices change across the absence and presence of the ‘made for’ claim and in the presence of the brands name, after tasting the different versions. This index takes a value of 1 when consumers choose the domestic version and 0 otherwise. 4.Results 4.1. Results of the discrete choice experiment Our sample provides a good representation across age, gender and income groups, and the descriptive statistics in Table 8.a and 8.b in the Appendix A show that randomization into the control and treatment groups was properly implemented. We present the results of our WTP comparisons for H1a and H1c for both generic and branded product versions in Table 1and Table 2in the Appendix A. WTP estimates are derived from conditional logit regressions where participants ’ utility for each product version is expressed as a function of country-specific product version dummies (composition and ingredients), their interac - tion with brand name, and price. Table 1in the Appendix A includes the average (hereafter indicated as }β†and standard deviations of the dif- ferences in WTPs for the different product versions when participants are confronted only with information on the composition and in- gredients of the generic product versions. The differences in WTPs are nonlinear combinations of parameter estimates from each logistic regression. Using the generic domestic version as a reference, a negative parameter estimate means that, on average, consumers are willing to pay less for the generic foreign product version than for the domestic version. For example, for German consumers the average WTP for the Lithuanian version of the seasoning mix for Bolognese pasta sauce }β WTP ˆ-0.21 € indicates that the average consumer values this (generic) version less than the (generic) domestic (i.e., German) version. From these results, we derive that consumers are willing to pay more for the domestic versions than the foreign versions in 48 % of country- product pairs when considering the generic versions (16 out of 33 cases). Similarly, the results in Table 2indicate that disclosing the brand name enhances consumers preference for domestic branded versions in 45 % of country-product pairs (15 out of 33 cases). However, focusing on the country-level results, we find heteroge - neity in preferences. Hungarian, Spanish and Swedish consumers show a stronger preference for their
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Europe will require a high degree of trade openness towards countries that provide key technologies in which the EU is currently deficient. For example, maintaining low trade barriers in digital goods, services and infrastructures with the US will be key to guarantee access to the latest AI models and processors. By contrast, a joint plan for decar - bonisation and competitiveness could entail, in specific circumstances, defensive trade measures to level the playing field globally and offset state-sponsored competition abroad, in line with the new EU Economic Security Strategy. When it comes to increasing security and reducing dependencies, the EU must ensure access to critical resources and protect key value chains. This may require securing preferential trade agree - ments with key partners and guaranteeing critical supplies, including through offtake agreements and direct investment in production facilities abroad. To avoid the pitfalls of protectionism, trade policy should be governed by a clear set of principles . First, the use of trade measures should be pragmatic and aligned with the overarching goal of raising the EU’s productivity growth. Unless there is an overriding geopolitical imperative, defensive measures should therefore not be applied systematically. Measures should aim to distinguish genuine innovation and produc - tivity improvements abroad, which are beneficial for Europe, from state-sponsored competition and demand suppression, which lead to lower employment for Europeans. Second, the EU’s trade policy should be consis - tent. Tariffs should avoid creating perverse incentives that undermine European industry, and therefore need to be assessed consistently across all stages of production. For example, imposing tariffs on imports of raw materials or intermediate goods, but not on final goods that use those materials intensively, could lead to de-localisation. Finally, trade measures must be balanced against consumer interests. Even in cases when the EU is the victim of foreign subsidies, there may be some industries where domestic producers have fallen so far behind, that making imports more expensive would only impose excessive deadweight costs on the economy. In these circumstances, it would be preferable for the EU to fund higher investments in more advanced technologies while allowing foreign taxpayers to contribute to higher consumption by European consumers. There should be enhanced coordination in the EU’s foreign direct investment (FDI) decisions . The US administration has recently imposed wide-ranging tariffs on Chinese imports, coupled with progressive measures tightening inward FDI rules, to protect strategic sectors. As a result, the
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H1b H1d German sample Danone Strawberry DE-HU 0.07 ✕-✕ DE-LI 0.11 ✕-✕ Milka Choco Cookies DE-HU 0.20 ✕-✕ DE-LI 0.17 ✕-✕ Hungarian sample Danone Strawberry HU-DE 0.05 ✕-✕ HU-LI 0.41* ✕- Milka Choco HU-DE 0.10 ✕-✕ HU-LI 0.10 ✕- Source: Authors ’ elaboration Table A6 Test of the Choice Preference Index (CPI) across regimes (t-test between R1 and R2 – Frame 1). Product Versions t-test difference]H2c – H2d German sample ​ ​ ​ Danone Strawberry DE 0.05 ✕-✕ LI 0.05 ✕-✕ HU 0.03 ✕-✕ Milka Choco DE 0.08 ✕-✕ LI 0.05 ✕-✕ HU 0.04 ✕-✕ Hungarian sample ​ ​ ​ Danone strawberry DE 0.09* ✓-✓ LI 0.06 ✕-✕ HU 0.08 ✕-✕ Milka Choco DE 0.07 ✕-✕ LI 0.06 ✕-✕ HU 0.20 ✕-✕ Source: Authors ’ elaboration ]Level of significance: 0.01 “***” 0.05 “**” 0.1 “*”. Table A7 Test of the Choice Preference Index (CPI) in the presence of brand name (t-test between R1 and R2 in Frame 2). Product Versions Difference H4a German sample ​ ​ ​ Danone Strawberry DE 0.09** ✓ LI 0.02 ✕ HU 0.05* ✓ Milka Choco DE 0.07* ✓ LI 0.04 ✕ HU 0.07** ✓ Hungarian sample ​ ​ ​ Danone strawberry DE 0.02 ✕ LI 0.04 ✕ HU 0.04 ✕ Milka Choco DE 0.02 ✕ LI 0.01 ✕ HU 0.03 ✕ Source: Authors ’ elaborationD.M. Federica et al. Food Policy 131 (2025) 102803 13 Table A8a Summary statistics DCE. DE LI HU ES RO SE Pooled CGDE LI HU ES RO SE Pooled (TG) Variables Control group (CG) ​ Information treatment group (TG) ​ Age 41.60 39.82 42.22 39.41 40.17 41.22 46.33 42.49 39.05 40.90 39.32 40.19 45.67 43.43 ​ (13.08) (14.40) (13.30) (12.81) (12.88) (15.45) ​ (12.97) (12.51) (13.14) (11.96) (13.58) (15.92) ​ Gender ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Male 50.40 46.20 44.80 51.80 48.60 49.40 51.50 50.00 48.00 43.20 55.60 46.00 51.00 49.50 Female 49.60 53.80 55.20 48.20 51.40 50.60 48.50 50.00 52.00 56.80 44.40 54.00 49.00 50.50 Educational Level​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Primary Education4.00 1.80 2.20 2.20 0.20 5.00 2.00 4.60 1.60 2.40 2.40 ​ 4.40 2.25 Secondary Education49.40 22.00 51.40 21.80 2.00 39.80 31.50 46.00 18.40 53.60 24.60 1.60 38.80 49.50 Tertiary Education25.40 18.40 44.00 23.20 26.60 13.20 34.25 28.60 17.40 41.60 21.20 25.60 12.40 45.75 University and higher21.20 57.80 2.40 52.80
