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measure short-lived
isomeric states populated by longer isomers.
The isomers studied in this work were also used to cali-
brate the IC for the nuclear charge of the fission fragments.
Even though such a measurement is difficult due to the
low kinetic energy of the fragments and the mass resolutioninduced by the double kinetic energy method, the results are
promising.
Based on all these encouraging results, a new generation
of the VESPA setup is being built. This augmented setupconsists in a larger array of both LaBr
3(Ce) and CeBr 3γ-ray
detectors placed around a twin Frisch-grid ionization cham-
ber, similar to the one used in this work. Adding these newdetectors improves the overall efficiency of the setup. This
will lead to better statistics and/or better selectivity, e.g., by
means of triple coincidences in relevant cases.
Acknowledgements This work was carried out in the framework of
the SINET project funded by the CEA.
Data Availability Statement Data will be made available on reason-
able request. [Author’s comment: The datasets generated during and/oranalysed during the current study are available from the correspondingauthor on reasonable request.]
Code Availability Statement This manuscript has no associated
code/software. [Author’s comment: Code/Software sharing not applica-ble to this article as no code/software was generated or analysed duringthe current study.]
123Eur. Phys. J. A (2025) 61:5 Page 11 of 12 5
References
1. M. Travar, V . Piau, A. Göök et al., Experimental information on
mass- and TKE-dependence of the prompt fission γ-ray multiplic-
ity. Phys. Lett. B 817, 136293 (2021). https://doi.org/10.1016/j.
physletb.2021.136293
2. V . Piau, O. Litaize, A. Chebboubi et al., Neutron and gamma mul-
tiplicities calculated in the consistent framework of the Hauser-Feshbach Monte Carlo code FIFRELIN. Phys. Lett. B 837, 137648
(2023). https://doi.org/10.1016/j.physletb.2022.137648
3. K. Skarsvåg, Time distribution of γ-rays from spontaneous fission
of
252Cf at short times. Nucl. Phys. A 253(2), 274–288 (1975).
https://doi.org/10.1016/0375-9474(75)90482-0
4. A. Chebboubi, G. Kessedjian, O. Litaize et al., Kinetic energy
dependence of fission fragment isomeric ratios for spherical nuclei
132Sn. Phys. Lett. B 775, 190–195 (2017). https://doi.org/10.1016/
j.physletb.2017.10.067
5. D. Gjestvang, J.N. Wilson, A. Al-Adili et al., Examination of how
properties of a fissioning system impact isomeric yield ratios ofthe fragments. Phys. Rev. C 108, 064602 (2023). https://doi.org/
10.1103/PhysRevC.108.064602
6. O. Litaize, O. Serot, L. Berge, Fission modelling with FIFRELIN.
Eur. Phys. J. A 51(12), 177 (2015). https://doi.org/10.1140/epja/
i2015-15177-9
7. A. Göök, W. Geerts, F.-J. Hambsch et al., A position-sensitive twin
ionization chamber for fission fragment and prompt neutron cor-relation experiments. Nucl. Instr. Meth. A 830, 366–374 (2016).
https://doi.org/10.1016/j.nima.2016.06.002
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phase is observed at 10 h during which the voltage rises quickly followed by a
voltage plateau at 1.5 V with very little increase until 16 h (see Figure 3f).Batteries 2025 ,11, 30 6 of 20
Batteries 2025, 11, x FOR PEER REVIEW 6 of 21
charge voltage profile shows a start charging voltage of ~1.2 V rising for 8 h until a voltage
of ~1.4 V, then a change phase is observed at 10 h during which the voltage rises quickly
followed by a voltage plateau at 1.5 V with very little increase until 16 h (see Figure 3f).
Figure 2. AA Energizer 2.5 Ah NiMH battery charge at 0.1 C and discharge at 0.2 C for different
charging durations. AS = ascending charging duration; DS = descending charging duration with (a)
charge capacity, (b) discharge capacity, (c) columbic efficiency, and (d) energy efficiency.
Figure 2. AA Energizer 2.5 Ah NiMH battery charge at 0.1 C and discharge at 0.2 C for different
charging durations. AS = ascending charging duration; DS = descending charging duration with
(a) charge capacity, ( b) discharge capacity, ( c) columbic efficiency, and ( d) energy efficiency.
The selected portable NiMH batteries exhibit different internal resistances ranging
from 2 m Ωup to 130 m Ω(see Figure 4). Increasing the size of the batteries tends to correlate
with lower internal resistance. A “D” NiMH battery has a larger cell construction than
a “AAA” battery (diameter of “AAA” 10 mm and “D” 33 mm); thus, the D battery has
a greater electrode contact area with the electrolyte, reducing the internal resistance [ 33].
While the internal resistance is lower in bigger NiMH batteries (C and D designations),
the specific energy density of the NiMH batteries is larger in AA and AAA batteries (see
Figure 4). In all cases, there are differences between the rated capacity (Wh/kg declared
label) and the tested capacity (Wh/kg JRC test). In most cases, the capacity declared by the
manufacturer is larger than the capacity measured in this study. These differences could be
related to the manufacturer’s date of production, testing equipment, changes in the cell
during transportation and distribution, to name a few.Batteries 2025 ,11, 30 7 of 20
Batteries 2025, 11, x FOR PEER REVIEW 7 of 21
Figure 3. NiMH battery charge profile at 0.1 C and 16 h of different battery manufacturers of (a)
AAA, (b) AA, (c) | [
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" ",
"(",
"a",
")",
" \n",
"AAA",
",",
" ",
"(",
"b",
")",
" ",
"AA",
",",
" ",
"(",
"c",
")",
" "
] | [] |
specialisation is above 1.5 highlighted
in dark green and industries where the degree of
specialisation is above 1.25 but below 1.5 high-
lighted in light green. Based on the results, for
each EaP country we can identify those industries
which have an innovation potential based on the
relative share of product and/or process innova-
tors, as follows.
40 Industry names are those used in the Enterprise Survey.
NACE codes match the corresponding ISIC codes. Industry
names therefore do not necessarily match NACE industry
names.
86
Part 2 Analysis of economic and innovation potential
■Armenia
• Paper (NACE 17)
• Basic metals (NACE 24)
• Construction (NACE F)
• Transport (NACE H)
■Azerbaijan
• Recycling (NACE 33)
• Services of motor vehicles (NACE 45)
• Wholesale (NACE 46)
■Georgia
• Garments (NACE 14)
• Publishing, printing and recorded media
(NACE 18)
• Chemicals (NACE 20+21)
• Non-metallic mineral products (NACE 23)
• Basic metals (NACE 24)
• Fabricated metal products (NACE 25)
• Furniture (NACE 31)
• Retail (NACE 47)
• Hotels and restaurants (NACE I)
■Moldova
• Wood (NACE 16)
• Paper (NACE 17)
• Chemicals (NACE 20+21)
• Plastics & rubber (NACE 22)
• Precision instruments (NACE 26)
• Machinery and equipment (NACE 28)
• Information and communication (NACE J) ■Ukraine
• Textiles (NACE 13)
• Services of motor vehicles (NACE 45)
The fact that only two industries emerge as spe-
cialised for Ukraine is a direct result of the fact
that the weighted number of enterprises is much
higher in Ukraine than in the other countries, with
a more equal distribution of enterprises across the
different industries.Armenia Azerbaijan Belarus Georgia Moldova Ukraine EaP
All industries 4 317 2 475 27 903 5 748 6 528 56 574 103 545Table 2.27. Weighted number of enterprises covered in the Enterprise Survey
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation87 88
Part 2 Analysis of economic and innovation potential
Share of product innovators Share of process innovators Share of product and/or process innovators
Name of industry Armenia Azerbaijan Belarus Georgia Moldova Ukraine EaP Armenia Azerbaijan Belarus Georgia Moldova Ukraine EaP Armenia Azerbaijan Belarus Georgia Moldova Ukraine EaP
Food (10+11) 37.0 42.6 63.3 33.7 42.9 39.1 43.1 23.3 3.7 43.2 25.8 50.1 19.2 27.5 45.5 46.3 65.5 42.5 61.7 44.6 51.0
Tobacco (12) -- -- -- -- 0.0 -- -- -- -- -- -- 0.0 -- -- | [
"specialisation",
"is",
"above",
"1.5",
"highlighted",
"\n",
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"green",
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".",
"Based",
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"for",
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"we",
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"\n",
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"based",
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"40",
"Industry",
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"Survey",
".",
"\n",
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"do",
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"NACE",
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"\n",
"names",
".",
"\n",
"86",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n ",
"■",
"Armenia",
"\n",
"•",
"Paper",
"(",
"NACE",
"17",
")",
"\n",
"•",
"Basic",
"metals",
"(",
"NACE",
"24",
")",
"\n",
"•",
"Construction",
"(",
"NACE",
"F",
")",
"\n",
"•",
"Transport",
"(",
"NACE",
"H",
")",
"\n ",
"■",
"Azerbaijan",
"\n",
"•",
"Recycling",
"(",
"NACE",
"33",
")",
"\n",
"•",
"Services",
"of",
"motor",
"vehicles",
"(",
"NACE",
"45",
")",
"\n",
"•",
"Wholesale",
"(",
"NACE",
"46",
")",
"\n ",
"■",
"Georgia",
"\n",
"•",
"Garments",
"(",
"NACE",
"14",
")",
"\n",
"•",
"Publishing",
",",
"printing",
"and",
"recorded",
"media",
"\n",
"(",
"NACE",
"18",
")",
"\n",
"•",
"Chemicals",
"(",
"NACE",
"20",
"+",
"21",
")",
"\n",
"•",
"Non",
"-",
"metallic",
"mineral",
"products",
"(",
"NACE",
"23",
")",
"\n",
"•",
"Basic",
"metals",
"(",
"NACE",
"24",
")",
"\n",
"•",
"Fabricated",
"metal",
"products",
"(",
"NACE",
"25",
")",
"\n",
"•",
"Furniture",
"(",
"NACE",
"31",
")",
"\n",
"•",
"Retail",
"(",
"NACE",
"47",
")",
"\n",
"•",
"Hotels",
"and",
"restaurants",
"(",
"NACE",
"I",
")",
"\n ",
"■",
"Moldova",
"\n",
"•",
"Wood",
"(",
"NACE",
"16",
")",
"\n",
"•",
"Paper",
"(",
"NACE",
"17",
")",
"\n",
"•",
"Chemicals",
"(",
"NACE",
"20",
"+",
"21",
")",
"\n",
"•",
"Plastics",
"&",
"rubber",
"(",
"NACE",
"22",
")",
"\n",
"•",
"Precision",
"instruments",
"(",
"NACE",
"26",
")",
"\n",
"•",
"Machinery",
"and",
"equipment",
"(",
"NACE",
"28",
")",
"\n",
"•",
"Information",
"and",
"communication",
"(",
"NACE",
"J",
")",
"■",
"Ukraine",
"\n",
"•",
"Textiles",
"(",
"NACE",
"13",
")",
"\n",
"•",
"Services",
"of",
"motor",
"vehicles",
"(",
"NACE",
"45",
")",
"\n",
"The",
"fact",
"that",
"only",
"two",
"industries",
"emerge",
"as",
"spe-",
"\n",
"cialised",
"for",
"Ukraine",
"is",
"a",
"direct",
"result",
"of",
"the",
"fact",
"\n",
"that",
"the",
"weighted",
"number",
"of",
"enterprises",
"is",
"much",
"\n",
"higher",
"in",
"Ukraine",
"than",
"in",
"the",
"other",
"countries",
",",
"with",
"\n",
"a",
"more",
"equal",
"distribution",
"of",
"enterprises",
"across",
"the",
"\n",
"different",
"industries",
".",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"EaP",
"\n",
"All",
"industries",
"4",
"317",
"2",
"475",
"27",
"903",
"5",
"748",
"6",
"528",
"56",
"574",
"103",
"545Table",
"2.27",
".",
"Weighted",
"number",
"of",
"enterprises",
"covered",
"in",
"the",
"Enterprise",
"Survey",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation87",
"88",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"Share",
"of",
"product",
"innovators",
"Share",
"of",
"process",
"innovators",
"Share",
"of",
"product",
"and/or",
"process",
"innovators",
"\n",
"Name",
"of",
"industry",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"EaP",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"EaP",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"EaP",
"\n",
"Food",
"(",
"10",
"+",
"11",
")",
"37.0",
"42.6",
"63.3",
"33.7",
"42.9",
"39.1",
"43.1",
"23.3",
"3.7",
"43.2",
"25.8",
"50.1",
"19.2",
"27.5",
"45.5",
"46.3",
"65.5",
"42.5",
"61.7",
"44.6",
"51.0",
"\n",
"Tobacco",
"(",
"12",
")",
"--",
"--",
"--",
"--",
"0.0",
"--",
"--",
"--",
"--",
"--",
"--",
"0.0",
"--",
"--"
] | [] |
was involved in the design of the experiment, specifically in
selecting the countries and products included in the study, but had no
further role in data collection, analysis, decision to publish, or prepa -
ration of the manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
We are grateful to the editor for his invaluable guidance throughout
the review process, which was essential in refining this research. We also
thank the reviewers for their constructive feedback and insightful
comments, which helped to improve and clarify our work. Our deepest
appreciation goes to the respondents who participated in the choice
experiments. We also extend our thanks to the London School of Eco-
nomics and Open Evidence for their professional and efficient handling
of the fieldwork as well as to the steering committee members at the
Commission ’s Directorate-General for Internal Market, Industry, Entre -
preneurship and SMEs (DG GROW).
Appendix A
Table A1
WTP premium for generic foreign versions over generic domestic versions in the absence of the ‘made for’ claim.
Country Groups Product Version Mean]Std Dev. H1a,G H1c,G
Western Countries
Germany Yogurt HU 0.22*** 0.04 ✕ ✕
(Euro) Yogurt LI 0.16*** 0.04 ✕ ✕
Spaghetti sauce HU 0.02 0.03 ✕ ✕
Spaghetti sauce LI 0.21*** 0.03 ✓ ✓
Cookies LI 0.1*** 0.03 ✓ ✓
Spain Soft drink RO 0.34*** 0.03 ✓ ✓
(Euro) Soft drink SE 0.32*** 0.03 ✓
Fish fingers RO 1.56*** 0.11 ✓ ✓
Fish fingers SE 0.22* 0.09 ✓
Crisps RO 0.62*** 0.05 ✓ ✓
Crisps SE 0.46*** 0.05 ✓
Sweden Soft drink ES 1.03** 0.35 ✕
(Krona) Soft drink RO 2.49*** 0.35 ✓ ✓
Fish fingers ES 14.25*** 1.24 ✓
Fish fingers RO 13.39*** 1.12 ✓ ✓
Crisps ES 1.33 0.80 ✕
Crisps RO 3.96*** 0.78 ✓ ✓
Eastern Countries
Hungary Yogurt DE 28.62*** 8.02 ✓ ✕
(Forint) Yogurt LI 0.99 7.72 ✕
Spaghetti sauce DE 156.14*** 18.12 ✓ ✕
(continued on next page)D.M. Federica et al. Food Policy 131 (2025) 102803
10 Table A1 (continued )
Country Groups Product Version Mean]Std Dev. H1a,G H1c,G
Spaghetti sauce LI 269.32*** 18.56 ✓
| [
"was",
"involved",
"in",
"the",
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"of",
"the",
"experiment",
",",
"specifically",
"in",
"\n",
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"countries",
"and",
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"included",
"in",
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",",
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",",
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",",
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"-",
"\n",
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".",
"\n",
"Declaration",
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"Competing",
"Interest",
"\n",
"The",
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"declare",
"that",
"they",
"have",
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"known",
"competing",
"financial",
"\n",
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"that",
"could",
"have",
"appeared",
"to",
"influence",
"\n",
"the",
"work",
"reported",
"in",
"this",
"paper",
".",
"\n",
"Acknowledgments",
"\n",
"We",
"are",
"grateful",
"to",
"the",
"editor",
"for",
"his",
"invaluable",
"guidance",
"throughout",
"\n",
"the",
"review",
"process",
",",
"which",
"was",
"essential",
"in",
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".",
"We",
"also",
"\n",
"thank",
"the",
"reviewers",
"for",
"their",
"constructive",
"feedback",
"and",
"insightful",
"\n",
"comments",
",",
"which",
"helped",
"to",
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"and",
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".",
"Our",
"deepest",
"\n",
"appreciation",
"goes",
"to",
"the",
"respondents",
"who",
"participated",
"in",
"the",
"choice",
"\n",
"experiments",
".",
"We",
"also",
"extend",
"our",
"thanks",
"to",
"the",
"London",
"School",
"of",
"Eco-",
"\n",
"nomics",
"and",
"Open",
"Evidence",
"for",
"their",
"professional",
"and",
"efficient",
"handling",
"\n",
"of",
"the",
"fieldwork",
"as",
"well",
"as",
"to",
"the",
"steering",
"committee",
"members",
"at",
"the",
"\n",
"Commission",
"’s",
"Directorate",
"-",
"General",
"for",
"Internal",
"Market",
",",
"Industry",
",",
"Entre",
"-",
"\n",
"preneurship",
"and",
"SMEs",
"(",
"DG",
"GROW",
")",
".",
"\n",
"Appendix",
"A",
"\n",
"Table",
"A1",
"\n",
"WTP",
"premium",
"for",
"generic",
"foreign",
"versions",
"over",
"generic",
"domestic",
"versions",
"in",
"the",
"absence",
"of",
"the",
"‘",
"made",
"for",
"’",
"claim",
".",
"\n",
"Country",
"Groups",
"Product",
"Version",
"Mean]Std",
"Dev",
".",
"H1a",
",",
"G",
"H1c",
",",
"G",
"\n",
"Western",
"Countries",
"",
"",
"",
"",
"",
"\n",
"Germany",
"Yogurt",
"HU",
"0.22",
"*",
"*",
"*",
"0.04",
"✕",
"✕",
"\n",
"(",
"Euro",
")",
"Yogurt",
"LI",
"0.16",
"*",
"*",
"*",
"0.04",
"✕",
"✕",
"\n",
"",
"Spaghetti",
"sauce",
"HU",
"0.02",
"0.03",
"✕",
"✕",
"\n",
"",
"Spaghetti",
"sauce",
"LI",
"\u00000.21",
"*",
"*",
"*",
"0.03",
"✓",
"✓",
"\n",
"",
"Cookies",
"LI",
"\u00000.1",
"*",
"*",
"*",
"0.03",
"✓",
"✓",
"\n",
"Spain",
"Soft",
"drink",
"RO",
"\u00000.34",
"*",
"*",
"*",
"0.03",
"✓",
"✓",
"\n",
"(",
"Euro",
")",
"Soft",
"drink",
"SE",
"\u00000.32",
"*",
"*",
"*",
"0.03",
"✓",
"\u0000",
"\n",
"",
"Fish",
"fingers",
"RO",
"\u00001.56",
"*",
"*",
"*",
"0.11",
"✓",
"✓",
"\n",
"",
"Fish",
"fingers",
"SE",
"\u00000.22",
"*",
"0.09",
"✓",
"\u0000",
"\n",
"",
"Crisps",
"RO",
"\u00000.62",
"*",
"*",
"*",
"0.05",
"✓",
"✓",
"\n",
"",
"Crisps",
"SE",
"\u00000.46",
"*",
"*",
"*",
"0.05",
"✓",
"\u0000",
"\n",
"Sweden",
"Soft",
"drink",
"ES",
"1.03",
"*",
"*",
"0.35",
"✕",
"\u0000",
"\n",
"(",
"Krona",
")",
"Soft",
"drink",
"RO",
"\u00002.49",
"*",
"*",
"*",
"0.35",
"✓",
"✓",
"\n",
"",
"Fish",
"fingers",
"ES",
"\u000014.25",
"*",
"*",
"*",
"1.24",
"✓",
"\u0000",
"\n",
"",
"Fish",
"fingers",
"RO",
"\u000013.39",
"*",
"*",
"*",
"1.12",
"✓",
"✓",
"\n",
"",
"Crisps",
"ES",
"1.33",
"0.80",
"✕",
"\u0000",
"\n",
"",
"Crisps",
"RO",
"\u00003.96",
"*",
"*",
"*",
"0.78",
"✓",
"✓",
"\n",
"Eastern",
"Countries",
"",
"",
"",
"",
"",
"\n",
"Hungary",
"Yogurt",
"DE",
"\u000028.62",
"*",
"*",
"*",
"8.02",
"✓",
"✕",
"\n",
"(",
"Forint",
")",
"Yogurt",
"LI",
"0.99",
"7.72",
"✕",
"\u0000",
"\n",
"",
"Spaghetti",
"sauce",
"DE",
"\u0000156.14",
"*",
"*",
"*",
"18.12",
"✓",
"✕",
"\n",
"(",
"continued",
"on",
"next",
"page)D.M.",
"Federica",
"et",
"al",
".",
" ",
"Food",
"Policy",
" ",
"131",
"(",
"2025",
")",
" ",
"102803",
" \n",
"10",
"Table",
"A1",
"(",
"continued",
")",
"\n",
"Country",
"Groups",
"Product",
"Version",
"Mean]Std",
"Dev",
".",
"H1a",
",",
"G",
"H1c",
",",
"G",
"\n",
"",
"Spaghetti",
"sauce",
"LI",
"\u0000269.32",
"*",
"*",
"*",
"18.56",
"✓",
"\u0000",
"\n",
""
] | [] |
ores X
7.2 Mining of non-ferrous metal ores X
C MANUFACTURING
10.4 Manufacture of vegetable and animal oils and fats X
10.9 Manufacture of prepared animal feeds X
16.1 Sawmilling and planing of wood X
19.1 Manufacture of coke oven products X
19.2 Manufacture of refined petroleum products X
20.1Manufacture of basic chemicals, fertilisers and nitrogen compounds, plastics
and synthetic rubber in primary formsX
20.4Manufacture of soap and detergents, cleaning and polishing preparations,
perfumes and toilet preparations X
23.5 Manufacture of cement, lime and plaster X
23.6 Manufacture of articles of concrete, cement and plaster XTable 2.5. Economic mapping results for Ukraine
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation47
NACE Industry nameCurrent
strengthEmerging
strength
23.9 Manufacture of abrasive products and non-metallic mineral products n.e.c. X
24.1 Manufacture of basic iron and steel and of ferro-alloys X
24.2 Manufacture of tubes, pipes, hollow profiles and related fittings, of steel X
24.3 Manufacture of other products of first processing of steel X
24.4 Manufacture of basic precious and other non-ferrous metals X
25.1 Manufacture of structural metal products X
25.6 Treatment and coating of metals; machining X X
25.9 Manufacture of other fabricated metal products X
27.1Manufacture of electric motors, generators, transformers and electricity
distribution and control apparatusX
28.1 Manufacture of general-purpose machinery X
28.3 Manufacture of agricultural and forestry machinery X X
28.9 Manufacture of other special-purpose machinery X
29.1 Manufacture of motor vehicles X
29.3 Manufacture of parts and accessories for motor vehicles X
30.2 Manufacture of railway locomotives and rolling stock X
30.3 Manufacture of air and spacecraft and related machinery X
33.1 Repair of fabricated metal products, machinery and equipment X
D ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY
35.1 Electric power generation, transmission and distribution X
35.3 Steam and air conditioning supply X X
EWATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION
ACTIVITIES
F CONSTRUCTION
41.1 Development of building projects X
41.2 Construction of residential and non-residential buildings X
42.1 Construction of roads and railways X
42.2 Construction of utility projects X
43.2 Electrical, plumbing and other construction installation activities X
GWHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND
MOTORCYCLES
45.3 Sale of motor vehicle parts and accessories X
46.1 Wholesale on a fee or contract basis X
46.2 Wholesale of agricultural raw materials and live animals X
46.3 Wholesale of food, beverages and tobacco X
46.6 Wholesale of other machinery, | [
"ores",
"X",
" \n",
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"Mining",
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"-",
"ferrous",
"metal",
"ores",
"X",
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associative learning.
Interestingly, subsets of pIC principal neurons are known torespond also to acute sensory nociceptive stimuli (tail shocks)
and their silencing impaired the emergence of sustained states
of anxiety without altering acute responses ( Gehrlach et al.,
2019 ), consistent with our findings in aIC VIP+ INs.
Cortical VIP INs were shown to play an important role in
shaping responses to sensory inputs. For example, the activity
Figure 7. Parallel coding of aversive and social stimuli in aIC VIP+ INs
(A) Example images of GCamP6s-dependent signals in aIC VIP+ INs within the FOV during the social preference test on day 1 (top left) and day 2 (top right) , during
fear conditioning (bottom left) and fear retrieval (bottom right).
(B) Percentage of mouse and object CNs on day 1 (left) and day 2 (right) that were also responding to the US, CS, both, or were unresponsive during fearconditioning.(C) Percentage of mouse and object CNs on day 1 (left) and day 2 (right) that were also responding to the US-, CS-R, both, or were unresponsive during fearretrieval.
12Cell Reports 39, 110893, May 31, 2022Articlell
OPEN ACCESSof VIP+ INs enhances the gain of visual and auditory responses
(Fu et al., 2014 ;Letzkus et al., 2011 ;Pi et al., 2013 ;Cone et al.,
2019 ;Keller et al., 2020 ), and in the visual cortex it was driven
by the presentation of novel images or stimuli with high contrast,whereas it was suppressed by familiar images ( Garrett et al.,
2020 ). These findings are consistent with our own data and sug-
gest a broad involvement of these INs in gain modulation to facil-itate the encoding of salient stimuli. Furthermore, VIP+ INs in theauditory and prefrontal cortex were shown to respond to rein-
forcement signals, including reward and punishment ( Letzkus
et al., 2011 ;Pi et al., 2013 ). Only a few studies, however, have
causally linked the function of VIP+ INs to distinct behavioral re-
sponses ( Lee et al., 2019 ;Krabbe et al., 2019 ). In these studies,
functional inhibition of VIP+ INs during aversive state processingled to decreased expression of anxiety or fearful states. This led
us to hypothesize that the activity of VIP+ INs has an impact on
the strength of learning associations and behavioral outcomes.In our work using closed-loop optogenetic experiments, wecould demonstrate that inhibition of aIC VIP+ INs resulted not
only in reduced fear memory retrieval, but also | [
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of these molecules is called the metabolome. Overall, these studies give a good view of the structure and function of simple metabolic pathways, but are inadequate when applied to more complex systems such as the metabolism of a complete cell.[132]
An idea of the complexity of the metabolic networks in cells that contain thousands of different enzymes is given by the figure showing the interactions between just 43 proteins and 40 metabolites to the right: the sequences of genomes provide lists containing anything up to 26.500 genes.[133] However, it is now possible to use this genomic data to reconstruct complete networks of biochemical reactions and produce more holistic mathematical models that may explain and predict their behavior.[134] These models are especially powerful when used to integrate the pathway and metabolite data obtained through classical methods with data on gene expression from proteomic and DNA microarray studies.[135] Using these techniques, a model of human metabolism has now been produced, which will guide future drug discovery and biochemical research.[136] These models are now used in network analysis, to classify human diseases into groups that share common proteins or metabolites.[137][138]
Bacterial metabolic networks are a striking example of bow-tie[139][140][141] organization, an architecture able to input a wide range of nutrients and produce a large variety of products and complex macromolecules using a relatively few intermediate common currencies.[142]
A major technological application of this information is metabolic engineering. Here, organisms such as yeast, plants or bacteria are genetically modified to make them more useful in biotechnology and aid the production of drugs such as antibiotics or industrial chemicals such as 1,3-propanediol and shikimic acid.[143][144][145] These genetic modifications usually aim to reduce the amount of energy used to produce the product, increase yields and reduce the production of wastes.[146]
History
Further information: History of biochemistry and History of molecular biology
The term metabolism is derived from the Ancient Greek word μεταβολή—"metabole" for "a change" which is derived from μεταβάλλειν—"metaballein", meaning "to change"[147]
Aristotle's metabolism as an open flow model
Greek philosophy
Aristotle's The Parts of Animals sets out enough details of his views on metabolism for an open flow model to be made. He believed that at each stage of the process, materials from food were transformed, with heat being released as the classical element of fire, and residual materials being excreted as urine, bile, or faeces.[148]
Ibn al-Nafis described metabolism in his 1260 AD | [
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] | [] |
the industry group
with the highest number of companies but ranks
significantly lower in terms of number of employ-
ees and estimated revenue than other countries.
Science & Engineering and Manufacturing, while
being the industry groups with the highest num-
ber of employees and highest estimated revenue
(respectively) are low-ranking in the other two
variables. Transportation, Energy and Natural Re-
sources have a high number of employees and es-
timated revenue, but account for few companies.
Financial Services ranks consistently high across
all three variables. Other industry groups relevant
in more than one variable are Lending & Invest-
ments, Hardware and Mobile. Information Technol-
ogy, while third in terms of number of companies,
is not as relevant in the other two variables as in
other countries.
104
Part 2 Analysis of economic and innovation potential
Armenia
# firms CM Firms # employees CM Employees # est. revenue CM Revenue
Software 63 Software 8 425 Hardware $753 m
Internet Services 28 Information Technology 7 085 Software $135 m
Mobile 28 Internet Services 4 375 Information Technology $129 m
Information Technology 27 Gaming 3 060 Financial Services $105 m
Apps 21 Sports 3 005Lending and
Investments$105 m
Hardware 18 Mobile 1 565 Gaming $92 m
Media and
Entertainment18 Sales and Marketing 1 525Commerce and
Shopping$84 m
Sales and Marketing 17 Apps 1 490 Travel and Tourism $78 m
Science and
Engineering15 Financial Services 1 405 Transportation $76 m
Other 15 Travel and Tourism 1 360 Administrative Services $76 mTable 2.39. Largest industry groups – Armenia
For Armenia, the table shows the raw number of companies, number of employees and estimated revenue featured in the
Crunchbase database by Industry Group.
Azerbaijan
# firms CM Firms # employees CM Employees # est. revenue CM Revenue
Software 35Science and
Engineering7 710 Manufacturing $5 530 m
Internet Services 32 Transportation 7 580 Transportation $5 506 m
Information Technology 22 Energy 3 085 Natural Resources $5 500 m
Financial Services 17 Natural Resources 3 080 Energy $5 500 m
Commerce and
Shopping15 Financial Services 2 825 Financial Services $401 m
Mobile 14Lending and
Investments1 900Lending and
Investments$330 m
Hardware 13 Hardware 1 820 Payments $60 m
Media and
Entertainment11 Professional Services 1 010 Software $52 m
Other 10 Mobile 880 Hardware $48 m
Lending and
Investments9 Other 875 Mobile $48 mTable 2.40. Largest industry groups – Azerbaijan
For Azerbaijan, the table shows the raw number of companies, | [
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"Part",
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in indifference between domestic
and foreign products in only 12 % more cases (4 out of 33). Only in
Lithuania and Romania preference for domestic products increased
when brand name was shown. This is in line with findings that in both
countries, as in most other Eastern European Member States, around 60
% of consumers consider branding an important factor when purchasing
food, as compared to an average of only 43 % in Western European
countries (European Commission, 2012 ), suggesting that the role of
brands is more important in Eastern European countries.
While we anticipated that the importance of brands in food product
choices (Vranesevic & Stancec, 2003) might veiling DQ differences (H3 a
and H3b), our findings provide no systematic support for the argument
that the presence of brands would influence consumers ’ responses to
DFQ practices as compared to generic products. This result is consistent
with the findings of Torelli et al. (2017) and Puzakoa and Aggarwal
(2018), who suggest that consumers may prioritize local or culturally
relevant attributes (such as the ’made for’ claim) rather than brand
identity, leading branding to have little effect on their evaluation of DFQ
differences.
6.Concluding remarks
The dual food quality (DFQ) issue has emerged as a case of potential
discrimination against Eastern European consumers, and still fuels an
intense political debate (Council of the European Union, 2024 ). The
conflicting views of the food industry, which justifies DFQ practices as
adjustments to consumer preferences, and consumer advocacy groups,
which allege unfair and discriminatory behaviour by companies to
maximise profit, have led to calls for informing consumers about the
existence of different versions across countries to ensure they can make a
conscious choice (Nes et al., 2024 , Z˘avadský and Hiadlovský, 2020;
Bartkov ˘a and Veselovsk ˘a, 2024 ). Against this background of diverging
stakeholder views, our study sheds light on the issue in three different
ways. First, it investigates whether consumer choices are in line with the
food industry providing different product versions to meet consumer
preferences. Second, it studies how consumers ’ preferences and pur-
chase decisions are affected by information disclosure about DFQ
practices using a ‘made for’ claim on packaging. Third, it assesses the
role of branding in veiling DFQ.
Our findings have significant implications, particularly regarding the
transparency and marketing of food products across different markets.
The lack of a clear preference for domestic versus foreign | [
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±SEM.
(G) Representative image of retrogradely labeled neurons presynaptic to aIC VIP+ INs in the MD. Scale bar, 500 mm.
(H) Representative image of retrogradely labeled neurons presynaptic to aIC VIP+ INs in the SS and IC. Scale bar, 500 mm.
(I) Representative image of ChR2-mCherry immunolabeled axons in the mPFC and aIC originating from the MD. Scale bar, 1 mm.(J) Representative high-magnification image of ChR2-mCherry-labeled axon terminals (green) and VIP immunoreactivity (magenta) in the aIC of a mous e injected
with ChR2-mCherry in the MD.Arrows indicate appositions of ChR2-mCherry-labeled boutons onto the soma and dendrites of VIP+ INs. Scale bar, 10 mm. Data are shown as mean + SEM. For
anatomical abbreviations, see STAR Methods .
Cell Reports 39, 110893, May 31, 2022 3Articlell
OPEN ACCESS(Figures 3 G–3I), although the response dynamic showed a slow
increase that reached the maximal steady-state activity 3–4 s af-
ter the test animal was in close proximity to the wire cage con-taining the novel conspecific. Moreover, a larger proportion of
aIC VIP+ INs were active during the interaction with the unfamil-
iar mouse in comparison with the object (42% and 13.6%,
Figure 2. Aversive stimuli activate aIC VIP+ INs
(A) Diagram of the 5-day-long behavioral paradigm, including two consecutive social preference tests, auditory test, fear conditioning, and retri eval tests.
(B) Schematic of the experimental approach used for freely moving Ca2+imaging of aIC VIP+ INs. Example of field-of-view (FOV) through the implanted GRIN lens
with representative overlaid cell contours (in green). Scale bar, 100 mm.
(C) Representative confocal images showing the Cre-dependent GCaMP6 expression in VIP+ aIC INs (left panel) and VIP immunostaining (right panel). S cale bar,
20mm.
(D) Ca2+traces during the fear-conditioning session recorded from five VIP+ INs. Arrowheads indicate the onset of CS and US stimuli. Colors indicate stimulus
presentation: yellow, CS; red, US; purple, ITI. Scale bars, horizontal 15 s, vertical 5 Zscore.
(E) Activity heatmap from all individual recorded aIC VIP+ INs (n = 85 cells from N = 7 mice), sorted by time of peak activity during US presentations, ave raged
across all five trials, during US (left panel) and CS presentations (right panel). Heatmap scale bar represents Zscore values.
(F) US and CS responses averaged from all recorded cells across all five trials.(G) Mean area under the curve (AUC) of Zscored activity responses was significantly higher during US presentations compared with CS presentations (Wilcoxon
signed rank, | [
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] | [] |
also clearly showed an additional
short-lived isomeric state with a half-life of (1 .6±0.1) ns
atE
∗=328 keV, that was never reported before. However,
this new isomeric state cannot explain the inconsistency we
observed for the other half-lives. These results indicate that
further investigations might be required regarding the94Rb
isomer.
3.1.4 Other noteworthy isomers
In this final subsection, we show how the present data could
be beneficial for the evaluation community, besides the newisomers previously reported.
A first interesting result is the half-life of the
97Sr isomer
atE∗=831 keV. In the evaluated data [ 23], an inconsistency
between two measurements of this half-life is reported by theevaluator. Since then, the half-life was re-measured a couple
of times, addressing this issue [ 16,49]. The result obtained
in the present work with VESPA is consistent with these newmeasurements.
Another point of interest concerns the short-lived isomer
in
92Rb ((0 .82±0.04) ns at E∗=142 keV). This is, to our
knowledge, the first time that a measurement of this half-
life is published, although the isomer is already known since
1972, but was only reported as private communication in theevaluated data [ 19].3.2 Charge calibration of the IC
The identified isomers constitute a large data set of known
post-neutron fission fragments, of various mass and nuclear
charge (see Fig. 7).
As initially demonstrated in the pioneer work from Ref.
[8], a twin Frisch-grid ionization chamber could be used
to estimate the nuclear charge of fission fragments from a
ratio between electron-ion pair track position in both sidesof the chamber. In Ref. [ 8], the authors based their analysis
on events having a high Total Kinetic Energy (TKE), cor-
responding to events of neutronless fission (i.e., the excita-tion energy of the fragments lies below the neutron emission
threshold), for a better mass determination. Here, we propose
to generalize their approach using the isomer determinationpreviously described. It should be noted that such a calibra-tion presents several difficulties due to the low kinetic energy
of the fragments, that is below 2 MeV/u, and the correlation
between the various fission fragment characteristics (mass,nuclear charge, and kinetic energy).
