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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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" \n",
"333Petroleum",
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"bituminous",
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",",
"\n",
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" ",
"X",
"1.3",
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"(",
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" ",
"X",
"0.8%Table",
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"Goods",
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"specialisation",
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"Georgia",
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"70",
"\n ",
"Part",
"2",
"Analysis",
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"economic",
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"innovation",
"potential",
"\n",
"SITC",
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"\n",
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"\n",
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"\n",
"strength%",
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"\n",
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"18",
"60.9",
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"411",
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"X",
"0.2",
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" \n",
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"n.e.s",
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"0.3",
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"X",
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"4.1",
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"553Perfumery",
",",
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"(",
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"0.6",
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"X",
"0.6",
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"6",
"Manufactured",
"goods",
"classified",
"chiefly",
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"material",
" \n",
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"interchangeable",
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"flaps",
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"\n",
"inner",
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"1.0",
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"hollow",
"profiles",
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"fittings",
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" ",
"X",
"0.4",
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"684",
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"X",
"0.3",
"%",
"\n",
"699",
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"base",
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",",
"n.e.s",
".",
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"X",
"0.2",
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"723Civil",
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"contractors",
"'",
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";",
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"parts",
"thereofX",
"0.3",
"%",
"X",
"0.3",
"%",
"\n",
"778",
"Electrical"
] | [] |
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
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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).
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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 -
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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, | [
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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 | [
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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-
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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
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Environmental sciences
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Governance, culture,
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(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
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")",
"\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
cCG H WTPsCIcCk
cCG Hℑ0 for c⊔F1CF2⊓(8).
H2aB: WTPsCImCk
cCB WTPsCIcCk
cCBℑ0 for c⊔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
cCG H WTPsCIcCk
cCG H〉
F0fors∃Eandc∃W
D0fors∃Wandc∃E(10)
H2bCBBWTPsCImCk
cCB WTPsCIcCk
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
cCBexceeds that for domestic generic product versions (
NsCIc
cCG H)
and whether these
preferences change across different regimes (i⊔IcCIm⊓). This translates into testing for:
H3a BNsCIcCk
cCG HDNsCIc
cCB (12)
H3b BNsCIm IcCk
cCG HDNsCIm IcCk
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
0.45 0.3 0.15 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 | [
"in",
"the",
"absence",
"of",
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"claim",
"(",
"absence",
"-",
"focused",
"regime",
"\n",
"Ic",
")",
"for",
"both",
"generic",
"and",
"branded",
"product",
"versions",
",",
"that",
"is",
":",
"\n",
"H2aG",
":",
"WTPsCImCk",
"\n",
"cCG\u0000H\u0000WTPsCIcCk",
"\n",
"cCG\u0000Hℑ0",
"for",
"c⊔F1CF2⊓(8",
")",
".",
"\n",
"H2aB",
":",
"WTPsCImCk",
"\n",
"cCB\u0000WTPsCIcCk",
"\n",
"cCBℑ0",
"for",
"c⊔F1CF2⊓(9",
")",
".",
"\n",
"A",
"negative",
"result",
"in",
"(",
"8)",
"and",
"(",
"9",
")",
"indicates",
"that",
"the",
"introduction",
"of",
"a",
"‘",
"made",
"for",
"’",
"claim",
"decreases",
"consumer",
"preference",
"for",
"the",
"foreign",
"version",
"relative",
"\n",
"to",
"the",
"domestic",
"one",
".",
"\n",
"To",
"assess",
"whether",
"these",
"effects",
"differ",
"between",
"Western",
"and",
"Eastern",
"consumers",
",",
"we",
"disentangle",
"equations",
"(",
"8)",
"and",
"(",
"9",
")",
"by",
"country",
"group",
".",
"We",
"expect",
"\n",
"that",
"in",
"the",
"presence",
"-",
"focused",
"regime",
",",
"the",
"social",
"status",
"stereotype",
"increases",
"Eastern",
"European",
"consumers",
"’",
"preference",
"for",
"Western",
"-",
"country",
"product",
"\n",
"versions",
",",
"since",
"these",
"trigger",
"higher",
"quality",
"expectations",
",",
"while",
"the",
"opposite",
"is",
"expected",
"for",
"Western",
"European",
"consumers",
".",
"This",
"yields",
"the",
"following",
"\n",
"specifications",
":",
"\n",
"H2bCGBWTPsCImCk",
"\n",
"cCG\u0000H\u0000WTPsCIcCk",
"\n",
"cCG\u0000H",
"〉",
"\n",
"F0fors∃Eandc∃W",
"\n",
"D0fors∃Wandc∃E(10",
")",
"\n",
"H2bCBBWTPsCImCk",
"\n",
"cCB\u0000WTPsCIcCk",
"\n",
"cCB",
"〉",
"\n",
"F0fors∃Eandc∃W",
"\n",
"D0fors∃Wandc∃E(11",
")",
"\n",
"To",
"investigate",
"the",
"brand",
"-",
"name",
"-",
"related",
"implications",
"empirically",
",",
"we",
"examine",
"the",
"proportion",
"of",
"consumers",
"whose",
"willingness",
"to",
"pay",
"for",
"the",
"different",
"\n",
"product",
"versions",
"varies",
"between",
"the",
"absence-",
"and",
"presence",
"-",
"focused",
"regimes",
".",
"More",
"specifically",
",",
"using",
"(",
"4",
")",
"to",
"(",
"10",
")",
"we",
"examine",
"the",
"number",
"of",
"cases",
"(",
"N",
")",
"in",
"\n",
"which",
"the",
"preference",
"for",
"domestic",
"branded",
"product",
"versions",
"(",
"NsCIc",
"\n",
"cCBexceeds",
"that",
"for",
"domestic",
"generic",
"product",
"versions",
"(",
"\n",
"NsCIc",
"\n",
"cCG\u0000H",
")",
"\n",
"and",
"whether",
"these",
"\n",
"preferences",
"change",
"across",
"different",
"regimes",
"(",
"i⊔IcCIm⊓",
")",
".",
"This",
"translates",
"into",
"testing",
"for",
":",
"\n",
"H3a",
"BNsCIcCk",
"\n",
"cCG\u0000HDNsCIc",
"\n",
"cCB",
"(",
"12",
")",
"\n",
"H3b",
"BNsCIm\u0000IcCk",
"\n",
"cCG\u0000HDNsCIm\u0000IcCk",
"\n",
"cCB",
"(",
"13",
")",
"\n",
"Appendix",
"C:.Price",
"levels",
"used",
"in",
"the",
"DCE",
"\n",
"The",
"price",
"vectors",
"were",
"constructed",
"starting",
"from",
"actual",
"prices",
"found",
"in",
"retails",
"stores",
"at",
"the",
"time",
"of",
"the",
"survey",
".",
"The",
"additional",
"levels",
"include",
"price",
"\n",
"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",
"\n",
"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",
"\n",
"these",
"are",
"usually",
"cheaper",
"in",
"the",
"market",
".",
"D.M.",
"Federica",
"et",
"al",
".",
" ",
"Food",
"Policy",
" ",
"131",
"(",
"2025",
")",
" ",
"102803",
" \n",
"15",
"%",
"deviation",
"from",
"original",
"price",
"\n",
"\u00000.45\u00000.3\u00000.15",
"0",
"0.15",
"0.3",
"\n",
"Product",
"Country",
"Currency",
"1",
"2",
"3",
"4",
"5",
"6",
"\n",
"DanoneActivia",
"Strawberry",
"DE",
"EUR",
"1.09",
"1.39",
"1.69",
"1.99",
"2.29",
"2.59",
"\n",
"HU",
"HUF",
"219",
"279",
"339",
"399",
"459",
"519",
"\n",
"LI",
"EUR",
"1.01",
"1.28",
"1.56",
"1.83",
"2.10",
"2.38",
"\n",
"KnorrSpaghettisauce",
"DE",
"EUR",
"0.43",
"0.55",
"0.67",
"0.79",
"0.91",
"1.03",
"\n",
"HU",
"HUF",
"219",
"279",
"339",
"399",
"459",
"519",
"\n",
"LI",
"EUR",
"0.34",
"0.43",
"0.53",
"0.62",
"0.71",
"0.81",
"\n",
"MilkaChocoCookies",
"DE",
"EUR",
"0.76",
"0.97",
"1.18",
"1.39",
"1.60",
"1.81",
"\n",
"HU",
"HUF",
"274",
"349",
"424",
"499",
"574",
"649",
"\n",
"LI",
"EUR",
"0.59",
"0.76",
"0.92"
] | [] |
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 | [
"the",
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"“",
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"\n",
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",",
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"In",
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",",
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",",
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",",
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",",
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",",
"11",
",",
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"https://doi.org/10.3390/batteries11010030Batteries",
"2025",
",",
"11",
",",
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"2",
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"\n",
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"\n",
"European",
"batteries",
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"in",
"Article",
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"[",
"1",
"]",
".",
"Our",
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"insights",
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"\n",
"manufacturers",
"and",
"consumers",
",",
"promoting",
"the",
"development",
"of",
"better",
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".",
"\n",
"NiMH",
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"At",
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"(",
"cathode",
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",",
"nickel",
"\n",
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"The",
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"(",
"anode",
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"is",
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"\n",
"an",
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"alloy",
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"a",
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"\n",
"(",
"nickel",
",",
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",",
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"and",
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"lanthanum",
",",
"cerium",
",",
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",",
"\n",
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"[",
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"The",
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"\n",
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",",
"usually",
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"-",
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"[",
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"[",
"10",
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",",
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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-
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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 | [
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[ 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
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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). | [
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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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"181Printing",
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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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Retrieved 3 July 2020.
Allen JP, Williams JC (October 1998). "Photosynthetic reaction centers". FEBS Letters. 438 (1–2): 5–9. Bibcode:1998FEBSL.438....5A. doi:10.1016/S0014-5793(98)01245-9. PMID 9821949. S2CID 21596537.