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[]
Joseph Weizenbaum. 1966. ELIZA—A computer pro- gram for the study of natural language communica- tion between men and machines. Communications of the ACM , 9:36–45. Jason Weston, Antoine Bordes, Sumit Chopra, Alexan- der M. Rush, Bart van Merri ¨enboer, Armand Joulin, and Tomas Mikolov. 2016. Towards AI-complete question answering: A set of prerequisite toy tasks. InProceedings of ICLR . Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sen- tence understanding through inference. In Proceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long Papers) , pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguis- tics. Ludwig Wittgenstein. 1953. Philosophical Investiga- tions . MacMillan, New York. Thomas Wolf. 2018. Learning meaning in natural language processing — The semantics mega-thread. Blog post, accessed 4/15/2020.5197Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Ling- peng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, and Phil Blunsom. 2019. Learning and evaluating general linguistic intelligence. ArXiv preprint. A GPT-2 on fighting bears with sticks In order to get a sense of how well O in x4 might be able to answer A’s plea for help with the bear, we tried using GPT-2 in place of O. Radford et al. (2019) claim that GPT-2 can solve non-LM tasks by predicting the next words of a prompt which captures the form in which that task might have oc- curred in the training text. We thus asked GPT-211 to complete the prompt Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, “Here’s what you can do. The first five random com- pletions (no cherrypicking) were as follows. 1.Robinson cried out, “Help! I’m being chased by a bear! All I have is these sticks. What should I do?” The survivalist said, “Here’s what you can do. Take one stick and punch the bear, and then run faster to the store. Wait there until the bear is gone, and then give her all of the sticks. Then go back inside and get your gun.” Advertisement Bobby told me that she began to cry. But when she was finished taking down the bear, she marched out into the yard, began collect- ing up the sticks and armed herself. She said she felt like a
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fabrics, whether or not knitted or crocheted (other than those for babies) X 848 Articles of apparel and clothing accessories of other than textile fabrics; headgear of all materials X 851 Footwear X X 871 Optical instruments and apparatus, n.e.s. X 872 Instruments and appliances, n.e.s., for medical, surgical, dental or veterinary purposes X 873 Meters and counters, n.e.s. X 874 Measuring, checking, analysing and controlling instruments and apparatus, n.e.s. X X X 881 Photographic apparatus and equipment, n.e.s. 882 Photographic and cinematographic supplies 883Cinematographic film, exposed and developed, whether or not incorporating soundtrack or consisting only of soundtrack 884 Optical goods, n.e.s. 885 Watches and clocks X 891 Arms and ammunition 892 Printed matter X X X X 893 Articles, n.e.s., of plastics X X X X X 894 Baby carriages, toys, games and sporting goods X 895 Office and stationery supplies, n.e.s. 896 Works of art, collectors' pieces and antiques 897 Jewellery, goldsmiths' and silversmiths' wares, and other articles of precious or semiprecious materials, n.e.s. X 898Musical instruments and parts and accessories thereof; records, tapes and other sound or similar recordings (excluding goods of groups 763 and 883) 899 Miscellaneous manufactured articles, n.e.s. X 9 Commodities and transactions not classified elsewhere in the SITC 911 Postal packages not classified according to kind 931 Special transactions and commodities not classified according to kind X X 941 Complete industrial plant not elsewhere specified 951 Confidential trade 961 Coin (other than gold coin), not being legal tender 971 Gold, non-monetary (excluding gold ores and concentrates) X X 972 Gold, monetary and gold coin and current coin 998 Total for countries whose data are confidential 999 Confidential trade Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation321 Annex 4. Concordance between IPC and NACE NACE IPC 10 A01H A21D A23B A23C A23D A23F A23G A23J A23K A23L A23P C12J C13B C13F C13J C13K 10.5 A01J 11 A23L C12C C12F C12G C12H 12 A24B A24D A24F 13 D04D D04G D04H D06C D06J D06M D06N D06P D06Q 14 A41B A41C A41D A41F 15 A43B A43C B68B B68C 16 B27D B27H B27M B27N 17 B42F D21C D21H D21J 18.1 B41M B42D B44F 19 C10G C10L 20.1B01J B09B B09C C01B C01C C01D C01F C01G C02F C05B C05C C05D C05F C05G C07B C07C C07F C07G C08B C08F C08G C08J C08K C08L C09B C09C C09K C10B C10C C10H C10J C10K C12S C25B F17C F17D
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[]
Eur. Phys. J. A (2025) 61:5 https://doi.org/10.1140/epja/s10050-024-01470-9 Regular Article - Experimental Physics Characterization of isomers produced by the spontaneous fission of252Cf with the VESPA setup V. P i a u1,2,a,A .G ö ö k3, S. Oberstedt4,b, A. Oberstedt5, A. Chebboubi1, O. Litaize1, M. Vidali4 1CEA/DES/IRESNE/DER/SPRC/LEPh, Cadarache, 13108 Saint-Paul lez Durance, France 2Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France 3Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden 4European Commission, Joint Research Centre, Directorate for Nuclear Safety and Security, 2440 Geel, Belgium 5Extreme Light Infrastructure-Nuclear Physics (ELI-NP), Horia Hulubei National Institute for Physics and Nuclear Engineering (IFIN-HH), 077125 Bucharest-Magurele, Romania Received: 24 June 2024 / Accepted: 7 December 2024 © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025 Communicated by Silvia Leoni Abstract Isomers produced by spontaneous fission of 252Cf were measured with the VESPA setup, composed of LaBr 3(Ce) detectors for fast γ-ray spectroscopy and an ion- ization chamber for detecting fission fragments. Identifica-tion of the isomers was derived from fission fragment- γ- γcoincidences. This paper presents the half-lives of 41 isomeric states measured with this setup, from less than ananosecond up to tens of microseconds. Short-lived isomers in 94Rb,108Tc, and147Ce are reported for the first time. In addition to this half-life analysis, the isomers are used todevelop and test a nuclear charge calibration of the ioniza- tion chamber. 