The IC used in the VESPA setup can return the pulse height
at each anode to obtain the energy of both fission fragments,as well as average electron drift times ( ¯t), to deduce their
position along the zaxis [ 7]. We define the drift-time ratio
(r
t) as the ratio between | [
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] | [] |
Fundamental physics and
mathematics1
Governance, culture, education
and the economy3
Health and wellbeing 4
ICT and computer science 1
Mechanical engineering and
heavy machinery3
Nanotechnology and materials 3
Optics and photonics 2
Transportation 3
*NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable 3.33. Selected S&T specialisation domains in Ukraine6.6 Ukraine – Summary of the
strengths of the S&T specialisations
Ukraine presents a very diversified S&T panorama.
It is not evident to highlight specialisation domains,
in part due to the disparate size of the country’s
S&T activity in relation to the EaP aggregate.
The following S&T domains have been highlighted:
■Health and wellbeing presents a notable
critical mass in publications and patents, as
well as a specialisation in patents and a rele-
vant number of EC projects. Its research is ori-
ented towards Cardiology and cardiovascular
medicine, as well as Ophthalmology;
■Energy presents a notable specialisation (in
publications and patents) and a high citation
impact in publications, as well as a relevant
number of EC projects. In this country, the do-
main correlates highly with Electric and elec-
tronic technologies and Fundamental physics. Scientific activity is related to Fuel technology
and Process chemistry and technology, and
technological development in the production
of electricity;
■Biotechnology, highly co-occurrent with
Chemistry and chemical engineering, presents
a high critical mass in patents, as well as a
specialisation in publications and patents. Its
research and technological development is
oriented towards organic chemistry and drug
discovery;
■Transportation, highly co-occurrent with ICT
and computer science (in areas such as auto-
matic control and UAVs) as well as Mechanical
engineering, presents the highest specialisa-
tion index in publications and patents, and a
high scientific citation impact. Its research is
geared towards road, rail, sea and air trans-
port, while patents cluster in Vehicles in gen-
eral, Combustion engines and Ships;
228
Part 3 Analysis of scientific and technological potential
■Mechanical engineering presents a high
critical mass in patents and specialisation and
citation impact in publications. It co-occurs
with a large number of domains, particularly
Transportation, Energy and Electric and elec-
tronic technologies;
■Nanotechnology and materials presents a
high critical mass in patents and publications,
and a relevant number of EC projects. This fairly transversal domain frequently co-occurs
with a large number of domains in the hard
and applied sciences, particularly Biotechnol-
ogy, Fundamental physics and mathematics
and Mechanical engineering and heavy ma-
chinery.
The following clouds | [
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"\n",
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"\n",
"Transportation",
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"Normalised",
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"development",
"is",
"\n",
"oriented",
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"chemistry",
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"drug",
"\n",
"discovery",
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"\n ",
"■",
"Transportation",
",",
"highly",
"co",
"-",
"occurrent",
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"ICT",
"\n",
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"computer",
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"(",
"in",
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"such",
"as",
"auto-",
"\n",
"matic",
"control",
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"UAVs",
")",
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"as",
"Mechanical",
"\n",
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",",
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"tion",
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",",
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"\n",
"geared",
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"road",
",",
"rail",
",",
"sea",
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"air",
"trans-",
"\n",
"port",
",",
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"Vehicles",
"in",
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"\n",
"eral",
",",
"Combustion",
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"and",
"Ships",
";",
"\n",
"228",
"\n ",
"Part",
"3",
"Analysis",
"of",
"scientific",
"and",
"technological",
"potential",
"\n ",
"■",
"Mechanical",
"engineering",
"presents",
"a",
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"critical",
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"in",
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"\n",
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"\n",
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",",
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"Transportation",
",",
"Energy",
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"elec-",
"\n",
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"\n ",
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"patents",
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"publications",
",",
"\n",
"and",
"a",
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".",
"This",
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"\n",
"with",
"a",
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"number",
"of",
"domains",
"in",
"the",
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"\n",
"and",
"applied",
"sciences",
",",
"particularly",
"Biotechnol-",
"\n",
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",",
"Fundamental",
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"and",
"mathematics",
"\n",
"and",
"Mechanical",
"engineering",
"and",
"heavy",
"ma-",
"\n",
"chinery",
".",
"\n",
"The",
"following",
"clouds"
] | [] |
to a lack of an industrial strategy equivalent to
other major regions . EU manufacturers are suffering primarily from a lack of stability of demand and from produc -
tion cost gaps, reinforced by an unlevel playing field with other major economies providing significant subsidies and
erecting trade barriers. The European Commission estimates that Chinese subsidies for clean tech manufacturing
have long been twice as high as those in the EU as a share of GDP, while the country has protected its home market
for solar PV, wind power-generation equipment and EV batteries. The US Inflation Reduction Act (IRA) is estimated
to provide USD 40 billion to USD 250 billion in support for manufacturing of clean tech and is projected to help to
bridge the US cost gap vis-à-vis producers in China. These policies have left the EU with a significant cost disad -
vantage: for example, solar PV manufacturing costs in China are around 35%-65% lower than in Europe and costs
for manufacturing battery cells are 20%-35% lowerx. The EU announced a comprehensive response in 2023 with
the Net Zero Industry Act (NZIA). However, EU financial support remains fragmented among different programmes,
characterised by higher complexity and lead times, and generally excludes operating costs where cost gaps are
greatest. Overall, financing for manufacturing at the EU level is five to ten times less generous than under the IRA.
Finally, while the NZIA specifies EU manufacturing targets, they are not backed by explicit minimum quotas for local
products and components – quotas which other regions regularly apply – meaning EU demand is not predictably
channelled towards EU clean tech output.
The EU’s improving outlook for its battery industry demonstrates that a focused policy effort can succeed,
even if non-EU players may benefit most . Although the EU’s market share in lithium-ion batteries globally stands
at just 6.5%, battery manufacturing output reached around 65 GWh in 2023 in the EU, growing by around 20% over
the previous year. For comparison, the US recorded 80 GWh of production and similar growth, while the figures
in China were 670 GWh and 50%, respectively. Public support for battery development has been key to strength -
ening Europe’s position. Public R&I spending on battery technology has risen by 18% per year on average over
the past decade, and Europe ranks only behind Japan and South Korea as a location for patent applications for
battery storage | [
" ",
"to",
"a",
"lack",
"of",
"an",
"industrial",
"strategy",
"equivalent",
"to",
"\n",
"other",
"major",
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".",
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"as",
"a",
"share",
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"GDP",
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".",
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"a",
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",",
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"manufacturing",
"costs",
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"China",
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"around",
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"%",
"lower",
"than",
"in",
"Europe",
"and",
"costs",
"\n",
"for",
"manufacturing",
"battery",
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".",
"The",
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"\n",
"the",
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".",
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",",
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"support",
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"\n",
"characterised",
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"quotas",
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"local",
"\n",
"products",
"and",
"components",
"–",
"quotas",
"which",
"other",
"regions",
"regularly",
"apply",
"–",
"meaning",
"EU",
"demand",
"is",
"not",
"predictably",
"\n",
"channelled",
"towards",
"EU",
"clean",
"tech",
"output",
".",
"\n",
"The",
"EU",
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"improving",
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"for",
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"battery",
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"a",
"focused",
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"can",
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",",
"\n",
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"EU",
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".",
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"share",
"in",
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"ion",
"batteries",
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"at",
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"6.5",
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",",
"battery",
"manufacturing",
"output",
"reached",
"around",
"65",
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"in",
"2023",
"in",
"the",
"EU",
",",
"growing",
"by",
"around",
"20",
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"over",
"\n",
"the",
"previous",
"year",
".",
"For",
"comparison",
",",
"the",
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"recorded",
"80",
"GWh",
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"and",
"similar",
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",",
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"figures",
"\n",
"in",
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"and",
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",",
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".",
"Public",
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"for",
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"development",
"has",
"been",
"key",
"to",
"strength",
"-",
"\n",
"ening",
"Europe",
"’s",
"position",
".",
"Public",
"R&I",
"spending",
"on",
"battery",
"technology",
"has",
"risen",
"by",
"18",
"%",
"per",
"year",
"on",
"average",
"over",
"\n",
"the",
"past",
"decade",
",",
"and",
"Europe",
"ranks",
"only",
"behind",
"Japan",
"and",
"South",
"Korea",
"as",
"a",
"location",
"for",
"patent",
"applications",
"for",
"\n",
"battery",
"storage"
] | [] |
for space
research and innovation should be supported by increased coordination, funding and the pooling of resources for
the development of new large EU joint programmes. Finally, as for the defence sector, the growth of innovative EU
space SMEs, start-ups and scale-ups should be enabled by improved access to finance and the introduction of
targeted European preference rules.
61THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 4ENDNOTESi Baba, C., Lan, T., Mineshima, A., Misch, F., Pinat, M., Shahmoradi,
A., Yao, J., & van Elkan, R., ‘ Geoeconomic Fragmentation: What’s
at Stake for the EU ’, IMF Working Paper No. 2023/245, 2023.
ii ECB, op cit., 2023.
iii Caldara, D., & Iacoviello, M., ‘ Measuring Geopolitical Risk ’,
American Economic Review, 112(4), 2022, p. 1194-1225.
iv European Commission, ‘ A new method to help policymakers
defend democracy against hybrid threats ’, 2023.
v IEA, Critical Minerals Market Review 2023 , p.5, 2023.
vi European Commission, Report on the state of the
Digital Decade 2023 , September 27 2023.vii Hein, J. R., Mizell, K., Koschinsky, A., & Conrad, T. A., Deep-ocean
mineral deposits as a source of critical metals for high- and
green-technology applications: Comparison with land-based
resources , Ore Geology Reviews, Volume 51, 2013, pages 1-14,
viii Eurométaux, Grégoir, L., van Acker, K., op. cit., 2022.
ix Microsoft, Unlocking a new era for scientific discovery
with AI: How Microsoft’s AI screened over 32 million
candidates to find a better battery , 2024.
x European Defence Agency.
xi Moretti et al., ‘The Intellectual Spoils of War? Defense
R&D, Productivity and International Spillovers’,
NBER Working Paper No. 26483, 2021.
62THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 45. Financing investments
The financing needs required for the EU to meet its objectives are massive, but productive investment is
weak despite ample private savings [see the chapter on investment] . To meet the objectives laid out in this report,
a minimum annual additional investment of EUR 750 to 800 billion is needed, based on the latest Commission esti -
mates, corresponding to 4.4-4.7% of EU GDP in 2023. For comparison, investment under the Marshall Plan between
1948-51 was equivalent to 1-2% of EU GDP. Delivering this increase would require the EU’s investment share to jump
from around 22% of GDP today to around 27%, reversing a multi-decade decline across most large EU economies.
However, productive investment in the EU is not | [
" ",
"for",
"space",
"\n",
"research",
"and",
"innovation",
"should",
"be",
"supported",
"by",
"increased",
"coordination",
",",
"funding",
"and",
"the",
"pooling",
"of",
"resources",
"for",
"\n",
"the",
"development",
"of",
"new",
"large",
"EU",
"joint",
"programmes",
".",
"Finally",
",",
"as",
"for",
"the",
"defence",
"sector",
",",
"the",
"growth",
"of",
"innovative",
"EU",
"\n",
"space",
"SMEs",
",",
"start",
"-",
"ups",
"and",
"scale",
"-",
"ups",
"should",
"be",
"enabled",
"by",
"improved",
"access",
"to",
"finance",
"and",
"the",
"introduction",
"of",
"\n",
"targeted",
"European",
"preference",
"rules",
".",
"\n",
"61THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER",
"4ENDNOTESi",
"Baba",
",",
"C.",
",",
"Lan",
",",
" ",
"T.",
",",
"Mineshima",
",",
"A.",
",",
"Misch",
",",
"F.",
",",
" ",
"Pinat",
",",
"M.",
",",
"Shahmoradi",
",",
"\n",
"A.",
",",
"Yao",
",",
" ",
"J.",
",",
"&",
"van",
"Elkan",
",",
"R.",
",",
"‘",
"Geoeconomic",
" ",
"Fragmentation",
":",
" ",
"What",
"’s",
"\n",
"at",
"Stake",
"for",
"the",
"EU",
"’",
",",
"IMF",
"Working",
" ",
"Paper",
"No",
".",
"2023/245",
",",
"2023",
".",
"\n",
"ii",
"ECB",
",",
"op",
"cit",
".",
",",
"2023",
".",
"\n",
"iii",
"Caldara",
",",
"D.",
",",
"&",
"Iacoviello",
",",
"M.",
",",
"‘",
"Measuring",
"Geopolitical",
"Risk",
"’",
",",
"\n",
"American",
"Economic",
"Review",
",",
"112(4",
")",
",",
"2022",
",",
"p.",
"1194",
"-",
"1225",
".",
"\n",
"iv",
"European",
"Commission",
",",
"‘",
"A",
"new",
"method",
"to",
"help",
"policymakers",
"\n",
"defend",
"democracy",
"against",
"hybrid",
"threats",
"’",
",",
"2023",
".",
"\n",
"v",
"IEA",
",",
"Critical",
"Minerals",
"Market",
"Review",
"2023",
",",
"p.5",
",",
"2023",
".",
"\n",
"vi",
"European",
"Commission",
",",
"Report",
"on",
"the",
"state",
"of",
"the",
"\n",
"Digital",
"Decade",
"2023",
",",
"September",
"27",
"2023.vii",
"Hein",
",",
"J.",
"R.",
",",
"Mizell",
",",
"K.",
",",
"Koschinsky",
",",
"A.",
",",
"&",
"Conrad",
",",
"T.",
"A.",
",",
"Deep",
"-",
"ocean",
"\n",
"mineral",
"deposits",
"as",
"a",
"source",
"of",
"critical",
"metals",
"for",
"high-",
"and",
"\n",
"green",
"-",
"technology",
"applications",
":",
"Comparison",
"with",
"land",
"-",
"based",
"\n",
"resources",
",",
"Ore",
"Geology",
"Reviews",
",",
"Volume",
"51",
",",
"2013",
",",
"pages",
"1",
"-",
"14",
",",
"\n",
"viii",
"Eurométaux",
",",
"Grégoir",
",",
"L.",
",",
"van",
"Acker",
",",
"K.",
",",
"op",
".",
"cit",
".",
",",
"2022",
".",
"\n",
"ix",
"Microsoft",
",",
"Unlocking",
"a",
"new",
"era",
"for",
"scientific",
"discovery",
"\n",
"with",
"AI",
":",
"How",
"Microsoft",
"’s",
"AI",
"screened",
"over",
"32",
"million",
"\n",
"candidates",
"to",
"find",
"a",
"better",
"battery",
",",
"2024",
".",
"\n",
"x",
"European",
"Defence",
"Agency",
".",
"\n",
"xi",
"Moretti",
"et",
"al",
".",
",",
"‘",
"The",
"Intellectual",
"Spoils",
"of",
"War",
"?",
"Defense",
"\n",
"R&D",
",",
"Productivity",
"and",
"International",
"Spillovers",
"’",
",",
"\n",
"NBER",
"Working",
"Paper",
"No",
".",
"26483",
",",
"2021",
".",
"\n",
"62THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER",
"45",
".",
"Financing",
"investments",
"\n",
"The",
"financing",
"needs",
"required",
"for",
"the",
"EU",
"to",
"meet",
"its",
"objectives",
"are",
"massive",
",",
"but",
"productive",
"investment",
"is",
"\n",
"weak",
"despite",
"ample",
"private",
"savings",
" ",
"[",
"see",
"the",
"chapter",
"on",
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the report recommends for the EU to establish industrial partnerships with third countries in the form of offtake
agreements across the supply chain or co-investment in manufacturing projects. The EU’s Global Gateway could
be leveraged for the necessary investment. However, in situations where otherwise productive EU companies are
being threatened by state-sponsored competition, the EU should be prepared to apply trade measures in line with
principles described above [see the Box in chapter 1 – the starting point] .
As part of its decarbonisation strategy, the EU should develop an industrial action plan for the automo -
tive sector [see the chapter on automotive] . In the short term, the main objective for the sector should be to
avoid a radical delocalisation of production away from the EU or the rapid takeover of EU plants and companies by
state-subsidised foreign producers, while continuing decarbonisation. The countervailing tariffs recently adopted
by the Commission against Chinese automotive companies making battery EVs will help level the playing field in this
regard while accommodating genuine productivity gains in China. Looking forward, the report recommends for the
EU to develop an industrial roadmap that accounts for the horizontal convergence (i.e. electrification, digitalisation
and circularity) and the vertical convergence (i.e. critical raw materials, batteries, transport and charging infrastruc -
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IPCEIs in the automotive sector. Scale, standardisation and collaboration will be crucial for EU manufacturers to
become competitive in areas such as small and affordable European EVs, software-defined vehicle and autonomous
driving solutions, and the circularity value chain. A coherent digital policy, encompassing the data ecosystem, should
support these developments. In building such a roadmap, the EU should follow a technology-neutral approach in
defining the path to CO2 and pollutant reductions and should take stock of market and technological developments.
The wider EU strategy towards cross-border and modal integration and sustainable transport needs to plan
for competitiveness and not only for cohesion [see the chapter on transport] . Transport should be based on a
new unified approach to planning at the EU and national levels, focused on harmonisation and interoperability as well
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Academic Editors: Pascal Venet and
Jiangfeng Ni
Received: 2 December 2024
Revised: 10 January 2025
Accepted: 13 January 2025
Published: 16 January 2025
Citation: Quintero Pulido, D.F.;
Covrig, C.F.; Bruchhausen, M. On the
Performance of Portable NiMH
Batteries of General Use. Batteries
2025 ,11, 30. https://doi.org/
10.3390/batteries11010030
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
On the Performance of Portable NiMH Batteries of General Use
Diego F. Quintero Pulido *
, Catalin Felix Covrig and Matthias Bruchhausen
European Commission, Joint Research Centre (JRC), 1755 LE Petten, The Netherlands;
[email protected] (C.F.C.); [email protected] (M.B.)
*Correspondence: [email protected]
Abstract: NiMH batteries are the most used technology of rechargeable batteries sold
directly to consumers. Herein, we study the performance of the most common sizes
of portable NiMH batteries (AA, AAA, D, C, and 9V). The performance and durability
parameters—capacity, charge retention, charge recovery, and endurance in cycles—are
measured for these types of batteries, according to the standard IEC 61951-2:2017 NiMH
batteries. The purpose of this study is to create a basis for setting minimum performance
requirements for the parameters in the European Regulation concerning batteries and waste
batteries, EU 2023/1542, Annex III, Part B. Results show that the charging time of 16 h could
be reduced to 8 h for verifying the rated capacity. The performance of commercial batteries
with regard to charge retention, charge recovery, and endurance in cycles is often found
to be 25–30% better than required in the relevant IEC standard. Furthermore, we present
a short comparative analysis of an application test (IEC 60086-2:2021 “toy”) for portable
NiMH batteries with primary batteries. Such data allow comparing the performance of
portable NiMH batteries compared to primary batteries in the application test “toy”.
Keywords: NiMH battery; performance; portable batteries of general use; IEC standard;
AA; AAA; C; D; 9V; battery regulation
1. Introduction
The European Parliament and the Council adopted Regulation EU 2023/1542 concern-
ing batteries and waste batteries in July 2023 [ 1]. It covers various categories of batteries,
and one of its goals is to guarantee minimum performance and durability of batteries in
the EU market to reduce environmental impacts. The Joint Research Centre (JRC) of the
European Commission supports technical aspects for the regulation’s implementation in
the coming years.
One category of batteries covered by | [
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without com-
plex sequence-based understanding. The simplest
baseline, TotalProb, which makes a decision based
on the likelihood of the sequence, performs sur-prisingly well (over 60% accuracy for all sampling
methods) relative to the methods which involve
training logistic regression models.
Logistic regression on bag-of-words is the best
of the baselines, beating out the histogram-based
methods. While Gehrmann et al. (2019) report an
AUC of 0.87 on classifying text as real or gener-
ated using logistic regression on the four buckets
of the GLTR system, we report AUC between 0.52
and 0.56 for this task. The discrepancy is likely
due to the fact that the human-written text in our
discriminator training set comes from the same
distribution as the text used to train the language
model, while in GLTR the human text comes from
children’s books, scientific abstracts, and news-
paper articles. The selection of training data for
learned detection systems is crucial. In real-world
applications, the choice ought to reflect the genres
that builders of text-generation systems are trying
to impersonate.
Fine-tuned BERT In Figure 1a, we begin by ob-
serving discriminator accuracy as a function of ex-
cerpt length and sampling method. As can be in-
tuitively expected, as sequence length increases,
so too does accuracy. For unconditioned text de-
coded with nucleus (p0.96) and untruncated (p1.0)
random sampling, we find discriminator accuracy
increases from 55%, near random, to about 81%
for the longest sequences tested. In contrast, dis-
criminators trained and evaluated on top- kachieve
over 80% accuracy even on 16-token excerpts.
Why are top- k’s samples so easy to detect? In
Figure 2b, we see the percentage of probability
mass concentrated in the kmost common token
types for each sampling method. While random
sampling and nucleus sampling are very similar to
human-written texts, we see top-k concentrating
up to 80% of its mass in the first 500 most com-
mon tokens. The other sampling methods as well
as human-written texts require at least 1,100 token
types for the same. It is clear that top- k’s distribu-50%55%60%65%70%75%80%85%90%95%100%
0326496128160192Accuracy
Sequence length in tokensAccuracy of BERT Fine-tuned Discriminator
k40-1wordcondk40-nowordcondp0.96-1wordcondp0.96-nowordcondp1.0-1wordcondp1.0-nowordcond(a)
00.10.20.30.40.50.60.70.80.91
24816326496128160192Sequence length in tokensFraction of BERT Discriminator Errors that are Machine-generated Labeled as Human-written
k40-1wordcondp0.96-1wordcondp1.0-1wordcond(b)
Figure 1: In (a), accuracy increases as the length of the sequences used to train the discriminator is increased.
In(b), we see that the BERT fine-tuned discriminator predicts about the same number of false-positives as | [
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DFQ issue). The within-subject comparison (R1 vs R2) in
Frame 1 examined whether consumer choice depends on the ‘made for’
condition (i.e., absence versus presence ‘made for’ claim), without
providing any additional information related to DFQ. In Frame 2 (“No
Blind ”) consumers received information on the existence of DFQ at the
beginning of the experiment. In this frame, comparison of the two
rounds (R1 vs R2) determines how brand name impacts preferences for
domestic product versions when the participants know that companies
use different recipes for the same product (‘made for’ claim). Second, in
each R1 we introduce a rank-order mechanism (Dong et al., 2010 ) for
participants to express their preferences based exclusively on taste.
As in experiment 1, we screened out respondents who do not
consume the three product categories. A total of 400 respondents from
Germany and Hungary (Mage 41.0 years; MFemale 62 %) qualified to
participate in the experiment. We randomly assigned respondents to one
of the two information frames in a between-subject design (NBlind 200,
Fig. 1.Overview of hypotheses.
Source: Authors ’ elaboration
Table 1
Main characteristics of the products used in each of the two country groups.
Germany, Lithuania,
and HungaryRomania, Spain,
and Sweden
Product
1Product
descriptionFlavoured Yogurt Crisps
Size/unit 4 units of 125 gr each 1 bag of 170 gr
Product
2Product
descriptionSeasoning mix for
Bolognese pasta sauceOrange flavoured
soft drink
Size/unit 1 packet of 42 g 1 bottle of 33 cl
Product
3Product
descriptionChocolate cookies Fish Fingers
Size/unit 1 pack of 168 gr 1 box of 450 gr
Source: Authors ’ elaboration
8Participants were invited to the lab at one-hour intervals, entering and
exiting from separate locations to avoid information disclosure. A monitor read
aloud to the group of max 20 people instructions provided by the research
group. They were instructed on the general content of the experiment (taste
three product versions, repeating four times with different products), how to
use the electronic device and the implications of their binding decision to
purchase using real money. Monitors ensured the smooth operation of the lab
experiment.D.M. Federica et al. Food Policy 131 (2025) 102803
4 NNoBlind 200). The experiment was implemented over four days in
2019. Participants in the two countries tasted the German, Hungarian
and Lithuanian versions of two branded food products: Danone Activia
Strawberry yogurt and Milka Choco Cookies. Note that the Hungarian
and German versions of Milka Choco Cookies | [
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effort
to support local processes.
This study has also analysed the potential con-
cordances or synergies between the above E&I
and S&T specialisation domains by means of ex-
isting concordance tables adapted and validat-
ed for the current effort – as described in Part
4. From these, several combined EIST domains
emerged, whereby scientific and technological ac-
tivities were found to directly connect with some
of the economic clusters selected for each country.
Thus, beyond the S&T domains identified via spe-
cialisation, critical mass and excellence indicators,
there is an additional set of S&T domains which
are relevant for priority-setting because they can
clearly contribute to knowledge-based develop-
ment pathways in the E&I specialisation domains.
The most common areas of combined E&I and
S&T specialisation throughout the EaP countries
are Agrifood and Electric and electronic technolo-
gies, while the top identified scientific domains in
the EaP region in publications are Nanotechnology
and materials, Fundamental physics and mathe-
matics, Health and wellbeing. The top technolog-
ical domains in numbers of patents in the EaP
region are Mechanical engineering and heavy ma-
chinery, Health and wellbeing, Electric and elec-
tronic technologies.
Domains where a direct concordance was found
would benefit the most from demand-side pol-
icies connecting companies with the knowledge
sector, such as subsidised contract research or in-
novation vouchers, from investment in technolog-
ical platforms and from networking instruments
such as clusters.
4
Executive summary
Main results – The
economic, innovation,
scientific and
technological (EIST)
specialisation domains
of Eastern Partnership
countries
The mapping phase indicated in the S3 Framework
is built on a set of recommended economic, in-
novation and scientometric indicators, which are
obtained from official national statistics and other
sources. This report follows this structure, using
international data sources to provide a general
overview of the EaP region and inform nation-
al-level processes, both during the design phase
and later updates.
To support the national-level mapping exercises,
the organisation of the entrepreneurial discovery
process and, eventually, Smart Specialisation pri-
ority-setting, this project aims to answer the fol-
lowing research questions:
1. What are the sub-sectoral specialisations of
the EaP countries in terms of economic critical
mass, emerging sectors and innovative activi-
ties of companies?
2. Which of these specialisations are common
in the EaP region and which specific to each
country?
3. What are the areas of specialisation and ex-
cellence in EaP STI systems that can be | [
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"ment",
"pathways",
"in",
"the",
"E&I",
"specialisation",
"domains",
".",
"\n",
"The",
"most",
"common",
"areas",
"of",
"combined",
"E&I",
"and",
"\n",
"S&T",
"specialisation",
"throughout",
"the",
"EaP",
"countries",
"\n",
"are",
"Agrifood",
"and",
"Electric",
"and",
"electronic",
"technolo-",
"\n",
"gies",
",",
"while",
"the",
"top",
"identified",
"scientific",
"domains",
"in",
"\n",
"the",
"EaP",
"region",
"in",
"publications",
"are",
"Nanotechnology",
"\n",
"and",
"materials",
",",
"Fundamental",
"physics",
"and",
"mathe-",
"\n",
"matics",
",",
"Health",
"and",
"wellbeing",
".",
"The",
"top",
"technolog-",
"\n",
"ical",
"domains",
"in",
"numbers",
"of",
"patents",
"in",
"the",
"EaP",
"\n",
"region",
"are",
"Mechanical",
"engineering",
"and",
"heavy",
"ma-",
"\n",
"chinery",
",",
"Health",
"and",
"wellbeing",
",",
"Electric",
"and",
"elec-",
"\n",
"tronic",
"technologies",
".",
"\n",
"Domains",
"where",
"a",
"direct",
"concordance",
"was",
"found",
"\n",
"would",
"benefit",
"the",
"most",
"from",
"demand",
"-",
"side",
"pol-",
"\n",
"icies",
"connecting",
"companies",
"with",
"the",
"knowledge",
"\n",
"sector",
",",
"such",
"as",
"subsidised",
"contract",
"research",
"or",
"in-",
"\n",
"novation",
"vouchers",
",",
"from",
"investment",
"in",
"technolog-",
"\n",
"ical",
"platforms",
"and",
"from",
"networking",
"instruments",
"\n",
"such",
"as",
"clusters",
".",
"\n",
"4",
"\n",
"Executive",
"summary",
"\n",
"Main",
"results",
"–",
"The",
"\n",
"economic",
",",
"innovation",
",",
"\n",
"scientific",
"and",
"\n",
"technological",
"(",
"EIST",
")",
"\n",
"specialisation",
"domains",
"\n",
"of",
"Eastern",
"Partnership",
"\n",
"countries",
"\n",
"The",
"mapping",
"phase",
"indicated",
"in",
"the",
"S3",
"Framework",
"\n",
"is",
"built",
"on",
"a",
"set",
"of",
"recommended",
"economic",
",",
"in-",
"\n",
"novation",
"and",
"scientometric",
"indicators",
",",
"which",
"are",
"\n",
"obtained",
"from",
"official",
"national",
"statistics",
"and",
"other",
"\n",
"sources",
".",
"This",
"report",
"follows",
"this",
"structure",
",",
"using",
"\n",
"international",
"data",
"sources",
"to",
"provide",
"a",
"general",
"\n",
"overview",
"of",
"the",
"EaP",
"region",
"and",
"inform",
"nation-",
"\n",
"al",
"-",
"level",
"processes",
",",
"both",
"during",
"the",
"design",
"phase",
"\n",
"and",
"later",
"updates",
".",
"\n",
"To",
"support",
"the",
"national",
"-",
"level",
"mapping",
"exercises",
",",
"\n",
"the",
"organisation",
"of",
"the",
"entrepreneurial",
"discovery",
"\n",
"process",
"and",
",",
"eventually",
",",
"Smart",
"Specialisation",
"pri-",
"\n",
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"-",
"setting",
",",
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"aims",
"to",
"answer",
"the",
"fol-",
"\n",
"lowing",
"research",
"questions",
":",
"\n",
"1",
".",
"What",
"are",
"the",
"sub",
"-",
"sectoral",
"specialisations",
"of",
"\n",
"the",
"EaP",
"countries",
"in",
"terms",
"of",
"economic",
"critical",
"\n",
"mass",
",",
"emerging",
"sectors",
"and",
"innovative",
"activi-",
"\n",
"ties",
"of",
"companies",
"?",
"\n",
"2",
".",
"Which",
"of",
"these",
"specialisations",
"are",
"common",
"\n",
"in",
"the",
"EaP",
"region",
"and",
"which",
"specific",
"to",
"each",
"\n",
"country",
"?",
"\n",
"3",
".",
"What",
"are",
"the",
"areas",
"of",
"specialisation",
"and",
"ex-",
"\n",
"cellence",
"in",
"EaP",
"STI",
"systems",
"that",
"can",
"be"
] | [] |
other publishing activities X 68 Non-ferrous metals X 64 Payments
60.2Television programming and
broadcasting activities X 69 Manufactures of metals X
61.1Wired telecommunications
activities X 71 Power-generating machinery and equipment X Clusters
61.2Wireless telecommunications
activities X 72 Machinery specialized for particular industries X Education and knowledge transfer
64.9 Other financial service activities X 74General industrial machinery and equipment, and
machine partsX Information and communication technologies
66.1Activities auxiliary to financial
services X 78 Road vehicles (including air-cushion vehicles) X Medicine and health
68.2Rental and operating of own or
leased real estateX 79 Other transport equipment X Industrial manufacturing and processes
68.3Real estate activities on a fee or
contract basis X 87Professional, scientific and controlling instruments
and apparatusX Tourism
69.1 Legal activities X
69.2Accounting and auditing activities;
tax consultancyX X EBOPS Services exports C E
71.1Architectural and engineering act.
