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non-relevant terms have been
manually discarded57.
The following keyword clouds (Figure 3.2 to 3.15)
showcase the distribution of keywords identified
in each domain, throughout the EaP countries,
where the size of the keyword corresponds to
the frequency at which it appears in the corpus
57 Discarded ‘non-relevant terms’ include those words that
frequently appear in a topic but do not hold any seman-
tic meaning, such as units of measure (e.g. ‘hour’, ‘min-
ute’), connectors (e.g. ‘one hand’, ‘other hand’) and generic
nouns (e.g. ‘author’, ‘result’, ‘invention’). They have been
removed from the table.of texts of the analysed records assigned to that
domain. The keyword clouds give an idea of the
content of each identified S&T specialisation do-
main. The S&T domains are characterised in de-
tail in Chapter 3.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation149
S&T domain Top keywords
Agrifoodplant, soil, seed, water, crop, flour, acid, extract, sugar, weight, leave, fruit, salt, condition, dough,
wine, fertiliser, treatment, milk, juice, grain, additive, concentration, mass, food, cultivation, meat,
oil, fermentation, glycoside, agriculture, source, wheat, bacteria, powder, specie, ingredient,
minute, compound, contamination, beverage, substance, drying, bread, pollution, starch, seedling,
cultivar, hour, vegetable
Biotechnologyacid, compound, synthesis, reaction, cell, derivative, substance, molecule, extract, strain, protein,
expression, oil, catalyst, water, gene, bacteria, waste, invention, oxidation, concentration, dye,
solvent, extraction, oxide, inhibitor, medium, medicine, salt, alcohol, polymer, formula, treatment,
form, bond, plant, culture, drug, ethanol, aqueous solution, dna, enzyme, temperature, micro-
organism, ion, fragment, cultivation, reagent, coli, pathway
Chemistry
and chemical
engineeringcompound, synthesis, reaction, acid, derivative, temperature, molecule, atom, hour, ligand, ion,
water, concentration, chloride, salt, bond, minute, min, catalyst, ring, treatment, cation, anion,
polymer, condition, substance, spectroscopy, crystal structure, reagent, amine, ester, fragment,
pressure, crystal, chain, sulphate, form, metal, alcohol, material, invention, substituent, solvent,
analogue, oxide, chemistry, aqueous solution, condensation, kinetic, oxidation
Electric and
electronic
technologiessignal, sensor, circuit, source, voltage, electrode, converter, transformer, block, frequency,
discharge, winding, power, generator, antenna, capacitor, switch, network, coil, amplifier, plasma,
pulse, pair, philtre, magnet, noise, inverter, transistor, sensitivity, distance, transducer, terminal,
measurement, conductor, electrolyte, amplitude, resistor, waveguide, screen, detection, connection,
cathode, rectifier, magnetic field, gate, resistance, adder, apparatus, insulation, bus
Energygas, energy, circuit, voltage, generator, tube, power, converter, transformer, consumption, air, disc,
fuel, plant, source, rotor, network, winding, exchanger, shaft, motor, battery, carrier, capacitor,
heater, combustion, pump, blade, heat, electrode, boiler, philtre, reactor, electrolyte, switch,
inverter, chamber, conversion, flow, heating, catalyst, water, | [
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Globally
At least 700 million people rely directly on melt from glaciers and
snow for their freshwater, including for food security, livelihood,
cultural and domestic needs; when seasonal supplies and services
are included, three billion people worldwide are affected.
The ongoing decline in glaciers has contributed significantly to
global sea-level rise, with today’s sea level about 30 cm higher
than in 1900, posing risks to coastal communities living far away
from glaciers. These changes also have global economic impacts,
affecting many sectors like agriculture, tourism, trade, and transportation. Sea-level rise, due in part to glacier melt, floods
low-lying areas and threatens coastal infrastructure. Preserving
glaciers is essential for environmental sustainability, economic
stability, and safeguarding cultural livelihoods, including tourism.
Monitoring Glaciers is Key for Adaptation
and Mitigation
Although scientists have monitored some glaciers for more than
130 years, other regions lack reliable and sufficiently funded
systematic monitoring efforts. Glacier inventories and datasets
on changes provide critical information for not only scientific
assessments, but political and economic decisions regarding
adaptation and mitigation strategies. Improved observation
coverage and resolution, data management, and data sharing can
enable analyses and prediction services that support timely
actions against threats, risks and impacts.
Glacier Loss Has Biodiversity and Ecosystem
Consequences
As glaciers shrink and disappear, sensitive and unique ecosystems
are lost; and with them, globally important biodiversity and
essential ecosystem services that support life, providing
freshwater to people, animals and plants alike. About 70% of
Earth’s freshwater exists as snow or ice; and runoff from glaciers
and snowpack is essential for drinking water, agriculture, industry
and clean energy production. Rising temperatures are affecting
the water cycle, including changing the timing of glacier melt
and snowmelt stream discharge. Less water availability as
glaciers continue to shrink and snow cover changes is expected
to contribute to greater competition for water, especially
in seasonally dry regions.Melting Glaciers and Thawing Mountain Permafrost
Create Hazards and Loss
Climate change has impacted the timing, frequency and location
of geohazard events with potential cascading effects. Slope
instability and flood risk increase with glacial loss and permafrost
thaw in frozen mountain regions. Continuous glacier retreat also
leads to extreme events. New and rising disaster risks such as
massive glacier lake outburst floods, glacier collapse or sudden
break-offs of large amounts of glacier ice, threaten downstream
populations and vulnerable transport and energy infrastructure.
Losing Cultural and Natural Glacier Heritage
Glaciers are part of fifty UNESCO World | [
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] | [] |
EaP countries ......... 55
Figure 2.3. Distribution of output in Manufacturing for five EaP countries ..................... 56
Figure 2.4. Distribution of goods exports (2012-2019) by SITC Rev. 4 one-digit class .. 64
Figure 2.5. Distribution of services exports (2011-2019) by EBOPS one-digit* ............ 79
Figure 3.1. A graphical representation of the results produced by topic modelling via
Latent Dirichlet Allocation .................................................................................................................... 147
Figure 3.2. Keyword cloud for Agrifood .......................................................................................... 151
Figure 3.3. Keyword cloud for Biotechnology .............................................................................. 151
Figure 3.4. Keyword cloud for Chemistry and chemical engineering ................................ 151
Figure 3.5. Keyword cloud for Electric and electronic technologies .................................. 151
Figure 3.6. Keyword cloud for Energy ............................................................................................. 151
Figure 3.7. Keyword cloud for Environmental sciences and industries ........................... 151
Figure 3.8. Keyword cloud for Fundamental physics and mathematics .......................... 151
Figure 3.9. Keyword cloud for Governance, culture, education and the economy ...... 151
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation259
Figure 3.10. Keyword cloud for Health and wellbeing ............................................................. 152
Figure 3.11. Keyword cloud for ICT and computer science ................................................... 152
Figure 3.12. Keyword cloud for Mechanical engineering and heavy machinery ......... 152
Figure 3.13. Keyword cloud for Nanotechnology and materials ......................................... 152
Figure 3.14. Keyword cloud for Optics and photonics ............................................................. 152
Figure 3.15. Keyword cloud for Transportation .......................................................................... 152
Figure 3.16. Number of records per labelled topic group (i.e. ‘domain’) in the EaP
region .............................................................................................................................................................. 155
Figure 3.17. Co-occurrence of S&T records in different domains across the whole EaP
region, colour-coded for the ratio with the total number of records in that domain
(column) ......................................................................................................................................................... 155
Figure 3.18. Share of records per S&T domain in the Eastern Partnership region .... 156
Figure 3.19. Share of records per S&T domain in the Eastern Partnership region,
calculated for each domain, relative to the total number of records per data
source ............................................................................................................................................... 157
Figure 3.20. Top 7 identified domains in each EaP country in publications (number of
identified publications in the domain | percentage of the total number of publications
analysed in the country) ......................................................................................................................... 167
Figure 3.21. Top 7 identified domains in each EaP country in patents (number of
identified patents in the domain | percentage of the total number of patents analysed
in the country) ............................................................................................................................................. 169
Figure 3.22. Top identified domains in each EaP country in EC projects (number of
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of total factor productivity within ten years could already
be sufficient to cover up to one third of the required fiscal spending. There are two key implications for the EU. First,
integrating Europe’s capital markets to better channel high household savings towards productive investments in
the EU will be essential. Second, the more willing the EU is to reform itself to generate an increase in productivity,
the easier it will be for the public sector to support the investment drive. This connection underscores why raising
productivity is fundamental. It also has implications for the issuance of common safe assets. To maximise productivity,
some joint funding for investment in key European public goods, such as breakthrough innovation, will be necessary.
At the same time, there are other public goods identified in this report – such as defence spending or cross-border
grids – that will be undersupplied without common action. If the political and institutional conditions are met, these
projects would also call for common funding.
The final building block is the will to reform the EU’s governance, increasing the depth of coordination and
reducing the regulatory burden . The “Community Method” has been a source of the EU’s success, but it was
established in a different era, when the Union was smaller and faced a different set of challenges. For much of the
EU’s history, the most important focus has been generating internal integration and cohesion, which Member States
could afford to address at their own pace. However, the EU is now much larger, creating more veto players, and the
challenges it faces are now often imposed on it from outside. To move forward, Europe must act as a Union in a way
it never has before, based around a renewed European partnership among Member States. It will require refocusing
the work of the EU on the most pressing issues, ensuring efficient policy coordination behind common goals, and
using existing governance procedures in a new way that allow Member States who want to move faster to do so. In
many areas, the EU can achieve a great deal by taking a large number of smaller steps, but doing so in a coherent way
that aligns all policies behind the common goal. There are other areas, however, where a small number of larger steps
are needed – delegating to the EU level tasks that can only be performed there. The case for | [
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Cone, J.J., Scantlen, M.D., Histed, M.H., and Maunsell, J.H.R. (2019). Different
inhibitory interneuron cell classes make distinct contributions to visual
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G. (2019). An amygdalar neural ensemble that encodes the unpleasantness ofpain. Science 363, 276–281. https://doi.org/10.1126/science.aap8586 .
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timer, K., Klein, D.F., Castellanos, F.X., and Milham, M.P. (2009). Relationshipbetween cingulo-insular functional connectivity and autistic traits in
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OPEN ACCESSneurotypical adults. Am. J. Psychiatry 166, 891–899. https://doi.org/10.1176/
appi.ajp.2009.08121894 .
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task relevance on the cortical response to changes in visual and auditory stim-uli: an event-related fmri study. Neuroimage 14, 1256–1267. https://doi.org/
10.1006/nimg.2001.0946 .
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10.1152/jn.00636.2001 .
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behavior via regulation of gamma-aminobutyric acidergic signaling and excita-tion/inhibition balance in the medial prefrontal cortex. Biol. Psychiatry 83,
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s41583-019-0253-y .
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tor 1a(mGluR1 a) in distinct classes of interneuron in the CA1 region of the rat
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050.
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",",
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".",
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"J.",
"Psychiatry",
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",",
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".",
"https://doi.org/10.1176/",
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"\n",
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",",
"J.",
",",
"Crawley",
",",
"A.P.",
",",
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",",
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"https://doi.org/10.1111/2041-210x.12584",
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",",
"Y.",
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",",
"J.M.",
",",
"Espinosa",
",",
"J.S.",
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"Sheng",
",",
"N.",
",",
"Darcy",
",",
"D.P.",
",",
"Nicoll",
",",
"R.A.",
",",
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"Huang",
",",
"Z.J.",
",",
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"M.P.",
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",",
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",",
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identification of
domains following the topic modelling, some do-
mains in some countries failed to emerge due to
being too small, in particular: Transportation in Ar-
menia, Azerbaijan, Georgia and Moldova; Electric
and electronic technologies in Azerbaijan and En-
ergy in Georgia.