1 Introduction At EC-JRC Geel, the VESPA setup [ 1], made of a position- sensitive double Frisch-grid ionization chamber surroundedby fast LaBr 3(Ce) scintillation detectors, is dedicated to multi-parameter measurements on spontaneous fission. It was recently used to study the prompt fission γ-ray emission correlated with fission fragments characteristics (mass and kinetic energy) for252Cf(sf) [ 1,2]. These studies focused on the prompt peak, i.e., γ-rays emitted within the first nanosec- ond after the scission of the fissioning nucleus. This promptpeak alone amounts to 90% of all γ-rays emitted by the fis- sion fragments [ 3]. The remaining γ-rays are emitted from isomeric states of fission fragments at a longer time scale (upto several µs). ae-mail: [email protected] (corresponding author) be-mail: [email protected] analysis of these isomeric states serves several inter- ests. First, isomers are a powerful probe for understanding the fission process. In particular, the angular momentum gen-eration of the fission fragments, which is not yet fully under-stood, can be obtained from isomeric yield ratios, see, e.g., Refs.
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briefly present the results for each individual country. 30 See note 29. 40 Part 2 Analysis of economic and innovation potential Current strengths Emerging strengths Degree of specialisationRelative size Change in degree of specialisation Georgia > 1.5 > 0.1% > 0 Moldova > 1.5 > 0.1% > 0 Ukraine > 1.5 > 0.1% > 0Table 2.2. Thresholds used to identify economic specialisations Agricultural sector Agriculture is an important sector in all EaP coun- tries, accounting for between 5% and 10% of GDP in Azerbaijan, Georgia and Ukraine, and for more than 10% in Armenia and Moldova (Figure 2.1). Employment in Agriculture accounts for almost 40% of the total employment in Azerbaijan and Georgia, between 20% and 25% in Armenia and Moldova and between 10% and 15% in Ukraine. It is important to note that the analyses in this report underestimate the relevance of the agri- cultural sector due to limited data availability for this industry. Economic data on employment, turn- over and wages are not available for Armenia and Azerbaijan, which not only excludes individual ag- ricultural industries from the potential list of prior-ity areas for these countries, but also makes it less likely that individual agricultural industries will be identified for Georgia, Moldova and Ukraine as the mapping analysis focuses on identifying relative specialisations compared to the unweighted EaP countries. With only three EaP countries contribut- ing to the EaP average for agricultural industries, an industry can be specialised for two of these countries at most. Mapping the economic potential – results for Georgia Results of the economic mapping for Georgia are shown in Table 2.3. In total, 28 industries have been identified as having a current strength and 40 industries have been identified as having an Figure 2.1. Importance of Agriculture in EaP countries Agriculture, forestry, and fishing, value added (% of GDP) Employment in agriculture (% of total employment) 20 18 16 14 12 10 8 6 4 2 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 201960 50 40 30 20 10 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Armenia Azerbaijan Belarus Georgia Moldova UkraineArmenia Azerbaijan Belarus Georgia Moldova Ukraine Source: World Bank World Development Indicators. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation41 emerging strength (these industries are high- lighted in the two columns on the right with an ‘X’ in a green-coloured cell).
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[]
(Covrig, Catalin Felix; Diego F Quintero Pulido; Matthias BRUCHHAUSEN (2024): On the performance of portable NiMH batteries of general use. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/838a8875-7634 -408b-b8ff-8a892cad4cb4) (accessed on 1 December 2024). Acknowledgments: The authors would like to thank Lucia Hegedusova for the X-ray tomography pictures, Andreas Pfrang and Marc Steen for the corrections of the manuscript. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Official Journal of the European Union Regulation (EU) 2023/1542 of the European Parliament and of the Council of 12 July 2023 Concerning Batteries and Waste Batteries, Amending Directive 2008/98/EC and Regulation (EU) 2019/1020 and Repealing Directive 2006/66/EC. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:32023R1542 (accessed on 1 January 2023). 2. Tenergy AA NiCd Rechargeable Battery for Solar/Garden Lights. Available online: https://power.tenergy.com/combo-48 -tenergy-aa-nicd-rechargeable-battery-for-solar-garden-lights/ (accessed on 30 October 2023). 3. Ansmann AG NiZN Akku Mignon AA 2500mWh 4er. Available online: https://shop.ansmann.de/de/nizn-aa-2500-4 (accessed on 30 October 2023). 4. EnerSys Cyclon Battery. Available online: https://www.enersys.com/en-gb/products/batteries/cyclon/cyclon/ (accessed on 30 October 2023). 5. IEC 61951-2:2017 ; Secondary Cells and Batteries Containing Alkaline or Other Non-Acid Electrolytes—Secondary Sealed Cells and Batteries for Portable Applications—Part 2: Nickel-Metal Hydride 2017. IEC: Geneva, Switzerland, 2017. 6. IEC 61951-2:2017+AMD1:2022 ; Secondary Cells and Batteries Containing Alkaline or Other Non-Acid Electrolytes—Secondary Sealed Cells and Batteries for Portable Applications—Part 2: Nickel-Metal Hydride 2022. IEC: Geneva, Switzerland, 2022. 7. Salehi, H.; Maroufi, S.; Mofarah, S.S.; Nekouei, R.K.; Sahajwalla, V . Recovery of Rare Earth Metals from Ni-MH Batteries: A Comprehensive Review. Renew. Sustain. Energy Rev. 2023 ,178, 113248. [CrossRef] 8. Kritzer, P . Separators for Nickel Metal Hydride and Nickel Cadmium Batteries Designed to Reduce Self-Discharge Rates. J. Power Sources 2004 ,137, 317–321. [CrossRef]Batteries 2025 ,11, 30 19 of 20 9. Shen, Y.; Noréus, D.; Starborg, S. Increasing NiMH Battery Cycle Life with Oxygen. Int. J. Hydrogen Energy 2018 ,43, 18626–18631. [CrossRef] 10. Zhao, Y.; Pohl, O.; Bhatt, A.I.; Collis, G.E.; Mahon, P .J.; Rüther, T.; Hollenkamp, A.F. A Review on Battery Market Trends, Second-Life Reuse, and Recycling. Sustain. Chem. 2021 ,2, 167–205. [CrossRef] 11. Constantine, J.; Lie, J.; Liu, J.-C. Recovery of Rare Earth Elements from Spent NiMH Batteries Using Subcritical Water Extraction with Citric Acid. J. Environ. Chem. Eng. 2022 ,10, 108000. [CrossRef] 12. Petranikova, M.; Ebin, B.; Tunsu, C. Selective Recovery of Cobalt from the Secondary Streams after NiMH Batteries Processing Using Cyanex 301. Waste Manag. 2019 ,83, 194–201. [CrossRef] [PubMed] 13. Innocenzi, V .; Ippolito, N.M.; De Michelis, I.; Prisciandaro, M.; Medici, F.; Vegli ò, F. A Review of the Processes and Lab-Scale Techniques for the Treatment of Spent Rechargeable NiMH Batteries. J. Power Sources 2017 ,362, 202–218.