Munekage Y, Hashimoto M, Miyake C, Tomizawa K, Endo T, Tasaka M, Shikanai T (June 2004). "Cyclic electron flow around photosystem I is essential for photosynthesis". Nature. 429 (6991): 579–82. Bibcode:2004Natur.429..579M. doi:10.1038/nature02598. PMID 15175756. S2CID 4421776.
Mandal A (26 November 2009). "What is Anabolism?". News-Medical.net. Archived from the original on 5 July 2020. Retrieved 4 July 2020.
Miziorko HM, Lorimer GH (1983). "Ribulose-1,5-bisphosphate carboxylase-oxygenase". Annual Review of Biochemistry. 52: 507–35. doi:10.1146/annurev.bi.52.070183.002451. PMID 6351728.
Dodd AN, Borland AM, Haslam RP, Griffiths H, Maxwell K (April 2002). "Crassulacean acid metabolism: plastic, fantastic". Journal of Experimental Botany. 53 (369): 569–80. doi:10.1093/jexbot/53.369.569. PMID 11886877.
Hügler M, Wirsen CO, Fuchs G, Taylor CD, Sievert SM (May 2005). "Evidence for autotrophic CO2 fixation via the reductive tricarboxylic acid cycle by members of the epsilon subdivision of proteobacteria". Journal of Bacteriology. 187 (9): 3020–7. doi:10.1128/JB.187.9.3020-3027.2005. PMC 1082812. PMID 15838028.
Strauss G, Fuchs G (August 1993). "Enzymes of a novel autotrophic CO2 fixation pathway in the phototrophic bacterium Chloroflexus aurantiacus, the 3-hydroxypropionate cycle". European Journal of Biochemistry. 215 (3): 633–43. doi:10.1111/j.1432-1033.1993.tb18074.x. PMID 8354269.
Wood HG (February 1991). "Life with CO or CO2 and H2 as a source of carbon and energy". FASEB Journal. 5 (2): 156–63. doi:10.1096/fasebj.5.2.1900793. PMID 1900793. S2CID 45967404.
Shively JM, van Keulen G, Meijer WG (1998). "Something from almost nothing: carbon dioxide fixation in chemoautotrophs". Annual Review of Microbiology. 52: 191–230. doi:10.1146/annurev.micro.52.1.191. PMID 9891798.
Boiteux A, Hess B (June 1981). "Design of glycolysis". Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 293 (1063): 5–22. Bibcode:1981RSPTB.293....5B. doi:10.1098/rstb.1981.0056. PMID 6115423.
Pilkis SJ, el-Maghrabi MR, Claus TH (June 1990). "Fructose-2,6-bisphosphate in control of hepatic gluconeogenesis. From metabolites to molecular genetics". Diabetes Care. 13 (6): 582–99. doi:10.2337/diacare.13.6.582. PMID 2162755. S2CID 44741368.
Ensign SA (July 2006). "Revisiting the glyoxylate cycle: alternate pathways for microbial acetate assimilation". Molecular Microbiology. 61 (2): 274–6. doi:10.1111/j.1365-2958.2006.05247.x. PMID 16856935. S2CID 39986630.
Finn PF, Dice JF (2006). "Proteolytic and lipolytic responses to starvation". Nutrition. 22 (7–8): 830–44. doi:10.1016/j.nut.2006.04.008. PMID 16815497.
Kornberg HL, Krebs HA (May 1957). "Synthesis of cell constituents from C2-units by a modified tricarboxylic acid cycle". Nature. 179 (4568): 988–91. Bibcode:1957Natur.179..988K. doi:10.1038/179988a0. PMID 13430766. S2CID 40858130.
Evans RD, Heather LC (June 2016). "Metabolic pathways and abnormalities". Surgery (Oxford). 34 (6): 266–272. doi:10.1016/j.mpsur.2016.03.010. ISSN 0263-9319. S2CID 87884121. | [
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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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(2011). Meta-analyses are in widespread use due to their ap-
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(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
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3 Results
We identify the regions (LAUs) in Europe exposed to multi-
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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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"acids.[45",
"]",
"\n\n\n",
"Carbon",
"Catabolism",
"pathway",
"map",
"for",
"free",
"energy",
"including",
"carbohydrate",
"and",
"lipid",
"sources",
"of",
"energy",
"\n",
"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",
"]",
"\n\n",
"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",
"]",
"\n\n",
"Energy",
"transformations",
"\n",
"Oxidative",
"phosphorylation",
"\n",
"Further",
"information",
":",
"Oxidative",
"phosphorylation",
",",
"Chemiosmosis",
",",
"and",
"Mitochondrion",
"\n",
"In",
"oxidative",
"phosphorylation",
",",
"the",
"electrons",
"removed",
"from"
] | [] |
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",
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"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",
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"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 | [
"equipment",
"and",
"supplies",
" ",
"X",
"\n",
"48",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"NACE",
"Industry",
"nameCurrent",
"\n",
"strengthEmerging",
"\n",
"strength",
"\n",
"46.9",
"Non",
"-",
"specialised",
"wholesale",
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" ",
"X",
"\n",
"47.1",
"Retail",
"sale",
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"non",
"-",
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"stores",
" ",
"X",
"\n",
"47.2",
"Retail",
"sale",
"of",
"food",
",",
"beverages",
"and",
"tobacco",
"in",
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"stores",
" ",
"X",
"\n",
"47.7",
"Retail",
"sale",
"of",
"other",
"goods",
"in",
"specialised",
"stores",
" ",
"X",
"\n",
"H",
"TRANSPORTATION",
"AND",
"STORAGE",
" \n",
"49.2",
"Freight",
"rail",
"transport",
"X",
"X",
"\n",
"49.4",
"Freight",
"transport",
"by",
"road",
"and",
"removal",
"services",
" ",
"X",
"\n",
"49.5",
"Transport",
"via",
"pipeline",
"X",
" \n",
"51.1",
"Passenger",
"air",
"transport",
"X",
" \n",
"52.1",
"Warehousing",
"and",
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"X",
" \n",
"I",
"ACCOMMODATION",
"AND",
"FOOD",
"SERVICE",
"ACTIVITIES",
" \n",
"56.1",
"Restaurants",
"and",
"mobile",
"food",
"service",
"activities",
" ",
"X",
"\n",
"J",
"INFORMATION",
"AND",
"COMMUNICATION",
" \n",
"59.1",
"Motion",
"picture",
",",
"video",
"and",
"television",
"programme",
"activities",
" ",
"X",
"\n",
"K",
"FINANCIAL",
"AND",
"INSURANCE",
"ACTIVITIES",
" \n",
"64.1",
"Monetary",
"intermediation",
" ",
"X",
"\n",
"64.9",
"Other",
"financial",
"service",
"activities",
",",
"except",
"insurance",
"and",
"pension",
"funding",
" ",
"X",
"\n",
"L",
"REAL",
"ESTATE",
"ACTIVITIES",
" \n",
"M",
"PROFESSIONAL",
",",
"SCIENTIFIC",
"AND",
"TECHNICAL",
"ACTIVITIES",
" \n",
"70.1",
"Activities",
"of",
"head",
"offices",
"X",
" \n",
"72.1",
"Research",
"and",
"experimental",
"development",
"on",
"natural",
"sciences",
"and",
"engineering",
"X",
" \n",
"73.1",
"Advertising",
" ",
"X",
"\n",
"N",
"ADMINISTRATIVE",
"AND",
"SUPPORT",
"SERVICE",
"ACTIVITIES",
" \n",
"77.3",
"Rental",
"and",
"leasing",
"of",
"other",
"machinery",
",",
"equipment",
"and",
"tangible",
"goods",
" ",
"X",
"\n",
"81.1",
"Combined",
"facilities",
"support",
"activities",
"X",
" \n",
"O",
"PUBLIC",
"ADMINISTRATION",
"AND",
"DEFENCE",
";",
"COMPULSORY",
"SOCIAL",
"SECURITY",
" \n",
"P",
"EDUCATION",
" \n",
"Q",
"HUMAN",
"HEALTH",
"AND",
"SOCIAL",
"WORK",
"ACTIVITIES",
" \n",
"86.2",
"Medical",
"and",
"dental",
"practice",
"activities",
" ",
"X",
"\n",
"R",
"ARTS",
",",
"ENTERTAINMENT",
"AND",
"RECREATION",
" \n",
"S",
"OTHER",
"SERVICE",
"ACTIVITIES",
" \n",
"n.e.c",
".",
"=",
"not",
"elsewhere",
"classified",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation49",
"\n",
"2.2",
".",
"Manufacturing",
"analysis",
"for",
"\n",
"five",
"EaP",
"countries",
"\n",
"Full",
"economic",
"mapping",
"of",
"the",
"economic",
"potential",
"\n",
"is",
"not",
"possible",
"for",
"three",
"Eastern",
"Partnership",
"coun-",
"\n",
"tries",
"due",
"to",
"a",
"lack",
"of",
"available",
"Orbis",
"data",
".",
"Partial",
"\n",
"mapping",
"for",
"the",
"Manufacturing",
"sector",
"is",
"feasible",
"\n",
"by",
"using",
"official",
"data",
"collected",
"by",
"UNIDO",
".",
"This",
"\n",
"section",
"uses",
"data",
"from",
"the",
"UNIDO",
"Industrial",
"\n",
"Statistics",
"Database",
"at",
"NACE",
"four",
"-",
"digit",
"level",
"\n",
"(",
"INDSTAT4)31",
",",
"which",
"contains",
"highly",
"disaggregat-",
"\n",
"ed",
"data",
"on",
"the",
"manufacturing",
"sector",
"for",
"the",
"peri-",
"\n",
"od",
"1990",
"onwards",
".",
"\n",
"31",
"UNIDO",
",",
"INDSTAT",
"4",
"Industrial",
"Statistics",
"Database",
"at",
"the",
"\n",
"3-",
"and",
"4",
"-",
"digit",
"level",
"of",
"ISIC",
"Revision",
"3",
"and",
"ISIC",
"Revision",
"4",
",",
"\n",
"Vienna",
",",
"2020",
".",
"Available",
"from",
"http://stat.unido.org",
".",
"Data",
"availability",
"\n",
"Data",
"availability",
"is",
"not",
"perfect",
"for",
"several",
"coun-",
"\n",
"tries",
",",
"not",
"even",
"at",
"NACE",
"three",
"-",
"digit",
"level",
",",
"as",
"shown",
"\n",
"in",
"Table",
"2.6",
".",
"However",
",",
"even",
"in",
"this",
"situation",
",",
"some",
"\n",
"sort",
"of",
"comparison",
"between",
"countries",
"is",
"still",
"pos-",
"\n",
"sible",
"and",
"has",
"thus",
"been",
"carried",
"out",
".",
"\n",
"Each",
"row",
"of",
"Table",
"2.6",
"represents",
"a",
"NACE",
"three",
"-",
"dig-",
"\n",
"it",
"Manufacturing",
"industry",
".",
"For",
"those",
"cases",
"high-",
"\n",
"lighted"
] | [] |
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 | [
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] | [] |
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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"This",
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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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"Class",
"25",
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"Clothing",
",",
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",",