The first graphic presents data for publications.
As already presented at the beginning of the cur-
rent chapter, large science-oriented S&T domains
dominated the EaP and the countries, notably
Fundamental physics and mathematics and Na-
notechnology and materials, particularly in Arme-
nia and Georgia. Health and wellbeing also ranks
highly in all countries, with small differences in
rank. Governance, culture, education and the
economy has a high rank in most countries, al-
though the share of publications in these domains
varies notably.
The second graphic presents data for patents, al-
beit the low number of patents compiled for some
of the countries (notably Armenia and Azerbaijan)
must be taken into account. As expected, Mechan-
ical engineering and heavy machinery dominates
the EaP, with a relevant weight for Health and
wellbeing (Azerbaijan, Moldova, Ukraine), Electric
and electronic technologies (peaking in Armenia),
Nanotechnology and materials and Agrifood (no-
tably in Armenia, Georgia, Moldova and Ukraine).
Finally, the third graphic presents data for EC
R&I projects. As noted earlier, the distribution of
S&T domains across EC projects is not only af-fected by the underlying specialisation and initi-
ative of EaP actors, but also by the orientation
of the programmes (7th Framework Programme
and Horizon 2020) as well as the differences in
accessing the funding by the EaP countries. Due
to the nature of the programmes and the coop-
eration framework between the EU and the EaP
countries, Governance, culture, education and
the economy is by far the largest S&T domain
in projects. Some differences then emerge, with
Nanotechnology and materials having consider-
able weight in Ukraine; Environmental sciences in
Azerbaijan, Georgia, Moldova and Ukraine; and ICT
and computer science across the board (peaking
in Armenia and Georgia).
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation167 168
Part 3 Analysis of scientific and technological potential
Figure 3.20. Top 7 identified domains in each EaP country in publications (number of identified publications in the
domain | percentage of the total number of publications analysed in the country)
Nanotechnology and
materials
(29 067 | 23.12%)
Nanotechnology and
materials
(1 265 | 14.56%)
Nanotechnology and
materials
(682 | 12.4%)Chemistry and | [
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] | [] |
OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2BOX 2
A closer look at the role of the ICT sector in the EU-US labour
productivity gap
The EU’s aggregate gap in labour productivity growth compared with the US reflects differences in industry
composition, sectoral innovation and technology diffusion. The EU economy has traditionally been strong
in all mid-technology sectors that are not at the centre of radical technological advances. The EU has less
activity in sectors in which much of the productivity growth has originated in recent years, notably the ICT
sector and the exploitation of large-scale digital services. Due to slow technology diffusion within industries,
the EU’s productivity growth gap compared to the US was particularly pronounced in these industries with
very high productivity growth.
Excluding the main ICT sectors (the manufacturing of computers and electronics and information and
communication activities) from the analysis, EU productivity has been broadly at par with the US in the period
2000-2019. The remaining disadvantage in productivity growth versus the US is significantly reduced to 0.2
percentage points (0.8% productivity growth for the US versus 0.6% for the EU). The actual EU-US gap can
be considered close to zero as EU 27 productivity growth is 0.2 to 0.3percentage points higher than the EU10
selection (for which EU KLEMS data is available). For 2013-2019 the role of ICT is even more striking, as the EU
productivity growth excluding the main ICT sectors exceeded that of the US by some margin.
This analysis may underestimate the total impact of ICT developments on the productivity gap. In addition
to ICT sectors, the US also has high productivity growth in professional services and finance and insurance,
reflecting strong ICT technology diffusion effects. These sectors are amongst the biggest contributors to
intangible investment in the total economy in the US. Also, some part of fintech is in the sector Finance and
Insurance. On the other hand, the EU outperforms the US in mid-technology sectors like manufacturing of
transport equipment, agriculture and in the wholesale and retail sectors. The latter reflects catching up effects
to key innovations that had been introduced in the US in the previous decade such as in e-commerce and
online retail reaching larger customer bases, implementation of advanced inventory management systems,
digital payment systems, data analytics and robotics, and automation.
27THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2Key barriers to innovation in | [
" ",
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"Excluding",
"the",
"main",
"ICT",
"sectors",
"(",
"the",
"manufacturing",
"of",
"computers",
"and",
"electronics",
"and",
"information",
"and",
"\n",
"communication",
"activities",
")",
"from",
"the",
"analysis",
",",
"EU",
"productivity",
"has",
"been",
"broadly",
"at",
"par",
"with",
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"US",
"in",
"the",
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"\n",
"2000",
"-",
"2019",
".",
"The",
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"is",
"significantly",
"reduced",
"to",
"0.2",
"\n",
"percentage",
"points",
"(",
"0.8",
"%",
"productivity",
"growth",
"for",
"the",
"US",
"versus",
"0.6",
"%",
"for",
"the",
"EU",
")",
".",
"The",
"actual",
"EU",
"-",
"US",
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"can",
"\n",
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"close",
"to",
"zero",
"as",
"EU",
"27",
"productivity",
"growth",
"is",
"0.2",
"to",
"0.3percentage",
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"higher",
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"the",
"EU10",
"\n",
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"(",
"for",
"which",
"EU",
"KLEMS",
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".",
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"2013",
"-",
"2019",
"the",
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"ICT",
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"even",
"more",
"striking",
",",
"as",
"the",
"EU",
"\n",
"productivity",
"growth",
"excluding",
"the",
"main",
"ICT",
"sectors",
"exceeded",
"that",
"of",
"the",
"US",
"by",
"some",
"margin",
".",
"\n",
"This",
"analysis",
"may",
"underestimate",
"the",
"total",
"impact",
"of",
"ICT",
"developments",
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"In",
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"\n",
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"the",
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"has",
"high",
"productivity",
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"and",
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"and",
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",",
"\n",
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"ICT",
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"These",
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"sector",
"Finance",
"and",
"\n",
"Insurance",
".",
"On",
"the",
"other",
"hand",
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"the",
"EU",
"outperforms",
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"US",
"in",
"mid",
"-",
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"of",
"\n",
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"key",
"innovations",
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"the",
"US",
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"decade",
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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",
"27THE",
"FUTURE",
"OF",
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"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
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"CHAPTER",
"2Key",
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"to",
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] | [] |
of economic and innovation potential
Table S.1. Summary table of mapping results for Armenia
NACE Economic – All industries C E SITC Goods exports C E NACE Innovation – Enterprise Survey
Results unavailable 0 Live animals other than animals of division 03 X 17 Paper
1 Meat and meat preparations X 24 Basic metals
NACE Economic – Manufacturing C E 2 Dairy products and birds' eggs X 41-43 Construction
11 Beverages X 3Fish (not marine mammals), crustaceans, molluscs
and aquatic invertebrates, and preparations
thereofX 49-53 Transport
12 Tobacco products X X 7Coffee, tea, cocoa, spices, and manufactures
thereof X
14 Manufacture of wearing apparel X 11 Beverages X NACE Innovation – Patents
16Wood products, cork, straw, plaiting
materials X 12 Tobacco and tobacco manufactures X 10 Manufacture of food products
28Manufacture of machinery and
equipment n.e.c. X 27Crude fertilizers, other than those of division 56,
and crude minerals (excluding coal, petroleum and
precious stones) X 11 Manufacture of beverages
32 Other manufacturing X X 28 Metalliferous ores and metal scrap X X 12 Manufacture of tobacco products
35 Electric current X 26.1 Manufacture of electronic components and boards
54 Medicinal and pharmaceutical products X 26.2 Manufacture of computers and peripheral equipment
66 Non-metallic mineral manufactures. X 26.6 Manufacture of irradiation
68 Non-ferrous metals X 27.2 Manufacture of batteries and accumulators
72 Machinery specialized for particular industries X
74General industrial machinery and equipment, and
machine parts X NACE Innovation – VC & start-ups
84 Articles of apparel and clothing accessories X 62, 63 Software
87Professional, scientific and controlling instruments
and apparatus. X 26, 61 Mobile
61, 62, 63 Information technology
EBOPS Services exports C E 53, 55, 79 Travel and tourism
1.3 Other transport X Gaming
2.2 Personal travel X 61, 63 Internet services
3 Communications services X 63 Apps
3.2 Telecommunications services X
4 Construction services X Clusters
5 Insurance services X X Green energy and environmental services
6 Financial services X Education and knowledge transfer
7.2 Information services X Food and agriculture
9 Other business services X Information and communication technologies
10.1 Audio-visual and related services X X
10.2 Other personal, cultural, and recreational services X X
11 Government services X
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation125 126
Part 2 Analysis of economic and innovation potential
Table S.2. Summary table of mapping results for Azerbaijan
NACE Economic – All industries C E | [
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"26.6",
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"X",
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"27.2",
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" ",
"X",
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"62",
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"X",
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"26",
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"61",
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"63",
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"exports",
"C",
"E",
" ",
"53",
",",
"55",
",",
"79",
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"\n",
"1.3",
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"transport",
" ",
"X",
" ",
"Gaming",
"\n",
"2.2",
"Personal",
"travel",
"X",
" ",
"61",
",",
"63",
"Internet",
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"\n",
"3",
"Communications",
"services",
" ",
"X",
" ",
"63",
"Apps",
"\n",
"3.2",
"Telecommunications",
"services",
" ",
"X",
" \n",
"4",
"Construction",
"services",
"X",
" ",
"Clusters",
"\n",
"5",
"Insurance",
"services",
"X",
"X",
" ",
"Green",
"energy",
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"environmental",
"services",
"\n",
"6",
"Financial",
"services",
" ",
"X",
" ",
"Education",
"and",
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"\n",
"7.2",
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" ",
"X",
" ",
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"9",
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"-",
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"126",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
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"innovation",
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"\n",
"Table",
"S.2",
".",
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"\n",
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] | [] |
the German (domestic), Hungarian (foreign country 1) and Lithuanian (foreign country 2) branded product
versions, respectively. Similarly, VHCGCVC1CGCVC2CG refer to the German, Hungarian and Lithuanian generic product versions. O refers to the outside (or
no-buy) option. β is the vector of coefficients to be estimated, and εsCiCk
rCtCl is the unobserved error term assumed to be Gumbel-distributed. We model the D.M. Federica et al. Food Policy 131 (2025) 102803
14 average WTP for the branded and generic product versions as:
WTPsCiCk
cCBβsCiCk
cCB⎣
αsCiCk
pforc⊔F1CF2⊓ (1)
WTPsCiCk
cCBβsCiCk
cCBEαsCiCk
pforc⊔F1CF2⊓1Setting the branded domestic product version as the reference, the WTP measures in (1), WTPsCiCk
cCB, represent
the average price premium (or price discount if the WTP is negative) that consumers are willing to pay for the foreign-country branded product version
over the domestic branded product version. Similarly, for the generic product versions we are interested in estimating how much more (or less) the
average consumer is willing to pay for the foreign-country generic product version compared to the domestic generic product version. To this end, we
first derive the WTP measures for the generic domestic and foreign-country product versions, relative to the branded domestic product version:
WTPsCiCk
cCGβsCiCk
cCG⎣
αsCiCk
pforc⊔HCF1CF2⊓ (2)
Then, we derive from (2) a new set of measures (3), WTPsCiCk
cCGH, which represent the price premium (discount) of the foreign generic product version
relative to the domestic generic product version, measured as the difference in the estimated WTP between the two versions of a product:
WTPsCiCk
cCGH(
βsCiCk
cCGβsCiCk
cCH)⎣
αsCiCk
pforc⊔F1CF2⊓ (3)
Based on equation (3), we model consumer preference for the domestic generic product version over the foreign generic product version in the
absence-focused regime as:
H1a,G: WTPsCIcCk
cCGHD0forc⊔F1CF2⊓(4).