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SEPTEMBER 2024The future of European competitiveness Part A | A competitiveness strategy for EuropeAcknowledgment This report would not exist without the generosity of many extraordinary people who have at heart the future of Europe. First, I would like to acknowledge the President of the European Commission Ursula von der Leyen for her constant support, and to thank the President of the European Central Bank Christine Lagarde for lending valuable resources. Paolo D’Aprile and Pauline Rouch coordinated all the work contained in the report. Jonathan Yiangou drafted exten - sive parts of it. Without their dedication and hard work this report would not have been possible. The analysis and the policy advice contained in the report owes much to the contributions of: Philippe Aghion, Laurence Boone, Vittorio Colao, Francesco Decarolis, Robbert Dijkgraaf, Francesco Giavazzi, Luigi Guiso, Claudio Michelacci, Marco Pagano, Raffaella Sadun, Fabiano Schivardi, Fiona M. Scott Morton, Michael Spence, Per Strömberg, Jean Tirole, and John Van Reenen. I would like to thank the Commission team that followed and contributed to the report from beginning to end: Alessandra Falcinelli, Miguel Gil Tertre, Alexandr Hobza, Thomas Hopkins, Sven Langedijk, Dimitri Lorenzani, Vukašin Ostojić, Núria Subirats Rebull, Dirk Van den Steen, Lukas Vogel, and Yoshua Witteveen. I would also like to thank our graphic designer, Camille Palandjian for her work. The team could count on the precious support of Isabela Di Pietro and Maria Grazia Ciorra. I am also grateful for the contributions of the following people and organisations, who were consulted in meetings and/or who sent written contributions. •D. Acemoglu, Massachusetts Institute of Technology •P. Antràs, Harvard University •P. Beria, Politecnico di Milano •O. Blanchard, Massachusetts Institute of Technology •J.P. Bourguignon, Institut des Hautes Études Scientifiques •M. Dewatripont, Université libre de Bruxelles •F. Dudenhoeffer, University of Duisburg-Essen •T. Duso, German Institute for Economic Research •L. Garicano, London School of Economics •F. Gianotti, CERN •D. Helm, University of Oxford •P. T. Jones, KU Leuven •M. Leptin, European Research Council •E. Marique, Radboud University Nijmegen •A. Mas-Colell, Pompeu Fabra University •J. J. Montero Pascual, Florence School of Regulation •E. Moretti, University of California, Berkeley •M. Motta, Pompeu Fabra University •M. Peitz, University of Mannheim •L.H.Roeller, European School of Management and Technology Berlin •A. Sapir, Université libre de Bruxelles •G. Siani, Banca d’Italia •N. Stern, London School of Economics 01 THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | ACKNOWLEDGMENTTHINK TANKS AND RESEARCH INSTITUTIONS BloombergNEF
[ "SEPTEMBER", "2024The", "future", "\n", "of", "European", "\n", "competitiveness", "\n", "Part", "A", "|", "A", "competitiveness", "strategy", "for", "EuropeAcknowledgment", "\n", "This", "report", "would", "not", "exist", "without", "the", "generosity", "of", "many", "extraordinary", "people", "who", "have", "at", "heart", "the", "future", "\n", "of", "Europe", ".", "\n", "First", ",", "I", "would", "like", "to", "acknowledge", "the", "President", "of", "the", "European", "Commission", "Ursula", "von", "der", "Leyen", "for", "her", "constant", "\n", "support", ",", "and", "to", "thank", "the", "President", "of", "the", "European", "Central", "Bank", "Christine", "Lagarde", "for", "lending", "valuable", "resources", ".", "\n", "Paolo", "D’Aprile", "and", "Pauline", "Rouch", "coordinated", "all", "the", "work", "contained", "in", "the", "report", ".", "Jonathan", "Yiangou", "drafted", "exten", "-", "\n", "sive", "parts", "of", "it", ".", "Without", "their", "dedication", "and", "hard", "work", "this", "report", "would", "not", "have", "been", "possible", ".", "\n", "The", "analysis", "and", "the", "policy", "advice", "contained", "in", "the", "report", "owes", "much", "to", "the", "contributions", "of", ":", "Philippe", "Aghion", ",", "\n", "Laurence", "Boone", ",", "Vittorio", "Colao", ",", "Francesco", "Decarolis", ",", "Robbert", "Dijkgraaf", ",", "Francesco", "Giavazzi", ",", "Luigi", "Guiso", ",", "Claudio", "\n", "Michelacci", ",", "Marco", "Pagano", ",", "Raffaella", "Sadun", ",", "Fabiano", "Schivardi", ",", "Fiona", "M.", "Scott", "Morton", ",", "Michael", "Spence", ",", "Per", "\n", "Strömberg", ",", "Jean", "Tirole", ",", "and", "John", "Van", "Reenen", ".", "\n", "I", "would", "like", "to", "thank", "the", "Commission", "team", "that", "followed", "and", "contributed", "to", "the", "report", "from", "beginning", "to", "end", ":", "\n", "Alessandra", "Falcinelli", ",", "Miguel", "Gil", "Tertre", ",", "Alexandr", "Hobza", ",", "Thomas", "Hopkins", ",", "Sven", "Langedijk", ",", "Dimitri", "Lorenzani", ",", "Vukašin", "\n", "Ostojić", ",", "Núria", "Subirats", "Rebull", ",", "Dirk", "Van", "den", "Steen", ",", "Lukas", "Vogel", ",", "and", "Yoshua", "Witteveen", ".", "I", "would", "also", "like", "to", "thank", "our", "\n", "graphic", "designer", ",", "Camille", "Palandjian", "for", "her", "work", ".", "The", "team", "could", "count", "on", "the", "precious", "support", "of", "Isabela", "Di", "Pietro", "\n", "and", "Maria", "Grazia", "Ciorra", ".", "\n", "I", "am", "also", "grateful", "for", "the", "contributions", "of", "the", "following", "people", "and", "organisations", ",", "who", "were", "consulted", "in", "meetings", "\n", "and/or", "who", "sent", "written", "contributions", ".", "\n ", "•D.", "Acemoglu", ",", "Massachusetts", "Institute", "\n", "of", " ", "Technology", "\n ", "•P.", "Antràs", ",", "Harvard", "University", "\n ", "•P.", "Beria", ",", "Politecnico", "di", "Milano", "\n ", "•O.", "Blanchard", ",", "Massachusetts", "Institute", "\n", "of", " ", "Technology", "\n ", "•J.P.", "Bourguignon", ",", "Institut", "des", "Hautes", "Études", " \n", "Scientifiques", "\n ", "•M.", "Dewatripont", ",", "Université", "libre", "de", "Bruxelles", "\n ", "•F.", "Dudenhoeffer", ",", "University", "of", "Duisburg", "-", "Essen", "\n ", "•T.", "Duso", ",", "German", "Institute", "for", "Economic", "Research", "\n ", "•L.", "Garicano", ",", "London", "School", "of", "Economics", "\n ", "•F.", "Gianotti", ",", "CERN", "\n ", "•D.", "Helm", ",", "University", "of", "Oxford", "\n ", "•P.", "T.", "Jones", ",", "KU", "Leuven", "•M.", "Leptin", ",", "European", "Research", "Council", "\n ", "•E.", "Marique", ",", "Radboud", "University", "Nijmegen", "\n ", "•A.", "Mas", "-", "Colell", ",", "Pompeu", "Fabra", "University", "\n ", "•J.", "J.", "Montero", "Pascual", ",", "Florence", "School", "\n", "of", " ", "Regulation", "\n ", "•E.", "Moretti", ",", " ", "University", "of", "California", ",", "Berkeley", "\n ", "•M.", "Motta", ",", "Pompeu", "Fabra", "University", "\n ", "•M.", "Peitz", ",", "University", "of", "Mannheim", "\n ", "•L.H.Roeller", ",", "European", "School", "of", "Management", "\n", "and", " ", "Technology", "Berlin", "\n ", "•A.", "Sapir", ",", "Université", "libre", "de", "Bruxelles", "\n ", "•G.", "Siani", ",", "Banca", "d’Italia", "\n ", "•N.", "Stern", ",", "London", "School", "of", "Economics", "\n", "01", "\n", "THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "ACKNOWLEDGMENTTHINK", "TANKS", "AND", "RESEARCH", "INSTITUTIONS", "\n", "BloombergNEF", "\n" ]
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our knowledge, does not affect the outcome of the experiment. 4. Charge Retention of Portable NiMH Batteries The charge retention test of portable NiMH batteries is performed by following the procedure in standard IEC 61951-2 clause 7.4. The results are presented in Figure 8. The test protocol consists of the following: 1. The battery is first discharged to 1 V at 0.2 C. 2. The battery is then charged for 16 h at 0.1 C. 3. After charging, the battery is stored for 28 days in a temperature-controlled chamber at 21◦C±2◦C 4. Following the storage period, the battery is discharged at a 0.2 C until 1 V , and the discharge duration is measured to determine the remaining capacity. A battery is considered to pass the IEC test if the discharge lasts for longer than 3 h before reaching the cut-off voltage. This means that the battery still held at least 60% of its initial charge after the 28-day storage period. Figure 8a,b shows the discharge of AAA and AA batteries after 28 days of storage, with their corresponding initial discharge and charging steps. The AAA and AA batteries have similar voltage profiles. However, as expected, the capacity of AAA and AA is different, with 646 mAh and 2101 mAh, respectively. The voltage and current profiles in the C and D portable NiMH batteries are shown in Figure 8c,d. The C battery has a capacity of 4319 mAh, and the D battery has a capacity of 7477 mAh. In the case of the 9V (Figure 8e), the battery shows a discharge voltage curve like the one observed in the other NiMH battery sizes, but at an average voltage of 8.4 V . Furthermore, all NiMH batteries tested during the charge retention experiment show a discharge current curve longer than 3 h (minimum for IEC 61951-2 clause 7.4), and in terms of columbic efficiency, the highest observed value is 70% for the D size and the lowest observed value is 58% for the 9V battery. Further discussion of these results is presented in Section 7. Batteries 2025, 11, x FOR PEER REVIEW   13 of 21      Figure 8. NiMH charge (capacity)  retention  analysis according  to IEC 61951-2 with the pre-charged   test for (a) AAA Duracell,  (b) AA Agfaphoto,  (c) C Ansmann,  (d) D Ansmann,  and (e) 9V Energizer   batteries.  5. Charge (Capacity)  Recovery  of Portable  NiMH Batteries  The charge recovery  test is 
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The analysis of endurance, measured in cycles, is performed using the method de- scribed by IEC 61951-2, clause 7.5.1.4. Figure 10 shows the cycling profiles of an AAA NiMH battery. The test procedure is as follows: 1. The battery is first discharged at a rate of 0.2 C until it reaches the cut-off voltage of 1 V . 2. The battery is then charged for 2 h at a 0.5 C rate. The charge termination criterion is either a ∆V of 5 mV to 10 mV (this criterion refers to a decline of voltage during charge after a certain time) or a maximum charge time of 132 min.Batteries 2025 ,11, 30 14 of 20 3. Immediately after charging, the battery is discharged at a 0.5 C rate until it reaches the cut-off voltage of 1 V . Batteries 2025, 11, x FOR PEER REVIEW   15 of 21    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  The analysis of endurance,  measured  in cycles, is performed  using the method de- scribed by IEC 61951-2, clause 7.5.1.4. Figure 10 shows the cycling profiles of an AAA  NiMH battery. The test procedure  is as follows:  1. The battery is first discharged  at a rate of 0.2 C until it reaches the cut-off voltage of  1 V.  2. The battery is then charged for 2 h at a 0.5 C rate. The charge termination  criterion is  either a ΔV of 5 mV to 10 mV (this criterion refers to a decline of voltage during  charge after a certain time) or a maximum  charge time of 132 min.  3. Immediately  after charging,  the battery is discharged  at a 0.5 C rate until it reaches  the cut-off voltage of 1 V.  If the discharge  duration  is less than 72 min, the experiment  is terminated.     Figure 10. NiMH endurance  in cycles analysis according  to IEC 61951-2 for an AAA Energizer  700  mAh battery (a) voltage profile, (b) current profile, (c) capacity,  and d) columbic  efficiency vs. cycle  number. The vertical red lines in (c,d) indicate checkup cycles.  Moreover,  every 50 cycles a checkup cycle is performed  using the standard  condi- tions for capacity rate calculation  (charge 