and related tech. consultancy X 1.1 Sea transport X
72.2Research and development on
social sciences and humanitiesX 2.1 Business travel X X
73.1 Advertising X X 2.2 Personal travel X
77.1Rental and leasing of motor
vehiclesX 3.1 Postal and courier services X
77.3Rental and leasing of other
machinery, equipment and tangible
goodsX 10.1 Audio-visual related services X
79.1Travel agency and tour operator
activitiesX 11 Government services X X
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation131 132
Part 2 Analysis of economic and innovation potential
Table S.4. Summary table of mapping results for Moldova
NACE Economic – All industries C E SITC Goods exports C E NACE Innovation – Enterprise Survey
1.2 Growing of perennial crops X X 0 Live animals other than animals of division 03 X 16 Wood
10.2Processing and preserving of fish,
crustaceans, molluscsX 4 Cereals and cereal preparations X X 17 Paper
10.3Processing and preserving of fruit
and vegetablesX 5 Vegetables and fruit X X 20+21 Chemicals
10.5 Manufacture of dairy products X 6 Sugars, sugar preparations and honey X 22 Plastics & rubber
10.6Man. of grain mill products,
starches and starch productsX 7Coffee, tea, cocoa, spices, and manufactures
thereofX 25 Precision instruments
13.9 Manufacture of other textiles X 21 Hides, skins and furskins, raw X 26 Machinery and equipment
14.1Manufacture of wearing apparel,
except fur apparelX 22 Oil-seeds and oleaginous fruits X X 58-63 Information and communication
15.2 Manufacture of footwear X 33Petroleum, petroleum products and related
materialsX
16.2Man. of products of wood, cork,
straw and plaiting materialsX 54 Medicinal and pharmaceutical products | [
"other",
"publishing",
"activities",
"X",
"68",
"Non",
"-",
"ferrous",
"metals",
"X",
"64",
"Payments",
"\n",
"60.2Television",
"programming",
"and",
"\n",
"broadcasting",
"activities",
"X",
"69",
"Manufactures",
"of",
"metals",
"X",
" \n",
"61.1Wired",
"telecommunications",
"\n",
"activities",
"X",
"71",
"Power",
"-",
"generating",
"machinery",
"and",
"equipment",
"X",
" ",
"Clusters",
"\n",
"61.2Wireless",
"telecommunications",
"\n",
"activities",
"X",
"72",
"Machinery",
"specialized",
"for",
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"industries",
"X",
" ",
"Education",
"and",
"knowledge",
"transfer",
"\n",
"64.9",
"Other",
"financial",
"service",
"activities",
"X",
" ",
"74General",
"industrial",
"machinery",
"and",
"equipment",
",",
"and",
"\n",
"machine",
"partsX",
" ",
"Information",
"and",
"communication",
"technologies",
"\n",
"66.1Activities",
"auxiliary",
"to",
"financial",
"\n",
"services",
"X",
"78",
"Road",
"vehicles",
"(",
"including",
"air",
"-",
"cushion",
"vehicles",
")",
"X",
" ",
"Medicine",
"and",
"health",
"\n",
"68.2Rental",
"and",
"operating",
"of",
"own",
"or",
"\n",
"leased",
"real",
"estateX",
" ",
"79",
"Other",
"transport",
"equipment",
"X",
" ",
"Industrial",
"manufacturing",
"and",
"processes",
"\n",
"68.3Real",
"estate",
"activities",
"on",
"a",
"fee",
"or",
"\n",
"contract",
"basis",
"X",
"87Professional",
",",
"scientific",
"and",
"controlling",
"instruments",
"\n",
"and",
"apparatusX",
" ",
"Tourism",
"\n",
"69.1",
"Legal",
"activities",
"X",
" \n",
"69.2Accounting",
"and",
"auditing",
"activities",
";",
"\n",
"tax",
"consultancyX",
"X",
"EBOPS",
"Services",
"exports",
"C",
"E",
" \n",
"71.1Architectural",
"and",
"engineering",
"act",
".",
"\n",
"and",
"related",
"tech",
".",
"consultancy",
"X",
" ",
"1.1",
"Sea",
"transport",
"X",
" \n",
"72.2Research",
"and",
"development",
"on",
"\n",
"social",
"sciences",
"and",
"humanitiesX",
" ",
"2.1",
"Business",
"travel",
"X",
"X",
" \n",
"73.1",
"Advertising",
"X",
"X",
" ",
"2.2",
"Personal",
"travel",
"X",
" \n",
"77.1Rental",
"and",
"leasing",
"of",
"motor",
"\n",
"vehiclesX",
" ",
"3.1",
"Postal",
"and",
"courier",
"services",
"X",
" \n",
"77.3Rental",
"and",
"leasing",
"of",
"other",
"\n",
"machinery",
",",
"equipment",
"and",
"tangible",
"\n",
"goodsX",
" ",
"10.1",
"Audio",
"-",
"visual",
"related",
"services",
"X",
" \n",
"79.1Travel",
"agency",
"and",
"tour",
"operator",
"\n",
"activitiesX",
" ",
"11",
"Government",
"services",
"X",
"X",
" \n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation131",
"132",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"Table",
"S.4",
".",
"Summary",
"table",
"of",
"mapping",
"results",
"for",
"Moldova",
"\n",
"NACE",
"Economic",
"–",
"All",
"industries",
"C",
"E",
"SITC",
"Goods",
"exports",
"C",
"E",
"NACE",
"Innovation",
"–",
"Enterprise",
"Survey",
"\n",
"1.2",
"Growing",
"of",
"perennial",
"crops",
"X",
"X",
"0",
"Live",
"animals",
"other",
"than",
"animals",
"of",
"division",
"03",
"X",
"16",
"Wood",
"\n",
"10.2Processing",
"and",
"preserving",
"of",
"fish",
",",
"\n",
"crustaceans",
",",
"molluscsX",
"4",
"Cereals",
"and",
"cereal",
"preparations",
"X",
"X",
"17",
"Paper",
"\n",
"10.3Processing",
"and",
"preserving",
"of",
"fruit",
"\n",
"and",
"vegetablesX",
"5",
"Vegetables",
"and",
"fruit",
"X",
"X",
"20",
"+",
"21",
"Chemicals",
"\n",
"10.5",
"Manufacture",
"of",
"dairy",
"products",
"X",
"6",
"Sugars",
",",
"sugar",
"preparations",
"and",
"honey",
"X",
"22",
"Plastics",
"&",
"rubber",
"\n",
"10.6Man",
".",
"of",
"grain",
"mill",
"products",
",",
"\n",
"starches",
"and",
"starch",
"productsX",
"7Coffee",
",",
"tea",
",",
"cocoa",
",",
"spices",
",",
"and",
"manufactures",
"\n",
"thereofX",
"25",
"Precision",
"instruments",
"\n",
"13.9",
"Manufacture",
"of",
"other",
"textiles",
"X",
"21",
"Hides",
",",
"skins",
"and",
"furskins",
",",
"raw",
"X",
"26",
"Machinery",
"and",
"equipment",
"\n",
"14.1Manufacture",
"of",
"wearing",
"apparel",
",",
"\n",
"except",
"fur",
"apparelX",
"22",
"Oil",
"-",
"seeds",
"and",
"oleaginous",
"fruits",
"X",
"X",
"58",
"-",
"63",
"Information",
"and",
"communication",
"\n",
"15.2",
"Manufacture",
"of",
"footwear",
"X",
"33Petroleum",
",",
"petroleum",
"products",
"and",
"related",
"\n",
"materialsX",
"\n",
"16.2Man",
".",
"of",
"products",
"of",
"wood",
",",
"cork",
",",
"\n",
"straw",
"and",
"plaiting",
"materialsX",
"54",
"Medicinal",
"and",
"pharmaceutical",
"products"
] | [] |
7 9 5 11 9 4
10.1 Processing/preserving of meat X X X X X X
10.2 Processing/preserving of fish, etc. X X X
10.3 Processing/preserving of fruit, vegetables X X X X
10.4 Vegetable and animal oils and fats X X X X X X
10.5 Dairy products X X X
10.6Grain mill products, starches and starch
products X X X X X X X X X X
10.7 Other food products X X X
10.8 Prepared animal feeds X X X X
11 Beverages X X X X X X X X X X X X
12 Tobacco products X X X X X X
13 Manufacture of textiles X X X X X X X
14 Manufacture of wearing apparel X X X X X X X
15 Manufacture of leather and related products X X X X 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 X X X
17 Manufacture of paper and paper products X X X X X X
18.1Printing and service activities related to
printing X X X X X X
18.2 Reproduction of recorded media
19Manufacture of coke and refined petroleum
products X X X X
20Manufacture of chemicals and chemical
products X X X X X X X X X X
21 Pharmaceuticals, medicinal chemicals, etc. X X X X X X
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation299 300
Annexes
ARMENIA AZERBAIJAN 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
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 Emerging Current Emerging Current Emerging Current Emerging Current Emerging
5 5 3 10 8 5 6 5 2 6 11 4 5 8 4 11 5 4 7 10 5 1 7 1 7 9 5 11 9 4
22 Manufacture of rubber and plastic products X X X X X
23Manufacture of other non-metallic mineral
products X X X X
24 Manufacture of basic metals X | [
"7",
"9",
"5",
"11",
"9",
"4",
"\n",
"10.1",
"Processing",
"/",
"preserving",
"of",
"meat",
" ",
"X",
" ",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
"\n",
"10.2",
"Processing",
"/",
"preserving",
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"fish",
",",
"etc",
".",
" ",
"X",
"X",
"X",
" \n",
"10.3",
"Processing",
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"preserving",
"of",
"fruit",
",",
"vegetables",
" ",
"X",
" ",
"X",
" ",
"X",
" ",
"X",
" \n",
"10.4",
"Vegetable",
"and",
"animal",
"oils",
"and",
"fats",
" ",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
"X",
" \n",
"10.5",
"Dairy",
"products",
" ",
"X",
" ",
"X",
" ",
"X",
" \n",
"10.6Grain",
"mill",
"products",
",",
"starches",
"and",
"starch",
"\n",
"products",
"X",
" ",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
"\n",
"10.7",
"Other",
"food",
"products",
"X",
" ",
"X",
" ",
"X",
" \n",
"10.8",
"Prepared",
"animal",
"feeds",
" ",
"X",
"X",
"X",
" ",
"X",
" \n",
"11",
"Beverages",
"X",
"X",
"X",
" ",
"X",
"X",
"X",
"X",
"X",
"X",
"X",
"X",
"X",
" \n",
"12",
"Tobacco",
"products",
"X",
"X",
"X",
"X",
"X",
"X",
" \n",
"13",
"Manufacture",
"of",
"textiles",
" ",
"X",
" ",
"X",
"X",
"X",
"X",
"X",
"X",
" \n",
"14",
"Manufacture",
"of",
"wearing",
"apparel",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
" \n",
"15",
"Manufacture",
"of",
"leather",
"and",
"related",
"products",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
"X",
"X",
"\n",
"16Manufacture",
"of",
"wood",
"and",
"of",
"products",
"of",
"wood",
"\n",
"and",
"cork",
",",
"except",
"furniture",
";",
"manufacture",
"of",
"\n",
"articles",
"of",
"straw",
"and",
"plaiting",
"materials",
" ",
"X",
" ",
"X",
" ",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
"\n",
"17",
"Manufacture",
"of",
"paper",
"and",
"paper",
"products",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
" ",
"X",
" \n",
"18.1Printing",
"and",
"service",
"activities",
"related",
"to",
"\n",
"printing",
"X",
" ",
"X",
"X",
"X",
"X",
" ",
"X",
" \n",
"18.2",
"Reproduction",
"of",
"recorded",
"media",
" \n",
"19Manufacture",
"of",
"coke",
"and",
"refined",
"petroleum",
"\n",
"products",
" ",
"X",
"X",
"X",
" ",
"X",
" \n",
"20Manufacture",
"of",
"chemicals",
"and",
"chemical",
"\n",
"products",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
" \n",
"21",
"Pharmaceuticals",
",",
"medicinal",
"chemicals",
",",
"etc",
".",
" ",
"X",
" ",
"X",
"X",
"X",
" ",
"X",
" ",
"X",
" \n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation299",
"300",
"\n",
"Annexes",
"\n",
"ARMENIA",
"AZERBAIJAN",
"GEORGIA",
"MOLDOVA",
"UKRAINEEmploy-",
"\n",
"ment",
"\n",
"Turnover",
"\n",
"Employ-",
"\n",
"ment",
"&",
"\n",
"turnover",
"\n",
"Employ-",
"\n",
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] | [] |
consider externalities.
One particularly damaging externality in the EU context is its adverse impact on the Single Market when the largest
countries with the most fiscal space can provide much more generous support than others [see Figure 8] . Second,
there is a lack of coordination among financing instruments. While the EU collectively spends a large amount on
its industrial goals, financing instruments are split along national lines and between Member States and the EU.
This fragmentation hampers scale, preventing the creation of large capital pools in particular for investments in
breakthrough innovation. It also hampers innovation by creating unnecessary complexity and bureaucracy for the
private sector. Third, there is a lack of coordination across policies. Industrial policies today – as seen in the US
and China – comprise multi-policy strategies, combining fiscal policies to incentivise domestic production, trade
policies to penalise anti-competitive behaviour abroad and foreign economic policies to secure supply chains. In
the EU context, linking policies in this way requires a high degree of coordination between national and EU policies.
However, owing to its complex governance structure and slow and disaggregated policymaking process, the EU is
less able to produce such a response.
FIGURE 8
Total State aid expenditure by Member State
2022, as % of GDP (top) and EUR billion (bottom)
Breakdown between COVID-19, State aid in response to the Russian invasion of Ukraine, and other State aid measures
Source: European Commission, 2024.
16THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1Towards a European response
GOALS
To manage these transformations, the report proposes a new industrial strategy for Europe . The three
main areas for action outlined in the report correspond to the three main transformations with which Europe must
contend. First, Europe needs to redress its slowing productivity growth by closing the innovation gap . This objective
will entail accelerating significantly technological and scientific innovation, improving the pipeline from innovation
to commercialisation, removing barriers that prevent innovative companies from growing and attracting finance,
and undertaking concerted efforts to close skills gaps. Second, to lower energy prices and capture the industrial
opportunities of decarbonisation, Europe needs a joint plan for decarbonisation and competitiveness . This plan will
have to ensure that Europe’s ambitious demand for decarbonisation can be matched by leadership on the technol -
ogies that will supply it. It will have to span industries that produce energy, those that enable decarbonisation, such
| [
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] | [] |
informing consumers about dual food quality.
Future research should explore alternative ways of conveying informa -
tion about DFQ to consumers to enhance transparency over composi -
tional difference, for example ‘Hungarian recipe ’ or visual cues like flags
or coloured scores, different colour of packaging, or adjusting product
names, which may also influence consumer perceptions.
Second, our analysis is limited by the small number of products
tested and the limited number of countries involved. Extending the
analysis to a larger number of products and EU countries —especially for
the laboratory setting where no clear trends emerged — would help
refine policy recommendations. Additionally, our study focuses on
different product versions, treating the overall list of ingredients as a
single attribute. Future research could examine the interplay between
specific ingredients and dual food quality (DFQ) to better understand
how individual components might influence consumer welfare and in
which cases DFQ might have a more significant impact on consumer
decision-making.
Third, the controlled and hypothetical nature of our experiments
may not fully reflect real-world consumer behaviour. Consumers often
spend minimal time making decisions and may overlook packaging
claims and ingredients, suggesting that differences in preferences could
be even smaller in real-life scenarios. Field experiments with a greater
degree of ecological validity would enhance our understanding of con-
sumer reactions to DFQ labelling in real shopping setting.
Disclaimers : The authors are solely responsible for the content of the
article. The views expressed are purely those of the authors and may not
in any circumstances be regarded as stating an official position of the
European Commission
CRediT authorship contribution statement
Di Marcantonio Federica: Conceptualization, Methodology, vali-
dation, Formal analysis, Investigation, Data Curation, Writing - Original
Draft, Writing - Review & Editing, Visualization, Supervision, Project
administration. Jesus Barreiro-Hurle: Conceptualization, Methodol -
ogy, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Project administration. Luisa Menapace: Writing - Original
Draft, Writing - Review & Editing, Validation, Investigation, Data
Curation, Methodology, Formal analysis, Conceptualization. Colen
Liesbeth: Writing - Original Draft, Writing - Review & Editing, Inves -
tigation, Conceptualization. Dessart Francois: Conceptualization,
Investigation, Writing - Review & Editing. Ciaian Pavel: Conceptuali -
zation, Project administration.
Funding
This research was funded by the Commission ’s Directorate-General
for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW)
under the Administrative Arrangement with the Joint Research Centre
(JRC), which supported the implementation of the experiments. The
funder | [
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] | [] |
in specific domains
by looking at scientific publications, research and
innovation projects and patents.
The mapping of scientific and technological poten-
tial is obtained by harvesting data from the fol-
lowing sources:
■Scopus25 by Elsevier, for scientific publica-
tions produced by the EaP countries. Publica-
tions are classified according to the All Science
Journal Classification Codes (ASJC) taxonomy;
24 https://www.clustercollaboration.eu/cluster-mapping
25 http://scopus.com/ ■the Community Research and Development
Information Service (CORDIS)26, for research
and innovation projects funded by the Euro-
pean Commission through the FP7 and H2020
framework programmes;
■the DOCDB database of the European Patent
Office, for patents whose applicant and/or in-
ventors were based in an EaP country, with no
restriction of the issuing patent office. In this
case, the textual data of each single record
was used, while only aggregate data was an-
alysed for the E&I potential.
2.6 Detailed step-by-step method-
ology
The overall work is carried out in a sequential
methodology, which makes it possible to progres-
sively tackle the above research questions and to
link E&I data and data from different S&T sources,
as follows:
26 https://cordis.europa.eu/home_en.html
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation31
Figure 1.1. Summary scheme of the methodological steps leading to the selection and definition of a list of
specialisation domains for each country and the potential cooperation areas for the whole region and with
international 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
As a result of Steps 1 and 2, a series of economic
and innovation (E&I) specialisation domains and,
in parallel, a series of scientific and technologi-
cal (S&T) specialisation domains are identified for
each EaP country. Step 3 then aims at finding con-
cordances between these two specialisation di-
mensions, leading to the identification of a subset
of combined EIST specialisation domains.
Step 1. Analysis of economic and inno-
vation (E&I) potential
This step focuses on the identification of statisti-
cal data of economic and innovation activities, the
calculation of degrees of specialisation and the
definition of specialisation niches, in terms of the
NACE taxonomy27, for each EaP country. The lists
27 NACE is a four-digit classification providing the frame-
work for collecting and presenting a large range of sta-
tistical data according to economic activity in the fields of NACE codes with positive indicators | [
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"24",
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"\n",
"26",
"https://cordis.europa.eu/home_en.html",
"\n",
"Smart",
"Specialisation",
"in",
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"Partnership",
"countries",
"-",
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"\n",
"Figure",
"1.1",
".",
"Summary",
"scheme",
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"steps",
"leading",
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"for",
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"\n",
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"\n",
"Economic",
"and",
"Innovation",
"(",
"E&I",
")",
"\n",
"specialisations",
"\n",
"EIST",
"specialisation",
"domains",
"\n",
"E&I",
"\n",
"preliminary",
"\n",
"prioritiesEIST",
"\n",
"preliminary",
"\n",
"prioritiesS&T",
"\n",
"preliminary",
"\n",
"prioritiesSTEP",
"1",
"\n",
"STEP",
"3STEP",
"2",
"\n",
"Science",
"and",
"Technology",
"(",
"S&T",
")",
"\n",
"specialisations",
"\n",
"As",
"a",
"result",
"of",
"Steps",
"1",
"and",
"2",
",",
"a",
"series",
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"economic",
"\n",
"and",
"innovation",
"(",
"E&I",
")",
"specialisation",
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",",
"\n",
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",",
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"series",
"of",
"scientific",
"and",
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"\n",
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"S&T",
")",
"specialisation",
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"are",
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".",
"Step",
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"\n",
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"Analysis",
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"\n",
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"\n",
"This",
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",",
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"NACE",
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",",
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"EaP",
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".",
"The",
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"\n",
"27",
"NACE",
"is",
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"-",
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"NACE",
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"positive",
"indicators"
] | [] |
0.783 1.329
20.4 Manufacture of soap and detergents 0.636 1.877 0.573 1.172 0.950 0.792
20.5 Manufacture of other chemical products 2.096 0.640 0.799 1.396 0.999
20.6 Manufacture of man-made fibres 5.609 0.391
21 Manufacture of basic pharmaceutical products 0.919 1.442 0.607 1.037 1.181 0.815
22 Manufacture of rubber and plastic products 2.410 1.580 0.254 0.981
23 Manufacture of other non-metallic mineral products 1.100 1.653 1.815 0.429
23.1 Manufacture of glass and glass products 1.064 2.604 1.816 0.517
23.3 Manufacture of clay building materials 1.643 1.416 0.757
23.4 Manufacture of other porcelain and ceramic products 5.372 0.252
23.5 Manufacture of cement, lime and plaster 1.382 1.789 1.413 0.350 0.802
24 Manufacture of basic metals 1.592 1.871 0.504 1.794
25.1 Manufacture of structural metal products 2.065
25.2 Manufacture of tanks 2.847 1.380
25.3 Manufacture of steam generators 4.246
25.4 Manufacture of weapons and ammunition 0.796 1.484 1.840 1.355
25.5 Forging, pressing, stamping and roll-forming of metal 1.351 2.982 0.508 0.946
25.6 Treatment and coating of metals; machining 1.405 1.543 2.483 0.568Table 2.33. Specialised industries using data for patent families*
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation97
NACE Industry name
Armenia
Azerbaijan
Belarus
Georgia
Moldova
Ukraine
25.7 Manufacture of cutlery, tools and general hardware 1.034 1.294 1.989 0.640 0.641
25.9 Manufacture of other fabricated metal products 3.988
26.1 Manufacture of electronic components and boards 2.217 0.513 1.248 0.605 0.909 0.509
26.2 Manufacture of computers and peripheral equipment 2.516 0.426 1.400 0.641 0.249 0.768
26.3 Manufacture of communication equipment 1.339 0.744 1.160 1.461 0.552 0.743
26.4 Manufacture of consumer electronics 3.412 0.221 1.652
26.5 Manufacture of instruments and appliances for measuring 0.279 0.795 1.345 0.621 1.025 1.936
26.6 Manufacture of irradiation 2.069 1.448 0.525 0.591 1.274
26.7Manufacture of optical instruments and photographic
equipment1.229 3.119 0.652 0.373 0.525
26.8 Manufacture of magnetic and optical media 0.802
27.1 Manufacture of electric motors 0.737 0.858 0.638 1.418 0.978 1.371
27.2 Manufacture of batteries and accumulator 2.255 2.126 1.077 0.335
27.3 Manufacture of wiring and wiring devices 2.432
27.4 Manufacture of electric lighting equipment 1.494 1.640 1.479 0.721
27.5 Manufacture of domestic appliances 0.630 0.754 1.855 1.027 1.316
27.9 Manufacture of other electrical equipment 1.556 0.702 1.242 1.698
28.1 Manufacture of general-purpose machinery 0.384 0.961 0.943 1.488 1.121 1.103
28.2 Manufacture of other general-purpose machinery 0.555 0.687 0.966 1.201 1.242 1.349
28.3 Manufacture of agricultural and forestry machinery 0.110 0.307 0.756 1.270 | [
"0.783",
"1.329",
"\n",
"20.4",
"Manufacture",
"of",
"soap",
"and",
"detergents",
"0.636",
"1.877",
"0.573",
"1.172",
"0.950",
"0.792",
"\n",
"20.5",
"Manufacture",
"of",
"other",
"chemical",
"products",
"2.096",
"0.640",
"0.799",
"1.396",
"0.999",
"\n",
"20.6",
"Manufacture",
"of",
"man",
"-",
"made",
"fibres",
"5.609",
"0.391",
"\n",
"21",
"Manufacture",
"of",
"basic",
"pharmaceutical",
"products",
"0.919",
"1.442",
"0.607",
"1.037",
"1.181",
"0.815",
"\n",
"22",
"Manufacture",
"of",
"rubber",
"and",
"plastic",
"products",
"2.410",
"1.580",
"0.254",
"0.981",
"\n",
"23",
"Manufacture",
"of",
"other",
"non",
"-",
"metallic",
"mineral",
"products",
"1.100",
"1.653",
"1.815",
"0.429",
"\n",
"23.1",
"Manufacture",
"of",
"glass",
"and",
"glass",
"products",
"1.064",
"2.604",
"1.816",
"0.517",
"\n",
"23.3",
"Manufacture",
"of",
"clay",
"building",
"materials",
"1.643",
"1.416",
"0.757",
"\n",
"23.4",
"Manufacture",
"of",
"other",
"porcelain",
"and",
"ceramic",
"products",
"5.372",
"0.252",
"\n",
"23.5",
"Manufacture",
"of",
"cement",
",",
"lime",
"and",
"plaster",
"1.382",
"1.789",
"1.413",
"0.350",
"0.802",
"\n",
"24",
"Manufacture",
"of",
"basic",
"metals",
"1.592",
"1.871",
"0.504",
"1.794",
"\n",
"25.1",
"Manufacture",
"of",
"structural",
"metal",
"products",
"2.065",
"\n",
"25.2",
"Manufacture",
"of",
"tanks",
"2.847",
"1.380",
"\n",
"25.3",
"Manufacture",
"of",
"steam",
"generators",
"4.246",
"\n",
"25.4",
"Manufacture",
"of",
"weapons",
"and",
"ammunition",
"0.796",
"1.484",
"1.840",
"1.355",
"\n",
"25.5",
"Forging",
",",
"pressing",
",",
"stamping",
"and",
"roll",
"-",
"forming",
"of",
"metal",
"1.351",
"2.982",
"0.508",
"0.946",
"\n",
"25.6",
"Treatment",
"and",
"coating",
"of",
"metals",
";",
"machining",
"1.405",
"1.543",
"2.483",
"0.568Table",
"2.33",
".",
"Specialised",
"industries",
"using",
"data",
"for",
"patent",
"families",
"*",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation97",
"\n ",
"NACE",
" ",
"Industry",
"name",
"\n",
"Armenia",
"\n",
"Azerbaijan",
"\n",
"Belarus",
"\n",
"Georgia",
"\n",
"Moldova",
"\n",
"Ukraine",
"\n",
"25.7",
"Manufacture",
"of",
"cutlery",
",",
"tools",
"and",
"general",
"hardware",
"1.034",
"1.294",
"1.989",
"0.640",
"0.641",
"\n",
"25.9",
"Manufacture",
"of",
"other",
"fabricated",
"metal",
"products",
"3.988",
"\n",
"26.1",
"Manufacture",
"of",
"electronic",
"components",
"and",
"boards",
"2.217",
"0.513",
"1.248",
"0.605",
"0.909",
"0.509",
"\n",
"26.2",
"Manufacture",
"of",
"computers",
"and",
"peripheral",
"equipment",
"2.516",
"0.426",
"1.400",
"0.641",
"0.249",
"0.768",
"\n",
"26.3",
"Manufacture",
"of",
"communication",
"equipment",
"1.339",
"0.744",
"1.160",
"1.461",
"0.552",
"0.743",
"\n",
"26.4",
"Manufacture",
"of",
"consumer",
"electronics",
"3.412",
"0.221",
"1.652",
"\n",
"26.5",
"Manufacture",
"of",
"instruments",
"and",
"appliances",
"for",
"measuring",
"0.279",
"0.795",
"1.345",
"0.621",
"1.025",
"1.936",
"\n",
"26.6",
"Manufacture",
"of",
"irradiation",
"2.069",
"1.448",
"0.525",
"0.591",
"1.274",
"\n",
"26.7Manufacture",
"of",
"optical",
"instruments",
"and",
"photographic",
"\n",
"equipment1.229",
"3.119",
"0.652",
"0.373",
"0.525",
"\n",
"26.8",
"Manufacture",
"of",
"magnetic",
"and",
"optical",
"media",
"0.802",
"\n",
"27.1",
"Manufacture",
"of",
"electric",
"motors",
"0.737",
"0.858",
"0.638",
"1.418",
"0.978",
"1.371",
"\n",
"27.2",
"Manufacture",
"of",
"batteries",
"and",
"accumulator",
"2.255",
"2.126",
"1.077",
"0.335",
"\n",
"27.3",
"Manufacture",
"of",
"wiring",
"and",
"wiring",
"devices",
"2.432",
"\n",
"27.4",
"Manufacture",
"of",
"electric",
"lighting",
"equipment",
"1.494",
"1.640",
"1.479",
"0.721",
"\n",
"27.5",
"Manufacture",
"of",
"domestic",
"appliances",
"0.630",
"0.754",
"1.855",
"1.027",
"1.316",
"\n",
"27.9",
"Manufacture",
"of",
"other",
"electrical",
"equipment",
"1.556",
"0.702",
"1.242",
"1.698",
"\n",
"28.1",
"Manufacture",
"of",
"general",
"-",
"purpose",
"machinery",
"0.384",
"0.961",
"0.943",
"1.488",
"1.121",
"1.103",
"\n",
"28.2",
"Manufacture",
"of",
"other",
"general",
"-",
"purpose",
"machinery",
"0.555",
"0.687",
"0.966",
"1.201",
"1.242",
"1.349",
"\n",
"28.3",
"Manufacture",
"of",
"agricultural",
"and",
"forestry",
"machinery",
"0.110",
"0.307",
"0.756",
"1.270"
] | [] |
rising to this challenge. Since the Great Financial Crisis (GFC), a
sizeable and persistent gap has opened between private productive investment01 in the EU and the US. At the same
time, the private investment gap across the two economies has not been offset by higher government investment,
which also dropped after the GFC and has been persistently lower in the EU compared to the US as a share of GDP.
EU households provide ample savings to finance higher investment, but at present these savings are not being
channelled efficiently into productive investments. In 2022, EU household savings were EUR 1,390 billion compared
with EUR 840 billion in the US. But, despite their higher savings, EU households have considerably lower wealth than
their US counterparts, largely because of the lower returns they receive from financial markets on their asset holdings.
The EU can meet these investment needs without overstretching the resources of the European economy,
but the private sector will need public support to finance the plan . The European Commission and the IMF’s
Research Department have simulated scenarios of a sustained EU investment push of around 5% of GDP, using their
multi-country models. The results suggest that investment of this magnitude would increase output by around 6%
within 15 years. Since supply adjusts more gradually than demand – as the build-up of additional capital takes time
– the transition phase implies some inflationary pressures, but these pressures dissipate over time. Unlocking the
investment will be challenging. Historically in Europe, around four-fifths of productive investment has been under -
taken by the private sector, and the remaining one-fifth by the public sector. Delivering private investment of around
4% of GDP through market financing alone would require a reduction in the private cost of capital – by approximately
250 basis points in the European Commission model. Although improved capital market efficiency (e.g. through
the completion of the Capital Markets Union) is expected to reduce private financing costs, the reduction will likely
be substantially smaller. Fiscal incentives to unlock private investment therefore appear necessary to finance the
investment plan, in addition to direct government investment.
The required stimulus to private investment will have some impact on public finances, but productivity gains
can reduce the fiscal costs . If the investment-related government spending is not compensated by budgetary
savings elsewhere, primary fiscal balances may temporarily deteriorate before the investment plan fully exerts its
positive | [
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a Commission proposal. As an illustration, if the EU is
unable to establish a special regime for innovative companies under normal procedures, a voluntary 28th company
rulebook harmonising legislation concerning corporate law and insolvency, as well as a few key aspects of labour law
and taxation, to be made progressively more ambitious, could be explored under enhanced cooperation by willing
Member States. As a last resort, intergovernmental cooperation should be considered. However, acting outside of
the Treaties creates parallel legal frameworks and implies the absence of judicial oversight by CJEU, democratic
legitimacy via the EP, and the Commission’s involvement in preparing texts.
SIMPLIFYING RULES
The regulatory burden on European companies is high and continues to grow, but the EU lacks a common
methodology to assess it . The Commission has been working for years to reduce the “stock” and “flow” of regula -
tion under the Better Regulation agenda. However, this effort has had limited impact so far. The stock of regulation
68THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 6remains large and new regulation in the EU is growing faster than in other comparable economies. While direct
comparisons are obscured by different political and legal systems, around 3,500 pieces of legislation were enacted
and around 2,000 resolutions were passed in the US at the federal level over the past three Congress mandates
(2019-2024). During the same period, around 13,000 acts were passed by the EU. Despite this increasing flow of
regulation, the EU lacks a quantitative framework to analyse the costs and benefits of new laws. Among the EU
institutions, only the Commission has developed a methodology (the Standard Cost Model) to calculate regulatory
burdens, but its concrete application varies across pieces of legislation. The co-legislators – the European Parlia -
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legislation. Moreover, there is no single methodology in place to assess the impact of EU legislation once transposed
at national level, with only a few Member States systematically measuring the impact of transposed EU law – in turn
making it harder for national parliaments to exercise scrutiny.
Companies in Europe face three main hindrances from the rising weight of regulation . First, they need to
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that NLP is currently in the
process of rapid hill-climbing. Every year, states of
the art across many NLP tasks are being improved
significantly — often through the use of better pre-
trained LMs — and tasks that seemed impossible
not long ago are already old news. Thus, every-
thing is going great when we take the bottom-up
view. But from a top-down perspective, the ques-
tion is whether the hill we are climbing so rapidly
is the right hill. How do we know that incremental
progress on today’s tasks will take us to our end
goal, whether that is “General Linguistic Intelli-
gence” (Yogatama et al., 2019) or a system that
passes the Turing test or a system that captures the
meaning of English, Arapaho, Thai, or Hausa to a
linguist’s satisfaction?
It is instructive to look at the past to appreci-
ate this question. Computational linguistics has
gone through many fashion cycles over the course
of its history. Grammar- and knowledge-based
methods gave way to statistical methods, and today
most research incorporates neural methods. Re-
searchers of each generation felt like they were
solving relevant problems and making constant
progress, from a bottom-up perspective. However,
eventually serious shortcomings of each paradigm
emerged, which could not be tackled satisfactorily
with the methods of the day, and these methods
were seen as obsolete. This negative judgment —
we were climbing a hill, but not the right hill — can
only be made from a top-down perspective. We
have discussed the question of what is required to5192learn meaning in an attempt to bring the top-down
perspective into clearer focus.
8.2 Hillclimbing diagnostics
We can only definitively tell if we’ve been climbing
the right hill in hindsight, but we propose some best
practices for less error-prone mountaineering:
First, above all, cultivate humility towards lan-
guage and ask top-down questions. Neural meth-
ods are not the first bottom-up success in NLP; they
will probably not be the last.
Second, be aware of the limitations of tasks: Arti-
ficial tasks like bAbI (Weston et al., 2016) can help
get a field of research off the ground, but there is no
reason to assume that the distribution of language
in the test data remotely resembles the distribution
of real natural language; thus evaluation results on
such tasks must be interpreted very carefully. Sim-
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datasets such as SQuAD | [
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circuits, for electric control or the distribution of electricity (excluding switching apparatus of subgroup 764.1) X X X
773 Equipment for distributing electricity, n.e.s. X X
774 Electrodiagnostic apparatus for medical, surgical, dental or veterinary purposes, and radiological apparatus
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation317 318
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
775 Household-type electrical and non-electrical equipment, n.e.s. X X
776Thermionic, cold cathode or photo-cathode valves and tubes (e.g., vacuum or vapour or gas-filled valves and tubes,
mercury arc rectifying valves and tubes, cathode-ray tubes, television camera tubes); diodes, transistors and similar
semiconductor devices; photosensitive semiconductor devices; light-emitting diodes; mounted piezoelectric crystals;
electronic integrated circuits and microassemblies; parts thereof X
778 Electrical machinery and apparatus, n.e.s. X X X
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 X
782 Motor vehicles for the transport of goods and special-purpose motor vehicles X X
783 Road motor vehicles, n.e.s. X
784 Parts and accessories of the motor vehicles of groups 722, 781, 782 and 783 X X
785 Motor cycles (including mopeds) and cycles, motorized and non-motorized; invalid carriages
786Trailers and semi-trailers; other vehicles, not mechanically-propelled; specially designed | [
"recording",
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" ",
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",",
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",",
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"X",
" ",
"X",
" \n",
"772Electrical",
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",",
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",",
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",",
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",",
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",",
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"-",
"Potential",
"for",
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"-",
"based",
"economic",
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"318",
"\n",
"Annexes",
"\n",
"ARMENIA",
"AZERBAIJAN",
"BELARUS",
"GEORGIA",
"MOLDOVA",
"UKRAINE",
"\n",
"SITC",
"Goods",
"name",
"Current",
"Emerging",
"Current",
"Emerging",
"Current",
"Emerging",
"Current",
"Emerging",
"Current",
"Emerging",
"Current",
"Emerging",
"\n",
"19",
"12",
"3",
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"65",
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"18",
"26",
"41",
"23",
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"\n",
"electronic",
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"parts",
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"X",
" \n",
"778",
"Electrical",
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",",
"n.e.s",
".",
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"X",
" ",
"X",
" ",
"X",
" \n",
"781Motor",
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",",
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",",
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" ",
"X",
" ",
"X",
" \n",
"782",
"Motor",
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"for",
"the",
"transport",
"of",
"goods",
"and",
"special",
"-",
"purpose",
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" ",
"X",
"X",
" \n",
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"Road",
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",",
"n.e.s",
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" ",
"X",
" \n",
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"722",
",",
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",",
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" ",
"X",
"X",
" \n",
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",",
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"vehicles",
",",
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"mechanically",
"-",
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";",
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] | [] |
have straight or branched structures.[77] The polysaccharides produced can have structural or metabolic functions themselves, or be transferred to lipids and proteins by the enzymes oligosaccharyltransferases.[78][79]
Fatty acids, isoprenoids and sterol
Further information: Fatty acid synthesis and Steroid metabolism
Simplified version of the steroid synthesis pathway with the intermediates isopentenyl pyrophosphate (IPP), dimethylallyl pyrophosphate (DMAPP), geranyl pyrophosphate (GPP) and squalene shown. Some intermediates are omitted for clarity.