The equivalent hypothesis for branded products leads to the following specification:
H1a,B: WTPsCIcCk
cCBD0 for c⊔F1CF2⊓(3).
With hypotheses H1c_G and H1c_B, we estimate the WTP of Eastern-country consumers E for generic (6) and branded (7) Western-country W product
versions.
H1c,G:WTPsCIcCk
cCGH〉
F0fors∃Eandc∃W
D0fors∃Wandc∃E (6).
H1c,B: WTPsCIcCk
cCB〉
F0fors∃Eandc∃W
D0fors∃Wandc∃E (7).WTPsCIcCk
cCGH〉
F0fors∃Eandc∃W
D0fors∃Wandc∃E
with E {Hungary, Lithuanian, Romania} and W {Germany, Spain, Sweden} representing the sets of Eastern and Western EU member states.
Our second set of hypotheses focuses on the impact of a potential policy requiring a ‘made for’ claim on product packaging. We model the impact of
a ‘made for’ claim (the presence-focused regime Im) on consumer choice, as compared to the WTP | [
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48
UA 5 5 22 13 1 238
PublicationsFigure 3.51. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Energy’ domain
Colour indicates the relative distribution of documents, computed row-wise.
AM
AZ
BY
GE
MD
UA
Other
1 1
1 3
2 5
1 3 6 8
3 3 15
1 2 6 3 41
EC projectsAM
AZ
BY
GE
MD
UA
Other
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AZ 10 7 16 4 15 164
BY 19 7 13 9 83 497
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UA 33 15 83 33 36 2 875
PublicationsFigure 3.52. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Environmental sciences and industries’ domain
Colour indicates the relative distribution of documents, computed row-wise.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation213
Regional collaboration in Fundamental
physics and mathematics
The domain of Fundamental physics and math-
ematics accounts for the vast majority of co-pub-
lications in the Eastern Partnership. Armenia and
Georgia collaborate most intensively with one an-
other, holding the highest number of publications
in their bilateral collaborations. For Azerbaijan,
Moldova and Ukraine, external partnerships ac-
count for the vast majority of their collaborations.
In terms of EC projects, the only intra-EaP collabo-
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sity of collaborations with external partners is less
significant in this case.Regional collaboration in Governance,
culture, education and the economy
In the case of Governance, culture education
and the economy publications, external collabo-
rations again have a significant weight across all
six EaP countries. Within the EaP, the pattern of
collaboration is very evenly distributed. Besides
Ukraine, Armenia-Georgia and Moldova-Ukraine
are the partnerships that stand out the most.
Collaboration in terms of EC projects is higher than
in other domains, and evenly distributed across
the countries. Georgia’s collaboration with Ukraine
and Armenia stands out.
AM
AZ
BY
GE
MD
UA
Other
2 2 3
1
2 4 7
1
2 4 8
EC projectsAM
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BY
GE
MD
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Other
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AZ 94 6 9 3 35 648
BY 1 412 6 1 377 10 673 2 619
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Retrieved 25 June 2020.
Nelson N, Ben-Shem A (December 2004). "The complex architecture of oxygenic photosynthesis". Nature Reviews. Molecular Cell Biology. 5 (12): 971–82. doi:10.1038/nrm1525. PMID 15573135. S2CID 5686066.
Madigan MT, Martinko JM (2006). Brock Mikrobiologie (11., überarb. Aufl ed.). München: Pearson Studium. pp. 604, 621. ISBN 3-8273-7187-2. OCLC 162303067.
Demirel Y (2016). Energy : production, conversion, storage, conservation, and coupling (Second ed.). Lincoln: Springer. p. 431. ISBN 978-3-319-29650-0. OCLC 945435943.
Häse CC, Finkelstein RA (December 1993). "Bacterial extracellular zinc-containing metalloproteases". Microbiological Reviews. 57 (4): 823–37. doi:10.1128/MMBR.57.4.823-837.1993. PMC 372940. PMID 8302217.
Gupta R, Gupta N, Rathi P (June 2004). "Bacterial lipases: an overview of production, purification and biochemical properties". Applied Microbiology and Biotechnology. 64 (6): 763–81. doi:10.1007/s00253-004-1568-8. PMID 14966663. S2CID 206934353.
Hoyle T (1997). "The digestive system: linking theory and practice". British Journal of Nursing. 6 (22): 1285–91. doi:10.12968/bjon.1997.6.22.1285. PMID 9470654.
Souba WW, Pacitti AJ (1992). "How amino acids get into cells: mechanisms, models, menus, and mediators". Journal of Parenteral and Enteral Nutrition. 16 (6): 569–78. doi:10.1177/0148607192016006569. PMID 1494216.
Barrett MP, Walmsley AR, Gould GW (August 1999). "Structure and function of facilitative sugar transporters". Current Opinion in Cell Biology. 11 (4): 496–502. doi:10.1016/S0955-0674(99)80072-6. PMID 10449337.
Bell GI, Burant CF, Takeda J, Gould GW (September 1993). "Structure and function of mammalian facilitative sugar transporters". The Journal of Biological Chemistry. 268 (26): 19161–4. doi:10.1016/S0021-9258(19)36489-0. PMID 8366068.
Bouché C, Serdy S, Kahn CR, Goldfine AB (October 2004). "The cellular fate of glucose and its relevance in type 2 diabetes". Endocrine Reviews. 25 (5): 807–30. doi:10.1210/er.2003-0026. PMID 15466941.
Alfarouk KO, Verduzco D, Rauch C, Muddathir AK, Adil HH, Elhassan GO, et al. (18 December 2014). "Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question". Oncoscience. 1 (12): 777–802. doi:10.18632/oncoscience.109. PMC 4303887. PMID 25621294.
Kruger, Nicholas J; von Schaewen, Antje (2003). "The oxidative pentose phosphate pathway: structure and organisation". Current Opinion in Plant Biology. 6 (3): 236–246. Bibcode:2003COPB....6..236K. doi:10.1016/S1369-5266(03)00039-6. PMID 12753973.
Wipperman MF, Sampson NS, Thomas ST (2014). "Pathogen roid rage: cholesterol utilization by Mycobacterium tuberculosis". Critical Reviews in Biochemistry and Molecular Biology. 49 (4): 269–93. doi:10.3109/10409238.2014.895700. PMC 4255906. PMID 24611808.
Sakami W, Harrington H (1963). "Amino Acid Metabolism". Annual Review of Biochemistry. 32: 355–98. doi:10.1146/annurev.bi.32.070163.002035. PMID 14144484.
Brosnan JT (April 2000). "Glutamate, at the interface between amino acid and carbohydrate metabolism". The Journal of Nutrition. 130 (4S Suppl): 988S – 90S. doi:10.1093/jn/130.4.988S. PMID 10736367. | [
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Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation309 310
Annexes
ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE
SITC Goods name Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging
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classification of socio-economic objectives
NACE statistical classification of economic activities
NCI normalised citation impact
NICE international classification of good and services
R&D research and development
R&I research and innovation
S&T scientific and technological
S3 smart specialisation strategies
SI specialisation index
SITC standard international trade classification
SME small medium enterprises
STI science, technology, innovation
UAV unmanned aerial vehicle
UNIDO United Nations Industrial Development
Organization
USPTO United States Patent and Trademark Office
WIPO World Intellectual Property OrganisationSmart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation 257
258
List of figures and tables
JRC Joint Research Centre
LDA Latent Dirichlet Allocation algorithm
LQ location quotient
NABS classification of socio-economic objectives
NACE statistical classification of economic activities
NCI normalised citation impact
NICE international classification of good and services
R&D research and development
R&I research and innovation
S&T scientific and technological
S3 smart specialisation strategies
SI specialisation index
SITC standard international trade classification
SME small medium enterprises
STI science, technology, innovation
UAV unmanned aerial vehicle
UNIDO United Nations Industrial Development
Organization
USPTO United States Patent and Trademark Office
WIPO World Intellectual Property OrganisationLIST OF FIGURES
Figure I. Summary scheme of the methodological steps leading to the selection
and definition of a list of specialisation domains for each country and the potential
cooperation areas for the whole region and with international partners ............................. 5
Figure IIa. Summary table for the EaP region, showcasing the alignment between the
country’s economic clusters (representing the E&I specialisation domains) and the S&T
specialisation domains .................................................................................................................................. 6
Figure IIb. The most relevant S&T specialisation domains by EaP countries ..................... 8
Figure III. Example of one such interactive visualisation tool, depicting the main
analysed actors and collaboration networks in the Eastern Partnership ........................... 22
Figure IV. Number of publications and EC projects in collaboration between EaP actors
in different countries .................................................................................................................................. 23
Figure V. Number of publications and EC projects in collaboration between EaP actors
in different countries .................................................................................................................................. 24
Figure VI. Number of publications and EC projects in collaboration between EaP actors
and partners outside of the EaP ............................................................................................................ 25
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 .......................... 31
Figure 2.1. Importance of Agriculture in EaP countries ............................................................. 40
Figure 2.2. Distribution of employment in Manufacturing for five | [
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] | [] |
3.472 0.417 0.730 0.394 0.831 0.407 0.967
NACE 60, 63 –
Transport and storage0.243 0.324 2.707 2.782 0.497 0.452 0.618 0.692 1.105 1.012 0.830 0.738
NACE 67 – Financial
intermediation0.262 0.262 1.691 0.000 0.536 0.930 1.575 2.007 0.740 1.298 1.196 1.503
NACE 72, 73 –
Computer-related
activity, research and
development1.107 0.335 1.145 2.299 1.359 1.385 0.394 0.464 0.731 0.591 1.264 0.925Table 2.36. Trademark specialisations for a number of combined manufacturing industries
sign Database46. Industrial designs are classified
into 32 classes using the Locarno Classification.
The full list is shown in Annex 6. Industrial design
data by Locarno class are also used in mapping the
46 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.technological potential of the Eastern Partnership
countries. The same industrial design data cannot
be used for NACE industries as an official Locarno
to NACE concordance does not exist. A Spanish Lo-
carno to NICE concordance47 shows that the level
of NACE detail is much higher than the correspond-
47 https://www.oepm.es/export/sites/oepm/comun/docu-
mentos_relacionados/varios_todas_modalidades/Con-
cordancia_CNAE_LOCARNO.pdf
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation101
ing level of Locarno detail, implying that a reversed
concordance is not feasible as an industrial design
could be allocated to multiple NACE industries.
The industrial design data will not be used to map
the innovation potential of the Eastern Partnership
countries. Design applications for the different Lo-
carno classes and for two time periods are shown
in Table 2.37. The distribution is highly skewed
with low numbers for Armenia, Azerbaijan and
Georgia, higher numbers for Moldova and much
higher numbers for Ukraine.
3.5. Start-ups and venture capi-
tal-backed companies
It is now well understood that start-up companies
are key in job creation48 and as a driving force in
local innovation and consequent economic devel-
opment49. To mitigate the lack of national data on
innovation potential, we resorted to Crunchba-
se50, one of the world’s leading data sources for
start-ups and venture capital-backed companies51.