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"duration", " ", "is", " ", "less", " ", "than", " ", "72", " ", "min", ",", " ", "the", " ", "experiment", " ", "is", " ", "terminated", ".", " \n \n", "Figure", " ", "10", ".", " ", "NiMH", " ", "endurance", " ", "in", " ", "cycles", " ", "analysis", " ", "according", " ", "to", " ", "IEC", " ", "61951", "-", "2", " ", "for", " ", "an", " ", "AAA", " ", "Energizer", " ", "700", " \n", "mAh", " ", "battery", " ", "(", "a", ")", " ", "voltage", " ", "profile", ",", " ", "(", "b", ")", " ", "current", " ", "profile", ",", " ", "(", "c", ")", " ", "capacity", ",", " ", "and", " ", "d", ")", " ", "columbic", " ", "efficiency", " ", "vs.", " ", "cycle", " \n", "number", ".", " ", "The", " ", "vertical", " ", "red", " ", "lines", " ", "in", " ", "(", "c", ",", "d", ")", " ", "indicate", " ", "checkup", " ", "cycles", ".", " \n", "Moreover", ",", " ", "every", " ", "50", " ", "cycles", " ", "a", " ", "checkup", " ", "cycle", " ", "is", " ", "performed", " ", "using", " ", "the", " ", "standard", " ", "condi-", "\n", "tions", " ", "for", " ", "capacity", " ", "rate", " ", "calculation", " ", "(", "charge", " " ]
[]
covering 17 million square kilometres, 4 times the EUs land surface01. FIGURE 1 Share of World GDP GDP at current prices, 2023 Source: IMF, 2024 01. The Exclusive Economic Zones (EEZs) are sea zones prescribed by the United Nations Convention on the Law of the Sea, extending up to 200 nautical miles from the coast of a country, within which the state has the rights to explore and exploit maritime resources. Leveraging this vast maritime area will contribute to competitiveness, security and sustainability. 11THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1FIGURE 2 Income and wage inequality in world regions Source: World Inequality Database (WID), 2024 Yet growth in the EU has been slowing, driven by weakening productivity growth, calling into question Europe’s ability to meet its ambitions . The EU has set out a range of ambitions – such as achieving high levels of social inclusion, delivering carbon neutrality and increasing geopolitical relevance – which depend on maintaining solid rates of economic growth. However, EU economic growth has been persistently slower than in the US over the past two decades, while China has been rapidly catching up. The EU-US gap in the level of GDP at 2015 prices02 has gradually widened from slightly more than 15% in 2002 to 30% in 2023, while on a purchasing power parity (PPP) basis a gap of 12% has emerged [see Figure 3] . The gap has widened less on per capita basis as the US has seen faster population growth, but it is still significant: in PPP terms, it has risen from 31% in 2002 to 34% today. The main driver of these diverging developments has been productivity. Around 70% of the gap in per capita GDP with US at PPP is explained by lower productivity in the EU [see Figure 4] . Slower productivity growth has in turn been asso - ciated with slower income growth and weaker domestic demand in Europe: on a per capita basis, real disposable income has grown almost twice as much in the US as in the EU since 2000. FIGURE 3 GDP evolution 2015 reference levels, in EUR trillion Source: OECD, 2024. 02. The value of the gap in GDP in any given year is only indicative. It should not be viewed as an exact estimate as price deflators and purchasing power adjustments are imperfect. When comparing GDP developments across countries, the price
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[]
consist of a categorical classification: a textual re- cord can have a strong affiliation to some topics, and a weak affiliation to others. LDA presents several advantages with respect to alternative quantitative analyses or other seman- tic analysis techniques (such as keyword extrac- tion). For instance, the topics emerge from – and are characterised by – the language actually used by specialists (sometimes at odds with external classification systems managed by third parties), the process allows for the identification of trans- 55 The weight is computed by numerically maximising a mathematical model based on the Dirichlet probability distribution. In a nutshell, the model assumes that every document in the corpus is associated with a weight rang- ing from 0 to 1 for each of the topics. Weights close to zero denote a weak linkage between a given document and a certain topic, while weights close to one indicate a strong connection between a document and a topic. Work- ing with topics and weights requires technical analyses that are beyond the scope of this work, therefore topics and weights are not discussed in this document. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation147 versal concepts which contribute to several topics and, finally, polysemy is well dealt with (at odds with keyword extraction). However, the fact that topics lack a label and that the classification of documents is not categorical may unnecessarily complicate the reading of the results with the aim of identifying niches of excellence and areas of specialisation in the context of S3. For this reason, the following applies in this work: 1. groups of topics linked to sub-fields of a giv- en S&T specialisation domain (as perceived by the eye of a non-specialist) were automati- cally clustered and then manually merged to- gether. In the following analyses, results are always presented in terms of these group- ings, because topics offer an excessively fine- grained resolution; 2. each topic group was manually labelled with a self-contained, intelligible name, which can be swiftly adopted in the policy-setting discourse; 3. document/topic weights were converted into a binary classification, so that in the following analyses each record is categorically assigned to the respective topics. Note that, in this set- ting, the association of a single record to mul- tiple topic groups is still allowed. With regard to point 1. above, the topic model- ling exercise yields a series