"headgear",
"\n",
"Class",
"26",
"–",
"Lace",
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"embroidery",
",",
"ribbons",
"and",
"\n",
"braid",
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"buttons",
",",
"hooks",
"and",
"eyes",
",",
"pins",
"and",
"needles",
";",
"\n",
"artificial",
"flowers",
";",
"hair",
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";",
"false",
"hair",
"\n",
"Class",
"27",
"–",
"Carpets",
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"rugs",
",",
"mats",
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"matting",
",",
"li-",
"\n",
"noleum",
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"existing",
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"(",
"non",
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"textile",
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"Class",
"28",
"–",
"Games",
",",
"toys",
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"playthings",
";",
"video",
"\n",
"game",
"apparatus",
";",
"gymnastic",
"and",
"sporting",
"articles",
";",
"\n",
"decorations",
"for",
"Christmas",
"trees",
"\n",
"Class",
"29",
"–",
"Meat",
",",
"fish",
",",
"poultry",
"and",
"game",
";",
"meat",
"ex-",
"\n",
"tracts",
";",
"preserved",
",",
"frozen",
",",
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"and",
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"milk",
"\n",
"and",
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"Class",
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"Coffee",
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",",
"cocoa",
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"cof-",
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"Class",
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",",
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"\n",
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",",
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"ani-",
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"\n",
"and",
"fruit",
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";",
"syrups",
"and"
] | [] |
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 | [
"for",
"Armenia",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
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"knowledge",
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"\n",
"SITC",
"Goods",
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"\n",
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"\n",
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"\n",
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"292",
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"X",
"0.3",
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" \n",
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"Mineral",
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" \n",
"351",
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" \n",
"661Lime",
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"(",
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"0.6",
"%",
" \n",
"667Pearls",
"and",
"precious",
"or",
"semiprecious",
"stones",
",",
"unworked",
"or",
"\n",
"workedX",
"5.0",
"%",
" \n",
"682",
"Copper",
"X",
"4.0",
"%",
" \n",
"684",
"Aluminium",
"X",
"5.0",
"%",
" \n",
"7",
"Machinery",
"and",
"transport",
"equipment",
" \n",
"8",
"Miscellaneous",
"manufactured",
"articles",
" \n",
"841Men",
"’s",
"or",
"boys",
"’",
"coats",
",",
"capes",
",",
"jackets",
",",
"suits",
",",
"blazers",
",",
"trousers",
",",
"\n",
"shorts",
",",
"shirts",
",",
"underwear",
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"nightwear",
"and",
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"articles",
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"\n",
"textile",
"fabrics",
",",
"not",
"knitted",
"or",
"crocheted",
"(",
"other",
"than",
"those",
"of",
"\n",
"subgroup",
"845.2)X",
"2.2",
"%",
"X",
"2.2",
"%",
"\n",
"842Women",
"’s",
"or",
"girls",
"’",
"coats",
",",
"capes",
",",
"jackets",
",",
"suits",
",",
"trousers",
",",
"\n",
"shorts",
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"shirts",
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"dresses",
"and",
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",",
"underwear",
",",
"nightwear",
"and",
"\n",
"similar",
"articles",
"of",
"textile",
"fabrics",
",",
"not",
"knitted",
"or",
"crocheted",
"\n",
"(",
"other",
"than",
"those",
"of",
"subgroup",
"845.2",
")",
" ",
"X",
"1.4",
"%",
"\n",
"845Articles",
"of",
"apparel",
",",
"of",
"textile",
"fabrics",
",",
"whether",
"or",
"not",
"knitted",
"\n",
"or",
"crocheted",
",",
"n.e.s",
".",
" ",
"X",
"0.6",
"%",
"\n",
"885",
"Watches",
"and",
"clocks",
"X",
"1.1",
"%",
" \n",
"897Jewellery",
",",
"goldsmiths",
"'",
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"silversmiths",
"'",
"wares",
",",
"and",
"other",
"\n",
"articles",
"of",
"precious",
"or",
"semiprecious",
"materials",
",",
"n.e.s",
".",
"X",
"1.9",
"%",
" \n",
"9Commodities",
"and",
"transactions",
"not",
"classified",
"elsewhere",
"in",
"\n",
"the",
"SITC",
" \n",
"971",
"Gold",
",",
"non",
"-",
"monetary",
"(",
"excluding",
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"ores",
"and",
"concentrates",
")",
"X",
"6.8",
"%",
"X",
"6.8",
"%",
"\n",
"68",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"Mapping",
"of",
"goods",
"export",
"specialisations",
"\n",
"–",
"results",
"for",
"Azerbaijan",
"\n",
"Results",
"of",
"the",
"export",
"mapping",
"for",
"Azerbaijan",
"are",
"\n",
"shown",
"in",
"Table",
"2.17",
".",
"The",
"3",
"goods",
"categories",
"with",
"\n",
"current",
"strength",
"represent",
"88",
"%",
"of",
"the",
"total",
"ex-",
"\n",
"ports",
"for",
"2012",
"-",
"2019",
".",
"This",
"very",
"high",
"share",
"is",
"due",
"\n",
"to",
"the",
"dominance",
"of",
"only",
"one",
"good",
"in",
"Azerbaijan",
"’s",
"\n",
"export",
":",
"export",
"of",
"Bituminous",
"minerals",
",",
"crude",
"(",
"SITC",
"\n",
"333",
")",
",",
"accounting",
"for",
"82",
"%",
"of",
"the",
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"exports",
".",
"\n",
"Azerbaijan",
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"are",
"therefore",
"extremely",
"spe-",
"\n",
"cialised",
",",
"putting",
"the",
"country",
"at",
"risk",
"of",
"negative",
"\n",
"market",
"developments",
"in",
"this",
"goods",
"category",
".",
"The",
"8",
"goods",
"categories",
"with",
"emerging",
"strength",
"\n",
"represent",
"almost",
"91",
"%",
"of",
"the",
"total",
"exports",
"as",
"Bitu-",
"\n",
"minous",
"minerals",
",",
"crude",
"(",
"SITC",
"333",
")",
"is",
"also",
"includ-",
"\n",
"ed",
".",
"Increasing",
"specialisations",
"are",
"also",
"observed",
"in",
"\n",
"Food",
"and",
"live",
"animals",
"(",
"SITC",
"0",
")",
"and",
"Manufactured",
"\n",
"goods",
"classified",
"chiefly",
"by",
"material"
] | [] |
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'>
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<div class="addthis_inline_share_toolbox_62ef"></div>
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<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 | [
"Hospital</p",
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"<",
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">",
"Percutaneous",
"coronary",
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"PCI",
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"a",
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"day",
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"In",
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",",
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",",
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",",
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"This",
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"align=\"center\">###</p",
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"CHAMPION",
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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 -- | [
"of",
"organisational",
"clusters",
".",
"\n",
"The",
"following",
"tables",
"showcase",
"the",
"sectoral",
"criti-",
"\n",
"cal",
"mass",
"and",
"specialisation",
"of",
"start",
"-",
"ups",
"and",
"ven-",
"\n",
"ture",
"capital",
"-",
"backed",
"companies",
"for",
"each",
"of",
"the",
"six",
"\n",
"Eastern",
"Partnership",
"countries",
",",
"and",
"for",
"each",
"of",
"the",
"\n",
"102",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"\n",
"Locarno2011-",
"\n",
"20142015-",
"\n",
"20182011-",
"\n",
"20142015-",
"\n",
"20182011-",
"\n",
"20142015-",
"\n",
"20182011-",
"\n",
"20142015-",
"\n",
"20182011-",
"\n",
"20142015-",
"\n",
"20182011-",
"\n",
"20142015-",
"\n",
"2018",
"\n",
"1",
"--",
"--",
"--",
"--",
"1",
"--",
"33",
"7",
"24",
"24",
"422",
"364",
"\n",
"2",
"--",
"--",
"--",
"--",
"--",
"--",
"38",
"13",
"44",
"22",
"303",
"391",
"\n",
"3",
"--",
"1",
"--",
"--",
"--",
"--",
"--",
"19",
"--",
"--",
"50",
"145",
"\n",
"4",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"1",
"19",
"\n",
"5",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"2",
"--",
"1",
"502",
"296",
"\n",
"6",
"--",
"--",
"--",
"2",
"--",
"--",
"96",
"24",
"321",
"41",
"632",
"678",
"\n",
"7",
"2",
"--",
"--",
"--",
"48",
"--",
"45",
"18",
"247",
"1",
"176",
"376",
"\n",
"8",
"--",
"--",
"--",
"--",
"--",
"--",
"8",
"4",
"18",
"1",
"236",
"787",
"\n",
"9",
"8",
"38",
"--",
"--",
"25",
"28",
"71",
"173",
"178",
"138",
"1",
"893",
"2",
"935",
"\n",
"10",
"--",
"--",
"--",
"--",
"--",
"1",
"--",
"9",
"--",
"2",
"146",
"305",
"\n",
"11",
"107",
"8",
"--",
"--",
"--",