Crunchbase compiles data on companies’ industri-
al sectors, revenue, acquisition, funding and more
via crowdsourcing. This allows for a considerable
degree of accuracy, but it also represents a pos-
sible shortcoming of the source itself: since it is
based on bottom-up initiatives to populate its
database, | [
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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 cooperation105
In Georgia, Financial Services has the highest
number of employees and estimated revenue
and the second highest number of companies,
while Software is first in the number of compa-
nies and third in the number of employees and
estimated revenue. Lending & Investment, while
having the second highest number of employees
and estimated revenue, has only 10 companies.
Internet Services has the third highest number of
companies and also ranks highly in the number
of employees and estimated revenue. Other in-
dustry groups relevant in more than one variable
are Payments, Sales & Marketing and Hardware.
Information Technology, while fourth in terms of
number of companies, is not as relevant as in oth-
er countries.
In Moldova, Software and Information Technology
are number one and number two across all three
variables, respectively. While Internet Services has
the third highest number of companies, it has few employees and a low estimated revenue. Lending
& Investments, Sustainability, Energy and Finan-
cial Services have a high estimated revenue and
number of employees, but very few companies.
Hardware is relevant across all three variables.
In Ukraine, the two bigger industry groups oth-
er than Software (in terms of number of com-
panies) are Sales & Marketing and Commerce &
Shopping. These three industry groups, particularly
Software, also have a high number of employees
and estimated revenue. The industry group with
the highest number of employees is Information
Technology, also with a high number of companies
and estimated revenue. Media & Entertainment,
while accounting for significantly few employees,
has the highest estimated revenue. The industry
groups with the third highest number of employ-
ees and estimated revenue are Hardware and Vid-
eo, respectively.
Georgia
# firms CM Firms # employees CM Employees # est. revenue CM Revenue
Software 40 Financial Services 21 880 Financial Services $1 808 m
Financial Services 25Lending and
Investments21 140Lending and
Investments$1 801 m
Internet Services 20 Software 12 265 Software $790 m
Information Technology 18 Payments 7 915 Payments $750 m
Sales and Marketing 14 Internet Services 4 145 Other $306 m
Travel and Tourism 13 Hardware 3 940 Internet Services $60 m
Hardware 13 Other 3 450 Information Technology $42 m
Commerce and
Shopping12 Sales and Marketing 3 270 Food and Beverage | [
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center of the IC cathode) and the detectors’ front faceand the azimuthal angle ϕ. The relative energy resolution (FWHM) is
also given for the137Cs full energy peak
LaBr 3(Ce) Position Resolution
serial no. Distance r Angle ϕ (662 keV)
Q489 13.98 cm 139◦2.81%
Q491 13.12 cm 41◦2.97%
5414 13.33 cm 91◦2.70%
5415 13.43 cm −2◦2.67%
5416 14.58 cm 180◦2.76%
Fig. 3 Time distribution of γ-rays detected in a LaBr 3(Ce) detector
with respect to fission trigger from the IC
tribution originated from the inelastic scattering of prompt
fission neutrons with the detectors or IC materials, mostly Al
and Fe, generating spurious γ-rays in the data. The width of
the prompt peak in Fig. 3was about 0.6 ns (FWHM), hence
demonstrating the good timing resolution of the ionization
chamber, as expected from this kind of detector [ 8].
The second purpose of the IC was the determination of
the mass and kinetic energy of the Fission Fragments (FFs).
These characteristics were estimated using the double kinetic
energy (2E) method (see, e.g., Ref. [ 9]). This method is based
on mass and linear momentum conservation laws and thesimultaneous measurement of post-neutron FF kinetic ener-
gies. Neutron emission was accounted for in an iterative pro-
cedure, taking the average multiplicity ¯ν(A)from Ref. [ 10],
and assuming isotropic neutron emission in the center of
mass frame. These simplifying assumptions were responsible
for smearing the mass distributions obtained by this method[11,12]. As a consequence, the post-neutron mass resolution
of the IC was about 5 u (FWHM). The angle θbetween the
fission and the IC axes was determined from the electron drifttime in the IC [ 7]. Then, FFs emitted at grazing angles, thatis cosθ< 0.5(θ< 60
◦), were rejected from the analysis.
Indeed, such fragments were subject to a large energy loss,
leading to incorrect energy and mass characterization.
Finally, the data obtained in this work was acquired for
about 3500 h (effectively), which amounts to 8 .7×109total
fission event. From those, 4 .8×109were selected according
to the cuts described above and analyzed in this work.
2.2 Data analysis
The isomers were selected by means of FF- γ-γcoincidences,
that is, a fission event followed by two γ-rays detected in
coincidence in LaBr 3(Ce) detectors. In this work, the coinci-
dence window between two γ-rays was set to ±2n s . T h i s
short window is consistent with the timing properties of
LaBr 3(Ce) | [
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] | [] |
example, a Business Europe gap analysis of 13 pieces of EU law flagged duplication across 169 require -
ments, including differences (29%) and outright inconsistencies (11%). Second, EU companies face an extra burden
due to national transposition, for instance as Member States “gold plate” of EU legislation or implement laws with
divergent requirements and standards from one country to another. As touched on in chapter 2, GDPR in particular
has been implemented with a large degree of fragmentation which undermines the EU’s digital goals. Third, EU regu -
lation imposes a proportionally higher burden on SMEs and small mid-caps than on larger companies, yet the EU
lacks a framework to assess these costs. About 80% of Commission Work Programme items are relevant to SMEs but
only around half of impact assessments substantially focused on these companies. The EU also lacks a commonly
agreed definition of small mid-caps and readily available statistical data.
To start lowering the “stock” of regulation, the report recommends appointing a new Commission Vice Pres -
ident for Simplification to streamline the acquis, while adopting a single, clear methodology to quantify the
cost of the new regulatory “flow” . At the start of each Commission mandate, before adopting new EU legislation,
a fixed period of at least six months should be devoted to systematically assessing and stress-testing all existing
regulation by sector of economic activity. On this basis, a second phase should focus on pursuing the codification
and consolidation of EU legislation by policy area. This process should include simplifying and removing overlap
and inconsistencies across the whole “legislative chain”, with priority given to those economic sectors where Europe
is particularly exposed to international competition. This exercise should be run by all members of the College of
Commissioners within their respective competencies and coordinated by a Vice-President for Simplification. To
ensure that new legislation is consistent with this simplification drive, a single methodology should be developed
and consistently applied within the Commission across its impact assessments. This methodology should be applied
to all new legislation and be adopted by co-legislators when amending legislation. It is also recommended to add
a new standard requirement in the article on the transposition of directives requiring Member States to systemati -
cally assess new legislation using the same methodology as the EU institutions. At the same time, the Single Market
Enforcement Taskforce (SMET) should be strengthened and focused on evaluating and | [
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), which examines con-
sumer preferences based on information about the country where a
product is made, these hypotheses test consumer preferences for
different product compositions, without revealing which recipe is sold in
which country. Specifically, these hypotheses test whether consumers
would prefer a foreign recipe over the domestic one, would they be
presented with both options. We test this by assessing consumers ’
willingness to pay as well as consumers ’ taste preference for different
versions. Additionally, for the WTP, we also assess whether the brand
versus generic nature of the product matters. This leads to testing the
following hypotheses:
H1a: Consumers are willing to pay more for domestic food product versions as compared to foreign-country versions, both when the prod-
ucts bear a generic2unknown (H1aG) or well-known brand (H1aB).
H1b: Consumers prefer the taste of domestic food product versions
over foreign-country versions.
These hypotheses could be challenged if product versioning reflects
opportunistic behaviour by firms that are not meant to meet consumer
preferences (Nes et al., 2021 ; Colombo et al., 2022; Commission, 2017,
2019a,b; Cummings and Taylor, 1999; Ding et al., 2005; Erdem et al.,
2006; Herz and Diamantopoulos, 2013; Kuhfeld, 2012; Nes et al., 2021;
Z˘avadský and Hiadlovský, 2020; Penn and Wuyang, 2018 ). This corre -
sponds to the argument of Eastern European consumers of being treated
as second-class by receiving inferior products compared to Western
counterparts (Borzan, 2017 ; Jancarikova, 2017 ; MPSR, 2017 ), which
could result in lower willingness to pay and taste preference for do-
mestic versions compared to Western products. In contrast, Western
European consumers would prefer their domestic version, as the Eastern
European product version would be of lower quality. If valid, these
hypotheses would not only provide empirical support for the argument
of Eastern European consumers and media, but also lay the groundwork
for actionable policy initiatives aimed at addressing opportunistic
behaviour of companies across EU MS. To this purpose, we put forward
the following hypotheses:
H1c: Eastern European consumers are willing to pay more for
Western-country food product versions, while Western European con-
sumers are willing to pay less for Eastern-country versions (for both the
generic H1c,G and branded H1c,B cases).
H1d: Consumers from Eastern countries prefer the taste of Western-
country food product versions, whereas Western-country consumers
prefer less the taste of Eastern-country versions.
However, consumers typically only see the product version available
in their domestic | [
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materials was
identified as an EIST niche in Georgia and Mol-
dova.
■Communications Equipment and Services
and ICT and computer science were identi-
fied as an E&I and a S&T domain, respectively,
in both Armenia and Moldova.
As stated above, these are transversal concord-
ances that are rendered by the data currently an-
alysed and the quantitative methodology hereby
applied. We could expect similar concordances for
countries which present similar E&I specialisa-
tions, on the one hand, or similar highlighted S&T
domains on the other. This would be the case of
Ukraine, for which Metalworking Technologies is
an E&I specialisation but no quantitative concord-
ance has been found with Nanotechnology and
materials. In that case, Ukraine could join Geor-
gia and Moldova in knowledge collaboration in this
specific EIST intersection.
PART
5Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation249
Part 5. Discussion of
results and final remarks
This study has aimed at identifying economic and
innovation and scientific and technological spe-
cialisations, and their possible concordances, to
support knowledge-based economic development
in the EaP countries as well as cooperation at re-
gional level.
E&I specialisations for the EaP region were cal-
culated by using relevant economic variables and
their growth dynamics, such as employment, turn-
over and average wages. Additionally, the critical
mass of venture capital-backed companies per
industry group and for patent, trademark and in-
dustrial design applications per respective class of
classification has been measured. Lastly, innova-
tion has been quantified by means of the results
of the innovation surveys.