Fatty acids are made by fatty acid synthases that polymerize and then reduce acetyl-CoA units. The acyl chains in the fatty acids are extended by a cycle of reactions that add the acyl group, reduce it to an alcohol, dehydrate it to an alkene group and then reduce it again to an alkane group. The enzymes of fatty acid biosynthesis are divided into two groups: in animals and fungi, all these fatty acid synthase reactions are carried out by a single multifunctional type I protein,[80] while in plant plastids and bacteria separate type II enzymes perform each step in the pathway.[81][82]
Terpenes and isoprenoids are a large class of lipids that include the carotenoids and form the largest class of plant natural products.[83] These compounds are made by the assembly and modification of isoprene units donated from the reactive precursors isopentenyl pyrophosphate and dimethylallyl pyrophosphate.[84] These precursors can be made in different ways. In animals and archaea, the mevalonate pathway produces these compounds from acetyl-CoA,[85] while in plants and bacteria the non-mevalonate pathway uses pyruvate and glyceraldehyde 3-phosphate as substrates.[84][86] One important reaction that uses these activated isoprene donors is sterol biosynthesis. Here, the isoprene units are joined to make squalene and then folded up and formed into a set of rings to make lanosterol.[87] Lanosterol can then be converted into other sterols such as cholesterol and ergosterol.[87][88]
Proteins
Further information: Protein biosynthesis and Amino acid synthesis
Organisms vary in their ability to synthesize the 20 common amino acids. Most bacteria and plants can synthesize all twenty, but mammals can only synthesize eleven nonessential amino acids, so nine essential amino acids must be obtained from food.[10] Some simple parasites, such as the bacteria Mycoplasma pneumoniae, lack all amino acid synthesis and take their amino acids directly from their hosts.[89] All amino acids are synthesized from intermediates in glycolysis, the citric acid cycle, or the pentose phosphate pathway. Nitrogen is provided by glutamate and glutamine. Nonessensial amino acid synthesis depends on the formation of | [
"have",
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",",
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"]",
"\n\n",
"Fatty",
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",",
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"\n",
"Further",
"information",
":",
"Fatty",
"acid",
"synthesis",
"and",
"Steroid",
"metabolism",
"\n\n",
"Simplified",
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"\n",
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"-",
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"The",
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",",
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"an",
"alcohol",
",",
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"it",
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"group",
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"it",
"again",
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"an",
"alkane",
"group",
".",
"The",
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"divided",
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"two",
"groups",
":",
"in",
"animals",
"and",
"fungi",
",",
"all",
"these",
"fatty",
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"synthase",
"reactions",
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"out",
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"multifunctional",
"type",
"I",
"protein,[80",
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"and",
"bacteria",
"separate",
"type",
"II",
"enzymes",
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"in",
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"]",
"\n\n",
"Terpenes",
"and",
"isoprenoids",
"are",
"a",
"large",
"class",
"of",
"lipids",
"that",
"include",
"the",
"carotenoids",
"and",
"form",
"the",
"largest",
"class",
"of",
"plant",
"natural",
"products.[83",
"]",
"These",
"compounds",
"are",
"made",
"by",
"the",
"assembly",
"and",
"modification",
"of",
"isoprene",
"units",
"donated",
"from",
"the",
"reactive",
"precursors",
"isopentenyl",
"pyrophosphate",
"and",
"dimethylallyl",
"pyrophosphate.[84",
"]",
"These",
"precursors",
"can",
"be",
"made",
"in",
"different",
"ways",
".",
"In",
"animals",
"and",
"archaea",
",",
"the",
"mevalonate",
"pathway",
"produces",
"these",
"compounds",
"from",
"acetyl",
"-",
"CoA,[85",
"]",
"while",
"in",
"plants",
"and",
"bacteria",
"the",
"non",
"-",
"mevalonate",
"pathway",
"uses",
"pyruvate",
"and",
"glyceraldehyde",
"3",
"-",
"phosphate",
"as",
"substrates.[84][86",
"]",
"One",
"important",
"reaction",
"that",
"uses",
"these",
"activated",
"isoprene",
"donors",
"is",
"sterol",
"biosynthesis",
".",
"Here",
",",
"the",
"isoprene",
"units",
"are",
"joined",
"to",
"make",
"squalene",
"and",
"then",
"folded",
"up",
"and",
"formed",
"into",
"a",
"set",
"of",
"rings",
"to",
"make",
"lanosterol.[87",
"]",
"Lanosterol",
"can",
"then",
"be",
"converted",
"into",
"other",
"sterols",
"such",
"as",
"cholesterol",
"and",
"ergosterol.[87][88",
"]",
"\n\n",
"Proteins",
"\n",
"Further",
"information",
":",
"Protein",
"biosynthesis",
"and",
"Amino",
"acid",
"synthesis",
"\n",
"Organisms",
"vary",
"in",
"their",
"ability",
"to",
"synthesize",
"the",
"20",
"common",
"amino",
"acids",
".",
"Most",
"bacteria",
"and",
"plants",
"can",
"synthesize",
"all",
"twenty",
",",
"but",
"mammals",
"can",
"only",
"synthesize",
"eleven",
"nonessential",
"amino",
"acids",
",",
"so",
"nine",
"essential",
"amino",
"acids",
"must",
"be",
"obtained",
"from",
"food.[10",
"]",
"Some",
"simple",
"parasites",
",",
"such",
"as",
"the",
"bacteria",
"Mycoplasma",
"pneumoniae",
",",
"lack",
"all",
"amino",
"acid",
"synthesis",
"and",
"take",
"their",
"amino",
"acids",
"directly",
"from",
"their",
"hosts.[89",
"]",
"All",
"amino",
"acids",
"are",
"synthesized",
"from",
"intermediates",
"in",
"glycolysis",
",",
"the",
"citric",
"acid",
"cycle",
",",
"or",
"the",
"pentose",
"phosphate",
"pathway",
".",
"Nitrogen",
"is",
"provided",
"by",
"glutamate",
"and",
"glutamine",
".",
"Nonessensial",
"amino",
"acid",
"synthesis",
"depends",
"on",
"the",
"formation",
"of"
] | [] |
energy prices . . . . . . . . 39
The threat to Europe’s clean tech sector . . . . . . 42
The challenges of asymmetric decarbonisation . 44
A joint plan for decarbonisation
and competitiveness . . . . . . . . . . . . . . . . . . 46
4. Increasing security and
reducing dependencies . . . . . . . . . . . . . . . 50
Reducing external vulnerabilities . . . . . . . . . . 52
Strengthening industrial capacity
for defence and space . . . . . . . . . . . . . . . . . 55
5. Financing investments . . . . . . . . . . . . . . 59
6. Strengthening governance . . . . . . . . . . . 63Contents
10THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1. The starting point:
a new landscape for Europe
Europe has the foundations in place to be a highly competitive economy . The European model combines an
open economy, a high degree of market competition and a strong legal framework and active policies to fight poverty
and redistribute wealth. This model has allowed the EU to marry high levels of economic integration and human
development with low levels of inequality. Europe has built a Single Market of 440 million consumers and 23 million
companies, accounting for around 17% of global GDP [see Figure 1] , while achieving rates of income inequality that
are around 10 percentage points below those seen in the United States (US) and China, according to some measures
[see Figure 2] . At the same time, the EU’s approach has delivered outstanding outcomes in terms of governance,
health, education and environmental protection. Of the world’s ten top-scoring countries for the application of the
rule of law, eight are EU Member Statesi. Europe leads the US and China in terms of life expectancy at birth and low
infant mortalityii. Europe’s education and training systems deliver strong educational attainment, with a third of adults
having completed higher educationiii. The EU is also the world leader in sustainability and environmental standards
and progress towards the circular economy, backed by the most ambitious global targets for decarbonisation, and
can benefit from the largest exclusive economic zone in the world, | [
"energy",
"prices",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
"39",
"\n",
"The",
"threat",
"to",
"Europe",
"’s",
"clean",
"tech",
"sector",
" ",
".",
".",
".",
".",
".",
".",
"42",
"\n",
"The",
"challenges",
"of",
"asymmetric",
"decarbonisation",
" ",
".",
"44",
"\n",
"A",
"joint",
"plan",
"for",
"decarbonisation",
" \n",
"and",
"competitiveness",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
"46",
"\n",
"4",
".",
"Increasing",
"security",
"and",
" \n",
"reducing",
"dependencies",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
"50",
"\n",
"Reducing",
"external",
"vulnerabilities",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
"52",
"\n",
"Strengthening",
"industrial",
"capacity",
"\n",
"for",
" ",
"defence",
" ",
"and",
" ",
"space",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
"55",
"\n",
"5",
".",
"Financing",
"investments",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
"59",
"\n",
"6",
".",
"Strengthening",
"governance",
" ",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
".",
"63Contents",
"\n",
"10THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER",
" ",
"1",
".",
"The",
"starting",
"point",
":",
" \n",
"a",
" ",
"new",
" ",
"landscape",
"for",
"Europe",
"\n",
"Europe",
"has",
"the",
"foundations",
"in",
"place",
"to",
"be",
"a",
"highly",
"competitive",
"economy",
".",
"The",
"European",
"model",
"combines",
"an",
"\n",
"open",
"economy",
",",
"a",
"high",
"degree",
"of",
"market",
"competition",
"and",
"a",
"strong",
"legal",
"framework",
"and",
"active",
"policies",
"to",
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of topics consisting of
groups of relevant words, as previously explained.
These topics generally have a much finer gran-
ularity than the S&T priority domains we would
typically define in the design of a Smart Speciali-
sation Strategy. Therefore, for practical reasons, to
best help in defining the different domains, topics
whose semantic content was largely overlapping
were grouped together. Additionally, some of the
extracted topics were finally discarded because of
a phenomenon commonly known as ‘topic drift’56,
56 Liu, Q., Huang, H. & Feng, C., ‘Micro-blog post topic drift
detection based on LDA model’, Behavior and Social Com-
puting, Springer, Cham., pp. 106-118, 2013.Figure 3.1. A graphical representation of the results produced by topic modelling via Latent Dirichlet Allocation
Documents are linked to several topics, while topics are composed of words appearing in the various texts. The same
words can appear in multiple topics, while the topics connected to some specific documents may feature some words
which are not actually contained in some of the texts, but are semantically related
148
Part 3 Analysis of scientific and technological potential
whereby the content of some topics is diverted by
recurrent words which do not convey meaningful
information (such as, for instance, terms related
to methods and instruments transversally used in
different scientific domains).
Once topics were grouped, to achieve point 2.
above, a series of domain names were chosen to
label each of those groups. These domains are
used as labels, transversely across EaP countries
so that topics featuring similar semantic content
from two different countries are eventually given
the same label. The choice of label is carried out
to facilitate use by stakeholders in a participatory
co-design of R&D&I policies, particularly towards
the EDP in the context of Smart Specialisation.
Lastly, to have a clear understanding of the num-
ber of records associated with each single do-
main, the fractional document/topic weights were
converted into a categorical association: docu-
ments were eventually associated with a specific
topic if the respective weight exceeded the aver-
age weight of the topic across records plus one
standard deviation. This allowed us to link docu-
ments with a limited subset to topics and, in turn,
domains.
2.3 Results
Table 3.3a presents the 14 labelled topic groups
obtained from the topic modelling process de-
scribed in the previous sections. A second table
includes the first 50 keywords linked to the topics
within each group; | [
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42.2 E03B E03C
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43 E03F E04B E04C E04D E04F E04G E04H
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324
Annexes
Annex 5. NICE
Classification for
trademarks
GOODS
Class 1 – Chemicals used in industry, science and
photography, as well as in agriculture, horticulture
and forestry; unprocessed artificial resins, unpro-
cessed plastics; manures; fire extinguishing com-
positions; tempering and soldering preparations;
chemical substances for preserving foodstuffs;
tanning substances; adhesives used in industry
Class 2 – Paints, varnishes, lacquers; preservatives
against rust and against deterioration of wood;
colorants; mordants; raw natural resins; metals in
foil and powder form for use in painting, decorat-
ing, printing and art
Class 3 – Bleaching preparations and other sub-
stances for laundry use; cleaning, polishing, scour-
ing and abrasive preparations; non-medicated
soaps; perfumery, essential oils, non-medicated
cosmetics, non-medicated hair lotions; non-med-
icated dentifrices
Class 4 – Industrial oils and greases; lubricants;
dust absorbing, wetting and binding compositions;
fuels (including motor spirit) and illuminants; can-
dles and wicks for lighting
Class 5 – Pharmaceuticals, medical and veterinary
preparations; sanitary preparations for medical
purposes; dietetic food and substances adapted
for medical or veterinary use, food for babies; di-
etary supplements for humans and animals; plas-
ters, materials for dressings; material for stopping
teeth, dental wax; disinfectants; preparations for
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] | [] |
defined as 0.1 C for a charging time of 16 h (after discharging the battery to 1 V at 0.2 C).
There is no definition of maximum cell voltage or maximum temperature. Furthermore, a
fast-charging methodology for rated capacity tests is not included in this standard. Section 7
presents this in more detail.
Figure 2 presents the charge (a) and discharge (b) capacities of two AA NiMH batteries
with a rated capacity of 2.5 Ah. The charging durations are varied between 8 h and 17 h.
For one battery, the charging time increased between cycles (AS); for the other battery, it
decreased (DS). Each battery has a twin battery to check repeatability. This is in order to
exclude any effects that might be caused by cycle aging. The experiments are performed
using a 0.1 C charge and 0.2 C discharge as specified in IEC-61951-2-2017-7.2.1 and 7.3.2.2.
We observed in Figure 2a that charging the AA NiMH battery for longer periods of
10 h does not affect the battery’s discharge capacity of 2.5 Ah (see Figure 2b). In terms of
efficiency, the highest columbic and energy efficiency is observed at 8 h for AS and DS
experiments (see Figure 2c,d).
Having a similar discharge capacity at different charging periods may be an effect of
the passivation of the negative electrode during charging of the NiMH battery, which can
affect the cycle life of the battery [ 32]. The IEC 61951-2017 charging methodology (7.2.1)
shows that a NiMH battery can have a columbic efficiency (charge–discharge) ranging from
62% (when charged for 16 h (see Figure 2d)) up to 99.1% (when charged for 8 h). Similarly,
the energy efficiency varies between 55 and 90% with the same rated capacity (in this case,
2.5 Ah).
To further analyze the charging of portable NiMH batteries, we have selected different
batteries available to consumers in Europe. These batteries are charged using the protocol
in IEC 61951-2. The charge profiles for AAA, AA, C, and D batteries are shown in Figure 3.
Portable NiMH batteries of different sizes have similar voltage profiles when charged at
0.1 C for 16 h (see Figure 3a–d). However, these batteries are from different manufacturers
and have different rated capacities and sizes (see Figure 3e). The average charge voltage
profile shows a start charging voltage of ~1.2 V rising for 8 h until a voltage of ~1.4 V , then
a change | [
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] | [] |
work, these parameters are evaluated using IEC
61951-2:2017 [5], the standard used as a basis for the ongoing work in WG 08.
It is observed that the performance of the tested commercial portable NiMH batteries
generally complies with the performance and durability requirements in the current IEC
61951-2 standard.
The capacity test is developed to charge batteries for long periods, sacrificing columbic
and energy efficiency. This method is looking at the maximum discharge output that a
battery can provide under a constant load. However, as suggested in Figure 2, NiMH
batteries can be charged for shorter periods (e.g., 8 h charge) to increase the electrical
efficiency of the system (e.g., to >90% columbic efficiency), thus reducing electrical energy
consumption. However, it is possible that reducing battery charging time may affect the
rated capacity of the battery. This requires further analysis.
In terms of the analysis of capacity, we observed that for the analyzed batteries, using
two cycles is sufficient to reach the rated capacity of the battery (see Figure 7), whereas
IEC 61951-2 allows cycling up to 5 times for checking the declared capacity of the battery.
Nevertheless, it is not possible to guarantee that any NiMH battery can pass the rate
capacity test with two or five cycles; based on our observations, one cycle is not enough to
pass the rated capacity test. Capacity increases after the first cycle may be related to the
easier access of NaOH to the electrode’s surface after the first cycle. Initially the NaOH is
not able to reach all areas inside the cell, and this can show less capacity.
In the case of the charge (capacity) retention of portable NiMH batteries, it is observed
that the batteries can provide up to 90% of the declared capacity (see Figure 8), and in
all cases, the discharge time is longer than 4 h (above the IEC requirement of 3 h). This
increase could be achieved thanks to new materials (e.g., better AB composites) and better
construction of NiMH batteries that can provide less self-discharge [ 38]. It is important
to note that the standard refers to this as a “charge (capacity) retention” test, but this
terminology can be misleading. What is actually being measured is the amount of charge
still available after storage, rather than the battery’s ability to retain its full initial capacity.
For the capacity (charge) recovery of NiMH batteries, it is observed | [
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] | [] |
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
identified E&I specialisations – grouped here in
economic clusters5.
The EaP region and the six country summary sche-
mas presented in the following pages provide syn-
thetic evidence for the identification of synergies
or concordances between the economic, innovation,
scientific and technological specialisations of the
EaP countries, as well as a complete overview of
all highlighted E&I and S&T specialisation domains.
In this sense, it is possible to find the following:
■E&I specialisation domains that do not con-
nect with any S&T domain;
■E&I specialisation domains that do connect
with S&T domains (EIST domains);
■S&T specialisation domains that do not con-
nect with any E&I domain.
5 Economic clusters are, in fact, aggregations of NACE
codes and are introduced in Part 4 of this document.As will be noted in the final remarks, beyond S3
priority-setting, the diverse nature of these spe-
cialisation domains may require targeted policy
instruments within a varied Smart Specialisation
policy mix.
Globally, this report provides complementary evi-
dence for these and many more potential E&I, S&T
and EIST domains, which can be reinterpreted, com-
plemented and further elaborated by experts and
stakeholders during the entrepreneurial discovery
process (EDP) to extend, replace or better specify
the economic, innovation, scientific and technolog-
ical specialisations and their concordances.
6
Executive summary
Economic cluster (i.e. E&I domain) Corresponding NACE code(s) Alignment with S&T domain(s)
Food processing and
manifacturing10 Manufacture of food products
11 Manufacture of beverages
Agrifood
Tobacco12 Manufacture of tobacco products
Leather, apparel, & footwear13 Manufacture of textiles
14 Manufacture of wearing apparel
15 Manufacture of leather and related
products
Nanotechnology and materials
Wood products16 Manufacture of wood and of
products of wood and cork, exept
furniture; manufacture of articles of
straw and plaiting materials
Chemistry and chemical engineering
Nanotechnology and materials
Media production and distribution18 Printing and reproduction of
recorded media
Oil and gas production and
trasportation19 Manufacture of coke and refined
petroleum products
Chemistry and chemical engineering
Energy
Nanotechnology and materials
Chemical products20 Manufacture of chemicals and
chemical products
Agrifood
Biotechnology
Chemistry and chemical engineering
Nanotechnology and materials
Vulcanized and fired materials23 Manufacture of other non-metallic
mineral products
Nanotechnology and materials
Metalworking technology25 Manufacture of fabricated metal
products, except machinery and
equipment
Nanotechnology and materials
Figure IIa. Summary table for the EaP | [
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] | [] |
.
Cell Reports 39, 110893, May 31, 2022 17Articlell
OPEN ACCESSSTAR+METHODS
KEY RESOURCES TABLE
RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Fran-
cesco Ferraguti ( [email protected] ).
Materials availability
This study did not generate new unique reagents.
Data and code availability
dThe data reported in this paper will be shared from the lead contact upon request.
dAll original codes have been deposited at Zenodo and is publicly available as of the date of publication. The DOI is listed in the
key resources table .
dAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
All animal procedures were performed according to institutional guidelines and were approved by the Austrian Animal Experimen-
tation Ethics Board (animal license numbers 2020–0.602.380, BMWFW-66.011/0123-WF/V/3b/2017) and in compliance with theREAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
VIP-rabbit Immunostar lot: 1744002; RRID:AB_572270
RFP-mouse Rockland lot: 37699; RRID:AB_2611063
GFP-goat Frontiers Institute lot: 2571574; RRID:AB_2571574
Bacterial and virus strains
AAV2/1.syn.FLEX.splitTVA-EGFP-tTA Addgene #100798
AAV2/1.TREtight.mTagBFP2-B19G Addgene #100799
RV.EnvA.dG.mCherry In house production Batch 1
AAV2/5.CAG.flex.ArchT-GFP UNC vector core https://www.med.unc.edu/genetherapy/
vectorcore/
AAV2/5.CAG.flex.GFP UNC vector core https://www.med.unc.edu/genetherapy/
vectorcore/
AAV2/5.CamKIIa-hChR2(HI34R)-mCherry Addgene #26975
AAV2/9.CAG.flex.GCaMP6s Addgene #100842
Experimental models: Organisms/strains
VIP-ires-cre mice Jackson Laboratory RRID:IMSR_JAX:010908
Ai9-tdTomato mice Jackson Laboratory RRID:IMSR_JAX:007909
Software and algorithms
MATLAB MathWorks https://www.mathworks.com/products/
matlab
Ca2+imaging processing pipeline GitHub https://github.com/bahanonu/
calciumImagingAnalysis
ANY-maze ANY-maze https://www.anymaze.co.uk
GraphPad GraphPad Prism https://www.graphpad.com
Python Python Software Foundation https://www.python.org
Boris BORIS http://www.boris.unito.it/
Original code This Publication https://doi.org/10.5281/zenodo.6477827
e1Cell Reports 39, 110893, May 31, 2022Articlell
OPEN ACCESSEuropean convention for the protection of vertebrate animals used for experimental and other scientific purposes, Animal Experi-
ments Act 2012 (TVG 2012) and the EU Directive 2010/63/EU. Every effort was taken to minimize animal suffering and the number
of animals used. Male heterozygous VIP-ires-cre mice (STOCK Viptm1(cre)Zjh/J; The Jackson Laboratory; RRID: IMSR_JAX:010908)
were used for all experiments. For VIP co-localization studies only, VIP-ires-cre homozygous mice were crossed with Ai9-tdTomatomice (B6.Cg-Gt(ROSA)
26Sortm9(CAG-tdTomato)Hze/J; The Jackson Laboratory; RRID:IMSR_JAX: 007909) to generate VIP-cre::Ai9 dou-
ble heterozygous mice. For behavioral and tracing experiments, only VIP-ires-cre adult male mice (aged 2–4 months at the time of
injection) were used. Mice were individually housed for at least 3 weeks before starting behavioral experiments. All optogenetic ex-periments were performed by an experimenter blind to the treatment group, and littermates of the same sex | [
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delegation applies
most of all to the type of European public goods described above. Such goods may not have direct spillovers on all
countries which are called to contribute, but they have large indirect spillovers for the whole EU. There are still other
areas where the EU should do less, applying the subsidiarity principle more rigorously and showing more “self-re -
straint”. It will also be crucial to reduce the regulatory burden on companies. Regulation is seen by more than 60% of
EU companies as an obstacle to investment, with 55% of SMEs flagging regulatory obstacles and the administrative
burden as their greatest challengexv. Kick-starting this partnership does not necessarily mean focusing all minds and
energies on the long and burdensome process of a Treaty change from day one. To begin with, a small number of
overarching, targeted institutional changes should be made – without the need for Treaty change.
05. The historical private-public split for investment in the EU is around 4/5 to 1/5.
18THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 1Preserving social inclusion
While the EU should aim to move closer to the US example in terms of productivity growth and innovation,
it should do so without the drawbacks of the US social model . As outlined above, the US has pulled ahead
of the EU owing to its stronger position in breakthrough technologies, yet it displays higher rates of inequality. A
European approach must ensure that productivity growth and social inclusion go hand-in-hand. Europe is entering
an unprecedented period in its history, where rapid technological change and sectoral transitions will combine
with a shrinking working age population. In this setting, Europe will have to ensure the best use of its available skills
while keeping the social fabric intact. Technological change can imply significant disruption for workers in previously
dominant industries that are no longer so, as well as increasing inequality: from 1980 to 2016, automation is found to
have accounted for 50-70% of the increase in wage inequality in the US between more and less educated workersxvi.
The European welfare state will therefore be critical to provide strong public services, social protection, housing,
transport and childcare during this transition. At the same time, Europe will need a fundamentally new approach to
skills. The EU must ensure that all workers have a right to education and retraining, allowing them to move into new
roles as | [
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17 256
UA 876 35 673 872 17 7 500
PublicationsFigure 3.53. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Fundamental physics and mathematics’ domain
Colour indicates the relative distribution of documents, computed row-wise.
AM
AZ
BY
GE
MD
UA
Other
10 18 25 19 18 29
10 7 11 8 9 14
18 7 19 15 18 34
25 11 19 23 30 50
19 8 15 23 23 57
18 9 18 30 23 115
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BY
GE
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UA 44 27 66 62 59 2 388
PublicationsFigure 3.54. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Governance, culture, education and the economy’ domain
Colour indicates the relative distribution of documents, computed row-wise.
214
Part 3 Analysis of scientific and technological potential
Regional collaboration in Health and
wellbeing
In the case of Health and wellbeing publications,
external collaborations again have a significant
weight across all six EaP countries. Within the EaP,
some of the highest-intensity collaborations are
Armenia and Azerbaijan with Georgia and Ukraine,
and Georgia and Moldova with Ukraine.
The highest number of collaborations in EC pro-
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intensity of external collaborations still significant.Regional collaboration in ICT and com-
puter science
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cations, external collaborations again have a sig-
nificant weight throughout all six EaP countries.
Azerbaijan, not usually standing out in other do-
mains, has the second-highest collaboration with
Ukraine. Georgia and Armenia also collaborate in-
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Collaborations in terms of EC projects are much
less concentrated on external partners and very
evenly distributed. Azerbaijan, however, has very
few EC projects.
AM
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GE
MD
UA
Other
4
1
2 2
1 1 3
1 3 10
2 1 3 40
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AZ
BY
GE
MD
UA
Other
AM 33 44 82 31 73 771
AZ 33 29 49 20 48 343
BY 44 29 59 33 195 1 364
GE 82 49 59 44 121 949
MD 31 20 33 44 62 444
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Dallo, I., Stauffacher, M., and Marti, M.: What defines the
success of maps and additional information on a multi-
hazard platform?, Int. J. Disast. Risk Re., 49, 101761,
https://doi.org/10.1016/j.ijdrr.2020.101761, 2020.
Daˇnhelka, J.: August 2002 flood in the Czech Republic: meteoro-
logical causes and hydrological response, Geografie, 109, 84–92,
https://doi.org/10.37040/geografie2004109020084, 2004.
De Angelis, S., Malamud, B. D., Rossi, L., Taylor, F. E., Trasforini,
E., and Rudari, R.: A multi-hazard framework for spatial–
temporal impact analysis, Int. J. Disast. Risk Re., 73, 102829,
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De Fino, M., Tavolare, R., Bernardini, G., Quagliarini, E.,
and Fatiguso, F.: Boosting urban community resilience to
multi-hazard scenarios in open spaces: A virtual reality –
serious game training prototype for heat wave protection
and earthquake response, Sustain. Cities Soc., 99, 104847,
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https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025302 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level
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Cover 2018 (CLC2018), https://www.eea.europa.eu/
data-and-maps/data/corine-land-cover-2018 (last access:
16 January 2025), 2018.
EUR-Lex: 52014SC0134 – EN – EUR-Lex, https://eur-lex.europa.
eu/legal-content/EN/TXT/?uri=CELEX%3A52014SC0134 (last
access: 18 October 2023), 2014.
European Civil Protection Knowledge Network: Data &
Tools, https://civil-protection-knowledge-network.europa.eu/
knowledge-network-science/data-tools (last access: 18 October
2023), 2021.
European Commission: Overview of Natural and Man
made Disaster Risks the European Union May Face,
https://op.europa.eu/en/publication-detail/-/publication/
285d038f-b543-11e7-837e-01aa75ed71a1 (last access: 16 Jan-
uary 2025), 2017.
European Commission: Overview of Natural and Man-
made Dis aster Risks the European Union May Face,
https://op.europa.eu/en/publication-detail/-/publication/
89fcf0fc-edb9-11eb-a71c-01aa75ed71a1/language-en/
format-PDF/source-236404726 (last access: 16 January 2025),
2020.
European Commission: Forging a climate-resilient Europe –
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CELEX:52021DC0082 (last access: 18 October 2023), 2021.
Figueiredo, R., Paupério, E., and Romão, X.: Understand-
ing the impacts of the October 2017 Portugal wild-
fires on cultural heritage, Heritage, 4, 2580–2598,
https://doi.org/10.3390/heritage4040146, 2021.
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Boyd, Edinburgh, ISBN 978-1178814261, 1932.
Fix, E. and Hodges, J. L.: Nonparametric Discrimination: Consis-
tency Properties, Randolph Field, Texas, Project 21-49-004, Re-
port No. 4, https://doi.org/10.2307/1403796, 1951.
Florczyk, A. J., Corbane, C., Schiavina, M., Pesaresi, M.,
Maffenini, L., Melchiorri, M., Politis, P., Sabo, F., Freire,
S., Ehrlich, D., Kemper, T., Tommasi, P., Airaghi, D., and
Zanchetta, L.: GHS-UCDB R2019A – GHS Urban Centre
Database 2015, multitemporal and multidimensional attributes,
JRC Data Catalogue, https://doi.org/10.2905/53473144-B88C-
44BC-B4A3-4583ED1F547E, 2019. | [
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to fund inno -
vative projects and faces several constraints . Although the GFC and the ensuing bank deleveraging led to a
greater role for capital markets and non-bank finance in Europe, bank loans are still the most important source of
external finance for companies. However, banks are typically ill-equipped to finance innovative companies: they lack
the expertise to screen and monitor them and have difficulties valuing their (largely intangible) collateral, especially
compared to angel financiers, venture capitalists and private equity providers. Banks in Europe also suffer from
lower profitability than their US counterparts – in large part because US banks gain higher net fee and commission
income from operating in their deeper capital markets – and lack scale relative to their US counterparts owing to the
incomplete Banking Union. EU banks also face some specific regulatory hurdles which constrain their capacity to
lend. In particular, EU banks cannot rely on securitisation to same extent as their US counterparts. Annual issuance
of securitisations in the EU stood at just 0.3% of GDP in 2022, while for the US the figure was 4%. Securitisation makes
banks’ balance sheets more flexible by allowing them to transfer some risk to investors, release capital and unlock
additional lending. In the EU context, it could also act as a substitute for lack of capital market integration by allowing
banks to package loans originating in different Member States into standardised and tradeable assets that can be
purchased also by non-bank investors.
At the same time, EU support for both public and private investment is constrained by the size of the EU
budget, its lack of focus and a too conservative attitude to risk . The EU’s annual budget is small, amounting to
just over 1% of EU GDP, while Member States’ budgets are collectively close to 50%. It is also not allocated towards
the EU’s strategic priorities: despite attempts at reform, the shares of the 2021-2027 Multiannual Financial Framework
(MFF) allocated to cohesion and the common agricultural policy are still 30.5% and 30.9%, respectively. Moreover,
the EU budget is fragmented across close to 50 spending programmes, preventing EU financing from reaching
sufficient scale for larger pan-European projects. Access to EU funding is complex and bureaucratic for private
actors, and there is limited room to accommodate new policy priorities or respond to unforeseen developments.
The capacity of the EU budget to mobilise private investment through risk-sharing instruments | [
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by each of these
countries. The analyses are performed by means of
topic modelling, an algorithmic approach that au-
tomatically extracts groups of sets of co-occurring
keywords (the topics) from large textual corpora.
Further details on this technique, the methodology
and the data coverage for each EaP country and
source is reported in Section 2.1, while additional
explanations on definitions and data sources are
provided in Part 1 of this document.
This report tackles Step 2 of the research proce-
dure (see Part 1. Introduction and methodology),
which has the following three objectives:
1. to make an initial assessment of the scien-
tific and technological (S&T) specialisa-
tions, in terms of emerging topics, supporting
the identification of S&T specialisation do-
mains in concordance with the E&I analysis;
2. to characterise these domains of S&T
specialisation for each EaP country and for
the EaP as a whole, providing finer-grained
taxonomic and semantic detail;
3. to identify key local and international actors
involved in these S&T specialisation domains
and to analyse the national and interna-
tional collaboration networks at institu-
tional level.Therefore, the work presented in this report ad-
dresses the following research questions:
■‘What are the areas of specialisation and ex-
cellence in EaP STI systems that can be mo-
bilised to support knowledge-based economic
transformation?’
■‘How are the international and national STI
collaboration networks structured and who
are the main stakeholders?’
2. Identification of the S&T
specialisation domains in the
Eastern Partnership
Science and technology activities span different
dimensions and their respective outputs differ in
nature and are distributed across multiple sources.
For instance, basic research tends to produce sci-
entific publications as an output, while technolog-
ical and applied research may ultimately result in
the production of patents protecting the respective
intellectual property. To obtain a comprehensive
view of the EaP S&T potential, all of these scientif-
ic endeavours and technological capabilities have
to be mapped, even though this task presents a
fundamental challenge: indeed, the records of the
outputs associated with all of these S&T activi-
ties are stored in different databases and they are
classified in accordance with different taxonomies,
each conceived within the specific boundary of the
respective S&T activity. For instance, patents are
categorised by means of patent classes, while pub-
lications are organised within bibliometric catego-
ries. To obtain a coherent view of the S&T potential
of the EaP countries across sources, it has | [
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1-Sexual health literacy and its related factors among couples: A population-based study in Iran
2-Effect of Educational Counseling on Knowledge and Attitude of Pregnant Women Towards Sex During Pregnancy
3-THE EFFECT OF INFERTILITY COUNSELING WITH COUPLE THERAPY APPROACH ON THE EMOTIONAL LEVEL OF INFERTILE COUPLES (RCT)
4-Effectiveness of Counseling Based on Functional Analytic Psychotherapy with Enhanced Cognitive Therapy on the Sexual Quality of Life of Married Adolescent Women
5-Efficacy of Dry Cupping versus Counselling with Mindfulness-based Cognitive Therapy Approach on Fertility Quality of Life and Conception Success in Infertile Women due to Polycystic Ovary Syndrome: A Pilot Randomized Clinical Trial
6-Comparison between the effect of the information–motivation–behavioral (IMB) model and
psychoeducational counseling on sexual satisfaction and contraception method used under the coercion of the spouse in Iranian women: A
Randomized, Clinical Trial
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also
chosen not to recharge the NiMH batteries for this test. In the case of NiMH batteries, the
standard charging method in IEC 61951-2 is used (16 h, 0.1 C). The application test “toy”
requires a battery to be discharged over a constant resistance for 1 h per day until a cut-off
voltage of 0.8 V is reached for AA, AAA, C, and D size batteries and to 5.4 V for 9V batteries.
A battery is considered to pass the test in IEC 60086-2 if the duration is higher than the
minimum average duration (MAD) values in Table 3.
Table 3. Parameters for the toy test per common designation in IEC 60086-2.
Common Designation Toy Test ΩNordic Swan MAD
(min)IEC 60086-2 MAD
(min)
AAA 5.1 190 120
AA 3.9 450 300
C 3.9 1260 840
D 2.2 1440 960
9V 270 1260 420
Figure 11 presents the voltage and current profiles of both primary and secondary
batteries using the “toy” application test. Figure 11a,b show the comparison of portable
NiMH batteries with primary batteries for the “toy” application test for the common
designations AA and AAA, respectively. Both the AA and AAA portable NiMH can pass
the “toy” application test (as well as the primary batteries) even without recharging. The
difference between the primary and the secondary AA and AAA batteries is 56 min (4.37%
capacity difference) and 54 min (8.76% capacity difference), respectively. In the case of the
C (see Figure 11c) and D (see Figure 11d) and 9V (see Figure 11e) NiMH batteries, they
cannot meet the duration requirement for the application test “toy” without recharging.
The difference between the C and D primary and secondary batteries is smaller than in the
case of the 9V battery (C = 608 min (44.8% capacity difference), D = 787.9 min (49% capacity
difference), and 9V = 179 min (400% capacity difference)).
Based on these results, although they cannot reach the pass value of the application
test “toy”, it is expected that the 9V , C, and D NiMH batteries can pass the test in the fourth
charge/discharge cycle for the 9V NiMH battery and the second cycle for the C and D (as
manufacturers declared a minimum of 400 cycles). For the AA and AAA batteries, the
results show that the NiMH can fulfill the application test without recharging and that
the gap between primary and secondary batteries for this | [
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there-
fore been decided to circumvent taxonomies and
to use the textual content of each record to ob-
tain fine-grained and homogeneous mapping of all
S&T activities across the EaP countries. To do so, a
series of topics have been extracted from the com-
bined corpus of scientific publications, EU-funded
research projects and patents by means of topic
modelling – an algorithmic approach to extract
condensed information from large textual corpora
in the form of ‘topics’.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation145
The result of topic modelling is a series of top-
ics, each characterised by a list of the main words
that tend to appear in them. This whole process is
carried out iteratively and with human revision in
order to identify and eliminate unrelated or irrel-
evant terms: proper names, acronyms, words with
little meaning or transversal words. Topics are
extracted individually for each country, allowing
for differences and nuances in national special-
isations. The individual topics are then clustered
together according to their semantic similarity,
leading to 14 S&T specialisation domains defined
across the EaP. This section presents and discuss-
es these results.
2.1 S&T data availability
Table 3.1 presents the data sources used in topic
modelling. Publications account for 64.48% of the
total number of records, while patents account for
32.36% and EC projects for only 0.16%.
Additionally, Table 3.2 showcases the specific dis-
tribution of records per type and per country. It is
important to note the significantly low number of
patents in comparison to the number of publica-
tions, which is especially pronounced in the cases
Scope Source Data extraction criteriaNo of records
(2012-2019)
Scientific publications
in internationally indexed
journalsScopus (Elsevier)Publications with at least
one author with an EaP
affiliation131 179
European Commission-
funded research and
innovation projectsCORDIS - Community
Research and
Development Information
ServiceFP7 and H2020 projects
with at least one EaP
partner324
PatentsWorldwide bibliographic
data (DOCDB) - European
Patent OfficeAt least one EaP inventor
or applicant for those
patents filed through a
patent office covered by
the EPO DOCDB database261 997
(2011-2018)Table 3.1. Characterisation of the data sources used in topic modelling, including the name of the source, its scope,
the data extraction criteria and the number of recordsof Armenia and Azerbaijan, where publications ac-
count for 96%, 92% and 87% of the total num-
ber of records in the country, respectively. This is
partially | [
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= 0.15; interaction ef fect:
p = 0.0001; Bonferroni multiple comparisons test, mouse CN versus object CN: object visit, p = 0.0007, mouse visit, p = 0.0001; object visit versus mous e visit:
mouse CN, p = 0.0024, object CN, p = 0.0006).(I) Schematic of the fear-conditioning paradigm.
(J) Percentage of inhibited or active aIC VIP+ CN during CS and US presentations (n = 85).
(K) Averaged responses from recorded aIC VIP+ US CN (red) and CS CN (blue) across all trials.(L) Mean AUC of Zscored activity responses following US presentations was higher for US CN than for CS CN, while the latter were preferentially active during CS
presentations (two-way ANOVA, main effect ensemble: p = 0.0004; main effect presentation: p = 0.042; interaction effect: p = 0.0001; Bonferronimultiple comparisons test, US CN versus CS CN: CS presentation, p = 0.0001, US presentation, p = 0.0001; CS versus US presentation: US+ CN, p = 0.0001, CS +
CN, p = 0.0028).(M) Schematic of the fear retrieval paradigm.(N) Percentage of aIC VIP+ INs with increased or decreased ( ±2sover baseline) Ca
2+responses during CS-R or US- (n = 79).
(O) Averaged responses from recorded aIC VIP+ US- CN (magenta) and CS-R CN (blue) across all trials.(P) Mean AUC of Zscored activity responses following CS-R presentations was higher for CS-R CN than for US- CN, while the latter were preferentially active
during US omissions (main effect ensemble: p = 0.407; main effect presentation: p = 0.719; interaction effect: p = 0.0001; Bonferroni multiple compar isons test,
US- CN versus CS-R CN: CS-R presentation, p = 0.0001, US- presentation, p = 0.0001; CS-R versus US- presentation: US- CN, p = 0.0001, CS-R CN, p = 0.0001).(Q) Population vector distances between clusters of mouse and object CN at different cumulative times of interaction with the object or mouse on socia l
preference test day 1 (Friedman test, p < 0.0001).(R) Population vector distances between clusters of mouse and object CN at different cumulative times of interaction with the object or mouse on socia l pref-
erence test day 2 (Friedman test, p = 0.13).(S) Population vector distances between clusters of CS and US CN at different CS-US pairings during fear conditioning (Friedman test, p = 0.93).(T) Population vector distances between clusters of CS-R and US- CN at different CS-R/US- presentations during fear retrieval testing (Friedman tes t, p = 0.13).
Data are shown as | [
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The EU should also put in place a common trading rulebook applying to both
spot and derivatives markets and ensure integrated supervision of energy and energy derivatives markets. Finally,
the EU should review the “ancillary activities exemption” to ensure that all trading entities are subject to the same
supervision and requirements.
At the same time, transferring the benefits of decarbonisation requires policies to better decouple the price
of natural gas from clean energy . The EU should decouple the remuneration of renewable energy and nuclear
from fossil-fuel generation by building on the tools introduced under the new Electricity Market Design – such
as PPAs and two-way CfDs – and progressively extending PPAs and CFDs to all renewable and nuclear assets in
a harmonised way. The marginal pricing system should be used to ensure efficient balance in the energy system.