"--",
"18",
"49",
"1",
"668",
"552",
"437",
"815",
"\n",
"12",
"--",
"--",
"--",
"--",
"4",
"31",
"4",
"3",
"10",
"12",
"598",
"993",
"\n",
"13",
"--",
"--",
"--",
"1",
"--",
"2",
"2",
"1",
"--",
"2",
"239",
"392",
"\n",
"14",
"--",
"--",
"--",
"--",
"1",
"13",
"--",
"11",
"4",
"--",
"78",
"208",
"\n",
"15",
"--",
"--",
"--",
"--",
"32",
"8",
"2",
"--",
"1",
"--",
"260",
"682",
"\n",
"16",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"1",
"--",
"12",
"\n",
"17",
"--",
"--",
"--",
"--",
"--",
"--",
"1",
"1",
"--",
"5",
"4",
"30",
"\n",
"18",
"--",
"--",
"--",
"--",
"--",
"--",
"35",
"56",
"3",
"--",
"16",
"14",
"\n",
"19",
"--",
"--",
"--",
"--",
"2",
"12",
"34",
"101",
"283",
"184",
"2",
"537",
"3",
"511",
"\n",
"20",
"--",
"--",
"--",
"--",
"--",
"--",
"9",
"17",
"247",
"62",
"403",
"377",
"\n",
"21",
"--",
"5",
"--",
"--",
"13",
"--",
"24",
"9",
"4",
"51",
"154",
"354",
"\n",
"22",
"--",
"--",
"--",
"--",
"--",
"--",
"1",
"--",
"--",
"--",
"22",
"84",
"\n",
"23",
"--",
"--",
"--",
"--",
"20",
"5",
"5",
"--",
"2",
"26",
"431",
"597",
"\n",
"24",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"--",
"22",
"5",
"76",
"166",
"\n",
"25",
"1",
"1",
"--",
"--",
"10",
"4",
"9",
"37",
"107",
"493",
"532",
"900",
"\n",
"26",
"--",
"--",
"--",
"--",
"--",
"1",
"--"
] | [] |
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 0.22 €cent, p D0.001 vs 0.39
€cent, p D0.001; }β LIYogurt 0.16 €cent, p D0.001 vs 0.34 €cent, p D
0.001; }β HUSauce 0.02 €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 -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 14.13 forint, p
D0.01) and the Spanish and Swedish versions of Lay’s potatoes to the
domestic versions (}β ESCrisps 4.21 leu, p D0.01 and }β SECrisps 4.95 leu,
p D0.05). This change reduces to one case when consumers also see the
| [
"the",
"effectiveness",
"of",
"the",
"‘",
"made",
"for",
"’",
"claim",
".",
"Thus",
",",
"we",
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"\n",
"confirm",
"that",
"the",
"‘",
"made",
"for",
"’",
"claim",
"is",
"raising",
"the",
"awareness",
"of",
"DFQ",
".",
"\n",
"Focusing",
"on",
"the",
"aggregated",
"results",
",",
"we",
"observe",
"that",
"when",
"making",
"\n",
"DFQ",
"salient",
",",
"in",
"the",
"majority",
"of",
"generic",
"country",
"-",
"product",
"pairs",
"(",
"69",
"%",
")",
",",
"the",
"\n",
"‘",
"made",
"for",
"’",
"claim",
"either",
"increases",
"preference",
"for",
"the",
"domestic",
"version",
"or",
"\n",
"reduces",
"preference",
"for",
"the",
"foreign",
"version",
",",
"resulting",
"in",
"a",
"higher",
"WTP",
"for",
"\n",
"the",
"domestic",
"product",
"or",
"a",
"lower",
"WTP",
"for",
"the",
"foreign",
"product",
".",
"For",
"the",
"\n",
"generic",
"versions",
",",
"results",
"from",
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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-
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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
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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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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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" ",
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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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"Leather",
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"\n",
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" ",
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" ",
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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
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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
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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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trace. For activity analysis across pairs of different behavioral paradigms, we matched neurons across days by using a centroid-
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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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.
Keller, A.J., Dipoppa, M., Roth, M.M., Caudill, M.S., Ingrosso, A., Miller, K.D.,
and Scanziani, M. (2020). A disinhibitory circuit for contextual modulation in
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inhibitory gating by vip interneurons permits associative learning. Nat. Neuro-
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Sten, T.A.H., Miller, K.D., and Froemke, R.C. (2017). Parallel processing bycortical inhibition enables context-dependent behavior. Nat. Neurosci. 20,
62–71. https://doi.org/10.1038/nn.4436 .
Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J.Z., and Kepecs,
A. (2013). Distinct behavioural and network correlates of two interneuron typesin prefrontal cortex. Nature 498, 363–366. https://doi.org/10.1038/na-
ture12176 .
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Ponnavolu, S., and Sohal, V.S. (2019). Vip interneurons contribute to avoid-ance behavior by regulating information flow across hippocampal-prefrontalnetworks. Neuron 102, 1223–1234.e4. https://doi.org/10.1016/j.neuron.
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Cell Reports 39, 110893, May 31, 2022 15Articlell
OPEN ACCESSLetzkus, J.J., Wolff, S.B.E., Meyer, E.M.M., Tovote, P., Courtin, J., Herry, C.,
and L €uthi, A. (2011). A disinhibitory microcircuit for associative fear learning
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forebrain encodes salience irrespective of valence. Neuron 59, 138–149.
https://doi.org/10.1016/j.neuron.2008.04.031 | [
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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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",",
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",",
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",",
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",",
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",",
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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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ability to leverage syntactic heuristics involving
overlap (of full constituents, subsequences, or sim-
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to frustrate such heuristics, BERT’s performance
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In this brief overview of BERTology papers we
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3 What is meaning?
We start by defining two key terms: We take form
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4But see Warstadt et al.’s (2019) cautionary note about how
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Vertuani S, Angusti A, Manfredini S (2004). "The antioxidants and pro-antioxidants network: an overview". Current Pharmaceutical Design. 10 (14): 1677–94. doi:10.2174/1381612043384655. PMID 15134565. S2CID 43713549.
von Stockar U, Liu J (August 1999). "Does microbial life always feed on negative entropy? Thermodynamic analysis of microbial growth". Biochimica et Biophysica Acta (BBA) - Bioenergetics. 1412 (3): 191–211. doi:10.1016/S0005-2728(99)00065-1. PMID 10482783.
Demirel Y, Sandler SI (June 2002). "Thermodynamics and bioenergetics". Biophysical Chemistry. 97 (2–3): 87–111. doi:10.1016/S0301-4622(02)00069-8. PMID 12050002. S2CID 3754065. Archived from the original on 4 August 2020. Retrieved 22 September 2019.
Albert R (November 2005). "Scale-free networks in cell biology". Journal of Cell Science. 118 (Pt 21): 4947–57. arXiv:q-bio/0510054. Bibcode:2005q.bio....10054A. doi:10.1242/jcs.02714. PMID 16254242. S2CID 3001195.
Brand MD (January 1997). "Regulation analysis of energy metabolism". The Journal of Experimental Biology. 200 (Pt 2): 193–202. doi:10.1242/jeb.200.2.193. PMID 9050227. Archived from the original on 29 March 2007. Retrieved 12 March 2007.
Soyer OS, Salathé M, Bonhoeffer S (January 2006). "Signal transduction networks: topology, response and biochemical processes". Journal of Theoretical Biology. 238 (2): 416–25. Bibcode:2006JThBi.238..416S. doi:10.1016/j.jtbi.2005.05.030. PMID 16045939.
Salter M, Knowles RG, Pogson CI (1994). "Metabolic control". Essays in Biochemistry. 28: 1–12. PMID 7925313.
Westerhoff HV, Groen AK, Wanders RJ (January 1984). "Modern theories of metabolic control and their applications (review)". Bioscience Reports. 4 (1): 1–22. doi:10.1007/BF01120819. PMID 6365197. S2CID 27791605.
Fell DA, Thomas S (October 1995). "Physiological control of metabolic flux: the requirement for multisite modulation". The Biochemical Journal. 311 (Pt 1): 35–9. doi:10.1042/bj3110035. PMC 1136115. PMID 7575476.
Hendrickson WA (November 2005). "Transduction of biochemical signals across cell membranes". Quarterly Reviews of Biophysics. 38 (4): 321–30. doi:10.1017/S0033583506004136. PMID 16600054. S2CID 39154236.
Cohen P (December 2000). "The regulation of protein function by multisite phosphorylation--a 25 year update". Trends in Biochemical Sciences. 25 (12): 596–601. doi:10.1016/S0968-0004(00)01712-6. PMID 11116185.
Lienhard GE, Slot JW, James DE, Mueckler MM (January 1992). "How cells absorb glucose". Scientific American. 266 (1): 86–91. Bibcode:1992SciAm.266a..86L. doi:10.1038/scientificamerican0192-86. PMID 1734513.
Roach PJ (March 2002). "Glycogen and its metabolism". Current Molecular Medicine. 2 (2): 101–20. doi:10.2174/1566524024605761. PMID 11949930.