Identified E&I specialisation domains for Arme-
nia are Food & beverages (NACE 10, 11), Tobac-
co (NACE 12), Travel and tourism (NACE 53, 55),
Information and communication (NACE 26, 61-
63). For Azerbaijan, the domains are as follows:
Coke and refined petroleum products (NACE 19),
Chemicals and related activities (NACE 20), Re-
pair and installation of machinery and equipment
(NACE 33), Computer programming, consultancy
and related activities (NACE 62), Financial services
(NACE 64). For Georgia, they are as follows: Food
and beverages (NACE 10, 11), Publishing, printing
and recorded media (NACE 18), Fabricated metal
products, except machinery and equipment (NACE
25), Tourism and travel (NACE 55, 56), Financial
service activities (NACE 62, 64). Moldova has the
following: Food & beverages (NACE 10, 11), Tex-
tiles & wearing apparel (NACE 13, 14), Leather
and related products (NACE 15), Wood and prod-
ucts of wood | [
"materials",
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"-",
"Potential",
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"-",
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"\n",
"Part",
"5",
".",
"Discussion",
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"\n",
"results",
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"\n",
"This",
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",",
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",",
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",",
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",",
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] | [] |
hydrogen, hydrogen sulfide or ferrous ions to oxygen, nitrate or sulfate. In animals, these reactions involve complex organic molecules that are broken down to simpler molecules, such as carbon dioxide and water. Photosynthetic organisms, such as plants and cyanobacteria, use similar electron-transfer reactions to store energy absorbed from sunlight.[34]
Classification of organisms based on their metabolism[35]
Energy source sunlight photo- -troph
molecules chemo-
Hydrogen or electron donor organic compound organo-
inorganic compound litho-
Carbon source organic compound hetero-
inorganic compound auto-
The most common set of catabolic reactions in animals can be separated into three main stages. In the first stage, large organic molecules, such as proteins, polysaccharides or lipids, are digested into their smaller components outside cells. Next, these smaller molecules are taken up by cells and converted to smaller molecules, usually acetyl coenzyme A (acetyl-CoA), which releases some energy. Finally, the acetyl group on acetyl-CoA is oxidized to water and carbon dioxide in the citric acid cycle and electron transport chain, releasing more energy while reducing the coenzyme nicotinamide adenine dinucleotide (NAD+) into NADH.[32]
Digestion
Further information: Digestion and Gastrointestinal tract
Macromolecules cannot be directly processed by cells. Macromolecules must be broken into smaller units before they can be used in cell metabolism. Different classes of enzymes are used to digest these polymers. These digestive enzymes include proteases that digest proteins into amino acids, as well as glycoside hydrolases that digest polysaccharides into simple sugars known as monosaccharides.[36]
Microbes simply secrete digestive enzymes into their surroundings,[37][38] while animals only secrete these enzymes from specialized cells in their guts, including the stomach and pancreas, and in salivary glands.[39] The amino acids or sugars released by these extracellular enzymes are then pumped into cells by active transport proteins.[40][41]
A simplified outline of the catabolism of proteins, carbohydrates and fats[image reference needed]
Energy from organic compounds
Further information: Cellular respiration, Fermentation (biochemistry), Carbohydrate catabolism, Fat catabolism, and Protein catabolism
Carbohydrate catabolism is the breakdown of carbohydrates into smaller units. Carbohydrates are usually taken into cells after they have been digested into monosaccharides such as glucose and fructose.[42] Once inside, the major route of breakdown is glycolysis, in which glucose is converted into pyruvate. This process generates the energy-conveying molecule NADH from NAD+, and generates ATP from ADP for use in powering many processes within the cell.[43] Pyruvate is an intermediate in several metabolic pathways, but the majority is converted to acetyl-CoA | [
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",",
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"the",
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] | [] |
exposed to multi-hazard
risk. After reaching a higher level of income (in the middle-
income category), the population exposed to multi-hazards
decreases towards the high-income category. This can sug-
gest that low-income countries have a major part of the pop-
ulation exposed in the rural areas compared to the high-
income countries, where most of the population exposed is
in densely populated urban areas and only a quarter of the
population exposed (25 %) lives in the rural area. The peak
in the countries with regions in the middle-income category
could suggest a balance between the high number of urbanareas (the largest across various income classes) and the ru-
ral areas with high densities of population.
(iii) Metropolitan areas. Using the Urban Audit 2018 def-
inition and based on correspondence to LAUs, we estab-
lish that 46 % of the urban/metropolitan areas in Europe
(442 of a total of 952) have populations exposed to multi-
hazards. These urban areas, totalling 46.8 million people,
are mostly in the high-income and high-middle-income cat-
egories (62.4 %). The high-income urban areas are mostly
found in the Netherlands (28), UK (23), Germany (20),
France (9), and Italy (9), while the low-income areas (110
at the European level) are found in Romania (17), Bulgaria
(16), Poland (15), Hungary (13), the Czech Republic (11),
and others (in Fig. S23 and Table S6).
https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025296 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level
Figure 6. Number of administrative areas (LAUs) with population exposed to multi-hazards by income level and urbanization level (a–
Europe-wide, b– the 15 highest-ranked countries )(upper part); population exposed to multi-hazards by income level and urbanization level
(c– Europe-wide, d– the 15 highest-ranked countries )(lower part).
Figure 7. Population exposed per income level. The markers represent countries’ population exposed to multi-hazards split by income level.
The blue line links the 75th quantile of the income classes.
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 297
Figure 8. Population exposed to multi-hazards at the level of metropolitan areas. (a)European countries’ population exposed within
metropolitan categories: city centres (C) and functional urban areas (F). The lower and upper whiskers represent the lowest 5 % and the
highest 95 %, respectively, of the calculated population exposed to multi-hazards for each | [
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applied in
the automotive sector. The ambitious target of zero tailpipe emissions by 2035 will lead to a de facto phasing out
of new registrations of vehicles with internal combustion engines and the rapid market penetration of EVs. Yet, the
EU has not followed up these ambitions with a synchronised push to convert the supply chain. For example, the
Commission only launched the European Battery Alliance to build a battery value chain in Europe in 2017, while
Europe as a whole is far behind on installing charging infrastructure. China, by contrast, has been focusing on the
full EV supply chain since 2012 and, as a result, it has moved faster and at a larger scale and is now one generation
ahead in EV technology in virtually all domains, while also producing at lower cost. European companies are already
losing market share and this trend may accelerate as shipping bottlenecks are overcome [see Figure 9] . Chinese
carmakers’ market share for EVs in Europe rose from 5% in 2015 to almost 15% in 2023, while the share of European
carmakers in the European EV market fell from 80% to 60%.
FIGURE 9
Electric car imports to Europe by country of production and manufacturer headquarters
Thousand vehicles, 2021-2022
Source: IEA, 2023
49THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3A joint plan for decarbonisation
and competitiveness
The first key goal for the energy sector is to lower the cost of energy for end users by transferring the
benefits of the decarbonisation [see the chapter on energy] . Natural gas will remain part of the energy mix in
Europe over the medium term – scenarios suggest that EU gas demand will fall by 8%-25% by 2030 – and so this
goal requires reducing the volatility of natural gas prices. The report recommends reinforcing joint procurement
– at least for LNG – to leverage Europe’s market power and establishing long-term partnerships with reliable and
diversified trade partners as part of a genuine EU gas strategy. Europe also needs to reduce its exposure to spot
market by encouraging a progressive move away from spot-linked sourcing and to reduce volatility in EU gas markets
by limiting the possibility of speculative behaviour. Following the US example, regulators should be able to apply
financial position limits as well as dynamic caps in circumstances when EU energy spot or derivatives prices diverge
markedly from global energy prices. | [
" ",
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"49THE",
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a noticeable critical mass per num-
ber of companies is also observed for the Industry
Groups Financial Services, Software and Informa-
tion Technology, respectively. Quite relevantly, the
employment critical mass and specialisation in the
Food and Beverage Industry Group is observed be-
cause of the presence of the Purcari winery, the
largest company based in Moldova indexed by
Crunchbase.
112
Part 2 Analysis of economic and innovation potential
ARMENIA
Firm critical
massEmployment
critical massFirm
specialisationEmployment
specialisation
Mobile
Apps
Travel and Tourism
Software
Internet Services
Information Technology
GamingTable 2.49. Armenia
AZERBAIJAN
Firm critical
massEmployment
critical massFirm
specialisationEmployment
specialisation
Financial Services
Natural Resources
Mobile
Lending and Investments
EnergyTable 2.50. Azerbaijan
GEORGIA
Firm critical
massEmployment
critical massFirm
specialisationEmployment
specialisation
Financial Services
Travel and Tourism
Software
Payments
Lending and Investments
Internet ServicesTable 2.51. Georgia
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation113
Ukraine
As previously explained, for Ukraine we will not con-
sider specialisation as per the other EaP countries.
The choice of the array of Industry Groups support-
ing the definition of innovation potential domains in
Ukraine is therefore predominantly based on criti-
cal mass figures and is supported by a qualitative
inspection of the industries for which the country
shows a relative specialisation of the EaP.
With this in mind, the most relevant Industry
Groups in Ukraine supporting the definition of in-
novation potential domains found via the Crunch-
base analysis are in Table 2.53. Sales and Marketing and Professional Services
are prominent per critical mass (both per num-
ber of companies and per number of employees)
and at the same time specialised for number of
companies and number of employees, respective-
ly. Manufacturing has both specialisations and is
prominent for employment critical mass. Software
is especially prominent per critical mass, while
Consumer Goods, Clothing and Apparel and Bio-
technology are seen to be niches of specialisation,
both per number of companies and employees,
with respect to the EaP region.
MOLDOVA
Firm critical
massEmployment
critical massFirm
specialisationEmployment
specialisation
Financial Services
Sustainability
Software
Lending and Investments
Information Technology
Food and BeverageTable 2.52. Moldova
UKRAINE
Firm critical
massEmployment
critical massFirm
specialisationEmployment
specialisation
Software
Sales and Marketing
Professional Services
Consumer Goods
Manufacturing
Clothing and Apparel
BiotechnologyTable 2.53. Ukraine
114
Part 2 Analysis of economic and innovation potential
Overview of the whole Eastern Partner-
ship and final recommendations
The analysis presented in this chapter is subject
to the limitations of Crunchbase, | [
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Koffas M, Roberge C, Lee K, Stephanopoulos G (1999). "Metabolic engineering". Annual Review of Biomedical Engineering. 1: 535–57. doi:10.1146/annurev.bioeng.1.1.535. PMID 11701499. S2CID 11814282.
"metabolism | Origin and meaning of metabolism by Online Etymology Dictionary". www.etymonline.com. Archived from the original on 21 September 2017. Retrieved 23 July 2020.
Leroi, Armand Marie (2014). The Lagoon: How Aristotle Invented Science. Bloomsbury. pp. 400–401. ISBN 978-1-4088-3622-4.
Al-Roubi AS (1982). Ibn Al-Nafis as a philosopher. Symposium on Ibn al-Nafis, Second International Conference on Islamic Medicine. Kuwait: Islamic Medical Organization.
Eknoyan G (1999). "Santorio Sanctorius (1561-1636) - founding father of metabolic balance studies". American Journal of Nephrology. 19 (2): 226–33. doi:10.1159/000013455. PMID 10213823. S2CID 32900603.
Williams HA (1904). Modern Development of the Chemical and Biological Sciences. A History of Science: in Five Volumes. Vol. IV. New York: Harper and Brothers. pp. 184–185. Retrieved 26 March 2007.
Manchester KL (December 1995). "Louis Pasteur (1822-1895)--chance and the prepared mind". Trends in Biotechnology. 13 (12): 511–5. doi:10.1016/S0167-7799(00)89014-9. PMID 8595136.
Kinne-Saffran E, Kinne RK (1999). "Vitalism and synthesis of urea. From Friedrich Wöhler to Hans A. Krebs". American Journal of Nephrology. 19 (2): 290–4. doi:10.1159/000013463. PMID 10213830. S2CID 71727190.
Eduard Buchner's 1907 Nobel lecture Archived 8 July 2017 at the Wayback Machine at http://nobelprize.org Archived 5 April 2006 at the Wayback Machine Accessed 20 March 2007
Kornberg H (December 2000). "Krebs and his trinity of cycles". Nature Reviews. Molecular Cell Biology. 1 (3): 225–8. doi:10.1038/35043073. PMID 11252898. S2CID 28092593.
Krebs HA, Henseleit K (1932). "Untersuchungen über die Harnstoffbildung im tierkorper". Z. Physiol. Chem. 210 (1–2): 33–66. doi:10.1515/bchm2.1932.210.1-2.33.
Krebs HA, Johnson WA (April 1937). "Metabolism of ketonic acids in animal tissues". The Biochemical Journal. 31 (4): 645–60. doi:10.1042/bj0310645. PMC 1266984. PMID 16746382.