To increase the uptake of PPAs into the industrial sector, the report recommends developing market platforms
to contract resources and pool demand between generators and offtakers. This initiative can be combined with
schemes to provide guarantees to mitigate the financial counterparty risks engendered by using such platforms,
thereby enlarging market access to SMEs. For example, the EIB and National Promotional Banks could provide
counter guarantees and specific financial products for small consumers or suppliers that lack a proper credit rating.
In parallel, a fundamental component of lowering energy costs for end users is reducing energy taxation, which can
be achieved by adopting a common maximum level of surcharges across the EU (including taxes, levies and network
charges). Legislative reform in this area is subject to unanimity, but cooperation among a subset of Member States
or guidance on energy taxation can be considered.
The second key goal is to accelerate decarbonisation in a cost-efficient way, leveraging all available solu -
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bioenergy and carbon capture, utilisation and storage, and should be backed by massive mobilisation of both public
and private finance (based on the proposals laid out in the chapter on investment. However, increasing the supply
of finance for clean energy deployment will not yield the desired results without increasing the pace of permitting
for installation. Different options are available to reduce permitting delays for new energy projects. Systematically
implementing existing legislation can make a major difference: for example, several Member States have experienced
double-digit increases in the volume of | [
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] | [] |
and financial capacity. First, the
programme should be refocused on a smaller number on commonly agreed priorities. Second, an increased share of
the budget allocation should be allocated towards financing disruptive innovation and, to make efficient use of this
funding, the EIC should be reformed to become a genuine “ARPA-type agency”, supporting high-risk projects with
the potential of delivering breakthrough technological advances. Third, the governance of the programme should
be managed by project managers and by people with proven track record at the frontier of innovation and – to
maximise access for young, innovative companies – application processes should be faster and less bureaucratic.
The organisation of the programme should be redesigned and streamlined to become more outcome-based and
efficient. Finally, conditional on reforms, the budget of the new Framework Programme should be doubled to EUR
200 billion per 7 years.
In parallel, better coordination of public R&I across Member States is necessary . A Research and Innova -
tion Union should be established and lead to a joint formulation of a common European R&I strategy and policy.
To improve coordination, the EU could promote a “European Research and Innovation Action Plan”, designed by
Member States, together with the Commission, the research community, and stakeholders from the private sector.
It is also essential to establish and consolidate European academic institutions at the forefront of global
research . The European Research Council (ERC) has been crucial to the competitiveness of European science
but many promising proposals remain unfunded owing to a lack of financial resources. The report recommends
doubling the support for fundamental research through the ERC, significantly increasing the number of grant recip -
ients without diluting the amount they receive. In parallel, the EU should introduce an excellence-based, highly
competitive “ERC for Institutions” programme to provide the required resources for academic institutions. A new
regime for world-class researchers (“EU Chair” position) is also proposed, to attract and retain the best academic
scholars by hiring them as European officials. This regime should be supported by a new EU framework for private
funding to enable public universities and research centres to design more competitive compensation policies for top
talents and to provide additional support for research. Beyond academic institutions, increased funding and stronger
coordination is required to develop world-leading research and technological infrastructures, when scale is needed.
Europe needs to make it easier for “inventors to become investors” and facilitate scaling | [
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] | [] |
Cookies LI 40.2*** 7.63 ✓
Lithuania Yogurt DE 0.1 0.07 ✕ ✕
(Euro) Yogurt HU 0.13 0.07 ✕
Spaghetti sauce DE 0.12 0.07 ✕ ✕
Spaghetti sauce HU 0.06 0.07 ✕
Cookies DE/HU]]0.08 0.05 ✕ ]]]
Romania Soft drink ES 42.93 126.99 ✕ ✕
(Leu) Soft drink SE 13.7 40.63 ✕ ✕
Fish fingers ES 1.83*** 0.25 ✕ ✓
Fish fingers SE 2.66*** 0.30 ✕ ✓
Crisps ES 3.76*** 0.96 ✕ ✓
Crisps SE 0.53 0.98 ✕ ✕
Source: Authors ’ elaboration
]The level of significance refers to H1a.
]]In Germany and Hungary, the two versions of Milka Choco Cookies were identical (valid for all tables).
]]]An East–West comparison for the Hungarian/German version of the Cookies is not possible as the two versions are identical (valid for all tables).
Table A2
WTP premium for branded-foreign over branded-domestic product versions in the absence of the ‘made for’ claim.
Country Groups Product Version Mean]Std Dev. H1a,B H1c,B
Western Countries
Germany Yogurt HU 0.29*** 0.04 ✕ ✕
(Euro) Yogurt LI 0.49*** 0.04 ✕ ✕
Spaghetti sauce HU 0.07* 0.03 ✕ ✕
Spaghetti sauce LI 0.03 0.03 ✕ ✕
Cookies LI 0.02 0.03 ✕ ✕
Spain Soft drink RO 0.35*** 0.03 ✓ ✓
(Euro) Soft drink SE 0.36*** 0.03 ✓
Fish fingers RO 1.4*** 0.11 ✓ ✓
Fish fingers SE 0.53*** 0.09 ✓
Crisps RO 0.63*** 0.05 ✓ ✓
Crisps SE 0.64*** 0.05 ✓
Sweden Soft drink ES 0.55 0.35 ✕
(Krona) Soft drink RO 1.95*** 0.35 ✓ ✓
Fish fingers ES 4.51*** 1.24 ✓
Fish fingers RO 4.8*** 1.12 ✓ ✓
Crisps ES 1.64* 0.80 ✕
Crisps RO 0.02 0.78 ✕ ✓
Eastern Countries
Hungary Yogurt DE 47.01*** 8.02 ✓ ✕
(Forint) Yogurt LI 31.89*** 7.72 ✕
Spaghetti sauce DE 205.87*** 18.12 ✓ ✕
Spaghetti sauce LI 73.05*** 18.56 ✓
Cookies LI 2.53 7.63 ✕
Lithuania Yogurt DE 0.25** 0.07 ✓ ✕
(Euro) Yogurt HU 0.15* 0.07 ✓
Spaghetti sauce DE 0.09 0.07 ✕ ✕
Spaghetti sauce HU 0.14 0.07 ✕
Cookies DE/HU 0.03 0.05 ✕
Romania Soft drink ES 54.47 126.99 ✕ ✕
(Leu) Soft drink SE 26.55 40.63 ✕ ✕
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"\u00000.03",
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"\u0000",
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"0.55",
"0.35",
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"\u0000",
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"\u00001.95",
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"0.35",
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"\u00004.51",
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"\u0000",
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"",
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] | [] |
Telecom1.63 Advertising 2.13 Financial Services 1.95Table 2.44. Specialised industry groups – Armenia
For Armenia, 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.
108
Part 2 Analysis of economic and innovation potential
Azerbaijan
Azerbaijan is highly specialised in Travel & Tour-
ism and Natural Resources across all three var-
iables. Transportation and Energy have a high
specialisation in terms of number of employees
and estimated revenue, but not so much in terms
of number of companies. Other industry groups
with high specialisation across more than one var-
iable are Lending & Investments and Financial
Services.
Georgia
Georgia’s main specialisation is in Payments,
which ranks the highest across all three variables.
Other industry groups with high specialisation in
all three variables are Lending & Investments,
Financial Services and Travel & Tourism. Oth-
er industry groups with high specialisation across
more than one variable are Food & Beverage,
Software and Agriculture & Farming. Soft-
ware and Internet Services, which ranked highly in terms of critical mass across all three variables,
only have a high specialisation in number of em-
ployees.
Moldova
Moldova is highly specialised in Sustainability,
Government & Military and Lending & In-
vestments across all three variables. Food &
Beverage is the industry group with the highest
specialisation in the number of companies and
number of employees. Other industry groups with
a high specialisation across more than one variable
are Privacy & Security, Energy and Financial
Services. Software and Information Technology,
which ranked first and second (respectively) in
terms of critical mass, have a low specialisation in
the number of companies.
Azerbaijan
# firms SI Firms # employees SI Employees # est. revenue SI Revenue
Travel and Tourism 3.93Agriculture and
Farming7.31 Energy 9.10
Lending and
Investments3.27 Natural Resources 6.33 Transportation 7.22
Messaging and
Telecom.2.93 Transportation 4.32 Natural Resources 4.63
Music and Audio 2.37 Travel and Tourism 3.91 Manufacturing 4.46
Natural Resources 2.34Science and
Engineering3.05 Travel and Tourism 2.18
Financial Services 2.30 Energy 2.22 Music and Audio 2.04
Internet Services 1.68 Food and Beverage 1.22 Financial Services 1.42
Food and Beverage 1.64 Mobile 1.06Lending and
Investments1.30
Energy 1.61 Financial Services 0.98 Payments 0.63
Community and
Lifestyle1.48Lending and
Investments0.97 Events 0.34Table 2.45. Specialised industry groups– Azerbaijan
For Azerbaijan, the table shows the relative specialisation in terms of number of companies, number of employees and
estimated | [
"Telecom1.63",
"Advertising",
"2.13",
"Financial",
"Services",
"1.95Table",
"2.44",
".",
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"\n ",
"Part",
"2",
"Analysis",
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"and",
"innovation",
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"\n",
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"Soft-",
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"\n",
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".",
"\n",
"Moldova",
"\n",
"Moldova",
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"highly",
"specialised",
"in",
"Sustainability",
",",
"\n",
"Government",
"&",
"Military",
"and",
"Lending",
"&",
"In-",
"\n",
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"across",
"all",
"three",
"variables",
".",
"Food",
"&",
"\n",
"Beverage",
"is",
"the",
"industry",
"group",
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"\n",
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"the",
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"\n",
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".",
"Other",
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"\n",
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"more",
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"\n",
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"&",
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",",
"Energy",
"and",
"Financial",
"\n",
"Services",
".",
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"and",
"Information",
"Technology",
",",
"\n",
"which",
"ranked",
"first",
"and",
"second",
"(",
"respectively",
")",
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"\n",
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",",
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"\n",
"the",
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".",
"\n",
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"\n",
"#",
"firms",
"SI",
"Firms",
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"SI",
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".",
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"\n",
"Travel",
"and",
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"3.93Agriculture",
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"\n",
"Farming7.31",
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"9.10",
"\n",
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"and",
"\n",
"Investments3.27",
"Natural",
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"6.33",
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"7.22",
"\n",
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"and",
"\n",
"Telecom.2.93",
"Transportation",
"4.32",
"Natural",
"Resources",
"4.63",
"\n",
"Music",
"and",
"Audio",
"2.37",
"Travel",
"and",
"Tourism",
"3.91",
"Manufacturing",
"4.46",
"\n",
"Natural",
"Resources",
"2.34Science",
"and",
"\n",
"Engineering3.05",
"Travel",
"and",
"Tourism",
"2.18",
"\n",
"Financial",
"Services",
"2.30",
"Energy",
"2.22",
"Music",
"and",
"Audio",
"2.04",
"\n",
"Internet",
"Services",
"1.68",
"Food",
"and",
"Beverage",
"1.22",
"Financial",
"Services",
"1.42",
"\n",
"Food",
"and",
"Beverage",
"1.64",
"Mobile",
"1.06Lending",
"and",
"\n",
"Investments1.30",
"\n",
"Energy",
"1.61",
"Financial",
"Services",
"0.98",
"Payments",
"0.63",
"\n",
"Community",
"and",
"\n",
"Lifestyle1.48Lending",
"and",
"\n",
"Investments0.97",
"Events",
"0.34Table",
"2.45",
".",
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"industry",
"groups",
"–",
"Azerbaijan",
"\n",
"For",
"Azerbaijan",
",",
"the",
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"the",
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"in",
"terms",
"of",
"number",
"of",
"companies",
",",
"number",
"of",
"employees",
"and",
"\n",
"estimated"
] | [] |
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or toilet preparations (excluding
soaps) X 0.3%
592Starches, inulin and wheat gluten; albuminoidal substances;
gluesX 0.1% X 0.1%
598 Miscellaneous chemical products, n.e.s. X 0.2%
6 Manufactured goods classified chiefly by material
611 Leather X 0.2%
634Veneers, plywood, particle board, and other wood, worked,
n.e.s.X 0.6% X 0.6%
635 Wood manufactures, n.e.s. X 0.4%
641 Paper and paperboard X 0.9%
642Paper and paperboard, cut to size or shape, and articles of
paper or paperboardX 0.5% X 0.5%
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation77
SITC Goods nameCurrent
strength% share
of
exportsEmerging
strength% share
of
exports
51 69.6% 52 47.0%
658 Made-up articles, wholly or chiefly of textile materials, n.e.s. X 0.2%
662Clay construction materials and refractory construction
materialsX 0.3%
663 Mineral manufactures, n.e.s. X 0.1%
665 Glassware X 0.2%
671Pig-iron, spiegeleisen, sponge iron, iron or steel granules
and powders and ferro-alloys X 3.6%
672Ingots and other primary forms, of iron or steel; semi-
finished products of iron or steelX 7.0%
673Flat-rolled products of iron or non-alloy steel, not clad,
plated or coatedX 5.3%
674Flat-rolled products of iron or non-alloy steel, clad, plated
or coatedX 0.3% X 0.3%
675 Flat-rolled products of alloy steel X 0.3% X 0.3%
676Iron and steel bars, rods, angles, shapes and sections
(including sheet piling)X 4.5%
678 Wire of iron or steel X 0.1%
679Tubes, pipes and hollow profiles, and tube or pipe fittings,
of iron or steelX 1.8%
682 Copper X 0.3%
684 Aluminium X 0.2%
689Miscellaneous non-ferrous base metals employed in
metallurgy, and cermets X 0.1%
697 Household equipment of base metal, n.e.s. X 0.2%
699 Manufactures of base metal, n.e.s. X 0.6% X 0.6%
7 Machinery and transport equipment
714Engines and motors, non-electric (other than those of
groups 712, 713 and 718); parts, n.e.s., of these engines
and motorsX 1.2%
716 Rotating electric plant and parts thereof, n.e.s. X 0.3%
718 Power-generating machinery and parts thereof, n.e.s. X 0.2%
721Agricultural machinery (excluding tractors) and parts
thereof X 0.2%
723Civil engineering and contractors' plant and equipment;
parts thereof X 0.2%
728Other machinery and equipment specialized for particular
industries; parts thereof, n.e.s.X 0.3%
737Metalworking machinery (other than machine tools) and
parts thereof, n.e.s.X 0.2% X 0.2%
742Pumps for liquids, whether or not fitted with a measuring
device; liquid elevators; parts for such pumps and liquid
elevatorsX 0.3%
744 Mechanical handling equipment and parts thereof, n.e.s. X 0.2%
746 Ball- | [
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"Aluminium",
" ",
"X",
"0.2",
"%",
"\n",
"689Miscellaneous",
"non",
"-",
"ferrous",
"base",
"metals",
"employed",
"in",
"\n",
"metallurgy",
",",
"and",
"cermets",
" ",
"X",
"0.1",
"%",
"\n",
"697",
"Household",
"equipment",
"of",
"base",
"metal",
",",
"n.e.s",
".",
" ",
"X",
"0.2",
"%",
"\n",
"699",
"Manufactures",
"of",
"base",
"metal",
",",
"n.e.s",
".",
"X",
"0.6",
"%",
"X",
"0.6",
"%",
"\n",
"7",
"Machinery",
"and",
"transport",
"equipment",
" \n",
"714Engines",
"and",
"motors",
",",
"non",
"-",
"electric",
"(",
"other",
"than",
"those",
"of",
"\n",
"groups",
"712",
",",
"713",
"and",
"718",
")",
";",
"parts",
",",
"n.e.s",
".",
",",
"of",
"these",
"engines",
"\n",
"and",
"motorsX",
"1.2",
"%",
" \n",
"716",
"Rotating",
"electric",
"plant",
"and",
"parts",
"thereof",
",",
"n.e.s",
".",
"X",
"0.3",
"%",
" \n",
"718",
"Power",
"-",
"generating",
"machinery",
"and",
"parts",
"thereof",
",",
"n.e.s",
".",
"X",
"0.2",
"%",
" \n",
"721Agricultural",
"machinery",
"(",
"excluding",
"tractors",
")",
"and",
"parts",
"\n",
"thereof",
" ",
"X",
"0.2",
"%",
"\n",
"723Civil",
"engineering",
"and",
"contractors",
"'",
"plant",
"and",
"equipment",
";",
"\n",
"parts",
"thereof",
" ",
"X",
"0.2",
"%",
"\n",
"728Other",
"machinery",
"and",
"equipment",
"specialized",
"for",
"particular",
"\n",
"industries",
";",
"parts",
"thereof",
",",
"n.e.s",
".",
"X",
"0.3",
"%",
" \n",
"737Metalworking",
"machinery",
"(",
"other",
"than",
"machine",
"tools",
")",
"and",
"\n",
"parts",
"thereof",
",",
"n.e.s",
".",
"X",
"0.2",
"%",
"X",
"0.2",
"%",
"\n",
"742Pumps",
"for",
"liquids",
",",
"whether",
"or",
"not",
"fitted",
"with",
"a",
"measuring",
"\n",
"device",
";",
"liquid",
"elevators",
";",
"parts",
"for",
"such",
"pumps",
"and",
"liquid",
"\n",
"elevatorsX",
"0.3",
"%",
" \n",
"744",
"Mechanical",
"handling",
"equipment",
"and",
"parts",
"thereof",
",",
"n.e.s",
".",
" ",
"X",
"0.2",
"%",
"\n",
"746",
"Ball-"
] | [] |
non-residential buildings X 55Essential oils and resinoids and perfume
materials; toilet, etc. X Green energy and environmental services
42.1 Construction of roads and railways X 57 Plastics in primary forms X Education and knowledge transfer
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation139 140
Part 2 Analysis of economic and innovation potential
42.2 Construction of utility projects X 58 Plastics in non-primary forms X Food and agriculture
43.2Electrical, plumbing and other
construction installation act. X 59 Chemical materials and products X Information and communication technologies
45.3Sale of motor vehicle parts and
accessories X 61Leather, leather manufactures, and dressed
furskins X Medicine and health
46.1Wholesale on a fee or contract
basis X 63 Cork and wood manufactures (excluding furniture) X Biotechnology
46.2Wholesale of agricultural raw
materials and live animals X 64Paper, paperboard and articles of paper pulp, of
paper or of paperboardX Pharmaceutics
46.3Wholesale of food, beverages and
tobacco X 65Textile yarn, fabrics, made-up articles, and related
products X Industrial manufacturing and processes
46.6Wholesale of other machinery,
equipment and supplies X 66 Non-metallic mineral manufactures X
46.9 Non-specialised wholesale trade X 67 Iron and steel X
47.1Retail sale in non-specialised
stores X 68 Non-ferrous metals X
47.2Retail sale of food, beverages and
tobacco in spec. stores X 71 Power-generating machinery and equipment X
47.7Retail sale of other goods in
specialised stores X 73 Metalworking machinery X X
49.2 Freight rail transport X X 74General industrial machinery and equipment, and
machine parts,X
49.4Freight transport by road and
removal services X 76Telecommunications and sound-recording and
reproducing apparatus etc.X
49.5 Transport via pipeline X 79 Other transport equipment X
82Furniture and parts thereof; bedding, mattresses,
mattress supports, etc. X
84 Articles of apparel and clothing accessories X
89 Miscellaneous manufactured articles, X
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation141
5. Common E&I specialisations
in the EaP region
During the preceding sections, several transversal
and country-level analyses have been presented,
based on different sources and indicators.
With regard to the transversal analysis, it has been
found that the primary sector (Agriculture, For-
estry and Fishing) is an important sector in all EaP
countries in terms of gross domestic product, and
agriculture in particular, very relevant in terms of
employment. Unfortunately, due to the data source
limitations of this study, no further characterisa-
tion of the primary sector has been produced at
sub-sectoral level for | [
"non",
"-",
"residential",
"buildings",
"X",
"55Essential",
"oils",
"and",
"resinoids",
"and",
"perfume",
"\n",
"materials",
";",
"toilet",
",",
"etc",
".",
"X",
" ",
"Green",
"energy",
"and",
"environmental",
"services",
"\n",
"42.1",
"Construction",
"of",
"roads",
"and",
"railways",
" ",
"X",
"57",
"Plastics",
"in",
"primary",
"forms",
" ",
"X",
" ",
"Education",
"and",
"knowledge",
"transfer",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation139",
"140",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"42.2",
"Construction",
"of",
"utility",
"projects",
" ",
"X",
"58",
"Plastics",
"in",
"non",
"-",
"primary",
"forms",
" ",
"X",
" ",
"Food",
"and",
"agriculture",
"\n",
"43.2Electrical",
",",
"plumbing",
"and",
"other",
"\n",
"construction",
"installation",
"act",
".",
"X",
"59",
"Chemical",
"materials",
"and",
"products",
" ",
"X",
" ",
"Information",
"and",
"communication",
"technologies",
"\n",
"45.3Sale",
"of",
"motor",
"vehicle",
"parts",
"and",
"\n",
"accessories",
"X",
"61Leather",
",",
"leather",
"manufactures",
",",
"and",
"dressed",
"\n",
"furskins",
"X",
" ",
"Medicine",
"and",
"health",
"\n",
"46.1Wholesale",
"on",
"a",
"fee",
"or",
"contract",
"\n",
"basis",
"X",
"63",
"Cork",
"and",
"wood",
"manufactures",
"(",
"excluding",
"furniture",
")",
"X",
" ",
"Biotechnology",
"\n",
"46.2Wholesale",
"of",
"agricultural",
"raw",
"\n",
"materials",
"and",
"live",
"animals",
"X",
"64Paper",
",",
"paperboard",
"and",
"articles",
"of",
"paper",
"pulp",
",",
"of",
"\n",
"paper",
"or",
"of",
"paperboardX",
" ",
"Pharmaceutics",
"\n",
"46.3Wholesale",
"of",
"food",
",",
"beverages",
"and",
"\n",
"tobacco",
"X",
"65Textile",
"yarn",
",",
"fabrics",
",",
"made",
"-",
"up",
"articles",
",",
"and",
"related",
"\n",
"products",
"X",
" ",
"Industrial",
"manufacturing",
"and",
"processes",
"\n",
"46.6Wholesale",
"of",
"other",
"machinery",
",",
"\n",
"equipment",
"and",
"supplies",
"X",
"66",
"Non",
"-",
"metallic",
"mineral",
"manufactures",
" ",
"X",
" \n",
"46.9",
"Non",
"-",
"specialised",
"wholesale",
"trade",
" ",
"X",
"67",
"Iron",
"and",
"steel",
"X",
" \n",
"47.1Retail",
"sale",
"in",
"non",
"-",
"specialised",
"\n",
"stores",
"X",
"68",
"Non",
"-",
"ferrous",
"metals",
" ",
"X",
" \n",
"47.2Retail",
"sale",
"of",
"food",
",",
"beverages",
"and",
"\n",
"tobacco",
"in",
"spec",
".",
"stores",
"X",
"71",
"Power",
"-",
"generating",
"machinery",
"and",
"equipment",
"X",
" \n",
"47.7Retail",
"sale",
"of",
"other",
"goods",
"in",
"\n",
"specialised",
"stores",
"X",
"73",
"Metalworking",
"machinery",
"X",
"X",
" \n",
"49.2",
"Freight",
"rail",
"transport",
"X",
"X",
"74General",
"industrial",
"machinery",
"and",
"equipment",
",",
"and",
"\n",
"machine",
"parts",
",",
"X",
" \n",
"49.4Freight",
"transport",
"by",
"road",
"and",
"\n",
"removal",
"services",
"X",
"76Telecommunications",
"and",
"sound",
"-",
"recording",
"and",
"\n",
"reproducing",
"apparatus",
"etc",
".",
"X",
" \n",
"49.5",
"Transport",
"via",
"pipeline",
"X",
" ",
"79",
"Other",
"transport",
"equipment",
"X",
" \n",
"82Furniture",
"and",
"parts",
"thereof",
";",
"bedding",
",",
"mattresses",
",",
"\n",
"mattress",
"supports",
",",
"etc",
".",
"X",
"\n",
"84",
"Articles",
"of",
"apparel",
"and",
"clothing",
"accessories",
" ",
"X",
"\n",
"89",
"Miscellaneous",
"manufactured",
"articles",
",",
" ",
"X",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation141",
"\n",
"5",
".",
"Common",
"E&I",
"specialisations",
"\n",
"in",
"the",
"EaP",
"region",
"\n",
"During",
"the",
"preceding",
"sections",
",",
"several",
"transversal",
"\n",
"and",
"country",
"-",
"level",
"analyses",
"have",
"been",
"presented",
",",
"\n",
"based",
"on",
"different",
"sources",
"and",
"indicators",
".",
"\n",
"With",
"regard",
"to",
"the",
"transversal",
"analysis",
",",
"it",
"has",
"been",
"\n",
"found",
"that",
"the",
"primary",
"sector",
"(",
"Agriculture",
",",
"For-",
"\n",
"estry",
"and",
"Fishing",
")",
"is",
"an",
"important",
"sector",
"in",
"all",
"EaP",
"\n",
"countries",
"in",
"terms",
"of",
"gross",
"domestic",
"product",
",",
"and",
"\n",
"agriculture",
"in",
"particular",
",",
"very",
"relevant",
"in",
"terms",
"of",
"\n",
"employment",
".",
"Unfortunately",
",",
"due",
"to",
"the",
"data",
"source",
"\n",
"limitations",
"of",
"this",
"study",
",",
"no",
"further",
"characterisa-",
"\n",
"tion",
"of",
"the",
"primary",
"sector",
"has",
"been",
"produced",
"at",
"\n",
"sub",
"-",
"sectoral",
"level",
"for"
] | [] |
98
Table 2.35. Descriptive statistics for trademarks in a number of combined manufacturing
industries .......................................................................................................................................................... 99
Table 2.36. Trademark specialisations for a number of combined manufacturing
industries ....................................................................................................................................................... 100
Table 2.37. Industrial design applications .................................................................................... 102
Table 2.38. Crunchbase – key descriptive statistics ................................................................. 103
Table 2.39. Largest industry groups – Armenia ......................................................................... 104
Table 2.40. Largest industry groups – Azerbaijan ..................................................................... 104
Table 2.41. Largest industry groups – Georgia ........................................................................... 105
Table 2.42. Largest industry groups – Moldova ......................................................................... 106
Table 2.43. Largest industry groups – Ukraine ........................................................................... 106
Table 2.44. Specialised industry groups – Armenia .................................................................. 107
Table 2.45. Specialised industry groups– Azerbaijan .............................................................. 108
Table 2.46. Specialised industry groups – Georgia ................................................................... 109
Table 2.47. Specialised industry groups – Moldova .................................................................. 109
Table 2.48. Specialised industry groups – Ukraine ................................................................... 110
Table 2.49. Armenia ................................................................................................................................ 112
Table 2.50. Azerbaijan ............................................................................................................................ 112
Table 2.51. Georgia .................................................................................................................................. 112
Table 2.52. Moldova ................................................................................................................................ 113
Table 2.53. Ukraine .................................................................................................................................. 113
266
List of figures and tables
Table 2.54. Recommended industry groups for start-ups and venture capital-backed
companies ..................................................................................................................................................... 115
Table 2.55. Concordance between NACE industries and recommended industry groups
for start-ups and venture capital-backed companies ............................................................... 116
Table 2.56. Number of cluster organisations by EaP country .............................................. 117
Table 2.57. Number of identified cluster organisations by country and sector .......... 119
Table S.1. Summary table of mapping results for Armenia .................................................. 123
Table S.2. Summary table of mapping results for Azerbaijan ............................................. 125
Table S.3. Summary table of mapping results for Georgia ................................................... 127
Table S.4. Summary table of mapping results for Moldova .................................................. 131
Table S.5. Summary table of mapping results for Ukraine ................................................... 135
Table 2.58. E&I specialisation domains in terms of NACE sectors .................................... 142
Table 3.1. Characterisation of the data sources used in topic modelling, including
the name of the source, its scope, the data extraction criteria and the number of
records .................................................................................................................................................... 145
Table 3.2. Distribution of records per type and per country ................................................. 146
Table 3.3a. List of identified S&T specialisation domains .................................................... 149
Table 3.3b. List of 50 most relevant keywords for each S&T domain ............................. 149
Table 3.4. Number of records per labelled topic group (i.e. ‘domain’) in the Eastern
Partnership region ..................................................................................................................................... 153
Table 3.5. Characterisation of the EaP S&T domains according to the internal distribution
of the S&T data sources ......................................................................................................................... 156
Table 3.6. Temporal evolution of S&T domains in the | [
"98",
"\n",
"Table",
"2.35",
".",
"Descriptive",
"statistics",
"for",
"trademarks",
"in",
"a",
"number",
"of",
"combined",
"manufacturing",
"\n",
"industries",
"..........................................................................................................................................................",
"99",
"\n",
"Table",
"2.36",
".",
"Trademark",
"specialisations",
"for",
"a",
"number",
"of",
"combined",
"manufacturing",
"\n",
"industries",
".......................................................................................................................................................",
"100",
"\n",
"Table",
"2.37",
".",
"Industrial",
"design",
"applications",
"....................................................................................",
"102",
"\n",
"Table",
"2.38",
".",
"Crunchbase",
"–",
"key",
"descriptive",
"statistics",
".................................................................",
"103",
"\n",
"Table",
"2.39",
".",
"Largest",
"industry",
"groups",
"–",
"Armenia",
".........................................................................",
"104",
"\n",
"Table",
"2.40",
".",
"Largest",
"industry",
"groups",
"–",
"Azerbaijan",
".....................................................................",
"104",
"\n",
"Table",
"2.41",
".",
"Largest",
"industry",
"groups",
"–",
"Georgia",
"...........................................................................",
"105",
"\n",
"Table",
"2.42",
".",
"Largest",
"industry",
"groups",
"–",
"Moldova",
".........................................................................",
"106",
"\n",
"Table",
"2.43",
".",
"Largest",
"industry",
"groups",
"–",
"Ukraine",
"...........................................................................",
"106",
"\n",
"Table",
"2.44",
".",
"Specialised",
"industry",
"groups",
"–",
"Armenia",
"..................................................................",
"107",
"\n",
"Table",
"2.45",
".",
"Specialised",
"industry",
"groups",
"–",
"Azerbaijan",
"..............................................................",
"108",
"\n",
"Table",
"2.46",
".",
"Specialised",
"industry",
"groups",
"–",
"Georgia",
"...................................................................",
"109",
"\n",
"Table",
"2.47",
".",
"Specialised",
"industry",
"groups",
"–",
"Moldova",
"..................................................................",
"109",
"\n",
"Table",
"2.48",
".",
"Specialised",
"industry",
"groups",
"–",
"Ukraine",
"...................................................................",
"110",
"\n",
"Table",
"2.49",
".",
"Armenia",
"................................................................................................................................",
"112",
"\n",
"Table",
"2.50",
".",
"Azerbaijan",
"............................................................................................................................",
"112",
"\n",
"Table",
"2.51",
".",
"Georgia",
"..................................................................................................................................",
"112",
"\n",
"Table",
"2.52",
".",
"Moldova",
"................................................................................................................................",
"113",
"\n",
"Table",
"2.53",
".",
"Ukraine",
"..................................................................................................................................",
"113",
"\n",
"266",
"\n",
"List",
"of",
"figures",
"and",
"tables",
"\n",
"Table",
"2.54",
".",
"Recommended",
"industry",
"groups",
"for",
"start",
"-",
"ups",
"and",
"venture",
"capital",
"-",
"backed",
"\n",
"companies",
".....................................................................................................................................................",
"115",
"\n",
"Table",
"2.55",
".",
"Concordance",
"between",
"NACE",
"industries",
"and",
"recommended",
"industry",
"groups",
"\n",
"for",
"start",
"-",
"ups",
"and",
"venture",
"capital",
"-",
"backed",
"companies",
"...............................................................",
"116",
"\n",
"Table",
"2.56",
".",
"Number",
"of",
"cluster",
"organisations",
"by",
"EaP",
"country",
"..............................................",
"117",
"\n",
"Table",
"2.57",
".",
"Number",
"of",
"identified",
"cluster",
"organisations",
"by",
"country",
"and",
"sector",
"..........",
"119",
"\n",
"Table",
"S.1",
".",
"Summary",
"table",
"of",
"mapping",
"results",
"for",
"Armenia",
"..................................................",
"123",
"\n",
"Table",
"S.2",
".",
"Summary",
"table",
"of",
"mapping",
"results",
"for",
"Azerbaijan",
".............................................",
"125",
"\n",
"Table",
"S.3",
".",
"Summary",
"table",
"of",
"mapping",
"results",
"for",
"Georgia",
"...................................................",
"127",
"\n",
"Table",
"S.4",
".",
"Summary",
"table",
"of",
"mapping",
"results",
"for",
"Moldova",
"..................................................",
"131",
"\n",
"Table",
"S.5",
".",
"Summary",
"table",
"of",
"mapping",
"results",
"for",
"Ukraine",
"...................................................",
"135",
"\n",
"Table",
"2.58",
".",
"E&I",
"specialisation",
"domains",
"in",
"terms",
"of",
"NACE",
"sectors",
"....................................",
"142",
"\n",
"Table",
"3.1",
".",
"Characterisation",
"of",
"the",
"data",
"sources",
"used",
"in",
"topic",
"modelling",
",",
"including",
"\n",
"the",
"name",
"of",
"the",
"source",
",",
"its",
"scope",
",",
"the",
"data",
"extraction",
"criteria",
"and",
"the",
"number",
"of",
"\n",
"records",
"....................................................................................................................................................",
"145",
"\n",
"Table",
"3.2",
".",
"Distribution",
"of",
"records",
"per",
"type",
"and",
"per",
"country",
".................................................",
"146",
"\n",
"Table",
"3.3a",
".",
"List",
"of",
"identified",
"S&T",
"specialisation",
"domains",
"....................................................",
"149",
"\n",
"Table",
"3.3b",
".",
"List",
"of",
"50",
"most",
"relevant",
"keywords",
"for",
"each",
"S&T",
"domain",
".............................",
"149",
"\n",
"Table",
"3.4",
".",
"Number",
"of",
"records",
"per",
"labelled",
"topic",
"group",
"(",
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grounded in the real world
the speaker and listener inhabit together. Commu-
nicative intents can also be about abstract worlds,
e.g. bank accounts, computer file systems, or a
purely hypothetical world in the speaker’s mind.
Linguists distinguish communicative intent from
conventional (orstanding ) meaning (Quine, 1960;
Grice, 1968). The conventional meaning of an
expression (word, phrase, sentence) is what is con-
stant across all of its possible contexts of use. Con-
ventional meaning is an abstract object that repre-
sents the communicative potential of a form, given
the linguistic system it is drawn from. Each lin-
guistic system (say, English) provides a relation
CES, which contains pairs (e; s)of expres-
sions eand their conventional meanings s.6The
field of linguistic semantics provides many com-
peting theories of what conventional meanings s
look like. For our purposes, we don’t need to select
among these theories; all we assume is that conven-
tional meanings must have interpretations, such as
a means of testing them for truth against a model
of the world. Thus, like the meaning relation M,C
connects language to objects outside of language.
5In spoken languages, the primary articulators are the com-
ponents of the vocal tract. In signed languages, they are
principally the hands and face.
6We abstract away here from the facts that linguistic sys-
temsCchange over time and are only incompletely shared
among different speakers. They are stable enough to function
as rich signals to communicative intent.Returning to the meaning relation Mfrom above,
it is best understood as mediated by the relation C
of a linguistic system shared between two inter-
locutors. The speaker has a certain communica-
tive intent i, and chooses an expression ewith a
standing meaning swhich is fit to express iin the
current communicative situation. Upon hearing e,
the listener then reconstructs sand uses their own
knowledge of the communicative situation and their
hypotheses about the speaker’s state of mind and
intention in an attempt to deduce i.
This active participation of the listener is cru-
cial to human communication (Reddy, 1979; Clark,
1996). For example, to make sense of (8) and (9)
(from Clark, 1996, p.144), the listener has to calcu-
late that Napoleon refers to a specific pose (hand
inside coat flap) or that China trip refers to a person
who has recently traveled to China.
(8) The photographer asked me to do a Napoleon for the
camera.
(9) Never ask two | [
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or roller bearings X 0.2%
78
Part 2 Analysis of economic and innovation potential
SITC Goods nameCurrent
strength% share
of
exportsEmerging
strength% share
of
exports
51 69.6% 52 47.0%
747Taps, cocks, valves and similar appliances for pipes, boiler
shells, tanks, vats or the like, including pressure-reducing
valves and thermostatically controlled valvesX 0.2%
748Transmission shafts (including camshafts and crankshafts)
and cranks; bearing housings and plain shaft bearings;
gears and gearing; ball or roller screws; gearboxes and
other speed changers (including torque converters);
flywheels and pulleys (including pulley blocks); clutches and
shaft couplings (including universal joints); articulated link
chain; parts thereofX 0.2%
764Telecommunications equipment, n.e.s., and parts, n.e.s., and
accessories of apparatus falling within division 76X 0.6% X 0.6%
773 Equipment for distributing electricity, n.e.s. X 2.6%
775Household-type electrical and non-electrical equipment,
n.e.s. X 0.5%
778 Electrical machinery and apparatus, n.e.s. X 0.4%
791Railway vehicles (including hovertrains) and associated
equipmentX 1.8%
793 Ships, boats (including hovercraft) and floating structures X 0.4% X 0.4%
8 Miscellaneous manufactured articles
812 Sanitary, plumbing and heating fixtures and fittings, n.e.s. X 0.2% X 0.2%
821Furniture and parts thereof; bedding, mattresses, mattress
supports, cushions and similar stuffed furnishings X 1.0%
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 0.4%
892 Printed matter X 0.2% X 0.2%
893 Articles, n.e.s., of plastics X 0.4%
9Commodities and transactions not classified elsewhere in
the SITC
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation79
2.4. Export performance for servic-
es
The UN Comtrade Database35 on exports of servic-
es contains up to four-digit export data according
to the EBOPS 2002 classification36. Specialisa-
tion in export performance can be used to iden-
tify those services categories in which countries
perform above average and are able to compete
successfully on international markets.