Newgard CB, Brady MJ, O'Doherty RM, Saltiel AR (December 2000). "Organizing glucose disposal: emerging roles of the glycogen targeting subunits of protein phosphatase-1" (PDF). Diabetes. 49 (12): 1967–77. doi:10.2337/diabetes.49.12.1967. PMID 11117996. Archived (PDF) from the original on 19 June 2007. Retrieved 25 March 2007.
Romano AH, Conway T (1996). "Evolution of carbohydrate metabolic pathways". Research in Microbiology. 147 (6–7): 448–55. doi:10.1016/0923-2508(96)83998-2. PMID 9084754. | [
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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 | [
"147Ce",
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".",
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",",
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"\n",
"123",
" ",
"5",
"Page",
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"Eur",
".",
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".",
"J.",
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"Interestingly",
",",
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",",
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"This",
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",",
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",",
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".",
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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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] | [] |
Gao, S., Grebitus, C., DeLong, K.L., 2024. Explaining consumer willingess to pay for
country-of-origin labeling with ethnocentrism, country image, and product image:
Examples from China ’s beef market. Canadian Journal of Agricultural Economics .
Graham, D.J., Orquin, J.L., Visschers, V.H., 2012. Eye tracking and nutrition label use: A
review of the literature and recommendations for label enhancement. Food Policy 37
(4), 378–382.
Grunert, K.G., 2005. Food quality and safety: consumer perception and demand.
European Review of Agricultural Economics 32 (3), 369–391.
Herz, M.F., Diamantopoulos, A., 2013. Activation of country stereotypes: automaticity,
consonance, and impact. Journal of the Academy of Marketing Science 41, 400–417.
Jancarikova, T., 2017. East Europeans decry ‘double standards ’ for food, seek change to
EU law. Reuters (march 2). https://www.reuters.com/article/centraleurope
-food-idUSL5N1GD4N4 .
Kuhfeld, W., 2012. “Experimental Design: Efficiency, Coding, and Choice Designs, ” in
Marketing research methods in SAS: Experimental design, choice, conjoint, and graphical
techniques . SAS-Institute TS-722, Cary, NC, pp. 47–97.
Liu-Thompkins, Y., Tam, L., 2013. Not All Repeat Customers Are the Same: Designing
Effective Cross-Selling Promotion on the Basis of Attitudinal Loyalty and Habit.
Journal of Marketing 77 (5), 21–36.
Marley, A.A.J., Louviere, J., 2005. Some Probabilistic Models of Best, Worst, and
Best–Worst Choices. Journal of Mathematical Psychology 49, 464–480.
Moreau, C.P., Herd, K.B., 2010. To Each His Own? HowComparisons to Others Influence
Consumer Self-Design. Journalof Consumer Research 36, 806–811.
MPSR (2017), “Kiadv ˘any a Magyarorsz ˘agon ˘es külfoldon forgalmazott ˘elelmiszerek
2017. ˘evi osszehasonlít ˘o vizsg ˘alat˘anak tapasztalatair ˘ol.” https://portal.nebih.gov.
hu/kutatas_kijelzo/-/asset_publisher/XZIUSA6aTnU0/content /magyarorszagon-es- D.M. Federica et al. Food Policy 131 (2025) 102803
16 kulfoldon-forgalmazott-elelmiszerek-2017-evi-nyari-osszehasonlito-vizsgalata/
europai-unios-projektek-archiv-cikkek.
Murphy, J.J., Geoffrey Allen, P., Stevens, T.H., Weatherhead, D., 2005. A Meta-analysis
of Hypothetical Bias in Stated Preference Valuation. Environmental and Resource
Economics 30, 313–325.
Nes, K., Ciaian, P., Di Marcantonio, F., 2021. Economic determinants of differences in the
composition of seemingly identical branded food products in the EU. Food Policy
100, 102020. https://doi.org/10.1016/j.foodpol.2020.102020 .
Nes, K., Ciaian, P., Di Marcantonio, F., 2021. Economic determinants of differences in the
composition of seemingly identical branded food products in the EU. Food Policy
100. https://doi.org/10.1016/j.foodpol.2020.102020 .
Nes, K., Antonioli, F., Di Marcantonio, F., Ciaian, P., 2023. EU-wide comparison of the
characteristics and presentation of branded food products. Publications Office of the
European Union, Luxembourg 2023. https://doi.org/10.2760/8399, JRC131303 .
Nes, K., Antonioli, F., Di Marcantonio, F., Ciaian, P., 2024. The impact of pre-empting
dual food quality regulation on product reformulation and packaging. Food Policy
128. https://doi.org/10.1016/j.foodpol.2024.102707 | [
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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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"and",
"computer",
"\n",
"science",
"\n",
"(",
"61",
"|",
"18.89",
"%",
")",
"\n",
"ICT",
"and",
"computer",
"\n",
"science",
"\n",
"(",
"2",
"|",
"10.53%)ICT",
"and",
"computer",
"\n",
"science",
"\n",
"(",
"18",
"|",
"31.03%)ICT",
"and",
"computer",
"\n",
"science",
"\n",
"(",
"19",
"|",
"25.0%)Environmental",
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"\n",
"and",
"industries",
"\n",
"(",
"3",
"|",
"15.79",
"%",
")",
"\n",
"Environmental",
"sciences",
"\n",
"and",
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"\n",
"(",
"8",
"|",
"13.79%)Environmental",
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"\n",
"and",
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"\n",
"(",
"15",
"|",
"19.74%)Environmental",
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"\n",
"and",
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"\n",
"(",
"63",
"|",
"19.5%)Environmental",
"sciences",
"\n",
"and",
"industries",
"\n",
"(",
"45",
"|",
"22.06",
"%",
")",
"\n",
"Energy",
"\n",
"(",
"5",
"|",
"11.63",
"%",
")",
"\n",
"Energy",
"\n",
"(",
"54",
"|",
"16.72%)Energy",
"\n",
"(",
"2",
"|",
"10.53",
"%",
")",
"\n",
"Energy",
"\n",
"(",
"11",
"|",
"16.42",
"%",
")",
"\n",
"Energy",
"\n",
"(",
"6",
"|",
"7.89%)Energy",
"\n",
"(",
"43",
"|",
"21.08%)Agrifood",
"\n",
"(",
"2",
"|",
"10.53",
"%",
")",
"\n",
"Agrifood",
"\n",
"(",
"5",
"|",
"8.62",
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")",
"\n",
"Agrifood",
"\n",
"(",
"7",
"|",
"9.21%)Optics",
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"\n",
"(",
"13",
"|",
"19.4%)Health",
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"\n",
"(",
"10",
"|",
"13.16",
"%",
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"\n",
"Health",
"and",
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"\n",
"(",
"4",
"|",
"6.9%)Health",
"and",
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"\n",
"(",
"41",
"|",
"20.1%)Health",
"and",
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"\n",
"(",
"56",
"|",
"17.34%)Health",
"and",
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"\n",
"(",
"4",
"|",
"9.3",
"%",
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"\n",
"Biotechnology",
"\n",
"(",
"3",
"|",
"6.98",
"%",
")",
"\n",
"Biotechnology",
"\n",
"(",
"29",
"|",
"8.98%)Biotechnology",
"\n",
"(",
"1",
"|",
"5.26",
"%",
")",
"\n",
"Biotechnology",
"\n",
"(",
"20",
"|",
"9.8%)Biotechnology",
"\n",
"(",
"3",
"|",
"5.17%)Fundamental",
"physics",
"\n",
"and",
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"\n",
"(",
"7",
"|",
"10.45",
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"\n",
"Fundamental",
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"\n",
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"\n",
"(",
"3",
"|",
"6.98%)Chemistry",
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"\n",
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"\n",
"(",
"1",
"|",
"5.26%)Chemistry",
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"\n",
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"6",
"|",
"8.96",
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"\n",
"Smart",
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"-",
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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 | [
"7",
"6",
"6",
"5",
"5",
"2",
"2",
"2",
"4",
"3",
"\n",
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"115",
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"93",
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"26",
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"27",
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"23",
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"17",
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"13",
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"13",
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"8",
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".",
"Number",
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"Colour",
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"\n",
"PART",
"\n",
"1Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation27",
"\n",
"Part",
"1",
".",
"Introduction",
"and",
"\n",
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"\n",
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".",
"Introduction",
",",
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] | [
{
"end": 2456,
"label": "CITATION-SPAN",
"start": 2219
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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 | [
"X",
" \n",
"63.9",