Further reading
Library resources about
Metabolism
Online books
Resources in your library
Resources in other libraries
Introductory
Rose S, Mileusnic R (1999). The Chemistry of Life. Penguin Press Science. ISBN 0-14-027273-9.
Schneider EC, Sagan D (2005). Into the Cool: Energy Flow, Thermodynamics, and Life. University of Chicago Press. ISBN 0-226-73936-8.
Lane N (2004). Oxygen: The Molecule that Made the World. USA: Oxford University Press. ISBN 0-19-860783-0.
Advanced
Price N, Stevens L (1999). Fundamentals of Enzymology: Cell and Molecular Biology of Catalytic Proteins. Oxford University Press. ISBN 0-19-850229-X.
Berg J, Tymoczko J, Stryer L (2002). Biochemistry. W. H. Freeman and Company. ISBN 0-7167-4955-6.
Cox M, Nelson DL (2004). Lehninger Principles of Biochemistry. Palgrave Macmillan. ISBN 0-7167-4339-6.
Brock TD, Madigan MR, Martinko J, Parker J (2002). Brock's Biology of Microorganisms. Benjamin Cummings. ISBN 0-13-066271-2. | [
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doi.org/10.1093/cercor/bhv202 .
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guti, F. (2019). An appraisal of the influence of the metabotropic glutamate 5(Mglu5) receptor on sociability and anxiety. Front Mol. Neurosci. 12, 30.
https://doi.org/10.3389/fnmol.2019.00030 .
Remedios, R., Kennedy, A., Zelikowsky, M., Grewe, B.F., Schnitzer, M.J., and
Anderson, D.J. (2017). Social behaviour shapes hypothalamic neuralensemble representations of conspecific sex. Nature 550, 388–392. https://
doi.org/10.1038/nature23885 .
Rhomberg, T., Rovira-Esteban, L., Viko ´r, A., Paradiso, E., Kremser, C., Nagy-
Pa´l, P., Papp, O.I., Tasan, R., Erde ´lyi, F., Szabo ´, G., et al. (2018). Vasoactive
intestinal polypeptide-immunoreactive interneurons within circuits of themouse basolateral amygdala. J. Neurosci. 38, 6983–7003. https://doi.org/
10.1523/jneurosci.2063-17.2018 .
Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H.,
Reiss, A.L., and Greicius, M.D. (2007). Dissociable intrinsic connectivity net-works for salience processing and executive control. J. Neurosci. 27, 2349–
2356. https://doi.org/10.1523/jneurosci.5587-06.2007 .
Sforazzini, F., Schwarz, A.J., Galbusera, A., Bifone, A., and Gozzi, A. (2014).
Distributed bold and cbv-weighted resting-state networks in the mouse brain.Neuroimage 87, 403–415. https://doi.org/10.1016/j.neuroimage.2013.09.050 .
Shi, T., Feng, S., Wei, M., and Zhou, W. (2020). Role of the anterior agranular
insular cortex in the modulation of fear and anxiety. Brain Res. Bull 155,
174–183. https://doi.org/10.1016/j.brainresbull.2019.12.003 .
Sreepathi, et al. (2012). Subpopulations of neurokinin 1 receptor-expressing
neurons in the rat lateral amygdala display a differential pattern of innervationfrom distinct glutamatergic afferents. Neuroscience 203.https://doi.org/10.
1016/j.neuroscience.2011.12.006 .
Sunkin, S.M., Ng, L., Lau, C., Dolbeare, T., Gilbert, T.L., Thompson, C.L., Ha-
wrylycz, M., and Dang, C. (2013). Allen brain atlas: an integrated spatio-tem-poral portal for exploring the central nervous system. Nucleic Acids Res. 41,
D996–D1008. https://doi.org/10.1093/nar/gks1042 | [
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350
Annexes
IN PERSON
All over the European Union there are hundreds of Europe Direct centres.
You can find the address of the centre nearest you online
(european-union.europa.eu/contact-eu/meet-us_en).
ON THE PHONE OR IN WRITING
Europe Direct is a service that answers your questions about the European Union.
You can contact this service:
• by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these
calls),
• at the following standard number: +32 22999696,
• via the following form: european-union.europa.eu/contact-eu/write-us_en.GETTING IN TOUCH WITH THE EU
FINDING INFORMATION ABOUT THE EU
ONLINE
Information about the European Union in all the official languages of the EU is
available on the Europa website (european-union.europa.eu).
EU PUBLICATIONS
You can view or order EU publications at op.europa.eu/en/publications. Multiple
copies of free publications can be obtained by contacting Europe Direct or your
local documentation centre (european-union.europa.eu/contact-eu/meet-us_en).
EU LAW AND RELATED DOCUMENTS
For access to legal information from the EU, including all EU law since 1951 in
all the official language versions, go to EUR-Lex (eur-lex.europa.eu).
OPEN DATA FROM THE EU
The portal data.europa.eu provides access to open datasets from the EU
institutions, bodies and agencies. These can be downloaded and reused for
free, for both commercial and non-commercial purposes. The portal also
provides access to a wealth of datasets from European countries.
JRC Mission
As the science and knowledge service
of the European Commission, the Joint
Research Centre’s mission is to support
EU policies with independent evidence
throughout the whole policy cycle.
@EU_ScienceHub
EU Science Hub - Joint Research Centre
EU Science, Research and Innovation
EU Science Hub
Eu Science EU Science Hub
joint-research-centre.ec.europa.euThe European Commission’s
science and knowledge service
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like syntax, such representa-
tions could perhaps be learned from form alone (He
et al., 2018; Hewitt and Manning, 2019). Equating
these with meaning ignores a core function of lan-
guage, which is to convey communicative intents.
“But meaning could be learned from . . . ”. As
we discussed inx7, if form is augmented with
grounding data of some kind, then meaning can
conceivably be learned to the extent that the com-
municative intent is represented in that data.
In addition, certain tasks are designed in a way
that specific forms are declared as representing cer-
tain semantic relations of interest. Examples of
this include NLI datasets (Dagan et al., 2006; Ra-
jpurkar et al., 2016; Ostermann et al., 2019) which
pair input/output tuples of linguistic forms with an
explicit semantic relation (e.g. text + hypothesis
+ “entailed”). Similarly, control codes, or tokens
liketl;dr, have been used to prompt large LMs to
perform summarization and other tasks (Radford
et al., 2019; Keskar et al., 2019). Here forms are
explicitly declared at test time to represent certain
semantic relations, which together with the dis-
tributional similarity between e.g. tl;dr and other
phrases such as in summary , may be enough to
bootstrap a successful neural summarizer. Depend-
ing on one’s perspective, one may argue that such
a system has learned to reliably find instances of
the relation without understanding the text; or that5193explicitly declaring cues like entailed ortl;dr as
representing certain semantic relations provides a
training signal that goes beyond pure form.
Analogously, it has been pointed out to us that
the sum of all Java code on Github (cf. x5) contains
unit tests, which specify input-output pairs for Java
code. Thus a learner could have access to a weak
form of interaction data, from which the meaning
of Java could conceivably be learned. This is true,
but requires a learner which has been equipped by
its human developer with the ability to identify and
interpret unit tests. This learner thus has access to
partial grounding in addition to the form.
“But there is so much form out there – surely
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analysis for each EaP
country. Some of these specialisations are shared
by more than one country, and offer opportunities
for cooperation and knowledge sharing.
The first clear transversal E&I domain of poten-
tial collaboration consists of NACE codes 10 Man-
ufacture of food products and 11 Manufacture of
beverages in the Food Processing and Manu-
facturing industry. This domain directly concerns
Armenia, Georgia, Moldova and Ukraine and is
aligned with the common strength in the Agricul-
ture industry identified in the previous paragraphs.
Manufacture of wood and of products of
wood and cork, except furniture; manufacture of
articles of straw and plaiting materials, NACE code
16, is identified as an E&I specialisation in two
countries: Moldova and Ukraine.
Manufacture of chemicals and chemical prod-
ucts, NACE code 20, is identified as an E&I special-
isation in two countries: Azerbaijan and Moldova.
Manufacture of fabricated metal products,
except machinery and equipment, NACE code
25, is identified as an E&I specialisation in two
countries: Georgia and Ukraine.
Manufacture of computer, electronic and op-
tical products, NACE code 26, is identified as an
E&I specialisation in two countries: Armenia and
Ukraine.
Accommodation, NACE code 25, is identified as
an E&I specialisation in two countries: Armenia
and Georgia.
Telecommunications, NACE code 61, is identi-
fied as an E&I specialisation in two countries: Ar-
menia and Moldova.
12
Overview of economic, innovation, scientific and technological specialisations
Several advanced services, notably NACE code
62 Computer programming, consultancy and
related activities; NACE code 63 Information
service activities; and NACE code 64 Finan-
cial service activities, except insurance and
pension funding, concern, in varying geometries,
4 EaP countries: Armenia, Azerbaijan, Georgia and
Moldova, creating an economic cluster of potential
collaboration.
The above E&I domains of potential collaboration
are solely those emerging from the selected E&I
domains for each country, and other potential are-
as of cooperation may exist. That being said, there
is obviously a cooperation-competition logic that
should be taken into account when addressing the
issue at EaP level.
Finally, several innovative and dynamic niches
were identified as relevant to the diversity of EaP
countries through the analysis of start-ups and
venture capital-backed companies using the data
source Crunchbase, notably6:
■Software for Armenia, Georgia, Moldova and
Ukraine,
■Mobile for Armenia and Azerbaijan,
■Gaming for Armenia.
The identification of cluster organisations in the
EaP countries also provides relevant areas of po-
tential intra-EaP collaboration, notably in Informa-
tion | [
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worldwide
patent offices, although with a varying frequency
of updates. Here, the same patent data will be
used after first transforming them into NACE in-
dustries using the IPC to NACE concordance from
Eurostat (cf. Annex 4)42.
For the analysis, aggregate IPC data at four-digit
level will be used for the years 2011 to 2018. Ta -
ble 2.30 summarises the number of patent docu-
ments and patent families for each of the Eastern
Partnership countries. Ukraine has by far the larg-
est number of patents, followed by Moldova. Num-
bers are small in Armenia, Azerbaijan and Georgia.
41 https://www.epo.org/searching-for-patents/data/bulk-
data-sets/docdb.html
42 https://ec.europa.eu/eurostat/ramon/documents/IPC_
NACE2_Version2_0_20150630.pdfThe IPC data at four-digit level are aggregated to
a mix of two-digit and three-digit NACE industries
using the concordance table in Annex 2. For each
country, the relative percentage share is calculat-
ed if the aggregate number of patents throughout
2011-2018 is at least 5 for Armenia, Azerbaijan,
Georgia and Moldova and at least 10 for Ukraine.
For each country, the degree of specialisation is
calculated for each industry as the ratio between
this relative percentage share and the unweight-
ed relative share of all six Eastern Partnership
countries combined. Results for patent documents
are shown in Table 2.32 and results for patent
families are shown in Table 2.33. Those indus-
tries where the degrees of specialisation is 1.5 or
above are highlighted in green. Combining these
results, the manufacturing industries shown in Ta -
ble 2.31 have innovation potential based on their
relative patent performance, including 7 industries
each for Armenia and Azerbaijan, 12 industries for
Georgia, 8 industries for Moldova and 11 indus-
tries for Ukraine.
Patent docs Patent families
Armenia 1 643 1 136
Azerbaijan 1 236 1 137
Georgia 1 833 1 689
Moldova 5 866 5 614
Ukraine 101 863 97 810Table 2.30. Number of patents for 2011-2018
Sources: WIPO Global Brand Database.