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revenue featured in the Crunchbase database by Industry Group. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation109 Georgia # firms SI Firms # employees SI Employees # est. revenue SI Revenue Payments 5.60 Payments 12.22 Payments 24.44 Travel and Tourism 5.42Lending and Investments6.06Lending and Investments22.11 Lending and Investments3.46 Financial Services 4.23 Financial Services 20.08 Financial Services 3.23 Apps 2.74 Other 8.68 Food and Beverage 3.12 Advertising 2.16 Consumer Electronics 3.54 Sports 2.64 Mobile 2.15Agriculture and Farming2.23 Artificial Intelligence 2.55 Software 1.51 Software 1.86 Agriculture and Farming2.01 Travel and Tourism 1.29 Privacy and Security 1.51 Government and Military1.79 Internet Services 1.24 Travel and Tourism 1.40 Transportation 1.52 Food and Beverage 1.10Government and Military1.38Table 2.46. Specialised industry groups – Georgia For Georgia, the table shows the relative specialisation in terms of number of companies, number of employees and estimated revenue featured in the Crunchbase database by Industry Group. Moldova # firms SI Firms # employees SI Employees # est. revenue SI Revenue Food and Beverage 2.98 Food and Beverage 7.68 Sustainability 224.20 Government and Military2.73 Sustainability 3.71Government and Military13.73 Gaming 2.40Government and Military1.88Lending and Investments9.41 Privacy and Security 2.30 Energy 1.73 Financial Services 8.51 Sustainability 2.28Lending and Investments1.49 Privacy and Security 4.61 Lending and Investments2.12 Apps 1.40 Energy 3.91 Travel and Tourism 1.91 Software 1.37 Other 3.68 Payments 1.90 Content and Publishing 1.30 Information Technology 3.11 Financial Services 1.78 Information Technology 1.18 Software 2.46 Music and Audio 1.73 Financial Services 1.16 Travel and Tourism 1.85Table 2.47. Specialised industry groups – Moldova For Moldova, the table shows the relative specialisation in terms of number of companies, number of employees and estimated revenue featured in the Crunchbase database by Industry Group. 110 Part 2 Analysis of economic and innovation potential Ukraine Ukraine accounts for the vast majority of compa- nies, employees and estimated revenues in the EaP region. This causes the sectoral specialisation index to be very close to 1 (whether lower or high- er) for all industry groups. The calculated indices are shown in the table below. However, a more informative specialisation analy- sis shall be performed differently to the other EaP countries: additional insights must be provided by qualitatively inspecting the distribution of industry groups and by suggesting pertinent sectors to be considered. For instance, we can define Ukraine’s specialised sectors, compared to the rest of the EaP region, as those industry groups in which the sectoral
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[]
. Gehrlach, D.A., Dolensek, N., Klein, A.S., Roy Chowdhury, R., Matthys, A., Jungha ¨nel, M., Gaitanos, T.N., Podgornik, A., Black, T.D., Reddy Vaka, N., et al. (2019). Aversive state processing in the posterior insular cortex. Nat. Neu-rosci. 22, 1424–1437. https://doi.org/10.1038/s41593-019-0469-1 . Gehrlach, D.A., Weiand, C., Gaitanos, T.N., Cho, E., Klein, A.S., Hennrich, A.A., Conzelmann, K.K., and Gogolla, N. (2020). A whole-brain connectivity map ofmouse insular cortex. Elife 9.https://doi.org/10.7554/elife.55585 . Ghosh, K.K., Burns, L.D., Cocker, E.D., Nimmerjahn, A., Ziv, Y., Gamal, A.E., and Schnitzer, M.J. (2011). Miniaturized integration of A fluorescence micro-scope. Nat. Methods 8, 871–878. https://doi.org/10.1038/nmeth.1694 . Goff, K.M., and Goldberg, E.M. (2019). Vasoactive intestinal peptide-express- ing interneurons are impaired in A mouse model of Dravet syndrome. Elife 8. https://doi.org/10.7554/elife.46846 . Gogolla, N. (2017). The insular cortex. Curr. Biol. 27, R580–R586. https://doi. org/10.1016/j.cub.2017.05.010 . Grewe, B.F., Gr €undemann, J., Kitch, L.J., Lecoq, J.A., Parker, J.G., Marshall, J.D., Larkin, M.C., Jercog, P.E., Grenier, F., Li, J.Z., et al. (2017). Neuralensemble dynamics underlying A long-term associative memory. Nature543, 670–675. https://doi.org/10.1038/nature21682 . Guet-Mccreight, A., Skinner, F.K., and Topolnik, L. (2020). Common principles in functional organization of vip/calretinin cell-driven disinhibitory circuitsacross cortical areas. Front. Neural Circuits 14.https://doi.org/10.3389/fncir. 2020.00032 . Hangya, B., Ranade, S.P., Lorenc, M., and Kepecs, A. (2015). Central cholin- ergic neurons are rapidly recruited by reinforcement feedback. Cell 162, 1155– 1168. https://doi.org/10.1016/j.cell.2015.07.057 . He, M., Tucciarone, J., Lee, S., Nigro, M.J., Kim, Y., Levine, J.M., Kelly, S.M., Krugikov, I., Wu, P., Chen, Y., et al. (2016). Strategies and tools for combina-torial targeting of gabaergic neurons in mouse cerebral cortex. Neuron 91, 1228–1243. https://doi.org/10.1016/j.neuron.2016.08.021 .Henson, R.N., and Rugg, M.D. (2003). Neural response suppression, haemo- dynamic repetition effects, and behavioural priming. Neuropsychologia 41, 263–270. https://doi.org/10.1016/s0028-3932(02)00159-8 . Hersman, S., Allen, D., Hashimoto, M., Brito, S.I., and Anthony, T.E. (2020). Stimulus salience determines defensive behaviors elicited by aversively condi-tioned serial compound auditory stimuli. Elife 9.https://doi.org/10.7554/elife. 