Data availability
Data availability is significantly reduced for ex-
ports of services, with data available for all
one-digit EBOPS classes, several two-digit EBOPS
35 https://comtrade.un.org/
36 https://unstats.un.org/wiki/display/comtrade/EB-
OPS+2002classes and only a limited number of three-dig-
it EBOPS classes. Data for exports of services for
Georgia are available up to 2019, up to 2019 for
Armenia, Azerbaijan and Moldova, up to 2017 for.
2018 data for Ukraine have been substituted with
2017 data.
Service exports are available for 11 one-digit EB-
OPS classes, but services | [
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"78",
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"Part",
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"Analysis",
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",",
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"-",
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"\n",
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achieve sufficient scale to support strategic projects and to simplify access to beneficiaries.
It is proposed to regroup and substantially decrease the number of all funding programmes. Dedicated funding
schemes should be put in place to address the investment gap for scale-up technology companies in the EU [see
the chapter on innovation] , as well as manufacturing capacities in certain cases, such as clean tech. The flexi -
bility of the EU budget should be enhanced to enable the reallocation of resources across and within programmes
and potential beneficiaries. The EU budget should also be better leveraged to support private investment through
different types of financial instruments and more risk appetite by implementing partners. In particular, it is recom -
mended to increase the size of the EU guarantee for the InvestEU Programme. The InvestEU programme should in
turn focus on financing higher risk and more scale-up investment. This objective will require the EIB Group to take
on more and larger high-risk projects, making greater use of the EIB Group’s own financial firepower.
Finally, the EU should move towards regular issuance of common safe assets to enable joint investment
projects among Member States and to help integrate capital markets . If the political and institutional conditions
are in place as outlined above, the EU should continue – building on the model of NGEU – to issue common debt
instruments, which would be used to finance joint investment projects that will increase the EU’s competitiveness
and security. As several of these projects are longer-term in nature, such as financing R&I and defence procurement,
common issuance should over time produce a deeper and more liquid market in EU bonds, allowing this market to
progressively support the integration of Europe’s capital markets. At the same time, together with the above reforms,
to finance a variety of programmes focused on innovation and on raising productivity, Member States could consider
increasing the resources available to the Commission by deferring the repayment of NGEU.
66THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 56. Strengthening
governance
A new industrial strategy for Europe will not succeed without parallel changes to the institutional setup and
functioning of the EU . As demonstrated throughout this report, successful industrial policies today require strate -
gies that span investment, taxation, education, access to finance, regulation, trade and foreign policy, united behind
an agreed strategic goal. Europe’s major competitors, as single | [
" ",
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"\n",
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"repayment",
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".",
"\n",
"66THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER",
"56",
".",
"Strengthening",
"\n",
"governance",
"\n",
"A",
"new",
"industrial",
"strategy",
"for",
"Europe",
"will",
"not",
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"without",
"parallel",
"changes",
"to",
"the",
"institutional",
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"and",
"\n",
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".",
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",",
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",",
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] | [] |
biological medicines in Europe in 2022, just two were
marketed by EU companies while six were marketed by US-based companies [see Figure 9] . The EU is struggling in
particular to establish its position in products with market exclusivity as orphan medicines06 and advanced therapy
medicinal products07. At the root of this emerging gap is lower spending on innovation. Total EU public sector R&I
spending on pharma stands at less than half the level of the US, while total EU private R&I investment is about a
quarter as large as the US. Innovation in the EU is also hindered by a slow and complex regulatory framework, which
is currently under review. In 2022, the median approval time for new medicines by regulatory agencies in Europe
was 430 days compared with 334 days in the US. Moreover, access to health data is one of the preconditions for the
development of AI in the pharma industry but is constrained by fragmentation. In particular, although GDPR contains
options to use patient data for health research, take up has been uneven across Member States, preventing the
industry from tapping into a wealth of available electronic data.
FIGURE 9
Market share erosion in the key segment of biologics
Note: Based on IQVIA MIDAS® quarterly volume sales data for period 2012 – 2022 reflecting estimates of real-world activity. Copyright IQVIA. All rights reserved.
Data for EEA markets (no data for CY, MT, IS and LI; retail data only for DK, EE, EL, LU, SI) and EC data (JRC R&D scoreboard) for regional allocation of companies.
Source: European Commission.
06. Orphan medicines are pharmaceutical products developed specifically to treat, prevent, or diagnose rare diseases or conditions. These
medications are called “orphan” because, under normal market conditions, pharmaceutical companies have little financial incentive
to develop and market products intended for only a small number of patients. Currently, 55% of orphan medicines are biologicals.
07. Advanced Therapy Medicinal Products (ATMPs) are innovative medicines for human use that
are based on genes, tissues, or cells. Many ATMPs are orphan medicines.
32THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2A programme to tackle the innovation deficit
Europe must improve the conditions for breakthrough innovation by addressing the weaknesses in its
common programmes for R&I [see the chapter on innovation] . The report recommends reforming the EU’s next
Framework Programme for R&I in terms of its focus, budget allocation, governance | [
" ",
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"in",
"2022",
",",
"just",
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"[",
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"\n",
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"FIGURE",
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"share",
"erosion",
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"\n",
"Note",
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"sales",
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"period",
"2012",
"–",
"2022",
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"-",
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"Copyright",
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".",
"\n",
"Data",
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"no",
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"CY",
",",
"MT",
",",
"IS",
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"LI",
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",",
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",",
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",",
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")",
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"(",
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".",
"\n",
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"Commission",
".",
"\n",
"06",
".",
"Orphan",
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"are",
"pharmaceutical",
"products",
"developed",
"specifically",
"to",
"treat",
",",
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",",
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" ",
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",",
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"conditions",
",",
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",",
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",",
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".",
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"ATMPs",
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".",
"\n",
"32THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER",
"2A",
"programme",
"to",
"tackle",
"the",
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"deficit",
"\n",
"Europe",
"must",
"improve",
"the",
"conditions",
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"breakthrough",
"innovation",
"by",
"addressing",
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"\n",
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" ",
"[",
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"next",
"\n",
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"in",
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",",
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",",
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] | [] |
62.0 Computer programming, consultancy and related activities; 63
Information service activities; 63.1 Data processing, hosting and related
activities; web portals; 63.9 Other information service activities
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation341
NABS NACE
NABS07 Health86 Human health activities; 86.1 Hospital activities; 86.2 Medical and dental
practice activities; 86.9 Other human health activities; 87 Residential care
activities; 87.1 Residential nursing care activities; 87.2 Residential care
activities for mental retardation, mental health and substance abuse; 87.3
Residential care activities for the elderly and disabled; 87.9 Other residential
care activities
NABS08 Agriculture1 Crop and animal production, hunting and related service activities; 1.1
Growing of non-perennial crops; 1.2 Growing of perennial crops; 1.3 Plant
propagation; 1.4 Animal production; 1.5 Mixed farming; 1.6 Support activities
to agriculture and post-harvest crop activities; 1.7 Hunting, trapping and
related service activities; 2 Forestry and logging; 2.1 Silviculture and other
forestry activities; 2.2 Logging; 2.3 Gathering of wild growing non-wood
products; 2.4 Support services to forestry; 20.2 Manufacture of pesticides
and other agrochemical products; 28.3 Manufacture of agricultural and
forestry machinery; 3 Fishing and aquaculture; 3.1 Fishing; 3.2 Aquaculture;
75 Veterinary activities; 75.0 Veterinary activities
NABS09 Education85 Education; 85.1 Pre-primary education; 85.2 Primary education; 85.3
Secondary education; 85.4 Higher education; 85.5 Other education; 85.6
Educational support activities
NABS10Culture, recreation, religion
and mass media58 Publishing activities; 58.1 Publishing of books, periodicals and other
publishing activities; 58.2 Software publishing; 59 Motion picture, video
and television programme production, sound recording and music
publishing activities; 59.1 Motion picture, video and television programme
activities; 59.2 Sound recording and music publishing activities; 60
Programming and broadcasting activities; 60.1 Radio broadcasting; 60.2
Television programming and broadcasting activities; 90 Creative, arts and
entertainment activities; 90.0 Creative, arts and entertainment activities;
91 Libraries, archives, museums and other cultural activities; 91.0 Libraries,
archives, museums and other cultural activities; 93 Sports activities
and amusement and recreation activities; 93.1 Sports activities; 93.2
Amusement and recreation activities; 94.9 Activities of other membership
organisations
NABS11Political and social systems,
structures and processes78 Employment activities; 78.1 Activities of employment placement
agencies; 78.2 Temporary employment agency activities; 78.3 Other
human resources provision; 84.1 Administration of the State and the
economic and social policy of the community; 84.2 Provision of services to
the community as a whole; 84.3 Compulsory social security activities; 88
Social work activities without accommodation; 88.1 Social work activities
without accommodation for the elderly and disabled; 88.9 | [
"62.0",
"Computer",
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",",
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";",
"63",
"\n",
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"63.1",
"Data",
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",",
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"86.1",
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";",
"1.2",
"Growing",
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"\n",
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"1.6",
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"\n",
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";",
"3",
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"75",
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"75.0",
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"NABS09",
"Education85",
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"85.1",
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"-",
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";",
"85.2",
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";",
"85.3",
"\n",
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";",
"85.4",
"Higher",
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";",
"85.5",
"Other",
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";",
"85.6",
"\n",
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"NABS10Culture",
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"recreation",
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"58.1",
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",",
"periodicals",
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";",
"90",
"Creative",
",",
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"91",
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",",
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",",
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";",
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"Libraries",
",",
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"and",
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"activities",
";",
"93.1",
"Sports",
"activities",
";",
"93.2",
"\n",
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";",
"94.9",
"Activities",
"of",
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"membership",
"\n",
"organisations",
"\n",
"NABS11Political",
"and",
"social",
"systems",
",",
"\n",
"structures",
"and",
"processes78",
"Employment",
"activities",
";",
"78.1",
"Activities",
"of",
"employment",
"placement",
"\n",
"agencies",
";",
"78.2",
"Temporary",
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";",
"78.3",
"Other",
"\n",
"human",
"resources",
"provision",
";",
"84.1",
"Administration",
"of",
"the",
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"and",
"the",
"\n",
"economic",
"and",
"social",
"policy",
"of",
"the",
"community",
";",
"84.2",
"Provision",
"of",
"services",
"to",
"\n",
"the",
"community",
"as",
"a",
"whole",
";",
"84.3",
"Compulsory",
"social",
"security",
"activities",
";",
"88",
"\n",
"Social",
"work",
"activities",
"without",
"accommodation",
";",
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"\n",
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"accommodation",
"for",
"the",
"elderly",
"and",
"disabled",
";",
"88.9"
] | [] |
sudden stops in trade can be extremely disruptive. As the era of
geopolitical stability fades, the risk of rising insecurity becoming a threat to growth and freedom is rising.
Europe is particularly exposed. We rely on a handful of suppliers for critical raw materials, especially China, even as
global demand for those materials is exploding owing to the clean energy transition. We are also hugely reliant on
imports of digital technology. For chips production, 75-90% of global wafer fabrication capacity is in Asia.
These dependencies are often two-way – for example, China relies on the EU to absorb its industrial overcapacity –
but other major economies like the US are actively trying to disentangle themselves. If the EU does not act, we risk
being vulnerable to coercion.
In this setting, we will need a genuine EU “foreign economic policy” to retain our freedom – a so-called statecraft.
The EU will need to coordinate preferential trade agreements and direct investment with resource-rich nations,
build up stockpiles in selected critical areas, and create industrial partnerships to secure the supply chain of key
technologies. Only together can we create the necessary market leverage to do all this.
Peace is the first and foremost objective of Europe. But physical security threats are rising and we must prepare. The
EU is collectively the world’s second largest military spender, but it is not reflected in the strength of our defence
industrial capacity.
The defence industry is too fragmented, hindering its ability to produce at scale, and it suffers from a lack of stan -
dardisation and interoperability of equipment, weakening Europe’s ability to act as a cohesive power. For example,
twelve different types of battle tanks are operated in Europe, whereas the US produces only one.
07
THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | FOREWORDWhat is standing in the way?
In many of these areas, Member States are already acting individually and industrial policies are on the rise. But it is
evident that Europe is falling short of what we could achieve if we acted as a community. Three barriers are standing
in our way.
First, Europe is lacking focus. We articulate common objectives, but we do not back them by setting clear priorities
or following up with joined-up policy actions.
For example, we claim to favour innovation, but we continue to add regulatory burdens onto European companies,
which are especially costly for SMEs | [
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"\n",
"THE",
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" ",
"—",
"PART",
"A",
"|",
"FOREWORDWhat",
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"?",
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",",
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",",
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"especially",
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"for",
"SMEs"
] | [] |
the spontaneous fission of252Cf, isomeric states in 25 dif-
ferent nuclei were identified, from A=88 to A=158.
These nuclei are depicted in Fig. 7. In total, the half-lives of
41 isomeric states were measured.
The half-lives measured in this work for all these isomers
are reported in Tables 2and3for light and heavy fragments,
respectively. In these tables, the total uncertainties corre-sponding to both statistical and systematic errors are given.
For each isomer, the systematic uncertainty was estimated
by varying the range of the fits and the binning of the timehistogram, as the fit result was sensitive to these parameters.Fig. 8 Ratios of half-lives measured in this work over literature values
3 Results and discussion
3.1 Half-life extraction of the identified isomers
The ratios between all the half-lives measured in this work
and the ones extracted from literature (see Tables 2and3)a r e
represented in Fig. 8, except for the newly identified isomers
in94Rb,108Tc and147Ce. From such a representation, the
extensive range of half-lives that could be accessed with the
VESPA setup is visible (5 orders of magnitude). We mayobserve that our results agree well with the literature values,although no perfect agreement is found. The ratios do not
seem to show any dependence on the half-life, indicating that
our method performs equally well over the whole time range.Most of the half-lives measured in this work (28 over 37)
agree within 4% (1 σ) to their corresponding literature value.
For some outliers, a more severe deviation is observed. Themost striking difference are the half-lives of the 1.039 MeV
isomeric state in
99Zr and the 2.075 and 1.485 MeV isomeric
states in94Rb.
In Tables 2and3, half-lives extracted from the RIPL3
database [ 15] (2023 update) are also compared to the ones
measured in this work. Indeed, as simulation codes may rely
on such databases, incorrect isomer half-lives may affect thecalculations.
The following subsections are mainly dedicated to those
isomers in
108Tc,147Ce, and94Rb, which were measured for
the first time.
3.1.1108Tc
Although γ-rays from the108Tc isomer were already
observed in the 1970 years (see, e.g., in Ref. [ 42]), no level
scheme was proposed at that time. Level schemes were pro-posed in Ref. [ 43](βdecay of
108Mo) and [ 44] (spontaneous
123 5 Page 6 of 12 Eur. Phys. J. A (2025) 61:5
Table 2 Half-lives of the isomers (light fragments) measured in this work ( Texp.
1/2) compared with literature ( Tlit.
1/2) | [
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".",
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",",
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".",
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"2and3",
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"15",
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",",
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",",
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".",
"\n",
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"those",
"\n",
"isomers",
"in",
"\n",
"108Tc,147Ce",
",",
"and94Rb",
",",
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"measured",
"for",
"\n",
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".",
"\n",
"3.1.1108Tc",
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"Although",
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"-",
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"from",
"the108Tc",
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"were",
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"\n",
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"in",
"the",
"1970",
"years",
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"\n",
"123",
" ",
"5",
"Page",
"6",
"of",
"12",
"Eur",
".",
"Phys",
".",
"J.",
"A",
" ",
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"2025",
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"61:5",
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"Table",
"2",
"Half",
"-",
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"the",
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"light",
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")"
] | [] |
specific industry group j and country i, and the
share of variable n in a specific industry group j for
the whole Eastern Partnership region.
As Ukraine accounts for most of the companies
listed in Crunchbase (85%), employees (82%)
and estimated revenue (76%) in the Eastern
Partnership region (for the data extracted from
Crunchbase), the contribution of the other Eastern
Partnership countries to the denominator on the
right-hand side of the above equation is negligible.
As a result, the specialisation analysis carried out
in this section is largely biased towards consider-
ing Ukraine as a baseline rather than the whole
Eastern Partnership, a fact that must be taken
into due account when drawing qualitative conclu-
sions. In particular, the above implies that special-
isation figures for the specific case of Ukraine are
not particularly informative. Therefore, this section
analyses the relative specialisation in each East-ern Partnership country, and provides a final re-
mark on Ukraine at the very end.
Here, for each EaP country, we focus on the top 10
Industry Groups in which each country is special-
ised, either per number of companies and/or num-
ber of employees (revenue is also reported, but
shall not be used as a weighing variable, because
observations are very sparse across countries). All
specialisation values sij of top-10 industries are
well above the typical 1.2 threshold applied for
selecting niches of high specialisation.
Armenia
Armenia is highly specialised in Travel & Tour-
ism and Gaming across all three variables.
Specialisation in Sports is very high in terms of
number of employees and estimated revenue, but
not so much in terms of number of companies.
Other industry groups with high specialisation
across more than one variable are Mobile and
Apps. Software, Information Technologies and In-
ternet Services, while highly relevant in terms of
critical mass across all three variables, have a low
relative specialisation.
Armenia
# firms SI Firms # employees SI Employees # est. revenue SI Revenue
Artificial Intelligence 3.71 Sports 24.13 Sports 32.01
Gaming 3.66 Travel and Tourism 9.36 Travel and Tourism 24.30
Travel and Tourism 3.64 Gaming 6.71 Gaming 17.07
Platforms 3.03 Music and Audio 4.43 Hardware 9.91
Mobile 2.17 Apps 3.32 Administrative Services 4.28
Music and Audio 1.97 Internet Services 3.31 Content and Publishing 4.04
Apps 1.96 Mobile 2.66 Real Estate 2.52
Education 1.81 Software 2.62 Artificial Intelligence 2.32
Data and Analytics 1.65 Information Technology 2.39Lending and
Investments2.16
Messaging and
| [
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",",
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"\n",
"#",
"firms",
"SI",
"Firms",
"#",
"employees",
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"Revenue",
"\n",
"Artificial",
"Intelligence",
"3.71",
"Sports",
"24.13",
"Sports",
"32.01",
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"Gaming",
"3.66",
"Travel",
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"9.36",
"Travel",
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"24.30",
"\n",
"Travel",
"and",
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"3.64",
"Gaming",
"6.71",
"Gaming",
"17.07",
"\n",
"Platforms",
"3.03",
"Music",
"and",
"Audio",
"4.43",
"Hardware",
"9.91",
"\n",
"Mobile",
"2.17",
"Apps",
"3.32",
"Administrative",
"Services",
"4.28",
"\n",
"Music",
"and",
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"1.97",
"Internet",
"Services",
"3.31",
"Content",
"and",
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"4.04",
"\n",
"Apps",
"1.96",
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"2.66",
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"Estate",
"2.52",
"\n",
"Education",
"1.81",
"Software",
"2.62",
"Artificial",
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"2.32",
"\n",
"Data",
"and",
"Analytics",
"1.65",
"Information",
"Technology",
"2.39Lending",
"and",
"\n",
"Investments2.16",
"\n",
"Messaging",
"and",
"\n"
] | [] |
domain.
Using this information, the collaboration network
patterns within and across EaP countries (as well
as international partners) for publications and re-
search projects funded by the EC have been iden-
tified. In the following analysis, for each domain, a
collaboration between any pair of countries (with-
in the EaP and beyond) is counted if an institu-
tion from both countries appears as an affiliation
of a publication, as co-owners of a patent or as
partners of an EC research and innovation project
classified within the respective S&T domains.
The main actors per EaP country and the na-
tional collaboration networks are presented and
discussed in Section 5.1, while international col-
laborations, within EaP countries and with external
partners, are presented in Section 5.2.
This chapter is complemented by the interactive
networks68, which provide a complete exploratory
depiction of all actors for a country, filterable by
domain.
68 Available on the Smart Specialisation platform webpage.5.1 Top actors in science and tech-
nology in the EaP countries
Methodology
This section focuses on identifying, for each EaP
country and S&T specialisation domain, the main
actors producing scientific publications, applying
for patents and receiving funding from the EC
for research and innovation projects. In doing so,
every single analysed record is linked back to the
respective extracted domain and the pertinent af-
filiation (for publications), applicant (for patents) or
beneficiary (for projects) is identified. While each
single document is linked to the respective actor
for publications and research projects, in the case
of patents, whole patent families are assigned to
their applicants.
The following figures aggregate the three data
sources together, providing a unified view of the
national research ecosystem. Since this aggre-
gation is normally biased towards publications
(which is by far the largest dataset), two comple-
mentary tables provide the top 10 organisations
classified as ‘private company, for profit’ and ‘pub-
lic administration’, which includes public hospitals
and state-owned companies.
Results
The following sections present the EaP science
and technology ecosystems country by country.
The national academies play a central role in Ar-
menia, Moldova and Ukraine. In all countries, one
or very few comprehensive universities, usually
located in the capital city (or some other main
cities for Ukraine), also have a backbone role in
the national ecosystem. After those, specialised
research institutions and universities present rel-
evant roles in specific S&T domains or related
sets of domains.
The presence of | [
"domain",
".",
"\n",
"Using",
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"\n",
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"\n",
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"\n",
"The",
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"and",
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"na-",
"\n",
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"\n",
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",",
"while",
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"col-",
"\n",
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",",
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"and",
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"external",
"\n",
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",",
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"Section",
"5.2",
".",
"\n",
"This",
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"is",
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"by",
"the",
"interactive",
"\n",
"networks68",
",",
"which",
"provide",
"a",
"complete",
"exploratory",
"\n",
"depiction",
"of",
"all",
"actors",
"for",
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",",
"filterable",
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"\n",
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".",
"\n",
"68",
"Available",
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"Specialisation",
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"webpage.5.1",
"Top",
"actors",
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"tech-",
"\n",
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"in",
"the",
"EaP",
"countries",
"\n",
"Methodology",
"\n",
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"identifying",
",",
"for",
"each",
"EaP",
"\n",
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"specialisation",
"domain",
",",
"the",
"main",
"\n",
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",",
"applying",
"\n",
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"receiving",
"funding",
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"\n",
"for",
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"\n",
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"respective",
"actor",
"\n",
"for",
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"research",
"projects",
",",
"in",
"the",
"case",
"\n",
"of",
"patents",
",",
"whole",
"patent",
"families",
"are",
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"\n",
"their",
"applicants",
".",
"\n",
"The",
"following",
"figures",
"aggregate",
"the",
"three",
"data",
"\n",
"sources",
"together",
",",
"providing",
"a",
"unified",
"view",
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"\n",
"national",
"research",
"ecosystem",
".",
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"this",
"aggre-",
"\n",
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"is",
"normally",
"biased",
"towards",
"publications",
"\n",
"(",
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"by",
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"the",
"largest",
"dataset",
")",
",",
"two",
"comple-",
"\n",
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"tables",
"provide",
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"top",
"10",
"organisations",
"\n",
"classified",
"as",
"‘",
"private",
"company",
",",
"for",
"profit",
"’",
"and",
"‘",
"pub-",
"\n",
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"administration",
"’",
",",
"which",
"includes",
"public",
"hospitals",
"\n",
"and",
"state",
"-",
"owned",
"companies",
".",
"\n",
"Results",
"\n",
"The",
"following",
"sections",
"present",
"the",
"EaP",
"science",
"\n",
"and",
"technology",
"ecosystems",
"country",
"by",
"country",
".",
"\n",
"The",
"national",
"academies",
"play",
"a",
"central",
"role",
"in",
"Ar-",
"\n",
"menia",
",",
"Moldova",
"and",
"Ukraine",
".",
"In",
"all",
"countries",
",",
"one",
"\n",
"or",
"very",
"few",
"comprehensive",
"universities",
",",
"usually",
"\n",
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"the",
"capital",
"city",
"(",
"or",
"some",
"other",
"main",
"\n",
"cities",
"for",
"Ukraine",
")",
",",
"also",
"have",
"a",
"backbone",
"role",
"in",
"\n",
"the",
"national",
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".",
"After",
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",",
"specialised",
"\n",
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"\n",
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"The",
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] | [] |
largest
scientific partner of EaP institutions, followed by
Germany, Poland and the United States. At some
distance, other European countries follow: France,
the UK, Italy, Spain and Switzerland can be ob-
served as recurrent partners. A considerable share
of these co-publications is the result of great en-
deavours in physics, mainly classified under the
Fundamental physics and mathematics domain.
China is the only Asian country appearing on the
list. In EC-funded projects, the first top European
partners are repeated, complemented by Belgium
and Netherlands – very active countries in Horizon
2020 – and Greece and Romania, which are geo-
graphically closer to the EaP countries.
The Russian Federation so far concentrated a larg-
er proportion of scientific collaborations within Bi-
otechnology, Chemistry and chemical engineering,
Energy, Nanotechnology and materials and Optics
and photonics. The remaining domains present
a more evenly distributed collaboration pattern,
notably in Agrifood, Environmental sciences and
industries or Electric and electronic technologies.
Some specific relationships emerge, notably with
Poland in Transportation and ICT and computer
science or with the United States in Health and
wellbeing.
6. Summary of the strengths
of each S&T specialisation do-
main for each EaP country
6.1 Selection of the most relevant
domains per country
Based on the evidence shown in Chapter 4 for
critical mass, specialisation and excellence per
domain, the following sections present synthet-
ic indicators supporting the selection of the
S&T specialisation domains for each Eastern Partnership country. For each country, each do-
main is characterised by the indicators below.
1. Critical mass
a. The domain is within the country’s top 5 by
number of publications
b. The domain is within the country’s top 5 by
number of patents
2. Specialisation
a. The domain is within the country’s top 5
by relative specialisation for publications, in
relation to the EaP
b. The domain is within the country’s top 5 by
relative specialisation for patents, in relation
to the EaP
3. Excellence
a. The domain is within the country’s top 5
by normalised citation impact for publications
b. The domain is within the country’s top 5 by
number of EU-funded projects (Horizon 2020)
The previous chapters have provided diverse and
complementary evidence on the thematics, col-
laborations and STI ecosystems related to each
domain. For this reason, while the table presents
a final column with the number of highlighted in-
dicators for a domain and country, no automatic
selection of domains | [
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"institutions",
",",
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",",
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"6",
".",
"Summary",
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] | [] |
years, suggesting that training next-genera -
tion AI systems could soon be as expensive as USD 1 billion and reach USD 10 billion by the end of the decadexiv. At
the same time, deploying AI will require faster, lower latency and more secure connections. Yet, the EU is behind its
2030 Digital Decade targets for fibre and 5G deployment. The investment levels required to support EU networks are
estimated at around EUR 200 billion to ensure full gigabit and 5G coverage across the EU. But Europe’s per capita
investment is markedly lower than other major economies [see Figure 9] . A key reason for lower rates of investment is
Europe’s fragmented market. For example, there are 34 mobile network operator groups in the EU and only a handful
in the US or China, in part because the EU and Member States have tended to view mergers in the sector negatively.
This fragmentation makes the fixed costs of investing in networks relatively more onerous for EU operators than for
continent-scale companies in the US or China. Fragmentation also makes it harder to capitalise on new technolo -
gies. Europe currently has virtually no presence in edge computing05, while opening network services to third-party
developers and innovators using Application Programming Interfaces (APIs) is hindered by lack of coordination of
standards.
05. Edge computing refers to the distribution of computational tasks across smaller nodes closer to customers, reducing data transport
to smaller distances. As the EU builds highly automated manufacturing plants requiring low latency and significant data volumes
steered by AI, edge computing for industrial applications could better enable performance and reduce latency for industrial
connected robotics, keeping data transfers more secure. While the Digital Decade sets the goal of deploying at least 10,000 climate-
neutral, secure edge nodes by 2030, there are today only three commercially deployed edge computing nodes in the EU.
31THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2FIGURE 8
Average monthly revenue per unit and CAPEX per capita
Source: ETNO, 2023
The EU’s position in other innovative sectors like pharma is declining due to the same challenges of low
investment in R&I and regulatory fragmentation [see the chapter on pharma] . While the EU’s pharma sector still
leads globally in trade measured by value, it is falling behind in the most dynamic market segments and losing market
share to US-based companies. Of the top ten best-selling | [
" ",
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".",
"\n",
"31THE",
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products, machinery and equipment X X X X X
33.2 Installation of industrial machinery and equipment X
D ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY
35 Electricity, gas, steam and air conditioning supply
35.1 Electric power generation, transmission and distribution X X X X X X X X X
35.2 Manufacture of gas; distribution of gaseous fuels through mains X X X X
35.3 Steam and air conditioning supply X X X X X X
EWATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND
REMEDIATION ACTIVITIES
36 Water collection, treatment and supply X X X X
37 Sewerage X X
38Waste collection, treatment and disposal activities; materials
recovery
38.1 Waste collection X
38.2 Waste treatment and disposal
38.3 Materials recovery X X X
39 Remediation activities and other waste management services
F CONSTRUCTION
41 Construction of buildings
41.1 Development of building projects X X X X X
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation281 282
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-
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ingEmerg-
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34 52 28 61 64 40 31 29 15 50 47 21 55 40 35 83 57 34
41.2 Construction of residential and non-residential buildings X X X X X X
42 Civil engineering
42.1 Construction of roads and railways X X X X X X X
42.2 Construction of utility projects X X X X X X X
42.9 Construction of other civil engineering projects X X X X X X X
43 Specialised construction activities
43.1 Demolition and site preparation X X X
43.2 Electrical, plumbing and other construction installation activities X X X X X X
43.3 Building completion and finishing X X X
43.9 Other specialised construction activities X X X X X X X
GWHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND
MOTORCYCLES
45Wholesale and retail trade and repair of motor vehicles and
motorcycles
45.1 Sale of motor vehicles X X X X X
45.2 Maintenance and repair of motor vehicles X X X
45.3 Sale of motor vehicle parts and accessories X X X X
45.4Sale, maintenance and repair of motorcycles | [
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"282",
"\n",
"Annexes",
"\n",
"GEORGIA",
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"motorcycles"
] | [] |
= 0.0001; US-2 versus CS-R2, p = 0.01; US- 3 versus
CS-R3, p = 0.09; US-4 versus CS-R4, p = 0.0003; US-5 versus CS-R5, p = 0.03).Data are shown as mean + or ±SEM. *p < 0.05, **p < 0.01, ***p < 0.001. Details of statistical analyses are provided in Table S1 .
Cell Reports 39, 110893, May 31, 2022 9Articlell
OPEN ACCESS(legend on next page)
10Cell Reports 39, 110893, May 31, 2022Articlell
OPEN ACCESSThese data reveal complex and dynamic changes of VIP+ IN re-
sponses during exploration of social and non-social cues.
Finally, to determine whether individual aIC VIP+ INs respond
specifically to either stimuli of social or aversive nature, we regis-tered the activity of these INs across the different behavioral par-
adigms ( Figure 7 A). A large proportion of mouse CNs on both
days 1 and 2 were found to respond to the US (day 1: n = 12/13, 92.3%; day 2: n = 5/7, 71.4%; Figure 7 B) during fear condi-
tioning, whereas only a minority were responsive to the CS
(day 1: n = 1/13, 7.7%; day 2: n = 1/7, 14.2%; Figure 7 B). During
fear retrieval, most of the mouse CNs either responded to theCS-R or stopped responding, whereas only a few were activated
during the US omission (day 1: CS-R n = 6/15, 40%, US- n =
1/15, 6.6%; day 2: CS-R n = 4/9, 44.4%, US- n = 1/9, 11.1%;Figure 7 C).
Taken together, our data show that individual VIP+ INs within
the aIC respond to both aversive and social stimuli irrespective ofsensory modality and potentially value.
DISCUSSION
Here, we show that aIC VIP+ INs respond, both at the population
and individual neuron level, to different sensory stimuli indepen-
dent of task and modality using single-cell Ca
2+imaging, and
that these INs are anatomically connected to a wide range of
sensory-related brain areas. Optogenetic inhibition of aIC VIP+
INs during the exposure of the animals to social or aversive stim-uli impaired the full expression of both social preference and fearmemory retrieval. Moreover, after deriving the coding specificity
of these INs, we observed that functional responses to different
stimuli, such as social versus non-social cues, are encoded by
specialized subsets of VIP+ INs, although their coding stabilityand specificity is flexible across days and behavioral paradigms.
Therefore, our findings suggest that the activity of aIC VIP+ INs is
required for the processing of | [
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] | [] |
We report that aIC VIP+ INs receive long-range inputs from a wide variety of sensory-related brain regions
and are activated, both at the population and individual cell level,
by sensory stimuli of different modalities. We further show that aICVIP+ IN activity influences adaptive behaviors, such as fear
Cell Reports 39, 110893, May 31, 2022 ª2022 The Author(s). 1
This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).ll
OPEN ACCESSFigure 1. Distribution of VIP+ INs in the IC and first-order long-range presynaptic inputs
(A) Example images of VIP expression throughout the IC in a VIP-ires-cre:Ai9-tdTomato reporter mouse (left and middle panels) and immunohistochemi cal quan-
tification of VIP+ INs throughout the different cortical layers of the aIC (right panel; N = 3 mice). Scale bars, 500 and 250 mm.
(B) Schematics of the mono-trans-synaptic retrograde tracing strategy employed to identify inputs to aIC VIP+ INs.(C) Example images of TVA-EGFP-tTA, G-BFP, and VIP expression in aIC VIP+ INs. Scale bar, 20 mm.
(D) Example images of TVA-EGFP-tTA and mCherry-RabV (RV) expression in aIC VIP+ INs. Scale bar, 100 mm.
(legend continued on next page)
2Cell Reports 39, 110893, May 31, 2022Articlell
OPEN ACCESSmemory retrieval and social preference. Finally, we reveal that,
although functionally different VIP+ INs exist, their coding speci-
ficity is flexible across days and behavioral paradigms.
RESULTS
Distribution of VIP+ INs in the IC and detection of their
first-order long-range presynaptic inputsAt first, we have investigated the general distribution of VIP+ INs
across different mouse insular cortex subdivisions and along its
entire rostro-caudal axis, by means of immunohistochemistry(IHC), since it has never been expressly studied before. The high-
est percentage of VIP+ INs was found in cortical layer II/III ( Fig-
ure 1 A), similar to other neocortical regions ( He et al., 2016 ;Mesik
et al., 2015 ;Pro¨nneke et al., 2015 ). The sensitivity of our VIP-IHC
analysis was confirmed in transgenic VIP:Ai9-tdTomato mice, in
which 99.7% of the VIP immunoreactive neurons co-expressedthe reporter tdTomato ( Figures S1 A–S1D). Among all insular cor-
tex subdivisions, the aIC and, in particular, the agranular compo-
nent, showed the highest density ( Figures S1 E and S1F).
Next, to identify the main brain regions projecting to aIC VIP+
INs and potentially driving their activity, we used a viral mono-
trans-synaptic tracing approach ( Liu et al., 2017 ;Lavin et al.,
2020 ) that restricted the retrograde tracing | [
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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)
88Br 0.270* 4500 (400) 5500 (100) [ 16] 5300
91Rb 1.134* 15 (4) 16 (1) [ 17] 16.6
92Rb 0.284* 54.4 (27) 54 (3) [ 18]5 4
0.142 0.82 (4) 0.75 (3) [ 19]0 . 7 5
93Rb 4.423* 97 (15) 111 (11) [ 17] 111
94Rb 2.075 65 (8) 107 (16) [ 20] 107
1.485* 42 (7) 18 (1) [ 20]1 8
0.328 1.6 (1) – – –
95Rb 0.835* 101 (24) 94 (7) [ 17]–
95Sr 0.556* 21.0 (5) 21.5 (3) [ 21] 21.9
97Sr 0.831* 530 (22) 504 (8) [ 16] 395
0.308 200 (10) 165 (4) [ 21] 169
98Y 1.181* 740 (30) 780 (30) [ 22] 780
0.496 6740 (140) 6900 (50) [ 22] 6900
0.375 37.8 (13) 35.2 (5) [ 22] 35.2
0.171 680 (30) 630 (20) [ 22] 630
97Zr 1.264* 106 (11) 102.8 (24) [ 23] 102.8
99Zr 1.039 29 (4) 54 (10) [ 24]5 4
0.252* 345 (12) 336 (5) [ 25] 293
0.122 1.01 (3) 1.08 (2) [ 26]1 . 0 7
101Zr 0.942* 18.2 (19) 16 (2) [ 27]1 6
108Tc 0.330(+ x)* 116 (3) 94 (10) [ 28]–
0.176(+ x)2 . 8 1 ( 4 ) – – –
0.106(+ x)0 . 9 4 ( 6 ) – – –
fission of252Cf), and are used in evaluated data. Although an
isomeric state was observed in Ref. [ 44], no absolute excita-
tion energy was assigned. However, in Ref. [ 45], the author
established a level scheme without floating level, based on
common γ-ray transitions (86 keV and 106 keV) seen in both
108Moβdecay and252Cf(sf). The γ-γcoincidences from our
data are in agreement with these three level schemes. Further-more, we observe a transition with E
γ/similarequal58 keV that was
also reported in Ref. [ 43]. We derived from our data that the
level at E∗=330 keV is a consistent candidate for the 116 ns
isomeric state, from all γ-γcoincidences reported in Fig. 9.