"Other",
"information",
"service",
"activities",
" ",
"X",
" ",
"X",
" \n",
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"-",
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"-",
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"288",
"\n",
"Annexes",
"\n",
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"ingEmerg-",
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"64.1",
"Monetary",
"intermediation",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
"X",
"X",
"\n",
"64.2",
"Activities",
"of",
"holding",
"companies",
" \n",
"64.3",
"Trusts",
",",
"funds",
"and",
"similar",
"financial",
"entities",
" \n",
"64.9Other",
"financial",
"service",
"activities",
",",
"except",
"insurance",
"and",
"pension",
"\n",
"fundingX",
"X",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
"X",
"X",
"\n",
"65Insurance",
",",
"reinsurance",
"and",
"pension",
"funding",
",",
"except",
"compulsory",
"\n",
"social",
"security",
" \n",
"65.1",
"Insurance",
" ",
"X",
" ",
"X",
" ",
"X",
" \n",
"65.2",
"Reinsurance",
" \n",
"65.3",
"Pension",
"funding",
" \n",
"66",
"Activities",
"auxiliary",
"to",
"financial",
"services",
"and",
"insurance",
"activities",
" \n",
"66.1Activities",
"auxiliary",
"to",
"financial",
"services",
",",
"except",
"insurance",
"and",
"\n",
"pension",
"funding",
" ",
"X",
"X",
"X",
" ",
"X",
" \n",
"66.2",
"Activities",
"auxiliary",
"to",
"insurance",
"and",
"pension",
"funding",
" ",
"X",
"X",
"X",
" \n",
"66.3",
"Fund",
"management",
"activities",
" \n",
"L",
"REAL",
"ESTATE",
"ACTIVITIES",
" \n",
"68",
"Real",
"estate",
"activities",
" \n",
"68.1",
"Buying",
"and",
"selling",
"of",
"own",
"real",
"estate",
" ",
"X",
"X",
"X",
"X",
"X",
"X",
" ",
"X",
" \n",
"68.2",
"Rental",
"and",
"operating",
"of",
"own",
"or",
"leased",
"real",
"estate",
"X",
"X",
"X",
"X",
" ",
"X",
"X",
"X",
" \n",
"68.3",
"Real",
"estate",
"activities",
"on",
"a",
"fee",
"or",
"contract",
"basis",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
" \n",
"M",
"PROFESSIONAL",
",",
"SCIENTIFIC",
"AND",
"TECHNICAL",
"ACTIVITIES",
" \n",
"69",
"Legal",
"and",
"accounting",
"activities",
" \n",
"69.1",
"Legal",
"activities",
"X",
"X",
"X",
"X",
" ",
"X",
" \n",
"69.2",
"Accounting",
",",
"bookkeeping",
"and",
"auditing",
"activities",
";",
"tax",
"consultancy",
"X",
"X",
"X",
"X",
"X",
"X",
" \n",
"70",
"Activities",
"of",
"head",
"offices",
";",
"management",
"consultancy",
"activities",
" \n",
"70.1",
"Activities",
"of",
"head",
"offices",
" ",
"X",
"X",
"X",
" ",
"X",
" \n",
"70.2",
"Management",
"consultancy",
"activities",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" \n",
"71Architectural",
"and",
"engineering",
"activities",
";",
"technical",
"testing",
"and",
"\n",
"analysis",
" \n",
"71.1Architectural",
"and",
"engineering",
"activities",
"and",
"related",
"technical",
"\n",
"consultancy",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" \n",
"71.2",
"Technical",
"testing",
"and",
"analysis",
" ",
"X",
" ",
"X",
" \n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation289",
"290",
"\n",
"Annexes",
"\n",
"GEORGIA",
"MOLDOVA",
"UKRAINEEmploy-",
"\n",
"ment",
"\n",
"Turnover",
"\n",
"Employ-",
"\n",
"ment",
"&",
"\n",
"turnover",
"\n",
"Employ-",
"\n",
"ment",
"\n",
"Turnover",
"\n",
"Employ-",
"\n",
"ment",
"&",
"\n",
"turnover",
"\n",
"Employ-",
"\n",
"ment",
"\n",
"Turnover",
"\n",
"Employ-",
"\n",
"ment",
"&",
"\n",
"turnover",
"\n",
"Employ-",
"\n",
"ment",
"\n",
"Turnover",
"\n",
"Employ-",
"\n",
"ment"
] | [] |
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 | [
"of",
"each",
"S&T",
"\n",
"specialisation",
"domain",
",",
"for",
"each",
"EaP",
"\n",
"country",
"\n",
"Based",
"on",
"the",
"evidence",
"presented",
"throughout",
"Part",
"\n",
"3",
"for",
"critical",
"mass",
",",
"specialisation",
"and",
"excellence",
"\n",
"per",
"domain",
",",
"the",
"following",
"tables",
"present",
"synthet-",
"\n",
"ic",
"indicators",
"supporting",
"the",
"selection",
"of",
"the",
"S&T",
"\n",
"specialisation",
"domains",
"for",
"each",
"Eastern",
"Partner-",
"\n",
"ship",
"country",
".",
"For",
"each",
"country",
",",
"each",
"domain",
"is",
"\n",
"characterised",
"by",
"its",
"critical",
"mass",
",",
"specialisation",
"EaP",
"S&T",
"specialisation",
"domains",
"\n",
"according",
"to",
"the",
"internal",
"distribution",
"of",
"S&T",
"data",
"sources",
"\n",
"Science",
"-",
"oriented",
"S&T",
"domains",
"\n",
"domains",
"where",
"scientific",
"publications",
"\n",
"are",
"most",
"relevantBalanced",
"S&T",
"domains",
"\n",
"domains",
"where",
"publications",
"and",
"\n",
"patents",
"have",
"a",
"similar",
"relative",
"weightTechnology",
"-",
"oriented",
"S&T",
"domains",
"\n",
"domains",
"where",
"patents",
"are",
"most",
"\n",
"relevant",
"\n",
"Fundamental",
"physics",
"\n",
"and",
"mathematicsHealth",
"and",
"wellbeingMechanical",
"engineering",
"\n",
"and",
"heavy",
"machinery",
"\n",
"Nanotechnology",
"and",
"\n",
"materialsBiotechnologyElectric",
"and",
"electronic",
"\n",
"technologies",
"\n",
"Optics",
"and",
"photonicsICT",
"and",
"computer",
"\n",
"scienceAgrifood",
"\n",
"Environmental",
"sciences",
"\n",
"and",
"industriesEnergy",
"\n",
"Chemistry",
"and",
"chemical",
"\n",
"engineeringTransportation",
"\n",
"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",
"\n",
"and",
"excellence8",
".",
"Below",
",",
"these",
"S&T",
"indicators",
"are",
"\n",
"presented",
"in",
"a",
"summary",
"table",
"for",
"each",
"country",
".",
"\n",
"Highlighted",
"domains",
"are",
"qualitatively",
"reflected",
"on",
"\n",
"for",
"each",
"country",
".",
"\n",
"8",
"Further",
"detail",
"on",
"the",
"choice",
"and",
"definition",
"of",
"indicators",
"\n",
"can",
"be",
"found",
"in",
"Part",
"3",
".",
"\n",
"16",
"\n",
"Overview",
"of",
"economic",
",",
"innovation",
",",
"scientific",
"and",
"technological",
"specialisations",
"\n",
"Armenia",
"–",
"Summary",
"of",
"the",
"strengths",
"of",
"\n",
"the",
"S&T",
"specialisations",
"\n",
"Armenia",
"is",
"very",
"specialised",
"in",
"the",
"hard",
"sciences",
".",
"\n",
"Beyond",
"those",
",",
"it",
"presents",
"a",
"rather",
"diversified",
"S&T",
"\n",
"panorama",
";",
"its",
"most",
"highlighted",
"S&T",
"domains",
"are",
"\n",
"the",
"following",
":",
"\n ",
"■",
"Fundamental",
"physics",
"and",
"mathematics",
"\n",
"presents",
"a",
"notable",
"critical",
"mass",
",",
"specialisation",
"\n",
"and",
"excellence",
",",
"both",
"in",
"scientific",
"publications",
"\n",
"and",
"patents",
".",
"The",
"presence",
"of",
"the",
"A.",
"I.",
"Alikhani-",
"\n",
"an",
"Yerevan",
"Institute",
"of",
"Physics",
",",
"which",
"houses",
"\n",
"a",
"particle",
"accelerator",
"amongst",
"other",
"advanced",
"\n",
"experimental",
"equipment",
",",
"leaves",
"a",
"strong",
"im-",
"\n",
"print",
"on",
"the",
"Armenian",
"science",
"and",
"technology",
"\n",
"ecosystem",
",",
"and",
"structures",
"an",
"important",
"share",
"\n",
"of",
"the",
"country",
"’s",
"international",
"collaborations",
";",
"\n ",
"■",
"Agrifood",
"presents",
"a",
"notable",
"specialisation",
"in",
"\n",
"publications",
"and",
"patents",
",",
"critical",
"mass",
"in",
"pat-",
"\n",
"ents",
"and",
"an",
"above",
"-",
"average",
"scientific",
"impact",
".",
"\n ",
"ARMENIA",
"Critical",
"mass",
"Specialisation",
"Excellence",
"Summary",
"\n",
"S&T",
"domain",
"Pubs",
".",
"Pat",
".",
"Pubs",
".",
"Pat",
".",
"NCI*EC",
"\n",
"projects*Total",
"\n",
"Agrifood",
"4",
"\n",
"Biotechnology",
"1",
"\n",
"Chemistry",
"and",
"chemical",
"\n",
"engineering0",
"\n",
"Electric",
"and",
"electronic",
"\n",
"technologies2",
"\n",
"Energy",
"1",
"\n",
"Environmental",
"sciences",
"and",
"\n",
"industries1",
"\n",
"Fundamental",
"physics",
"and",
"\n",
"mathematics5",
"\n",
"Governance",
",",
"culture",
",",
"education",
"\n",
"and",
"the",
"economy3",
"\n",
"Health",
"and",
"wellbeing",
"3",
"\n",
"ICT",
"and",
"computer",
"science",
"1",
"\n",
"Mechanical",
"engineering",
"and",
"\n",
"heavy",
"machinery2",
"\n",
"Nanotechnology",
"and",
"materials",
"3",
"\n",
"Optics",
"and",
"photonics",
"2",
"\n",
"*",
"NCI",
"=",
"Normalised",
"citation",
"impact",
"*",
"EC",
"projects",
"=",
"EU",
"-",
"funded",
"R&I",
"projectsTable"
] | [] |
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, | [
"elevators",
";",
"parts",
"for",
"such",
"pumps",
"and",
"liquid",
"\n",
"elevatorsX",
"0.4",
"%",
" \n",
"745Non",
"-",
"electrical",
"machinery",
",",
"tools",
"and",
"mechanical",
"apparatus",
"\n",
"and",
"parts",
"thereof",
",",
"n.e.s",
".",
"X",
"0.1",
"%",
" \n",
"771Electric",
"power",
"machinery",
"(",
"other",
"than",
"rotating",
"electric",
"plant",
"\n",
"of",
"group",
"716",
")",
"and",
"parts",
"thereof",
" ",
"X",
"0.4",
"%",
"\n",
"772Electrical",
"apparatus",
"for",
"switching",
"or",
"protecting",
"electrical",
"\n",
"circuits",
"or",
"for",
"making",
"connections",
"to",
"or",
"in",
"electrical",
"circuits",
"\n",
"(",
"e.g.",
",",
"switches",
",",
"relays",
",",
"fuses",
",",
"lightning",
"arresters",
",",
"voltage",
"\n",
"limiters",
",",
"surge",
"suppressors",
",",
"plugs",
"and",
"sockets",
",",
"lamp-",
"\n",
"holders",
"and",
"junction",
"boxes",
")",
";",
"electrical",
"resistors",
"(",
"including",
"\n",
"rheostats",
"and",
"potentiometers",
")",
",",
"other",
"than",
"heating",
"resistors",
";",
"\n",
"printed",
"circuits",
";",
"boards",
",",
"panels",
"(",
"including",
"numerical",
"\n",
"control",
"panels",
")",
",",
"consoles",
",",
"desks",
",",