92
Part 2 Analysis of economic and innovation potential
Armenia10 Manufacture of food products
11 Manufacture of beverages
12 Manufacture of tobacco products
26.1 Manufacture of electronic components and boards
26.2 Manufacture of computers and peripheral equipment
26.6 Manufacture of irradiation
27.2 Manufacture of batteries and accumulators
Azerbaijan19 Manufacture of coke and refined petroleum products
20.1 Manufacture of basic chemicals
20.4 Manufacture of soap and detergents
20.5 Manufacture of other chemical products
42.9 Construction of other civil engineering projects
43 Specialised construction activities
62 Computer programming. consultancy and | [
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The paper presents an innovative framework for understanding political scandals, focusing on their role in shaping public discourse and political mobilization, with case studies from Eastern Europe.
5
Smith, J., “Translating Transparency: The Case of Political Finance in Post-Communist Europe,” Political Studies Review, vol. 40, no. 1, 2018, pp. 77-102.
This article contributes to the study of political finance by examining the challenges of introducing transparency measures in post-communist countries, focusing on case studies from the Czech Republic and Slovakia.
Data set, software, source code, data paper, etc./ Jeux de données, codes sources, logiciels, data paper[3], etc.
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Valorisation/Valorization
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Participation in the production of the radio documentary “Central European Politics: Between Tradition and Transformation,” Radio Europe, broadcasted in April 2022.
Publications for a wide audience (selection):
Smith, J., “How Central Europe is Shaping the Future of European Politics,” The Political Observer, March 4, 2024.
Smith, J., “The Changing Face of Nationalism in Slovakia,” Foreign Policy Review, December 14, 2023.
Press interview John Smith: “The Future of the EU: A View from Central Europe,” Politico, May 18, 2023 (online).
Regular media interviews for:
Radio Europe, BBC News, Der Spiegel.
Organization of events for a non-academic audience:
2023 - 2025 / 12 seminars of the Central and Eastern Europe Research Network, co-organized with Sara Müller and Marko Ivanov. | [
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Kwan, M.-P.: The Uncertain Geographic Context Prob-
lem, Ann. Assoc. Am. Geogr., 102, 958–968,
https://doi.org/10.1080/00045608.2012.687349, 2012.
Lancaster, H.: The combination of probabilities: An application of
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Lee, R., White, C. J., Adnan, M. S. G., Douglas, J., Mahecha,
M. D., O’Loughlin, F. E., Patelli, E., Ramos, A. M., Roberts,
M. J., Martius, O., Tubaldi, E., van den Hurk, B., Ward,
P. J., and Zscheischler, J.: Reclassifying historical disasters:
From single to multi-hazards, Sci. Total Environ., 912, 169120,
https://doi.org/10.1016/j.scitotenv.2023.169120, 2024.
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Akad. Mat. Kutato Int. Közl, 3, 171–196, 1958.
Liu, B., Siu, Y . L., and Mitchell, G.: Hazard interaction analysis for
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16, 629–642, https://doi.org/10.5194/nhess-16-629-2016, 2016.
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E., and Gasparini, P.: Principles of Multi-Risk Assessment:
Interactions Amongst Natural and Man-Induced Risks, Euro-
pean Commission, Directorate-General for Research, Environ-
ment Directorate, Luxembourg, 72 pp., ISBN 978-92-79-07963-
4, https://doi.org/10.2777/30886, 2009.Marzocchi, W., Garcia-Aristizabal, A., Gasparini, P., Mastellone,
M., and Di Ruocco, A.: Basic principles of multi-risk assess-
ment: a case study in Italy, Nat. Hazards, 62, 551–573, 2012.
McFarland, L., Huang, Y ., Wang, L., and Malfertheiner, P.: Sys-
tematic review and meta-analysis: Multi-strain probiotics as ad-
junct therapy for Helicobacter pylori eradication and prevention
of adverse events, United Eur. Gastroenterol. J., 4, 546–561,
https://doi.org/10.1177/2050640615617358, 2015.
Openshaw, S.: The Modifiable Areal Unit Problem, CATMOG 38,
GeoBooks, Norwich, https://doi.org/10.1007/s11069-012-0092-
x, 1984.
Peter, A. G. and van Bergeijk, L.: Macroeconomics of Natural Dis-
asters: Strengths and Weaknesses of Meta-Analysis Versus Re-
view of Literature, Risk Anal., 35, 1050–1072, 2015.
Rey, S. J. and Anselin, L.: PySAL: A Python Library of Spatial An-
alytical Methods, Review of Regional Studies, 37, 5–27, 2007.
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is used, Math. Model., 9, 161–176, https://doi.org/10.1016/0270-
0255(87)90473-8, 1987.
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O., Sanders, B., Matthew, R., and Agha Kouchak, A.: Multihaz-
ard scenarios for analysis of compound extreme events, Geophys.
Res. Lett., 45, 5470–5480, 2018.
SciPy: Statistics (scipy.stats) – SciPy v1.10.1 Manual, https://
docs.scipy.org/doc/scipy/tutorial/stats.html (last access: 16 Jan-
uary 2025), 2024.
Stillwell, J., Daras, K., Bell, M., and Lomax, N.: The IMAGE stu-
dio: a tool for internal migration analysis and modelling, Appl.
Spat. Anal. Polic., 7, 5–23, 2014. | [
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offer relevant areas of po-
tential intra-EaP collaboration between clusters,
notably in Information and communication tech-
nologies, Industrial manufacturing and processes,
Green energy and environmental services, Medi-
cine and health, Biotechnology, Pharmaceuticals
and Food and agriculture.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation119
Armenia Azerbaijan Belarus Georgia Moldova Ukraine Total
Green energy and
environmental services1 1 3 5
Petrochemicals 1 1
Education and knowledge
transfer1 1 2 3 7
Food and agriculture 1 4 5
Information and
commu¬nication technologies3 2 1 10 16
Medicine and health 1 1 1 3
Biotechnology 1 1 1 3
Pharmaceutics 1 1 2
Industrial manufacturing and
processes* 2 1 6 9
Tourism 1 1
High technology 1 1Table 2.57. Number of identified cluster organisations by country and sector
* Includes clusters in diverse industries such as textiles, aerospace, electronics, automation, furniture and printing.
4. Specialisations resulting
from the economic and innova-
tion analysis
This section summarises the results from the dif-
ferent mapping analyses (the economic mapping
analysis using Orbis data, the economic analysis
using INDSTAT data, the analysis of goods exports
and services exports and the innovation mapping
analysis using Enterprise Survey data, patent data,
data on venture capital and start-ups and clus-
ters) and identifies E&I specialisations for each of
the Eastern Partnership countries. To reduce the
amount of information and to keep the tables rel-
atively short, different results have been used for
specialisations in goods exports. The results of
a similar analysis to that in Section 2.3 are there-
fore used but using a more restricted list of
SITC Rev. 4 two-digit goods only.E&I specialisations for Armenia
Summary table S.1 for Armenia combines the re-
sults of the various economic and innovation map-
pings. Different colours have been used to identify
commonalities matching descriptions of indus-
try names, export categories and cluster names,
where possible. The economic and innovation anal-
ysis shows the following E&I specialisations:
■food & beverages (NACE 10, 11) based on
an economic specialisation in beverages, spe-
cialised performance in related patents, the
identification of a food and agriculture cluster
and specialised performance in related goods
exports;
■tobacco (NACE 12) based on an economic
specialisation in beverages, specialised perfor-
mance in related patents and specialised per-
formance in related goods exports;
120
Part 2 Analysis of economic and innovation potential
■travel and tourism (NACE 53, 55) based
on specialised performance in | [
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] | [] |
assistance. They also thank Maria Sol Fustin ˜ana, Claudia Schmuckermair,
and Biafra Ahanonu for providing advice on deep-brain Ca2+imaging data an-
alyses, and Miodrag Mitric for helping with the setup of optogenetics experi-ments. The authors are grateful to E. Boyden, K. Deisseroth, D. Kim, I. Wick-ersham, J. Naughton, E. Callaway, and The Vector Core at the University ofNorth Carolina at Chapel Hill (UNC Vector Core) for viral constructs. The au-thors also thank Helena Arin ˜o for providing scientific illustrations. This workwas supported by the Austrian Science Fund grants F44-17-B23 and
W012060-10 to F.F. and the Eurolife consortium fellowship to M.Y.M.
AUTHOR CONTRIBUTIONS
Conceptualization, A.R.-P., E.P., and F.F.; software, A.R.-P. and M.S.; valida-
tion, A.R.-P. and F.C.; formal analysis, A.R.-P., E.P., F.C., F.F., H.H., andM.Y.M.; investigation, A.R.-P., E.P., and F.C.; writing – original draft, A.R.-P.,E:P., and F.F.; writing – review & editing, A.R.-P., E.P., F.F., and M.S.; visual-ization, A.R.-P., E.P., F.C., and F.F.; supervision, F.F., E.P., and G.G.; fundingacquisition, F.F.
DECLARATION OF INTERESTS
The authors declare no competing interests.
Received: May 20, 2021
Revised: January 20, 2022Accepted: May 9, 2022Published: May 31, 2022
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Cerebellar modulation of the reward circuitry and social behavior. Science
363, Eaav0581. https://doi.org/10.1126/science.aav0581 .
| [
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"\n",
"Alvarez",
",",
"R.P.",
",",
"Kirlic",
",",
"N.",
",",
"Misaki",
",",
"M.",
",",
"Bodurka",
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"J.",
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"Psychiatry",
"5",
",",
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"https://doi",
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"\n",
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"/",
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"Anastasiades",
",",
"P.G.",
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"Collins",
",",
"D.P.",
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",",
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and communication technologies, Industrial
manufacturing and processes, Green energy and
environmental services, Medicine and health, Bi-
otechnology, Pharmaceuticals and Food and ag-
riculture.
6 As presented previously, other niches were also identified
for more than one country (financial services, information
technology, lending and investments, travel and tourism),
but these are already directly covered by the preceding
analysis based on full economic and innovation mapping
– NACE codes.2. Scientific and technologi-
cal (S&T) potential in the Eastern
Partnership countries
Part 3 of this report presents the analysis of the
scientific and technological (S&T) specialisations
in the Eastern Partnership countries. This exercise
is carried out by extracting and further analys-
ing the textual content of scientific publications,
research projects funded by the European Com-
mission and patents (as indexed by the Europe-
an Patent Office) produced by each of the six EaP
countries. The analyses are performed by means
of topic modelling, an algorithmic approach that
automatically extracts groups of co-occurring key-
words (the topics) from large textual corpora, and
which allows emerging thematic specialisations
across data sources to be identified in a transver-
sal fashion.
The objectives of these analyses are the follow-
ing: to identify a set of scientific and technological
(S&T) specialisation domains in terms of emerg-
ing topics; to further characterise these domains
for each EaP country and for the EaP as a whole;
and to identify key local and international actors
involved in these domains and their collaboration
networks.
Identification and characterisation of
S&T specialisation domains across the
EaP
Table I presents the 14 labelled topic groups ob-
tained from the topic modelling process, described
in detail in Part 3. These initial ‘topic groups’ are
the S&T specialisation domains, which are char-
acterised and further defined throughout the
analysis.
Overall, the EaP science and technology system
is most active in experimental and fundamen-
tal sciences (physics, mathematics, chemistry)
and their technical applications (materials, me-
chanical engineering, ICT and computer science,
biotechnology, chemical engineering), including
emerging areas such as nanotechnology and op-
tics and photonics.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation13
In any case, fundamental and applied fields do
have direct and indirect impacts on the techno-
logical development and innovativeness of sec-
tors, as is further explored in Part 4 of this report.