For completeness, we report in the figure the γ-ray energies
measured by our setup.
Besides, we measured the half-life of two short-lived states
below the main isomer for the first time, at E∗=176.2 and
106.3 keV. From the multiple isomers analysis, we undoubt-edly concluded that the 176.2 keV state, populated | [
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] | [] |
p = 0.0001, n = 85 cells).Data are shown as mean + or ±SEM. Details of statistical analyses are provided in Table S1 .
4Cell Reports 39, 110893, May 31, 2022Articlell
OPEN ACCESSFigure 3. Auditory and social stimuli activate aIC VIP+ INs
(A) Schematic of the auditory response test, in which 30 tone presentations of 50 or 80 dB (6,000 Hz, for 1 s) were pseudo-randomly presented.
(B) Activity maps from all individual recorded aIC VIP+ INs (n = 76 cells from N = 7 mice), sorted by time of peak activity during the 80 dB tone presentatio ns,
averaged across all 80 dB (left panel) or 50 dB tone presentations (right panel).
(C) Eighty and 50 dB tone responses averaged from all recorded aIC VIP+ INs across all tone presentations.(D) Mean AUC of Zscored activity responses was higher on 80 dB compared with 50 dB tone presentations (Wilcoxon signed rank, p = 0.003, n = 76 cells).
(E) Schematic of the social preference test on days 1 and 2 of testing. The position of the interactor mouse was counterbalanced between days.(F) Time spent in interaction with the novel conspecific mouse or object during the two days of testing (mixed-effects model REML; main effect zone: p = 0 .025;
main effect day: p = 0.55; interaction effect: p = 0.26).(G) Activity maps from all individual recorded aIC VIP+ INs on social preference test day 1 (n = 88 cells from N = 7 mice), sorted by time of peak activity du ring
interactions with another conspecific mouse, averaged across all interactions with the conspecific (left panel) or object (right panel).(H) Mouse and object interaction responses averaged from all recorded aIC VIP+ INs across all interactions.
(legend continued on next page)
Cell Reports 39, 110893, May 31, 2022 5Articlell
OPEN ACCESSrespectively, chi-square, p = 0.0001; Figure S4 A). On day 2, the
number of active VIP+ INs responding to the new conspecific re-
mained proportionally similar (38%) to day 1, likewise the global
Ca2+activity increased following the entry in the close proximity
area and displayed a comparable response dynamic to the first
trial. On the other hand, the number of VIP+ INs active during
visits to the novel object doubled (30%; Figure S4 A) when the
mice were re-tested on the second day, and the Ca2+activity
showed a transient increase of a similar magnitude to the one
observed during social visits | [
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0.1 C for 16 h and discharge 0.2 C until 1 V); the
experiment stops when the discharge time is less than 3 h. However, it is possible to per-
form a second capacity test, and if the discharge capacity is less than 3 h, the experiment
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.
If the discharge duration is less than 72 min, the experiment is terminated.
Moreover, every 50 cycles a checkup cycle is performed using the standard conditions
for capacity rate calculation (charge 0.1 C for 16 h and discharge 0.2 C until 1 V); the
experiment stops when the discharge time is less than 3 h. However, it is possible to
perform a second capacity test, and if the discharge capacity is less than 3 h, the experiment
is terminated. A battery has passed the test if the number of cycles is equal to or exceeds
200 (in the case of an AAA NiMH battery).
The voltage and current profiles of an AAA battery are presented in Figures 10a and 10b ,
respectively. The charge termination voltage tends to increase from 1.49 V in the first cycle to
1.59 V at the end of cycle 300; this effect in voltage can be related to an increase in resistance
in the cell after cycling and possible side reactions, e.g., gas formation [ 36]. Furthermore,
the average capacity of the AAA NiMH battery during cycling is 660 mAh, and during
the checkup cycle, a capacity of 698 mAh is observed (see Figure 10c). Additionally, the
columbic efficiency of the AAA NiMH battery varies from 86% to 82% during the cycles
and ~62% during the checkup cycle (see Figure 10d). This is consistent with the kinetics
of the cathode (Ni(OH) 2) and anode (MH), leading to higher capacity with lower C rates,
which we have presented in Section 3.Batteries 2025 ,11, 30 15 of 20
7. Discussion
Portable NiMH batteries are tested for different parameters that are included in the
European Regulation EU 2023/1542 (see Table 1). The procedures for measuring these
parameters will be laid down in harmonized standards currently under development in
CENELEC/TC 21X/WG 08 [ 37]. In this | [
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consumer discontent. In line with this, Article 6(2)(c)
of the UCPD Directive, introduced by the Enforcement and Moderniza -
tion Directive (EU2019/2161), emphasizes the importance of ensuring
transparency regarding DFQ practices. Currently, the UCPD Directive
allows traders to claim ‘legitimate and objective factors ’ to justify
product differentiation across the single market, stressing that they
should still inform consumers. It also states that, to inform consumers of
this product differentiation, traders should generally prefer advertising,
information at retailer premises or through online means, over labels
(European Commission, 2021 ). This could be achieved by providing a
clear labelling, such as a ‘made for’ claim, which could help consumers
make more informed purchasing decisions.
However, there is a risk that manufacturers could misuse such labels
to create territorial supply constraints, artificially segmenting markets in
Europe, which warrants further investigation (Russo et al., 2020 ).
Alternatively, policymakers could introduce a ‘DFQ-detector ’—such
as a barcode-scanning app that informs consumers of product variations
across Member States —to prevent misleading branding (Di Marcantonio
et al., 2024 ). ECO (2021) supports this approach, advocating for
equipping national and EU authorities with tools, like its existing online
DFQ comparison database, aiming to enforce a harmonized trans -
parency standard and ensure consistency across Member State. The
suggested approaches align with the econometric estimates of Nes et al.
(2024) , which suggests that companies respond to critiques about DFQ
in their products by differentiating front-packaging, indicating this as a
feasible practice to distinguish products across markets. Insights on
effective front-packaging modifications to raise consumers awareness of
differences in composition between product versions has been provided
by Solano-Hermosilla et al. (2023) . Some companies, such as PepsiCo
(2024) , have already started informing consumers about differences of
products across EU countries.
6.2. Limitations and Outlook
While this study provides the first empirical comparison of consumer
valuation for different versions of similarly branded products, there are 9As a matter of fact, Nes et al. (2023) find only seven products out of 125
with significant differences in composition and similar packaging. Moreover,
when inspecting Annex 1 of this report one can see that even for these seven
products the differences are marginal. Unfortunately, due to confidentiality
issues we cannot disclose the seven products in that category.D.M. Federica et al. Food Policy 131 (2025) 102803
9 some limitations to consider. First, the ‘made for’ claim represents only
one possible way of | [
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spe-
cialisation index sij (defined at the beginning of this section) calculated for Ukraine is larger than 1 and
more than 25% larger than the average sectoral
specialisation index for the rest of the EaP region.
This leads us to identify Consumer Goods, Bi-
otechnology, Manufacturing, Advertising,
Events, Clothing and Apparel, Sales and
Marketing, Energy, Content and Publishing,
Design, as the industry groups that fulfil the
aforementioned condition in terms of the number
of companies. Sustainability must be added to
this list of 10, which is 11th in the ranking, for its
relevance in this innovation landscape.
With regard to specialisation in terms of the num-
ber of employees, the list in the second column of
the table turns out to be modified only by the ad-
vancement of Consumer Goods and Professional
Services to a top-10 ranking in place of Sustain-
ability and Content and Publishing.
Ukraine
# firms SI Firms # employees SI Employees # est. revenue SI Revenue
Consumer Goods 1.13 Biotechnology 1.21 Biotechnology 1.22
Manufacturing 1.11 Clothing and Apparel 1.20 Video 1.22
Biotechnology 1.11 Health Care 1.19 Consumer Goods 1.21
Sustainability 1.11Navigation and
Mapping1.19 Design 1.21
Energy 1.10 Sustainability 1.19Media and
Entertainment1.20
Advertising 1.10 Education 1.18 Food and Beverage 1.20
Sales and Marketing 1.08 Video 1.17 Clothing and Apparel 1.20
Clothing and Apparel 1.08 Manufacturing 1.17Navigation and
Mapping1.19
Content and Publishing 1.07 Consumer Electronics 1.16 Data and Analytics 1.19
Design 1.07 Content and Publishing 1.16 Sales and Marketing 1.19Table 2.48. Specialised industry groups – Ukraine
For Ukraine, 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.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation111
Relevant start-ups and venture capi-
tal-backed companies Industry Groups
supporting the definition of potential in-
novation domains
Based on the evidence shown in the previous sec-
tions for critical mass and specialisation per In-
dustry Group, here we shall identify a series of
start-ups and venture-backed companies Industry
Groups that may be helpful in the definition of in-
novation potential domains for the Smart Special-
isation Strategies across the Eastern Partnership.
We hereby identify a series of prominent Industry
Groups per Eastern Partnership country by adopt-
ing the following rationale: each Industry Group
identified in this section shall be featured in the
top 10 Industry Groups per critical mass of | [
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"1.13",
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"1.21",
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"1.22",
"\n",
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"1.11",
"Clothing",
"and",
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"1.20",
"Video",
"1.22",
"\n",
"Biotechnology",
"1.11",
"Health",
"Care",
"1.19",
"Consumer",
"Goods",
"1.21",
"\n",
"Sustainability",
"1.11Navigation",
"and",
"\n",
"Mapping1.19",
"Design",
"1.21",
"\n",
"Energy",
"1.10",
"Sustainability",
"1.19Media",
"and",
"\n",
"Entertainment1.20",
"\n",
"Advertising",
"1.10",
"Education",
"1.18",
"Food",
"and",
"Beverage",
"1.20",
"\n",
"Sales",
"and",
"Marketing",
"1.08",
"Video",
"1.17",
"Clothing",
"and",
"Apparel",
"1.20",
"\n",
"Clothing",
"and",
"Apparel",
"1.08",
"Manufacturing",
"1.17Navigation",
"and",
"\n",
"Mapping1.19",
"\n",
"Content",
"and",
"Publishing",
"1.07",
"Consumer",
"Electronics",
"1.16",
"Data",
"and",
"Analytics",
"1.19",
"\n",
"Design",
"1.07",
"Content",
"and",
"Publishing",
"1.16",
"Sales",
"and",
"Marketing",
"1.19Table",
"2.48",
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"Partnership",
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"-",
"Potential",
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"knowledge",
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"based",
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"\n",
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] | [] |
Conclusions
Portable NiMH batteries of general use are an important technology and require
specific performance testing. The performance and durability parameters—rated capacity,
charge retention, capacity recovery, and endurance in cycles—are measured in this study
according to the applicable standard IEC 61951-2:2017/AMD1:2022.
Our findings show that better energy efficiency is achieved by reducing charging times
(e.g., 8 h), as compared to 16 h defined for charging of NiMH batteries at 0.1 C in this
standard. However, reducing charging time must be analyzed in more detail to observe if
this change does not affect the battery’s capacity.Batteries 2025 ,11, 30 18 of 20
Furthermore, the charge retention tests show that batteries lose 5% to 10% capacity
after the 28-day storage period. This suggests that the minimum discharge time in IEC
61951 can be modified from 3 h to 4 h for these batteries. For the charge recovery, our
results suggest that stored batteries after 8 months can pass the performance test of IEC
61951-2 and that the required discharge time could be increased from 4 h to, e.g., 5 h.
In addition, the endurance test shows that a maximum charge voltage may be added
as an end-of-charge criterion to reduce the charging time, and the minimum number of
cycles could be increased to be more in line with manufacturer-declared values; however,
this needs to be analyzed more in detail to evaluate a realistic value for battery cycles
per size.
Lastly, the application test shows that portable NiMH batteries in general can perform
the “toy” test intended for primary batteries (IEC 60086-2); our results suggest that NiMH
batteries can be used for this specific application as portable primary batteries.
Results confirm the common experience that NiMH batteries can replace primary
batteries in a toy application. While a primary typically lasts longer than a NiMH battery
on a single charge, a NiMH battery can be recharged.
Author Contributions: D.F.Q.P . performed the experiments, analyzed the data, and wrote the paper;
C.F.C. wrote the materials part and corrected the paper; and M.B. supervised the research and
corrected the paper. All authors have read and agreed to the published version of the manuscript.
Funding: This research was partly funded by the Administrative Agreement No. 36075 between the
European Commission services JRC, DG ENV and DG GROW.
Data Availability Statement: The original data presented in the study are openly available in the
Joint Research Centre data catalogue | [
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attention to whether a person’s eyes are
open or not in terms of whether to follow their gaze,
and the degree to which infants in fact follow gaze
at 10–11 months while vocalizing themselves pre-
dicts vocabulary comprehension 7–8 months later
(Brooks and Meltzoff, 2005).8
In summary, the process of acquiring a linguis-
tic system, like human communication generally,
relies on joint attention and intersubjectivity: the
ability to be aware of what another human is attend-
ing to and guess what they are intending to commu-
nicate. Human children do not learn meaning from
form alone and we should not expect machines to
do so either.
7 Distributional semantics
Distributional semanticists have long been aware
that grounding distributional representations in the
real world is challenging. The lexical similarity
relations learned by distributional models trained
on text don’t in themselves connect any of those
words to the world (Herbelot, 2013; Baroni et al.,
2014; Erk, 2016; Emerson, 2020), and the distribu-
tions of words may not match the distribution of
things in the world (consider four-legged dogs ).
One approach to providing grounding is to train
distributional models on corpora augmented with
perceptual data, such as photos (Hossain et al.,
2019) or other modalities (Kiela and Clark, 2015;
Kiela et al., 2015). Another is to look to interaction
data, e.g. a dialogue corpus with success annota-
tions, including low-level success signals such as
8These three studies do not name the language that the
children were learning. It appears to have been English.5191emotional stress (McDuff and Kapoor, 2019) or
eye gaze (Koller et al., 2012), which contains a
signal about the felicitous uses of forms. The idea
that as the learner gets access to more and more
information in addition to the text itself, it can learn
more and more facets of meaning is worked out in
detail by Bisk et al. (2020). We agree that this is an
exciting avenue of research.
From this literature we can see that the slogan
“meaning is use” (often attributed to Wittgenstein,
1953), refers not to “use” as “distribution in a text
corpus” but rather that language is used in the
real world to convey communicative intents to real
people. Speakers distill their past experience of
language use into what we call “meaning” here,
and produce new attempts at using language based
on this; this attempt is successful if the listener
correctly deduces the speaker’s | [
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(EIST domains),
for all cases in which a concordance could be iden-
tified for at least two countries. The final column
of the table reports a ‘potential for EIST collab-
oration’ indicator, based on the number of times
the E&I and S&T pair has been identified across
the EaP.
As can be observed, the table is rather sparse:
most of the possible E&I and S&T concordance
pairs in fact are only observed in one country (and
thus not reported in the table), implying that the
observed transversality of EIST domains through-
out the EaP is fairly limited. Of course, this is a
result based on data obtained from internation-
al sources, the caveats of which have been dis-
cussed previously in this document. Additionally,
the methodology behind those numbers is pure-ly quantitative and fails to see actual synergies
between S&T and E&I currently happening on the
ground which escape the logic of concordance ta-
bles applied to derive the above results.
Bearing in mind the above limitations, the following
EIST concordances can be observed across the EaP.
■The E&I-S&T pair Food Processing and
Manufacturing - Agrifood is by far the most
recurrent in the EaP, as it appears as a niche
of EIST potential in Armenia, Georgia, Moldova
and Ukraine.
■The second most frequent E&I-S&T pair is
Information Technology and Analytical
Instruments - Electric and electronic
technologies, which appears in Armenia and
Ukraine.
■Other concordances of the cluster Informa-
tion Technology and Analytical Instru-
ments were identified with both of the S&T
domains ICT and computer science and Op-
tics and photonics in Ukraine.
■Chemical Products is a cluster for which po-
tential cooperation could be inferred between
Economic cluster S&T domainsEaP countriesCoop.
potentialAM AZ BY GE MD UA
Food Processing and
ManufacturingAgrifood 4
Chemical ProductsBiotechnology 2
Chemistry and chemical
engineering2
Nanotechnology and
materials2
Metalworking TechnologyNanotechnology and
materials2
Information Technology and
Analytical InstrumentsElectric and electronic
technologies3
ICT and computer science 2
Optics and photonics 2
Communications Equipment and
ServicesICT and computer science 2Table 4.7. Pairs of economic clusters and S&T domains that can be identified in at least two countries
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation247
Azerbaijan and Moldova, as the EIST niches
Chemical Products - Biotechnology, Chem-
istry and chemical engineering and Na-
notechnology and materials could all be
identified in both countries.
■The E&I-S&T pair Metalworking Technolo-
gies - Nanotechnology and | [
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"photonics",
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"\n ",
"■",
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"a",
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"for",
"which",
"po-",
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] | [] |
Member States.
The entire permit granting process for onshore wind farms can take up to 9 years in some Member States, compared
with under 3 years in the most efficient ones. Ground-mounted solar PV systems can take 3-4 years to approve in
some countries but 1 year in others. The time devoted to analyses of environmental impacts represents a significant
share of the difference between best and worst performers. The EU has developed initiatives to shorten permitting
(such as the Article 122 emergency proposals), but there are still significant hurdles to implementation, in particular
lack of administrative capacity and digitalisation. 69% of municipalities report a lack of skills related to environmental
and climate assessments.
Finally, over time energy taxation has become an important source of budget revenues, contributing to
higher retail prices . While taxation can be a policy tool to encourage decarbonisation, significant variation exists
among Member States concerning taxes and price relief schemes. In contrast to the EU, the US does not levy any
federal taxes on electricity or natural gas consumption. Moreover, as power generation falls under the scope of
the EU’s ETS, its carbon intensity is priced in electricity generation costs. This cost is high and volatile in the EU
(amounting to EUR 20-25/MWh for gas-fired generation in EU), while in California the same cost stands at around
EUR 10-15/MWh. Excluding the CO₂ costs paid by producers (which are estimated to lie in the range of 15-20% the
commodity costs in 2022), generation cost is in the range of 45% for households and 65% of industrial retail prices.
The residual costs were approximately equally shared between the network and taxes.
45THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3The threat to Europe’s clean tech sector
Although Europe is a world leader in clean tech innovation, it is squandering early-stage advantages owing
to the weaknesses in its innovation ecosystem [see the chapter on clean technologies] . More than one-fifth of
clean and sustainable technologies worldwide are developed in the EU and the pipeline is still strong: around half
of EU clean tech innovations at a launch or early revenue stage, 22% at scale-up stage and 10% already matureviii.
However, since 2020 patenting in low-carbon innovation has slowed down in Europe, while in recent years the sector
has seen its early-stage advantages being challenged. For example, from 2015 to 2019 the EU represented 65% of
global | [
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"45THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER",
"3The",
"threat",
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"Europe",
"’s",
"clean",
"tech",
"sector",
"\n",
"Although",
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"\n",
"global"
] | [] |
distribution of documents, computed row-wise.The Russian Federation so far concentrated a
larger proportion of scientific collaborations in Bi-
otechnology, Chemistry and chemical engineering,
Energy, Nanotechnology and materials and Optics
and photonics. The remaining domains present
a more evenly distributed collaboration pattern,
notably varied in Agrifood, Environmental scienc-
es and industries or Electric and electronic tech-nologies. Some specific relationships emerge, for
instance with Poland in Transportation and ICT
and computer science or with the United States in
Health and wellbeing.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation25
Russian
federation
Germany
Poland
United
States
France
United
Kingdom
Italy
Spain
China
Switzerland
Agrifood 289 140 127 150 105 84 113 66 42 51
Biotechnology 966 580 671 661 548 241 156 146 159 45
Chemistry and chemical engineering 1 186 633 399 356 287 116 143 119 86 54
Electric and electronic technologies 304 173 248 182 134 68 82 84 81 22
Energy 361 149 233 171 72 52 66 73 44 19
Environmental sciences and industries 1 331 614 710 623 368 383 263 253 172 188
Fundamental physics and mathematics 5 398 4 606 3 907 4 495 3 734 3 438 3 608 3 123 3 028 2 832
Governance, culture, education and the economy 903 438 687 675 279 453 240 211 146 155
Health and wellbeing 1 731 1 414 1 249 2 126 986 1 465 925 707 366 599
ICT and computer science 678 496 823 466 383 201 189 137 281 121
Mechanical engineering and heavy machinery 388 182 319 96 67 60 55 30 55 23
Nanotechnology and materials 4 197 2 452 2 526 1 568 1 202 649 435 461 523 283
Optics and photonics 1 054 573 372 483 300 200 147 182 211 57
Transportation 70 67 104 42 15 24 8 9 23 2
PublicationsGermany
France
United
Kingdom
Spain
Italy
Belgium
Greece
Netherlands
Poland
Romania
Agrifood 12 14 7 14 14 11 9 13 5 8
Biotechnology 12 10 13 9 7 5 5 6 10 4
Chemistry and chemical engineering 9 5 5 4 4 4 3 3 3 3
Electric and electronic technologies 6 4 5 3 5 4 4 1
Energy 40 23 22 24 25 28 17 14 15 19
Environmental sciences and industries 51 42 36 42 41 36 26 31 23 28
Fundamental physics and mathematics | [
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"388",
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"24",
"8",
"9",
"23",
"2",
"\n",
"PublicationsGermany",
"\n",
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"\n",
"United",
"\n",
"Kingdom",
"\n",
"Spain",
"\n",
"Italy",
"\n",
"Belgium",
"\n",
"Greece",
"\n",
"Netherlands",
"\n",
"Poland",
"\n",
"Romania",
"\n",
"Agrifood",
"12",
"14",
"7",
"14",
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"11",
"9",
"13",
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"8",
"\n",
"Biotechnology",
"12",
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"5",
"5",
"6",
"10",
"4",
"\n",
"Chemistry",
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"9",
"5",
"5",
"4",
"4",
"4",
"3",
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"3",
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"Electric",
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"6",
"4",
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"4",
"4",
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"40",
"23",
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"24",
"25",
"28",
"17",
"14",
"15",
"19",
"\n",
"Environmental",
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"51",
"42",
"36",
"42",
"41",
"36",
"26",
"31",
"23",
"28",
"\n",
"Fundamental",
"physics",
"and",
"mathematics"
] | [] |
Nanotechnology
and materials in Georgia
Georgia
For the case of Georgia, only in the case of two
E&I domains was it possible to find a S&T coun-
terpart, namely:
■for the ‘Food Processing and Manufacturing’
cluster, the ‘Agrifood’ S&T domain could be
aligned with the manufacturing of food and
beverages E&I domains. This concordance was
produced by both patents and publications;
■for the ‘Metalworking Technology’ cluster, the
‘Nanotechnology and materials’ S&T domain could be matched with the ‘Manufacture of
fabricated metal products’ E&I domain. The
mapping was, in this case, elicited by publi-
cations only, but a closer inspection of the se-
mantic content of the S&T domain confirms
that the alignment is well-suited here.
The most relevant keywords for these Georgian
S&T domains that match E&I domains can be
found in the figures below; similar figures were
also shown in Part 3 when characterising the S&T
for the whole EaP region.
GEORGIA
Concordance between E&I analysis and S&T analysis
Economic clusterE&I domains
(NACE sectors)S&T domains
Food Processing and
Manufacturing10 Manufacture of food products
11 Manufacture of beverages• Agrifood
Media Production and Distribution18 Printing and reproduction of
recorded media
Metalworking Technology25 Manufacture of fabricated metal
products, except machinery and
equipment• Nanotechnology and materials
Hospitality and Tourism55 Accommodation
56 Food and beverage service
activities
Computer Programming and
Financial Services62 Computer programming,
consultancy and related activities
64 Financial service activities, except
insurance and pension fundingTable 4.4. Combined EIST specialisation domains in Georgia
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation241
Moldova
For Moldova, the following concordances between
E&I and S&T domains were identified:
■for the cluster ‘Food Processing and Manu-
facturing’, the ‘Agrifood’ S&T domain could
be matched with the corresponding food and
beverage manufacturing E&I domains. The
concordance was triggered by both patents
and publications;
■for the cluster ‘Leather, Apparel & Footwear’,
a concordance between the ‘Nanotechnology
and materials’ S&T domain could be identified
with the textile and leather manufacturing E&I
domains. The concordance was, in this case,
produced by publications exclusively. A qual-
itative inspection of the semantic content of
the ‘Nanotechnology and materials’ S&T do-
main suggests, however, that this S&T domain
is primarily focused on the characterisation of metallic materials. For this reason, it was de-
cided to manually remove the identified con-
cordance;
■for the ‘Wood Products’ cluster, a concordance
was obtained between the respective E&I do-
main and the ‘Chemistry and | [
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"56",
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"\n",
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"Computer",
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"\n",
"Financial",
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"Computer",
"programming",
",",
"\n",
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"\n",
"64",
"Financial",
"service",
"activities",
",",
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"\n",
"insurance",
"and",
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"4.4",
".",
"Combined",
"EIST",
"specialisation",
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"in",
"Georgia",
"\n",
"Smart",
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"in",
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"Eastern",
"Partnership",
"countries",
"-",
"Potential",
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"-",
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"\n",
"Moldova",
"\n",
"For",
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",",
"the",
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"E&I",
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"and"
] | [] |
sectoral, strategic and struc-
tural properties. The platform mapping cluster
organisation is curated by the ECCP and is openly
accessible. For each cluster, the data source pro-
vides an indication of its areas of specialisation in
the form of NACE codes, scientific disciplines and
policy objectives.
The assessment of existing cluster organisations
in EaP countries is complemented by the informa-
tion provided in the 2017 report Review of the
state of development of clusters in EaP coun-
tries, elaborated as part of the Horizon 2020 pro-
ject ‘STI International Cooperation Network for
Eastern Partnership Countries – PLUS’. The report
presents additional clusters or emerging cluster
organisations to those identified in the mapping
tool, which are defined as ‘closest to meeting the
European definition criteria for “cluster” as of Jan-
uary 2017’ and selected by local experts. The re-
port also includes some additional considerations
on organisation cluster policy and development.
Table 2.56 presents all of the identified cluster
organisations by EaP country, by selected source.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation115
Armenia Azerbaijan Belarus Georgia Moldova Ukraine
Software
Mobile
Financial Services
Information Technology
Lending and Investments
Travel and Tourism
Gaming
Internet Services
Natural Resources
Payments
Sustainability
Professional Services
Hardware
Sales and Marketing
Food and Beverage
Science and Engineering
Energy
Data and Analytics
Commerce and Shopping
AppsTable 2.54. Recommended industry groups for start-ups and venture capital-backed companies
The table shows the selected Industry Groups supporting the definition of innovation potential domains, for each EaP country.
116
Part 2 Analysis of economic and innovation potential
NACE code Industry Group(s) Number of EaP countries
B6 – Extraction of crude petroleum and natural gas Natural Resources 1
C10 – Manufacture of food products Food and Beverage 1
C11 – Manufacture of beverages Food and Beverage 1
C26 – Manufacture of computer, electronic and optical
productsHardware; Mobile 4
D35 – Electricity, gas, steam and air conditioning supply Natural Resources; Energy 1
G46 – Wholesale trade, except of motor vehicles and
motorcyclesCommerce and Shopping 1
G47 – Retail trade, except of motor vehicles and motorcyclesCommerce and Shopping;
Sales and Marketing1
H53 – Warehousing and support activities for transportation Travel and Tourism 2
I55 – Accommodation Travel and Tourism 2
I56 – Food and beverage service activities Food and Beverage 1
J61 – TelecommunicationsMobile; Information
Technology; Internet
Services5
J62 – Computer programming, consultancy and related
activitiesInformation Technology;
Software; Data and
| [
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"-",
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"-",
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"\n",
"Armenia",
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"\n",
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"116",
"\n ",
"Part",
"2",
"Analysis",
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"\n",
"NACE",
"code",
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"C26",
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] | [] |
China trips to the same party.
We humans are also very willing, as we will see
inx4 below, to attribute communicative intent to a
linguistic signal of a language we speak, even if the
originator of the signal is not an entity that could
have communicative intent.
To summarize, as we strive to understand how
NLU tasks and system performance on those tasks
relates to the bigger picture goals of building
human-analogous natural language understanding
systems, it is useful to distinguish cleanly between
form, conventional meaning, and communicative
intent. Furthermore, we should be careful not to
confuse communicative intent with ground truth
about the world, as speakers can of course be mis-
taken, be intentionally dissembling, etc.
We argue that a model of natural language that
is trained purely on form will not learn meaning:
if the training data is only form, there is not suffi-
cient signal to learn the relation Mbetween that
form and the non-linguistic intent of human lan-
guage users, nor Cbetween form and the standing
meaning the linguistic system assigns to each form.
3.2 Meaning and intelligence
Meaning and understanding have long been seen
as key to intelligence. Turing (1950) argued that a
machine can be said to “think” if a human judge
cannot distinguish it from a human interlocutor af-
ter having an arbitrary written conversation with5188each. However, humans are quick to attribute mean-
ing and even intelligence to artificial agents, even
when they know them to be artificial, as evidenced
by the way people formed attachments to ELIZA
(Weizenbaum, 1966; Block, 1981).
This means we must be extra careful in devising
evaluations for machine understanding, as Searle
(1980) elaborates with his Chinese Room experi-
ment: he develops the metaphor of a “system” in
which a person who does not speak Chinese an-
swers Chinese questions by consulting a library of
Chinese books according to predefined rules. From
the outside, the system seems like it “understands”
Chinese, although in reality no actual understand-
ing happens anywhere inside the system.
Searle’s thought experiment begins from the
premise that it is possible to manipulate forms
well enough to be indistinguishable from a system
that understands the meaning of the forms, reasons
about it, and responds appropriately. We observe
that much recent work in NLP claims to be build-
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but in fact also the process for building it only | [
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in the
presence of the ‘made for’- claim.
Table A4
Difference between the absence and presence of ‘made for’ claim of the difference in WTP of branded foreign versus branded domestic versions.
Mean Std Dev. H2aB H2bB
Western Countries
Germany Yogurt HU 0.37*** 0.06 ✓ ✓
(Euro) Yogurt LI 0.53*** 0.06 ✓ ✓
Spaghetti sauce HU 0.17*** 0.05 ✓ ✓
Spaghetti sauce LI 0.26*** 0.05 ✓ ✓
Cookies HU 0.35*** 0.05 ✓ ✓
Cookies LI 0.40*** 0.05 ✓ ✓
Spain Soft drink RO 0.17*** 0.06 ✓ ✓
(Euro) Soft drink SE 0.06 0.05 ✕
Fish fingers RO 0.96*** 0.18 ✓ ✓
Fish fingers SE 0.34*** 0.13 ✓
Crisps RO 0.74*** 0.11 ✓ ✓
Crisps SE 0.14* 0.08 ✓
Sweden Soft drink ES 4.11*** 0.65 ✓
(Krona) Soft drink RO 8.02*** 0.82 ✓ ✓
Fish fingers ES 5.72*** 1.61 ✓
Fish fingers RO 9.73*** 1.57 ✓ ✓
Crisps ES 11.99*** 1.49 ✓
Crisps RO 20.75*** 1.53 ✓ ✓
Eastern Countries
Hungary Yogurt DE 0.69 13.52 ✕ ✕
(Forint) Yogurt LI 145.63*** 14.40 ✓
Spaghetti sauce DE 5.5 28.74 ✕ ✕
Spaghetti sauce LI 226.99*** 35.67 ✓
Cookies DE 40.38*** 14.82 ✓
Cookies LI 201.44*** 16.10 ✓
Lithuania Yogurt DE 0.03 0.10 ✕ ✕
(Euro) Yogurt HU 0.21** 0.10 ✓
Spaghetti sauce DE 0.11 0.09 ✕ ✕
Spaghetti sauce HU 0.27*** 0.08 ✓
Cookies DE 0.19** 0.07 ✕ ✓
Cookies HU 0.07 0.07 ✕ ✕
Romania Soft drink ES 49.03 158.31 ✕ ✕
(Leu) Soft drink SE 24.27 79.27 ✕ ✕
Fish fingers ES 0.91*** 0.32 ✓ ✕
Fish fingers SE 0.23 0.39 ✕ ✕
(continued on next page)D.M. Federica et al. Food Policy 131 (2025) 102803
12 Table A4 (continued )
Mean Std Dev. H2aB H2bB
Crisps ES 1.77 2.18 ✕ ✕
Crisps SE 9.37*** 2.60 ✓ ✕
Source: Authors ’ elaboration
]These coefficients represent the difference between the WTP of the generic product in the absence of the ‘made for’ claim and the WTP of the generic product in the
presence of the ‘made for’- claim.
Table A5
Test of the Sensory preference score (SPS) across product versions (rating pool R1 – R2 in Frame 1).
Product Versions t-test difference | [
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Conclusions (Sect. 6).
2 Data and methodologies
We present the methodological approach in three steps: first,
we describe the underlying exposure data and methodology
that create the basis for our single- and multiple-hazard anal-
ysis (1). We then present the methodological approach used
to identify hotspots for single hazards’ exposure (2). Finally,
we present the meta-analysis methodology used to combine
the hotspots of single hazards’ exposure and to identify re-
gions with significant multi-hazard potential (3). Figure 1 de-
picts a representation of the entire methodological chain.
2.1 The exposure data and methodology
2.1.1 The areal dimension
The multi-hazard spatial coincidence is assessed at the level
of the areal dimension, represented by local administrative
units (LAUs). LAUs are the finest hierarchical subdivision
of the European economic territory for which statistics are
available. LAUs are provided by the statistical office of the
European Union (Eurostat) and represent the administrative
units of municipalities and communes of Europe. In the
present study, we use the 2013 version of LAUs covering the
EU27+UK and the European Free Trade Association (EFTA)
countries. LAUs are used as statistical areas for multi-hazard
exposure and hotspot analysis. Administrative directives, or-
ganizations, and operational services are coordinated at the
level of administrative entities, and they become of high rel-
evance when linked down to the local level, challenging the
gap in the scale of policy and scale of practice (Gaillard and
Mercer, 2013).
Here we consider 122 034 LAUs on which we perform the
aggregations and statistical analysis. Their average area is
39.6 km2, the maximum area is 20 688 km2(Kiruna, Swe-
den), and the minimum area is 0.2 km2(Thorpe Hamlet,
UK). LAUs present heterogeneities across Europe in terms
of area covered, especially in northern part of Europe (e.g.Scandinavia) but remain rather homogeneously distributed
within the national boundaries (Fig. 2).
Despite being a well-established geographic concept, the
process of aggregating higher-resolution data to larger ad-
ministrative units comes with a potential source of error
known as the modifiable areal unit problem (MAUP). Two is-
sues related to the MAUP presented in the literature (Fother-
ingham and Wong, 1991; Jelinski and Wu, 1996; Openshaw,
1984) are scaling and zonation effects (Charlton and Kemp,
2008). These generally alter the variance structure of the data
when aggregated due to disconnection across scales and to
different ways of subdividing the geographical space at the
same scale (Stillwell et al., 2014). In order to | [
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replying
to A’s messages. Can O successfully pose as B
without making A suspicious? This constitutes
a weak form of the Turing test (weak because A
has no reason to suspect she is talking to a non-
human); the interesting question is whether O fails
it because he has not learned the meaning relation,
having seen only the form of A and B’s utterances.
The extent to which O can fool A depends on
the task — that is, on what A is trying to talk about.
A and B have spent a lot of time exchanging trivial
notes about their daily lives to make the long island
evenings more enjoyable. It seems possible that O
would be able to produce new sentences of the kind
B used to produce; essentially acting as a chatbot.
This is because the utterances in such conversations
have a primarily social function, and do not need to
be grounded in the particulars of the interlocutors’
actual physical situation nor anything else specific
about the real world. It is sufficient to produce text
that is internally coherent.
Now say that A has invented a new device, say
a coconut catapult. She excitedly sends detailed
instructions on building a coconut catapult to B,
and asks about B’s experiences and suggestions for
improvements. Even if O had a way of construct-
ing the catapult underwater, he does not know what
words such as rope andcoconut refer to, and thus
can’t physically reproduce the experiment. He can5189only resort to earlier observations about how B re-
sponded to similarly worded utterances. Perhaps O
can recognize utterances about mangos andnails as
“similarly worded” because those words appeared
in similar contexts as coconut andrope. So O de-
cides to simply say “Cool idea, great job!”, because
B said that a lot when A talked about ropes and
nails. It is absolutely conceivable that A accepts
this reply as meaningful — but only because A does
all the work in attributing meaning to O’s response.