"cabinets",
"and",
"other",
"bases",
",",
"\n",
"equipped",
"with",
"two",
"or",
"more",
"apparatus",
"for",
"switching",
",",
"\n",
"protecting",
"or",
"for",
"making",
"connections",
"to",
"or",
"in",
"electrical",
"\n",
"circuits",
",",
"for",
"electric",
"control",
"or",
"the",
"distribution",
"of",
"electricity",
"\n",
"(",
"excluding",
"switching",
"apparatus",
"of",
"subgroup",
"764.1)X",
"0.6",
"%",
"X",
"0.6",
"%",
"\n",
"773",
"Equipment",
"for",
"distributing",
"electricity",
",",
"n.e.s",
".",
"X",
"12.1",
"%",
" \n",
"781Motor",
"cars",
"and",
"other",
"motor",
"vehicles",
"principally",
"designed",
"\n",
"for",
"the",
"transport",
"of",
"persons",
"(",
"other",
"than",
"motor",
"vehicles",
"for",
"\n",
"the",
"transport",
"of",
"ten",
"or",
"more",
"persons",
",",
"including",
"the",
"driver",
")",
",",
"\n",
"including",
"station",
"-",
"wagons",
"and",
"racing",
"cars",
" ",
"X",
"0.4",
"%",
"\n",
"792Aircraft",
"and",
"associated",
"equipment",
";",
"spacecraft",
"(",
"including",
"\n",
"satellites",
")",
"and",
"spacecraft",
"launch",
"vehicles",
";",
"parts",
"thereof",
" ",
"X",
"0.2",
"%",
"\n",
"8",
"Miscellaneous",
"manufactured",
"articles",
" \n",
"821Furniture",
"and",
"parts",
"thereof",
";",
"bedding",
",",
"mattresses",
",",
"mattress",
"\n",
"supports",
",",
"cushions",
"and",
"similar",
"stuffed",
"furnishingsX",
"5.1",
"%",
"X",
"5.1",
"%",
"\n",
"831Trunks",
",",
"suitcases",
",",
"vanity",
"cases",
",",
"executive",
"cases",
",",
"briefcases",
",",
"\n",
"school",
"satchels",
",",
"spectacle",
"cases",
",",
"binocular",
"cases",
",",
"camera",
"\n",
"cases",
",",
"musical",
"instrument",
"cases",
",",
"gun",
"cases",
",",
"holsters",
"\n",
"and",
"similar",
"containers",
";",
"travelling",
"bags",
",",
"insulated",
"food",
"or",
"\n",
"beverages",
"bags",
",",
"toilet",
"bags",
",",
"rucksacks",
",",
"handbags",
",",
"shopping",
"\n",
"bags",
",",
"wallets",
",",
"purses",
",",
"map",
"cases",
",",
"cigarette",
"cases",
",",
"tobacco",
"\n",
"pouches",
",",
"tool",
"bags",
",",
"sports",
"bags",
",",
"bottle",
"cases",
",",
"jewellery",
"\n",
"boxes",
",",
"powder",
"boxes",
",",
"cutlery",
"cases",
"and",
"similar",
"containers",
",",
"\n",
"of",
"leather",
"or",
"of",
"composition",
"leather",
",",
"of",
"sheeting",
"of",
"plastics",
",",
"\n",
"of",
"textile",
"materials",
",",
"of",
"vulcanized",
"fibre",
"or",
"of",
"paperboard",
",",
"or",
"\n",
"wholly",
"or",
"mainly",
"covered",
"with",
"such",
"materials",
"or",
"with",
"paper",
";",
"\n",
"travel",
"sets",
"for",
"personal",
"toilet",
",",
"sewing",
"or",
"shoe",
"or",
"clothes",
"\n",
"cleaningX",
"0.7",
"%",
" \n",
"841Men",
"’s",
"or",
"boys",
"’",
"coats",
",",
"capes",
",",
"jackets",
",",
"suits",
",",
"blazers",
",",
"trousers",
",",
"\n",
"shorts",
",",
"shirts",
",",
"underwear",
",",
"nightwear",
"and",
"similar",
"articles",
"of",
"\n",
"textile",
"fabrics",
",",
"not",
"knitted",
"or",
"crocheted",
"(",
"other",
"than",
"those",
"of",
"\n",
"subgroup",
"845.2)X",
"2.4",
"%",
" \n",
"74",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"SITC",
"Goods",
"nameCurrent",
"\n",
"strength%",
"share",
"\n",
"of",
"\n",
"exportsEmerging",
"\n",
"strength%",
"share",
"\n",
"of",
"\n",
"exports",
"\n",
"41",
"78.7",
"%",
"23",
"33.8",
"%",
"\n",
"842Women",
"’s",
"or",
"girls",
"’",
"coats",
",",
"capes",
",",
"jackets",
",",
"suits",
",",
"trousers",
",",
"\n",
"shorts",
",",
"shirts",
",",
"dresses",
"and",
"skirts",
",",
"underwear",
",",
"nightwear",
"and",
"\n",
"similar",
"articles",
"of",
"textile",
"fabrics",
",",
"not",
"knitted",
"or",
"crocheted",
"\n",
"(",
"other",
"than",
"those",
"of",
"subgroup",
"845.2)X",
"3.5",
"%",
" \n",
"843Men",
"’s",
"or",
"boys",
"’",
"coats",
","
] | [] |
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;
| [
"appliances",
"\n",
"[",
"27.5]A21B",
";",
"B01B",
";",
"F24D",
";",
"A45D",
";",
"D06F",
";",
"F25C",
";",
"\n",
"A47",
"G",
";",
"E06C",
";",
"F25D",
";",
"A47J",
";",
"F24B",
";",
"H05B",
";",
"\n",
"A47L",
";",
"F24C",
"\n",
"27.9Manufacture",
"of",
"other",
"electrical",
"\n",
"equipment",
"[",
"27.9]B60",
"M",
";",
"H01",
"T",
";",
"B61L",
";",
"H02H",
";",
"G08",
"G",
";",
"H02",
"M",
";",
"\n",
"G10",
"K",
";",
"H05C",
";",
"H01P",
"\n",
"28.1Manufacture",
"of",
"general",
"-",
"purpose",
"\n",
"machinery",
"[",
"28.1]A47",
"K",
";",
"F01",
"K",
";",
"B23F",
";",
"F01",
"M",
";",
"F01B",
";",
"F01N",
";",
"\n",
"F01C",
";",
"F01P",
";",
"F01D",
";",
"F02C",
";",
"F02",
"G",
";",
"F02",
"K",
";",
"\n",
"F03B",
";",
"F03C",
";",
"F03D",
";",
"F03",
"G",
";",
"F04B",
";",
"F04C",
";",
"\n",
"F04D",
";",
"F15B",
";",
"F16C",
";",
"F16D",
";",
"F16F",
";",
"F16H",
";",
"\n",
"F16",
"K",
";",
"F16",
"M",
";",
"F23R",
";",
"G05D",
";",
"G05",
"G",
"\n",
"28.2Manufacture",
"of",
"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 | [
"related",
"activities",
"\n",
"Belarus10.5",
"Manufacture",
"of",
"dairy",
"products",
"\n",
"13",
" ",
"Manufacture",
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"\n",
"16",
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"\n",
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"\n",
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"\n",
"22",
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"\n",
"23",
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"\n",
"23.3",
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"\n",
"23.5",
"Manufacture",
"of",
"cement",
",",
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"\n",
"25.4",
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"\n",
"26.4",
"Manufacture",
"of",
"consumer",
"electronics",
"\n",
"26.7",
"Manufacture",
"of",
"optical",
"instruments",
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"photographic",
"equipment",
"\n",
"27.2",
"Manufacture",
"of",
"batteries",
"and",
"accumulator",
"\n",
"27.9",
"Manufacture",
"of",
"other",
"electrical",
"equipment",
"\n",
"29.1",
"Manufacture",
"of",
"motor",
"vehicles",
"\n",
"29.3",
"Manufacture",
"of",
"parts",
"and",
"accessories",
"for",
"motor",
"vehicles",
"\n",
"30",
" ",
"Manufacture",
"of",
"other",
"transport",
"equipment",
"\n",
"32",
" ",
"Other",
"manufacturing",
"\n",
"32.9",
"Manufacturing",
"n.e.c",
".",
"Table",
"2.31",
".",
"Industries",
"with",
"innovation",
"potential",
"based",
"on",
"2011",
"-",
"2018",
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"\n",
"Smart",
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"-",
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"-",
"based",
"economic",
"cooperation93",
"\n",
"Georgia12",
" ",
"Manufacture",
"of",
"tobacco",
"products",
"\n",
"15",
" ",
"Manufacture",
"of",
"leather",
"and",
"related",
"products",
"\n",
"23",
" ",
"Manufacture",
"of",
"other",
"non",
"-",
"metallic",
"mineral",
"products",
"\n",
"23.1",
"Manufacture",
"of",
"glass",
"and",
"glass",
"products",
"\n",
"23.4",
"Manufacture",
"of",
"other",
"porcelain",
"and",
"ceramic",
"products",
"\n",
"24",
" ",
"Manufacture",
"of",
"basic",
"metals",
"\n",
"25.5",
"Forging",
",",
"pressing",
",",
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"metal",
"\n",
"25.6",
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"coating",
"of",
"metals",
";",
"machining",
"\n",
"25.7",
"Manufacture",
"of",
"cutlery",
",",
"tools",
"and",
"general",
"hardware",
"\n",
"27.5",
"Manufacture",
"of",
"domestic",
"appliances",
"\n",
"29.1",
"Manufacture",
"of",
"motor",
"vehicles",
"\n",
"31",
" ",
"Manufacture",
"of",
"furniture",
"\n",
"Moldova16",
" ",
"Manufacture",
"of",
"wood",
"and",
"of",
"products",
"of",
"wood",
"and",
"cork",
"\n",
"18.1",
"Printing",
"and",
"service",
"activities",
"related",
"to",
"printing",
"\n",
"20.2",
"Manufacture",
"of",
"pesticides",
"and",
"other",
"agrochemical",
"products",
"\n",
"23.1",
"Manufacture",
"of",
"glass",
"and",
"glass",
"products",
"\n",
"25.2",
"Manufacture",
"of",
"tanks",
"\n",
"25.6",
"Treatment",
"and",
"coating",
"of",
"metals",
";",
"machining",
"\n",
"28.3",
"Manufacture",
"of",
"agricultural",
"and",
"forestry",
"machinery",
"\n",
"28.4",
"Manufacture",
"of",
"metal",
"forming",
"machinery",
"and",
"machine",
"tools",
"\n",
"Ukraine14",
" ",
"Manufacture",
"of",
"wearing",
"apparel",
"\n",
"24",
" ",
"Manufacture",
"of",
"basic",
"metals",
"\n",
"25.1",
"Manufacture",
"of",
"structural",
"metal",
"products",
"\n",
"25.3",
"Manufacture",
"of",
"steam",
"generators",
"\n",
"25.9",
"Manufacture",
"of",
"other",
"fabricated",
"metal",
"products",
"\n",
"26.4",
"Manufacture",
"of",
"consumer",
"electronics",
"\n",
"26.5",
"Manufacture",
"of",
"instruments",
"and",
"appliances",
"for",
"measuring",
"\n",
"27.3",
"Manufacture",
"of",
"wiring",
"and",
"wiring",
"devices",
"\n",
"27.9",
"Manufacture",
"of",
"other",
"electrical",
"equipment",
"\n",
"28.4",
"Manufacture",
"of",
"metal",
"forming",
"machinery",
"and",
"machine",
"tools",
"\n",
"32.5",
"Manufacture",
"of",
"medical",
"and",
"dental",
"instruments",
"and",
"supplies",
"\n",
"94",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n ",
"NACE",
" ",
"Industry",
"name",
"\n",
"Armenia",
"\n",
"Azerbaijan",
"\n",
"Belarus",
"\n",
"Georgia",
"\n",
"Moldova",
"\n",
"Ukraine",
"\n",
"10",
"Manufacture",
"of",
"food",
"products",
"3.209",
"0.243",
"0.336",
"0.936",
"0.705",
"0.570",
"\n",
"10.5",
"Manufacture",
"of",
"dairy",
"products",
"1.647",
"1.220",
"\n",
"11",
"Manufacture",
"of",
"beverages",
"3.987",