53803 . Holland, P.C. (1980). Influence of visual conditioned stimulus characteristics on the form of pavlovian appetitive conditioned responding in rats. J. Exp. Psy- chol. Anim. Behav. Process. 6, 81–97. https://doi.org/10.1037/0097-7403.6.1. 81. Ishai, A., Pessoa, L., Bikle, P.C., and Ungerleider, L.G. (2004). Repetition sup- pression of faces is modulated by emotion. Proc. Natl. Acad. Sci. U S A 101, 9827–9832. https://doi.org/10.1073/pnas.0403559101 . Kapur, S. (2003). Psychosis as A state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia. Am. J. Psychi-atry 160, 13–23. https://doi.org/10.1176/appi.ajp.160.1.13 . Kastli, R., Vighagen, R., Van Der Bourg, A., Argunsah, A.O ¨., Iqbal, A., Voigt, F.F., Kirschenbaum, D., Aguzzi, A., Helmchen, F., and Karayannis, T. (2020).Developmental divergence of sensory stimulus representation in cortical inter-neurons. Nat. Commun. 11, 5729. https://doi.org/10.1038/s41467-020- 19427-z
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Most active public actors are primarily concentrat- ed in the domain of Health and wellbeing – and, to a lesser extent, Energy and Agrifood. Medical centres account for most of the top-ranked public actors, but it is the Ministries of Health and Edu- cation & Science in which most of the records are concentrated. On the other hand, private actors account for very few of the analysed records, and no particular do- main of activity stands out. 198 Part 3 Analysis of scientific and technological potential ARMENIA Top 10 actors classified as ‘Public sector (excluding higher education and research institutions)’ NameNo of recordsMain S&T domains Ministry of Health of Armenia 55Health and wellbeing; Governance, culture, education and the economy; Agrifood Ministry of Education and Science of Armenia35Health and wellbeing; Environmental sciences and industries; Agrifood Nuclear and Radiation Safety Center 18Energy; Governance, culture, education and the economy; Health and wellbeing Erebouni Medical Center 14Health and wellbeing; Biotechnology; Chemistry and chemical engineering Shengavit Medical Center 13 Health and wellbeing Armenian State Hydrometeorological and Monitoring Service10Energy; Governance, culture, education and the economy; Health and wellbeing Armenia Republican Medical Center 9 Health and wellbeing National Center for Disease Control and Prevention9Health and wellbeing; Governance, culture, education and the economy National Center of Oncology 9Health and wellbeing; Governance, culture, education and the economy; Biotechnology Ministry of Nature Protection of Armenia8Environmental sciences and industries; Agrifood; Governance, culture, education and the economyTable 3.19. Top private actors in Armenia by number of records, across all domains ARMENIA Top actors classified as ‘Private company, for-profit’ NameNo of recordsMain S&T domains Center of Medical Genetics and Primary Health Care16Health and wellbeing; Biotechnology; Governance, culture, education and the economy Synopsys Armenia 13ICT and computer science; Chemistry and chemical engineering; Electric and electronic technologies Armenian Institute of Applied Chemistry8Chemistry and chemical engineering; Fundamental physics and mathematics; ICT and computer science ECOSERV REMOTE OBSERVATION CENTRE CO LTD6Fundamental physics and mathematics; Electric and electronic technologies Darmantest Laboratories LLC 5Health and wellbeing; Nanotechnology and materials; Chemistry and chemical engineering National Instruments AM LLC 4 Health and wellbeing; Optics and photonics Spectralus CJSC 4Fundamental physics and mathematics; Nanotechnology and materials; Optics and photonics Arpimed LLC 3 Health and wellbeing SI Ecoatom LLC 2Chemistry and chemical engineering; Environmental sciences and industriesTable 3.20. Top private actors in Armenia by number of records, across all domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation199
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[]
and related parts and accessories 46 Wholesale trade, except of motor vehicles and motorcycles 46.1 Wholesale on a fee or contract basis X X X X 46.2 Wholesale of agricultural raw materials and live animals X X X X 46.3 Wholesale of food, beverages and tobacco X X X X X 46.4 Wholesale of household goods X X X 46.5 Wholesale of information and communication equipment X X X X 46.6 Wholesale of other machinery, equipment and supplies X X X X 46.7 Other specialised wholesale X X X X X 46.9 Non-specialised wholesale trade X X X X X 47 Retail trade, except of motor vehicles and motorcycles 47.1 Retail sale in non-specialised stores X X X X X X 47.2 Retail sale of food, beverages and tobacco in specialised stores X X X X X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation283 284 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 47.3 Retail sale of automotive fuel in specialised stores X X X 47.4Retail sale of information and communication equipment in specialised stores X X 47.5 Retail sale of other household equipment in specialised stores X X X X X X X 47.6 Retail sale of cultural and recreation goods in specialised stores X X X X X 47.7 Retail sale of other goods in specialised stores X X X X 47.8 Retail sale via stalls and markets X X X X 47.9 Retail trade not in stores, stalls or markets X X X X H TRANSPORTATION AND STORAGE 49 Land transport and transport via pipelines 49.1 Passenger rail transport, interurban X X X X 49.2 Freight rail transport X X X X X X 49.3 Other passenger land transport X X X X X X X 49.4 Freight transport by road and removal services X X X X X X X 49.5 Transport via pipeline X X X X 50 Water transport 50.1 Sea and coastal
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