It is not because O understood the meaning of A’s
instructions or even his own reply.
Finally, A faces an emergency. She is suddenly
pursued by an angry bear. She grabs a couple of
sticks and frantically asks B to come up with a way
to construct a weapon to defend herself. Of course,
O has no idea what A “means”. Solving a task like
this requires | [
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71.20 42.00 32.25 20.80 62.60 2.40 51.80 72.80 44.40 2.50
Observations 500 500 500 500 500 500 500 500 500 500 500 500
]In italic percentage and standard deviation in parentheses.
Table A8b
Pairwise comparison of relevant sample characteristics across treatment in the DCE.
Germany Lithuania Hungary Sweden Spain Romania
Age 0.276 0.366 0.114 0.000 0.917 0.987
Gender 0.899 0.569 0.611 0.613 0.229 0.411
Educational Level]0.619 0.430 0.894 0.870 0.709 0.704
]Chi2 test.
Table A9
Summary statistics and pairwise comparison of relevant sample characteristics across treatment in the Lab experiment.
Variables Germany Hungary
Control group
(Blind)Treatment group
(No Blind)Pairwise comparison Control group
(Blind)Treatment group
(No Blind)Pairwise comparison
Age 48.53 44.14 0.000 45.33 41.53 0.000
Gender
Male 55.50 47.50 0.001 54.00 45.00 0.000
Female 44.50 52.50 46.00 55.00
Educational Level]
Primary Education 2.50 1.50 0.013 1.00 3.50 0.000
Secondary Education 31.50 31.50 56.50 42.50
Tertiary Education 37.00 31.50 40.00 51.50
University and higher 29.00 35.50 2.50 2.50
Observation 200 200 200 200
]Chi2 test.
Fig. A1. Quality perception and valuation of DFQ models, based on Total Food Quality Model by Grunert (2005).
Appendix B:.Statistical model
The key objective of this study is to assess which composition of the same product consumers prefer. We focus on two primary consumer behaviours
that reflect the two alternative arguments put forward by stakeholders to explain consumer preference in the presence of DFQ. We model consumer
choice on a purchase occasion as a discrete-choice variable and adopt a random utility approach. The indirect utility obtained by a consumer r from
country s∃S, under claim regime i∃I (with Ic identifying the absence-focused regime and Im the presence-focused ‘made for’ regime), for each
product k∃K in any of the six choice situations t∃T from his/her preferred and least preferred alternative l∃L among four possible alternatives and
P price vector can be written as:
UsCiCk
rCtClβsCiCk
lXsCiCk
lαsCiCkpsCiCk
lεsCiCk
rCtClC
where
Xl∃
VF1CBCVF2CBCVHCGCVF1CGCVF2CGCO)
and VcCj indicates the country-specific ‘list of ingredients and nutritional facts ’. Subscript c refers to the product version sold in domestic country H
or the two alternatives sold in foreign countries F1 and F2, and j indicates the branded ⊔B⊓or generic product ⊔G⊓, such that for the German sample (i.e.
s Germany), VHCBCVF1CBCVF2CB refer to | [
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"A9",
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are mostly systematic, corresponding to a ran-dom fluctuation of the charge, uniformly distributed in the
range [ −0.3,0.3], to take into account the systematic devia-
tion observed for isomers (Fig. 13b). The average measured
nuclear charge is then /angbracketleftˆZ
L/angbracketright=43.42±0.17. Note that Fig. 14
represents the raw nuclear charge yield as measured with
the IC. Unfolding this distribution with an accurate responsefunction obtained from the isomers data (Fig. 13) would lead
to a more physical distribution, but this complex analysis
would be the subject of a forthcoming article. This distribu-
tion can still be compared to the Z
pmodel introduced by
Wahl [ 51], as represented in Fig. 14. This nuclear charge
distribution was obtained from the Zpmodel using the pre-
neutron mass distribution Y(A)measured in this work and
was folded by the charge resolution of the IC obtained from
Fig.13c. In this figure, we represented two different sets of
parameters for this model, namely the original one from 1988[51] and a more recent one from 2000 [ 52].4 Conclusion
This paper has presented the results of a thorough analysis
of fission fragment isomers produced in the spontaneous fis-
sion of
252Cf using the VESPA setup at EC-JRC Geel. First, a
systematic study of the half-lives of various isomers was con-ducted using two complementary analysis methods. First, γ-
γcoincidences of late-emission prompt fission γ-rays were
used to identify isomers and estimate half-lives in simplecases. Second, a multiple isomers analysis was carried out to
disentangle isomeric states populating each other. The results
obtained in this work can be valuable for the data evaluationcommunity. In particular, half-lives of short-lived isomeric
states in
108Tc and94Rb were measured for the first time.
Also, our data indicate the existence of a short-lived (6 ns)isomeric state at E
∗= 401 keV that was assigned to147Ce. In
total, 41 half-lives were measured, from the nanosecond scale
to tens of microseconds, with a careful estimate of associated
uncertainties. This work demonstrates the great strength andversatility of the VESPA setup, consisting of a twin Frisch-
grid ionization chamber in conjunction with fast scintillators
such as LaBr
3(Ce) detectors. The main advantage of such a
setup is to be sensitive to half-lives across several orders of
magnitude and to short-lived isotopes, using a rather simple
analysis method. In particular, the multiple isomers analy-sis, combined with the fast timing properties of LaBr
3(Ce)
detectors, proved to be a powerful tool to | [
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] | [] |
Valenzuela, A., Dhar, R., Zettelmeyer, F., 2009. Contingent Response to Self-
Customization Procedures: Implications for Decision Satisfaction and Choice.
Journal of Marketing Research 46 (6), 754–763.
Verlegh, P.W., Steenkamp, J.B.E., 1999. A review and meta-analysis of country-of-origin
research. Journal of Economic Psychology 20 (5), 521–546.
Webster, Jim (2014), “Nutella, imported vs. domestic: Is there a difference? ” The
Washington Post (May 30) https://www.washingtonpost.com/lifestyle/food/
nutella-imported-vs-domestic-is-there-a-difference/2014/05/30/3fe79e68-e5bb-
11e3-8f90-73e071f3d637_story.html.
Z˘avadský, J., Hiadlovský, V., 2020. Economic problems of dual quality of everyday
consumer goods. Economic Annals-XXI 185 (9–10), 70–78. https://doi.org/
10.21003/ea.V185-07 .D.M. Federica et al. Food Policy 131 (2025) 102803
17 | [
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are often associated with higher quality (Verlegh and Steen -
kamp, 1999; Chu et al., 2010; Newman et al., 2014; Thøgersen, 2023 )
and Russian, Polish and Hungarian (Ettenson, 1993 ) and Croatian
(Ozretic-Dosen et al., 2007 ) consumers were found to prefer products
made in Western countries over those made in Eastern or Central
Europe. On the other hand, consumers may favor products made for
their own country, believing they are tailored to national tastes, which
could lead to higher WTP (Franke et al., 2009; Franke et al., 2010;
Moreau and Herd, 2010; Valenzuela et al., 2009 ).
We anticipate that highlighting different product versions will lead
to the following responses: (a) in Western countries, positive stereotypes
may enhance WTP for tailored domestic versions; (b) in Eastern coun-
tries, negative stereotypes may reduce WTP for domestic versions,
possibly outweighing the benefits of tailoring to local taste.
Additionally, since the political debate focused on branded products,
we study a third hypothesis examining the role of brand names in
influencing consumer perceptions of DFQ. The importance of this aspect
stems from the fact that brands often signal quality consistency (Erdem
et al., 2006), and can overshadow other quality cues, such as price
(Dodds et al., 1991 ) or intrinsic product attributes. For example, an
Italian consumer may overlook compositional differences in Nutella
purchased in the US (Webster, 2014 ), because what matters most to
them is enjoying their favourite product alongside the trusted brand
(Allison and Uhl, 1964 ). Thus, while an exhaustive review of the liter-
ature surrounding the role of brands in consumers ’ purchase decisions is
beyond the scope of this article, we hypothesize that the presence of a
strong extrinsic cues, like a well-known brand, will reduce the attention
to compositional differences and diminish the role of the ‘made for’
claim in consumer decision making — an aspect that cannot be ignored
during the policy design. In this case:
H3: Preference for the domestic product version over a foreign
version reduces (or possibly disappears) when products carry a well-
known brand both in the absence (H3 a) and the presence of the ‘made
for’ claim (H3 b).
Fig. 1summarizes our main hypotheses.
3.Material and methods
To test the above-mentioned hypotheses, we employ two comple -
mentary approaches. The first is an online discrete choice experiment
(DCE) assessing consumers ’ differences in WTPs between different
product versions to | [
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Vander Heiden MG, DeBerardinis RJ (February 2017). "Understanding the Intersections between Metabolism and Cancer Biology". Cell. 168 (4): 657–669. doi:10.1016/j.cell.2016.12.039. PMC 5329766. PMID 28187287.
Cooper GM (2000). "The Molecular Composition of Cells". The Cell: A Molecular Approach (2nd ed.). Archived from the original on 27 August 2020. Retrieved 25 June 2020.
Michie KA, Löwe J (2006). "Dynamic filaments of the bacterial cytoskeleton". Annual Review of Biochemistry. 75: 467–92. doi:10.1146/annurev.biochem.75.103004.142452. PMID 16756499. S2CID 4550126.
Nelson DL, Cox MM (2005). Lehninger Principles of Biochemistry. New York: W. H. Freeman and company. p. 841. ISBN 978-0-7167-4339-2.
Kelleher JK, Bryan BM, Mallet RT, Holleran AL, Murphy AN, Fiskum G (September 1987). "Analysis of tricarboxylic acid-cycle metabolism of hepatoma cells by comparison of 14CO2 ratios". The Biochemical Journal. 246 (3): 633–9. doi:10.1042/bj2460633. PMC 1148327. PMID 3120698.
Hothersall JS, Ahmed A (2013). "Metabolic fate of the increased yeast amino Acid uptake subsequent to catabolite derepression". Journal of Amino Acids. 2013: 461901. doi:10.1155/2013/461901. PMC 3575661. PMID 23431419.
Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH, Murphy RC, et al. (May 2005). "A comprehensive classification system for lipids". Journal of Lipid Research. 46 (5): 839–61. doi:10.1194/jlr.E400004-JLR200. PMID 15722563.
"Lipid nomenclature Lip-1 & Lip-2". qmul.ac.uk. Archived from the original on 6 June 2020. Retrieved 6 June 2020.
Berg JM, Tymoczko JL, Gatto Jr GJ, Stryer L (8 April 2015). Biochemistry (8 ed.). New York: W. H. Freeman. p. 362. ISBN 978-1-4641-2610-9. OCLC 913469736.
Raman R, Raguram S, Venkataraman G, Paulson JC, Sasisekharan R (November 2005). "Glycomics: an integrated systems approach to structure-function relationships of glycans". Nature Methods. 2 (11): 817–24. doi:10.1038/nmeth807. PMID 16278650. S2CID 4644919.
Sierra S, Kupfer B, Kaiser R (December 2005). "Basics of the virology of HIV-1 and its replication". Journal of Clinical Virology. 34 (4): 233–44. doi:10.1016/j.jcv.2005.09.004. PMID 16198625.
Wimmer MJ, Rose IA (1978). "Mechanisms of enzyme-catalyzed group transfer reactions". Annual Review of Biochemistry. 47: 1031–78. doi:10.1146/annurev.bi.47.070178.005123. PMID 354490.
Mitchell P (March 1979). "The Ninth Sir Hans Krebs Lecture. Compartmentation and communication in living systems. Ligand conduction: a general catalytic principle in chemical, osmotic and chemiosmotic reaction systems". European Journal of Biochemistry. 95 (1): 1–20. doi:10.1111/j.1432-1033.1979.tb12934.x. PMID 378655.
Dimroth P, von Ballmoos C, Meier T (March 2006). "Catalytic and mechanical cycles in F-ATP synthases. Fourth in the Cycles Review Series". EMBO Reports. 7 (3): 276–82. doi:10.1038/sj.embor.7400646. PMC 1456893. PMID 16607397.
Bonora M, Patergnani S, Rimessi A, De Marchi E, Suski JM, Bononi A, et al. (September 2012). "ATP synthesis and storage". Purinergic Signalling. 8 (3): 343–57. doi:10.1007/s11302-012-9305-8. PMC 3360099. PMID 22528680. | [
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and self-defeating for those in the digital sectors. More than half of SMEs in
Europe flag regulatory obstacles and the administrative burden as their greatest challenge.
We have also left our Single Market fragmented for decades, which has a cascading effect on our competitiveness.
It drives high-growth companies overseas, in turn reducing the pool of projects to be financed and hindering the
development of Europe’s capital markets. And without high-growth projects to invest in and capital markets to
finance them, Europeans lose opportunities to become wealthier. Even though EU households save more than their
US counterparts, their wealth has grown by only a third as much since 2009.
Second, Europe is wasting its common resources. We have large collective spending power, but we dilute it across
multiple different national and EU instruments.
For instance, we are still not joining forces in the defence industry to help our companies to integrate and reach
scale. European collaborative procurement accounted for less than a fifth of spending on defence equipment
procurement in 2022. We also do not favour competitive European defence companies. Between mid-2022 and
mid-2023, 78% of total procurement spending went to non-EU suppliers, out of which 63% went to the US.
Likewise, we do not collaborate enough on innovation, even though public investments in breakthrough technolo -
gies require large capital pools and the spillovers for everyone are substantial. The public sector in the EU spends
about as much on R&I as the US as a share of GDP, but just one-tenth of this spending takes place at the EU level.
Third, Europe does not coordinate where it matters.
Industrial strategies today – as seen in the US and China – combine multiple policies, ranging from fiscal policies
to encourage domestic production, to trade policies to penalise anti-competitive behaviour, to foreign economic
policies to secure supply chains.
In the EU context, linking policies in this way requires a high degree of coordination between national and EU efforts.
But owing to its slow and disaggregated policymaking process, the EU is less able to produce such a response.
Europe’s decision-making rules have not substantially evolved as the EU has enlarged and as the global environ -
ment we face has become more hostile and complex. Decisions are typically made issue-by-issue with multiple veto
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The outcome is a legislative process with an average time of 19 months to agree new | [
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destroying vermin; fungicides, herbicides
Class 6 – Common metals and their alloys, ores;
metal building materials for building and construc-
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metal for railway tracks; non-electric cables and
wires of common metal; ironmongery, small items of metal hardware; pipes and tubes of metal; met-
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Class 7 – Machines and machine tools; motors and
engines (except for land vehicles); machine cou-
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land vehicles); agricultural implements other than
hand-operated; incubators for eggs; automatic
vending machines
Class 8 – Hand tools and implements (hand-oper-
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switching, transforming, accumulating, regulating
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transmission or reproduction of sound or images;
magnetic data carriers, recording discs; compact
discs, DVDs and other digital recording media;
mechanisms for coin-operated apparatus; cash
registers, calculating machines, data process-
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fire-extinguishing apparatus
Class 10 – Surgical, medical, dental and veterinary
apparatus and instruments; artificial limbs, eyes
and teeth; orthopaedic articles; suture materials;
therapeutic and assistive devices adapted for the
disabled; massage apparatus; apparatus, devices
and articles for nursing infants; sexual activity ap-
paratus, devices and articles
Class 11 – Apparatus for lighting, heating, steam
generating, cooking, refrigerating, drying, ventilat-
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Class 12 – Vehicles; apparatus for locomotion by
land, air or water
Class 13 – Firearms; ammunition and projectiles;
explosives; fireworks
Class 14 – Precious metals and their alloys; jewel-
lery, precious and semi-precious stones; horologi-
cal and chronometric instruments
Class 15 – Musical instruments
Class 16 – Paper and cardboard; printed matter;
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation325
bookbinding material; photographs; stationery
and office requisites, except furniture; adhesives
for stationery or household purposes; artists’ and
drawing materials; paintbrushes; typewriters and
office requisites (except furniture); instructional
and teaching materials (except apparatus); plastic
materials forsheets, films and bags for wrapping
and packaging; printers’ type,; printing blocks
Class 17 – Unprocessed and semi-processed rub-
ber, gutta-percha, gum, asbestos, mica and sub-
stitutes for all these materials; plastics and resins
in extruded form for use in manufacture; packing,
stopping and insulating materials; flexible pipes,
tubes and hoses, not of metal
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] | [] |
in the (less densely populated) functional
urban areas.
Whilst we believe that the disaster risk management for
multi-hazard assessment is brought forward by the abil-
ity of the proposed approach to identify LAUs exposed to
multi-hazards with high significance, we acknowledge sev-
eral shortcomings. Among these, a notable limitation of our
approach is that vulnerability (an aspect of the research de-
sign considered) is not taken into account in the assessment
of asset exposure (population and residential built-up areas)
to multi-hazards. The multi-hazard potential of each region is
simply expressed by means of exposure (or assets exposed).
Nevertheless, the overall analytical approach detects signifi-
cant patterns of multi-hazard potential across regions, reveal-
ing spatially explicit clusters and thereby setting the basis
for more precise and focused analysis. The areal-dimension
approach excludes a detailed level of study that could more
accurately examine the spatial coincidence of multiple haz-
ards at localized levels. Moreover, subdividing the exposuredata at the level of areal dimensions that are heterogeneous
in size (as described in Sect. 2.1.1) may introduce biases
into the cluster analysis, particularly when neighbouring re-
lations are defined by distance. This can lead to underesti-
mations or overestimations of the clusters. However, we mit-
igated this issue by identifying the optimal kvalue, which
is dynamically determined for each hazard–asset relation-
ship, in order to minimize the influence of noise and out-
liers on the clustering analysis. Future research opportuni-
ties include revising the meta-analysis approach (based on
Stouffer’s method) employed in this study, specifically ex-
ploring the use of weighted or unweighted versions of the
Z-transform test for the Stouffer method when aggregating
single-hazard hotspots into multi-hazard hotspots. There is
evidence in the statistical literature (Whitlock, 2005) sug-
gesting that the weighted Zapproach may be preferable, par-
ticularly when there is variation in the sample size across
studies or clusters, as is the case in our study, where the num-
ber of regions varies depending on the exposure type. How-
ever, the choice between the weighted and unweighted ver-
sions of this test remains an open question in meta-analysis,
as highlighted by Becker (1994).
6 Conclusions
To our knowledge, this study is the first that uses spatial pat-
terns (clusters/hotspots) and meta-analysis to identify the re-
gions at a European level that are exposed to multi-hazards.
The methodology presented in this study offers valuable in-
sights into the European multi-hazard landscape, thereby in- | [
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CNs were strongly inhibited. Conversely, dur-ing US omissions US- CNs were highly active, while CS-R CNs
were strongly inhibited ( Figures 6 O and 6P). Altogether, these re-
sults suggest that, despite the fact that the presentation of socialand aversive stimuli leads to an overall increase in the activity of
aIC VIP+ INs, a broad functional heterogeneity exists in the indi-
vidual responses of these cells.
We next addressed whether a loss in coding fidelity of CNs
underlies the decay in the general activity of VIP+ INs
upon repeated stimulus presentations. To this aim, we calcu-
lated the Mahalanobis population vector distance (PVD) toassess the differentiability of the responses ( Grewe et al.,
2017 ). During the social preference behavioral paradigm, the
minimum duration of the interaction with a given stimulus acrossall imaged mice (i.e., maximum object interaction during day 1 or
2 of the social interaction test) was used as the maximum period
considered for analysis of PVD. We found that, only during thefirst day of testing, object and mouse CN PVDs significantly
increased over time, owing to increased fidelity of the VIP+ IN
coding ensembles to the social or object cues ( Figures 6 Q and
6R). In the course of fear conditioning and retrieval, althoughthe activity of CS, US, CS-R, or US- CNs changed across suc-
cessive trials ( Figures S6 C and S6F), PVDs between CN ensem-
bles remained unchanged ( Figures 6 S and 6T). These data show
that the observed decrease in general activity upon repeated
stimulus presentation does not arise from a loss or switch in
(E) The time spent in the social interaction zone was similar between ArchT- and GFP-injected animals during periods of OFF (first 5 min) and ON (last 5 mi n) laser-
mediated inhibition (two-way ANOVA, main effect group: p = 0.67; main effect time: p = 0.001; interaction effect: p = 0.27; Bonferroni multiple compar isons test,
OFF GFP versus ON GFP, p = 0.001; OFF ArchT versus ON ArchT, p = 0.001).(F) Social interaction ratios during periods of OFF (first 5 min) and ON (last 5 min) laser-mediated inhibition in ArchT- and GFP-injected animals (two -way ANOVA,
main effect group: p = 0.45; main effect time: p = 0.001; interaction effect: p = 0.15; Bonferroni multiple comparisons test, OFF GFP versus ON GFP, p = 0. 15; OFF
ArchT versus ON ArchT, p = 0.001).(G) Social preference | [
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2.195 1.362
28.4Manufacture of metal forming machinery and machine
tools0.347 1.382 1.019 1.568 1.684
28.9 Manufacture of other special-purpose machinery 0.182 1.154 1.513 0.881 0.827 1.442
29.1 Manufacture of motor vehicles 0.554 0.836 1.573 1.587 0.554 0.897
29.3 Manufacture of parts and accessories for motor vehicles 4.864 0.729
30 Manufacture of other transport equipment 0.357 1.009 1.736 1.092 0.365 1.441
31 Manufacture of furniture 0.738 1.756 1.469 1.185
32 Other manufacturing 0.403 0.782 2.075 0.830 0.891 1.019
32.5Manufacture of medical and dental instruments and
supplies0.292 1.233 0.819 0.582 1.163 1.912
32.9 Manufacturing n.e.c. 1.220 2.076 0.395 1.168 0.938
42.2 Construction of utility projects 1.320 0.633
42.9 Construction of other civil engineering projects 4.249 1.327 0.334
43 Specialised construction activities 0.200 1.628 1.077 0.933 1.511 0.650
62 Computer programming. consultancy and related activities 0.825 2.059 0.715 1.263 0.375 0.763
* A patent family is a collection of patent applications covering the same or similar technical content. The applications in
a family are related to one another through priority claims. Source: https://www.epo.org/searching-for-patents/helpful-
resources/first-time-here/patent-families.html
98
Part 2 Analysis of economic and innovation potential
3.3. Trademark applications
Trademark data for each Eastern Partnership
country are available from the WIPO Global Brand
Database43. Trademarks are classified into 45
classes using the NICE Classification, 34 of which
for goods and 11 for services. The full list is shown
in Annex 5. Trademark data by NICE class are also
used in mapping the technological potential of
the Eastern Partnership countries. Here, the same
trademark data could be used for NACE industries
if an official NICE to NACE concordance existed.
Such a concordance is however unavailable. A
Spanish NACE four-digit to NICE two-digit con-
cordance44 shows that the level of NACE detail is
much higher than the corresponding level of NICE
detail, implying that a reversed concordance is not
43 Disclaimer: The World Intellectual Property Organization
(WIPO) bears no responsibility for the integrity or accuracy
of the data contained herein, in particular due, but not
limited, to any deletion, manipulation, or reformatting of
data that may have occurred beyond its control.
44 https://www.oepm.es/export/sites/oepm/comun/docu-
mentos_relacionados/varios_todas_modalidades/Con-
cordancia_CNAE_NIZA.pdffeasible as a single trademark could be allocated
to too many NACE industries.
The analysis will therefore take an ad hoc ap-
proach by using a partial concordance covering a
smaller number of combined manufacturing in-
dustries. Millot (2009)45 developed the following
correspondence, based on a sample of 1 000 Eu-
ropean firms with | [
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Optics and photonics 171 -10.0% 16 2 189Table 3.15. Number of records per S&T specialisation domain in Moldova
188
Part 3 Analysis of scientific and technological potential
Figure 3.33. Number of records per S&T specialisation domain in Moldova
0 200 400 600 800 1 000 1 200
Number of records
publicationsNanotechnology and materials
Health and wellbeing
Mechanical engineering and heavy machinery
Governance, culture, education and the economy
Chemistry and chemical engineering
Fundamental physics and mathematics
Electric and electronic technologies
Biotechnology
Agrifood
Environmental sciences and industries
Energy
ICT and computer science
Optics and photonics
patents EC projectsHealth and wellbeing are domains in which Moldo-
va’s S&T ecosystem presents a high critical mass,
relative specialisation and scientific impact simul-
taneously.
In patents, Biotechnology (1.7), Agrifood (1.3),
Health and wellbeing (1.3), Mechanical engineer-
ing and heavy machinery (1.1) and Electric and
electronic technologies (1.1) have a positive spe-
cialisation. Given the patent counts in the coun-
try for these domains, such figures provide a
relevant indication of the country’s technological
specialisation.
Finally, Table 3.16 presents the change in the
share of each domain within the S&T data sourc-
es, comparing the 2011-2014 period to the more
recent 2015-2018 period66. The small number of
records, particularly patents, gathered for Moldo-
va affects the temporal evolution indicators sig-
nificantly, and thus some domains have not been
considered.
66 See section ‘Temporal evolution of the S&T specialisation
domains’ for further methodological information.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation189
Figure 3.34. Specialisation index across domains of Moldova’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 3.35. Specialisation index across domains of Moldova’s S&T ecosystem against the EaP average, for patents
0.8 0.6 0.4 1.0
Specialisation indexBiotechnology
Agrifood
Health and wellbeing
Mechanical engineering and heavy machinery
Electric and electronic technologies
Nanotechnology and materials
ICT and computer science
Energy
Chemistry and chemical engineering
Optics and photonics
Fundamental physics and mathematics
Governance, culture, education and the economy
Environmental sciences and industries
Transportation
190
Part 3 Analysis of scientific and technological potential
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no sense to choose the best one: every approach works underwith different conditions like various text generation strategies, different datasetcapacity, quality of data and other. Thus, before choosing which method to use, one
needs to determine the features of arti ficial content generating method. Anyway, each
approach involves trade off that requires further evaluation.
In future, we plan to compare all the presented approaches on standardized datasets
and to do a robustness analysis across different datasets.
References
1. Grechnikov, E.A., Gusev, G.G., Kustarev, A.A., Raigorodsky, A.M.: Detection of arti ficial
texts, digital libraries: advanced methods and technologies, digital collections. In: Proceedings
of XI All-Russian Research Conference RCDL 2009, KRC RAS, Petrozavodsk, pp. 306 –308
(2009)Table 1. Summing up the methods
The method Dataset/Language The best result
Frequency counting method [ 1] 2000 original texts, 250
artificial; Russian90.61 % accuracy
The method of linguistic features [ 2] 2 k, 5 k, 10 k of generated
words; Spanish-English100 % F-measure
for world stuf fing
Phrase analysis method [ 6] 2 k, 5 k, 10 k of generated
words100 % F-measure
for word stuf fing
Lexicographic features method [ 14] 2 k, 5 k, 10 k of generated
words99 % F-measure for
patchwork
Perplexity-based filtering [ 14] English-Japanese parallel
documents97 % F-measure for
2ndMarkov model
A fake content detector based on
relative entropy [ 14]English and French
parallel corpora99 % F-measure for
2ndMarkov model
Hidden Style Similarity method [ 3] A corpus of 5 million html
pages100 % accuracy at
certain threshold
SciDetect method [ 19] 1600 arti ficial
documents + 8200original; English100 % accuracyComputer-Generated Text Detection Using Machine Learning 4252. Corston-Oliver, S., Gamon, M., Brockett, C.: A machine learning approach to the automatic
evaluation of machine translation. In: Proceeding of 39th Annual Meeting on Association for
Computational Linguistics, ACL 2001, pp. 148 –155 (2001)
3. Urvoy, T., Lavergne, T., Filoche, P.: Tracking web spam with hidden style similarity. In:
AIRWEB 2006, Seattle, Washington, USA, 10 August 2006
4. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques
with Java Implementations. Morgan Kaufmann Publishers, Burlington (2011)
5. Arase, Y., Zhou, M.: Machine translation detection from monolingual web-text. In:
Proceedings of 51st Annual Meeting of the Association for Computational Linguistics,Sofia, Bulgaria, pp. 1597 –1607, 4 –9 August 2013
6. Baayen, R.H.: Word Frequency Distributions. Kluwer Academic Publishers, Amsterdam
(2001)
7. Clarkson, P., Rosenfeld, R.: Statistical language modeling using the CMU-Cambridge
toolkit. In: Proceedings of Eurospeech 1997, pp. 2707 –2710 (1997)
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(Gary Marcus)
(5) Though BERT passed the lab’s common-sense test, ma-
chines are still a long way from an artificial version of
a human’s common sense. (Oren Etzioni)
However, there are plenty of instances where
the popular press gets it wrong, such as (6) from
the B2C website,2apparently based on the Google
Blog post about BERT and search, which includes
numerous statements like (7).3
(6) BERT is a system by which Google’s algorithm uses
pattern recognition to better understand how human
beings communicate so that it can return more relevant
results for users.
(7) Here are some of the examples that showed up our
evaluation process that demonstrate BERTs ability to
understand the intent behind your search.
In sum, it is not clear from our academic literature
whether all authors are clear on the distinction be-
tween form and meaning, but it is clear that the
way we speak about what neural LMs are doing is
misleading to the public.
Part of the reason for this tendency to use impre-
cise language may well be that we do not yet fully
understand what exactly it is about language that
the large LMs come to implicitly represent. Their
success, however, has sparked a subfield (‘BERTol-
ogy’) that aims to answer this question. The
methodology of probing tasks (e.g. Adi et al., 2017;
Ettinger et al., 2018) has been used to show that
1https://www.nytimes.com/2018/11/18/technology/artific
ial-intelligence-language.html, accessed 2019/12/04
2https://www.business2community.com/seo/what-t
o-do-about-bert-googles-recent-local-algorithm-updat
e-02259261, accessed 2019/12/04
3https://www.blog.google/products/search/search-langu
age-understanding-bert/, accessed 2019/12/04large LMs learn at least some information about
phenomena such as English subject-verb agreement
(Goldberg, 2019; Jawahar et al., 2019), constituent
types, dependency labels, NER, and (core) seman-
tic role types (again, all in English) (Tenney et al.,
2019).4Hewitt and Manning (2019) find informa-
tion analogous to unlabeled dependency structures
in the word vectors provided by ELMo and BERT
(trained on English). And of course it is well estab-
lished that vector-space representations of words
pick up word classes, both syntactic (POS, e.g. Lin
et al., 2015) and semantic (lexical similarity, e.g.
Rubenstein and Goodenough, 1965; Mikolov et al.,
2013).
Others have looked more closely at the success
of the large LMs on apparently meaning sensitive
tasks and found that in fact, far from doing the “rea-
soning” ostensibly required to complete the tasks,
they were instead simply more effective at leverag-
ing artifacts in the data than previous approaches.
Niven and Kao (2019) find that BERT’s | [
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the Global
Human Settlement Layer (GHSL) (Freire et al., 2015). These
are two main groups of assets that are currently present
across all types of analysis within the DRMKC Risk Data
Hub. The residential built-up category is represented as built-
up area (km2), while the population is expressed as the num-
ber of people within a 100 m 100 m grid cell.
To discriminate between the residential typology for both
built-up areas and population, the CORINE Land Cover(EEA, 2018) code 1.111 (continuous urban fabric) and 2.112
(discontinuous urban fabric) is used as the artificial explana-
tory layer.
2.2 Single-hazard hotspot analysis
The study uses a hotspot analysis to identify clusters (con-
centrations) of regions – LAUs – with assets (or elements at
risk) exposed to single hazards. The chosen approach facili-
tates the identification of spatial patterns and trends that are
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 291
Table 1. Description of the hazard scenarios and datasets considered and their characteristics. RP denotes return period; PGA denotes peak
ground acceleration.
Component Scenario Description Spatial
resolutionData source
River flood RP: 1 event in 200 years Areal extent of the river-flood-prone areas 100 m EFAS (European Flood
Awareness System),
KULTURisk project
Landslide High susceptibility and very high
susceptibility classesPhysical characteristics of various terrain
factors that cause a high predisposition to
landslide occurrence (ELSUS 100 layers)200 m ESDAC (European Soil
Data Centre)
Coastal
inundationRP: 1 event in 200 years Areal extent of coastal inundation as the
extreme total water level (TWL) result of
the contributions from the mean sea level
(MSL), the tide, and the combined effect of
waves and storm surge100 m HELIX project, JRC
coastal risk and GAP
PESETA II projects
Earthquake PGA =0:18 (g) for a probability
of exceedance of 10 % in 50 years
(475-year RP)Areal extent of PGA =0:18 (g),
equivalent of “Moderate”, “Moderate to
heavy”, “Heavy”, and “Very heavy”
potential damage level of the USGS
intensity scale1000 m SHARE project
Subsidence
(from drought)Soils with clay content greater
than 35 %Areal extent of fine and very fine soil texture
(particles<2 mm size) and with clay
content greater than 35 %1000 m ESDAC, IPL project
Wildfire Wildland–urban interface
(WUI) areaWUI areas within 10 km limit range from the
historical burned areas (2000–2016)100 m EFFIS based
not readily apparent in raw data, thereby revealing | [
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strategic priority, with well-defined objectives, governance, and financing . For the first cycle, the objectives
could correspond to the goals set out in this report. Governance of the Action Plans should aim to minimise bureau -
cracy and involve a wide range of stakeholders: Member States, technical experts, the private sector, and EU insti -
tutions and agencies. The Commission should have a mandate for horizontal actions and exclusive competencies
of the EU, such as revamping competition policy and reducing administrative and regulatory burdens. For shared
competencies like closing the skills gap and accelerating innovation, the Commission should provide guidelines and
share the institutional setup for implementation with relevant national bodies and industry experts, as discussed in
the relevant chapters of this report. In specific sectors of the economy, a new setup could be envisaged bringing
together the Commission, industry and Member States, as well as relevant sectoral agencies.
01. During the first half of the 2019-2024 parliamentary term.
02. Article 121 TFEU provides a legal basis for establishing a Competitiveness Coordination
Framework. The procedure involves the Council and the European Council.
67THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 6The consolidation of the EU’s various coordination mechanisms should be matched by a consolidation of
its budgetary resources . EU resources should focus on funding public goods that are critical to the EU’s strategic
priorities and which would otherwise be undersupplied by Member States or the private sector [see the chapter on
investment] . Already under the current Multiannual Financial Framework (MFF), programmes such as InvestEU could
be made more effective by adjusting the mandates of the implementing partners to enable more risk-taking. Under
the next MFF, the report recommends defining a “Competitiveness Pillar” with funding hypothecated to delivering
the Action Plans. The EU also needs to leverage better the large spending power of the Member States – which
is collectively equivalent to other major economies – by improving cooperation and focus. It is recommended to
create nationally pre-allocated envelopes in the MFF to incentivise and co-finance multi-country industrial projects,
which can be activated by a sub-group of interested Member States if necessary. It is also proposed to deploy two
revamped tools: a new Competitiveness IPCEI allowing State aid for cross-border projects, including industrial infra -
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] | [] |
five of the top ten tech
companies globally in terms of quantum investment are based in the US and four in China. None are based in the EU.
FIGURE 2
Decomposition of average annual labour productivity growth
Selected sectors, US and EU (pp, 2000-2019)
Note: EU is the GDP-weighted average of AT, BE, DE, DK, ES, FI, FR, IT, NL, SE. The values are the average annual labour productivity (GVA per hour worked)
growth contributions over the period 2000-2019.
Source: Nikolov, P., Simons, W., Turrini, A. Voigt, P., forthcoming.
While some digital sectors are likely already “lost”, Europe still has an opportunity to capitalise on future
waves of digital innovation . The EU’s competitive disadvantage will likely widen in cloud computing, as the market
is characterised by continuous massive investments, economies of scale and multiple services offered by a single
provider. However, there are multiple reasons why Europe should not give up on developing its domestic tech sector.
First, it is important that EU companies maintain a foothold in areas where technological sovereignty is required,
such as security and encryption (“sovereign cloud” solutions). Second, a weak tech sector will hinder innovation
performance in a wide range of adjacent fields, such as pharma, energy, materials and defence. Third, AI – and
particularly generative AI – is an evolving technology in which EU companies still have an opportunity to carve out
a leading position in selected segments. Europe holds a strong position in autonomous robotics, hosting around
22% of worldwide activity, and in AI services, hosting around 17% of activity02. But innovative digital companies are
generally failing to scale up in Europe and attract finance, reflected in a huge gap in later-stage financing between
the EU and the US [see Figure 3] . 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 in the US all six companies with a valuation above EUR
1 trillion have been created over this period03.
02. JRC, Examples of AI services , Policy Brief, 2024. Examples of AI services include the use of any AI technology, such as
machine learning, computer vision, natural language processing, to perform high level applications such as business
intelligence, predictive analytics, forecasting, optimisation, failure detection, applied to different business functions.
03. “From scratch” refers to starting a company from its inception as | [
" ",
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"None",
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"FIGURE",
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" \n",
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",",
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] | [] |
Subsets and Splits