"0.120",
"0.277",
"0.726"
] | [] |
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 | [
"Other",
"social",
"work",
"\n",
"activities",
"without",
"accommodation",
"\n",
"NABS12General",
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"of",
"\n",
"knowledge",
":",
"R&D",
"financed",
"\n",
"from",
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"\n",
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"GUF)72",
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"72.1",
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"72.2",
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"72.2",
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",",
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"10",
".",
"NACE",
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",",
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",",
"\n",
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"&",
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"\n",
"NACE",
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"13",
"Manufacture",
"of",
"textiles",
"Surfaces",
",",
"Coatings",
"and",
"Films",
";",
"Materials",
"Chemistry",
"\n",
"14",
"Manufacture",
"of",
"wearing",
"apparel",
"Surfaces",
",",
"Coatings",
"and",
"Films",
";",
"Materials",
"Chemistry",
"\n",
"15",
"Manufacture",
"of",
"leather",
"and",
"related",
"products",
"Surfaces",
",",
"Coatings",
"and",
"Films",
";",
"Materials",
"Chemistry",
"\n",
"16Manufacture",
"of",
"wood",
"and",
"of",
"products",
"of",
"wood",
"and",
"\n",
"cork",
",",
"except",
"furniture",
";",
"manufacture",
"of",
"articles",
"of",
"\n",
"straw",
"and",
"plaiting",
"materialsMaterials",
"Chemistry",
"\n",
"18",
"Printing",
"and",
"reproduction",
"of",
"recorded",
"media",
"Surfaces",
",",
"Coatings",
"and",
"Films",
";",
"Materials",
"Chemistry",
"\n",
"19",
"Manufacture",
"of",
"coke",
"and",
"refined",
"petroleum",
"productsEnergy",
"Engineering",
"and",
"Power",
"Technology",
";",
"Fuel",
"\n",
"Technology",
";",
"General",
"Chemical",
"Engineering",
";",
"\n",
"Geochemistry",
"and",
"Petrology",
";",
"Surfaces",
",",
"Coatings",
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"\n",
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"\n",
"20",
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"of",
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"\n",
"23",
"Manufacture",
"of",
"other",
"non",
"-",
"metallic",
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"Technology",
";",
"General",
"Chemical",
"Engineering",
";",
"\n",
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",",
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"Films",
";",
"Materials",
"Chemistry",
"\n",
"25Manufacture",
"of",
"fabricated",
"metal",
"products",
",",
"except",
"\n",
"machinery"
] | [] |
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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] | [] |
8. Chickering, D.M., Heckerman, D., Meek, C.: A Bayesian approach to learning Bayesian
networks with local structure. In: Geiger, D., Shenoy, P.P. (eds.) Proceedings of 13thConference on Uncertainty in Arti ficial Intelligence, pp. 80 –89 (1997)
9. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
10. Chen, S.F., Goodman, J.T.: An empirical study of smoothing techniques for language
modeling. In: Proceedings of 34th Annual Meeting of the Association for Computational
Linguistics (ACL), Santa Cruz, pp. 310 –318 (1996)
11. Honore, A.: Some simple measures of richness of vocabulary. Assoc. Lit. Linguist. Comput.
Bull. 7(2), 172 –177 (1979)
12. Sichel, H.: On a distribution law for word frequencies. J. Am. Stat. Assoc. 70, 542 –547
(1975)
13. Lavergne, T., Urvoy, T., Yvon, F.: Detecting fake content with relative entropy scoring. In:
PAN 2008 (2008)
14. Seymore, K., Rosenfeld, R.: Scalable backoff language models. In: ICSLP 1996,
Philadelphia, PA, vol. 1, pp. 232 –235 (1996)
15. Stolcke, A.: Entropy-based pruning of backoff language models (1998)
16. Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing.
The MIT Press, Cambridge (1999)
17. Gyongyi, Z., Garcia-Molina, H.: Web spam taxonomy. In: 1st International Workshop on
Adversarial Information Retrieval on the Web (AIRWeb 2005) (2005)
18. Heymann, P., Koutrika, G., Garcia-Molina, H.: Fighting spam on social web sites: a survey
of approaches and future challenges. IEEE Mag. Internet Comput. 11(6), 36 –45 (2007)
19. Labb é, C., Labb é, D.: Duplicate and fake publications in the scienti fic literature: how many
SCIgen papers in computer science? Scientometrics, Akad émiai Kiad ó, p. 10 (2012)426 D. Beresneva | [
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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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"278",
"\n",
"Annexes",
"\n",
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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 }βand 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
-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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Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation321
Annex 4. Concordance
between IPC and NACE
NACE IPC
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"9",
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" \n",
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" \n",
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" \n",
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"-",
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"\n",
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"4",
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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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] | [
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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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"\n",
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".",
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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
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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
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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
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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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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-
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cally clustered and then manually merged to-
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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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"Sustainability",
"224.20",
"\n",
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"\n",
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"Sustainability",
"3.71Government",
"and",
"\n",
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"\n",
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"and",
"\n",
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"and",
"\n",
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"\n",
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"2.30",
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"1.73",
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"Privacy",
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"4.61",
"\n",
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"\n",
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"Apps",
"1.40",
"Energy",
"3.91",
"\n",
"Travel",
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"1.91",
"Software",
"1.37",
"Other",
"3.68",
"\n",
"Payments",
"1.90",
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"1.30",
"Information",
"Technology",
"3.11",
"\n",
"Financial",
"Services",
"1.78",
"Information",
"Technology",
"1.18",
"Software",
"2.46",
"\n",
"Music",
"and",
"Audio",
"1.73",
"Financial",
"Services",
"1.16",
"Travel",
"and",
"Tourism",
"1.85Table",
"2.47",
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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.
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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 .
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ergic neurons are rapidly recruited by reinforcement feedback. Cell 162, 1155–
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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,
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pression of faces is modulated by emotion. Proc. Natl. Acad. Sci. U S A 101,
9827–9832. https://doi.org/10.1073/pnas.0403559101 .
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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-
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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-
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Turnover
Employ-
ment &
turnover
Employ-
ment
Turnover
Employ-
ment &
turnover
NACE Industry name Current Current CurrentEmerg-
ingEmerg-
ingEmerg-
ingCurrent Current CurrentEmerg-
ingEmerg-
ingEmerg-
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ingEmerg-
ingEmerg-
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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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] | [] |
Subsets and Splits