Furthermore, the identified S&T domains are not
hermetic: since S&T records are analysed and
classified individually, some | [
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] | [] |
average is performed to obtain the final indicator.
In line with what was done for the SI, the average
61 https://en.wikipedia.org/wiki/Citation_impact.
174
Part 3 Analysis of scientific and technological potential
number of citations per publication for the EaP re-
gion within each bibliometric category is used as a
baseline to compute the NCI.
It is important to consider that Ukraine produces
most of the records in the Eastern Partnership and
its publications therefore have a greater impact
and are cited more often (proportionately). There-
fore, most of the computed NCI indicators for
Ukraine are higher than 1. This is considered when
analysing the results in the following section.
4.2 Results by country
For each of the six countries, results are present-
ed and discussed in the following pages. Firstly,
the bare numbers (i.e. critical mass) of records are
presented per S&T specialisation domain, both in
the form of a table and a stacked bar chart. Those
domains with the highest number of records are
highlighted, and special consideration is given to
their evolution in recent years, as captured by the
compound annual growth rate (CAGR). The dis-
tribution of records (by type) per domain is also
discussed.
The citation impact (for publications) and the
specialisation index (both for publications and patents) are then presented for each domain for
individual countries62. For publications, a two-di-
mensional graph is provided: the graph shows the
specialisation index on the x-axis and the normal-
ised citation impact on the y-axis. In each graph,
EaP countries feature a higher specialisation and
higher citation impact for domains scattered to-
wards the right and the top parts of the graph,
respectively. In the case of patents, a simple bar
chart is featured; each bar represents the speciali-
sation index of a domain, and special consideration
is given to those domains with an SI higher than
1, which signals a relative specialisation of the
country in that particular domain. These graphs,
elaborated for each of the six EaP countries, are
exemplified in the following pages.
62 As per how the specialisation index has been defined, it
would make no sense to present its results for the Eastern
Partnership region as a whole.
Figure 3.23. Example of the figures used to present the citation impact and specialisation index for publications (left)
and the specialisation index for patents (right)
Specialisation index Specialisation indexNo pubs.
100
500
1 000Normalised citation impact2
| [
"average",
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"Partnership",
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"Example",
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"\n",
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"impact2",
"\n"
] | [] |
sented in the data sources and thus in the
results of the analysis, while universities and
research organisations feature in particular.
Additional care must be taken when analysing
results and interpreting conclusions related to
priority-setting and the market or society-ori-
ented innovation and application capacity of
the preliminary priority domains. In Chapter 5
‘Identification of the main actors and collabo-
ration patterns within the S&T specialisation
domains’, private companies have been high-
36
Part 1 Introduction and methodology
lighted to facilitate an interpretation of the
specialisations of the private sector.
6. Large number of individual patent appli-
cants jeopardising a representative char-
acterisation of patenting activity by EaP
organisations. The six EaP countries present
a very large number of individual persons as
applicants in the patent data source, amount-
ing to 40.7%. That is, slightly less than 60%
of EaP patents can be directly connected to
an organisation (academic institutions, com-
panies or some other organisation). There may
certainly be a number of individual inventors
and patent agents, but there are also cases
where university staff, for instance, apply for
patents as individuals. EaP legislation as well
as the internal regulations and organisation of
academic institutions and R&D grants could
facilitate and incentivise institutional patent
applications, always guaranteeing the intel-
lectual and economic rights of the inventors.
7. Relative weight of Ukraine in trans-EaP
analyses. From each data source, the num-
ber of records available for Ukraine outnum-
bers those for the rest of the EaP countries
combined. This means that statistics and
record figures for the whole EaP region are
highly congruent with those of Ukraine; as a
consequence, the analyses carried out at en-
tire-EaP regional level are inevitably skewed
towards the Ukrainian profile. Indicators of
critical mass and the internal distribution of
S&T domains within each country complement
specialisation indicators to offer a more com-
prehensive view of national specialisation ar-
eas and niches.
8. Computation of specialisation and excel-
lence indicators against the EaP aggre-
gate. Due to the size of the data sources and
the methodological approach, specialisation
and excellence indicators for each country
have been computed against the aggregate
S&T activity of the six EaP countries. This
provides a notion of relative specialisation
and excellence, relevant in terms of Eastern
Partnership initiatives and useful as a gener-al contextualisation, but incapable of offering
insight supporting differentiation versus Euro-
pean, Asian or global partners and competi-
| [
"sented",
"in",
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"\n",
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",",
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"\n ",
"Part",
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"Introduction",
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furniture; manufacture of articles of
straw and plaiting materialsMaterials Chemistry; Mechanics of Materials; Process
Chemistry and Technology
346
Annexes
UKRAINE
Concordances between NACE sectors and the intersection of ASJC subject fields & S&T domains
NACE sector ASJC Scopus subject field
10 Manufacture of food products Food Science
13 Manufacture of textiles Materials Chemistry; Mechanics of Materials
14 Manufacture of wearing apparel Materials Chemistry; Mechanics of Materials
15 Manufacture of leather and related products Materials Chemistry; Mechanics of MaterialsMOLDOVA
Concordances between NACE sectors and the intersection of ASJC subject fields & S&T domains
NACE sector ASJC Scopus subject field
18 Printing and reproduction of recorded mediaSurfaces, Coatings and Films; Materials Chemistry;
Process Chemistry and Technology
19 Manufacture of coke and refined petroleum productsEnergy Engineering and Power Technology; General
Chemical Engineering; Surfaces, Coatings and
Films; Materials Chemistry; Process Chemistry and
Technology
20 Manufacture of chemicals and chemical productsBiotechnology; Biochemistry; Drug Discovery; General
Chemical Engineering; Surfaces, Coatings and
Films; Materials Chemistry; Process Chemistry and
Technology
23 Manufacture of other non-metallic mineral productsGeneral Chemical Engineering; Surfaces, Coatings
and Films; Materials Chemistry; Mechanics of
Materials; Process Chemistry and Technology
25Manufacture of fabricated metal products, except
machinery and equipmentElectronic, Optical and Magnetic Materials; Mechanics
of Materials
26Manufacture of computer, electronic and optical
productsElectrical and Electronic Engineering; Electronic,
Optical and Magnetic Materials
27 Manufacture of electrical equipmentElectrical and Electronic Engineering; Instrumentation;
Electronic, Optical and Magnetic Materials; Control
and Systems Engineering
28 Manufacture of machinery and equipment n.e.c. Instrumentation; Mechanical Engineering
29Manufacture of motor vehicles, trailers and semi-
trailersInstrumentation
33 Repair and installation of machinery and equipmentInstrumentation; Electronic, Optical and Magnetic
Materials; Mechanical Engineering
61 TelecommunicationsInformation Systems; Computer Networks and
Communications; Computer Science Applications;
General Computer Science; Modelling and Simulation
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation347
UKRAINE
Concordances between NACE sectors and the intersection of ASJC subject fields & S&T domains
NACE sector ASJC Scopus subject field
16Manufacture of wood and of products of wood and
cork, except furniture; manufacture of articles of
straw and plaiting materialsMaterials Chemistry; Mechanics of Materials
18 Printing and reproduction of recorded media Materials Chemistry
19 Manufacture of coke and refined petroleum productsEnergy Engineering and Power Technology; General
Chemical Engineering; Materials Chemistry; Catalysis
20 Manufacture of chemicals and chemical productsBiochemistry; Drug Discovery; General Chemical
Engineering; Materials Chemistry; Catalysis
23 Manufacture of other non-metallic mineral productsGeneral Chemical Engineering; Materials Chemistry;
Mechanics of Materials; Catalysis
25Manufacture of fabricated | [
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ship countries, data on export values are available
up until 2019, except for Ukraine where 2019 data
are not available. For Ukraine, 2019 data have
been substituted with 2018 data.
32 https://comtrade.un.org/
33 https://unstats.un.org/unsd/trade/sitcrev4.htmEight years have been used (2012-2019) for the
mapping analysis data, divided into three periods
for measuring changes over time similar to the
economic mapping using Orbis data, i.e. 2012-
2015, 2014-2017 and 2016-2019. Countries do
not have exports for all goods categories. Ukraine
has the largest number of goods categories with
exports; Azerbaijan has the lowest number, which
has been increasing over time. For several goods
categories for the EaP, averages will thus not be
calculated using data for all countries but only for
countries for which there are exports.
Goods exports are available for 10 one-digit SITC
Rev. 4 classes: 0 Food and live animals; 1 Beverag-
es and tobacco; 2 Crude materials, inedible, except
fuels; 3 Mineral fuels, lubricants and related mate-
rials; 4 Animal and vegetable oils, fats and waxes;
5 Chemicals and related products, n.e.s34.; 6 Man-
ufactured goods classified chiefly by material; 7
Machinery and transport equipment; 8 Miscellane-
ous manufactured articles; and 9 Commodities and
transactions not classified elsewhere in the SITC.
34 ‘n.e.s. stands for ‘not elsewhere specified’.
64
Part 2 Analysis of economic and innovation potential
There are large differences in the share of these
export classes throughout the EaP countries. More
than 90% of goods exports in Azerbaijan are in
Mineral fuels, lubricants and related materials,
a share which is much higher than in any of the
other countries. For Amenia, the largest export
classes include Crude materials, inedible, except
fuels (25%); Manufactured goods classified chief-ly by material (21%); and Beverages and tobac-
co (20%). For Georgia, the largest export class is
Machinery and transport equipment (21%). For
Moldova, the largest export classes include Food
and live animals (23.5%) and Miscellaneous man-
ufactured articles (22%). For Ukraine, the largest
export class is Mineral fuels, lubricants and relat-
ed materials (22%).
2012 2013 2014 2015 2016 2017 2018 2019
Armenia 204 212 204 227 219 219 227 228
Azerbaijan 196 192 192 197 217 223 220 220
Georgia 231 231 231 230 235 235 239 243
Moldova 221 226 221 225 229 226 222 219
Ukraine 255 253 254 251 252 254 249 --Table 2.14. Available three-digit goods export data (number of goods categories)
Sources: UN Comtrade Database.
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"\n",
"64",
"\n ",
"Part",
"2",
"Analysis",
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"\n",
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"\n",
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"22",
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".",
"\n",
"2012",
"2013",
"2014",
"2015",
"2016",
"2017",
"2018",
"2019",
"\n",
"Armenia",
"204",
"212",
"204",
"227",
"219",
"219",
"227",
"228",
"\n",
"Azerbaijan",
"196",
"192",
"192",
"197",
"217",
"223",
"220",
"220",
"\n",
"Georgia",
"231",
"231",
"231",
"230",
"235",
"235",
"239",
"243",
"\n",
"Moldova",
"221",
"226",
"221",
"225",
"229",
"226",
"222",
"219",
"\n",
"Ukraine",
"255",
"253",
"254",
"251",
"252",
"254",
"249",
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"2.14",
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Potential for knowledge-based economic cooperation271 272
Annexes
GEORGIA MOLDOVA UKRAINEEmploy-
ment
Turnover
Employ-
ment &
turnover
Employ-
ment
Turnover
Employ-
ment &
turnover
Employ-
ment
Turnover
Employ-
ment &
turnover
Employ-
ment
Turnover
Employ-
ment &
turnover
Employ-
ment
Turnover
Employ-
ment &
turnover
Employ-
ment
Turnover
Employ-
ment &
turnover
NACE Industry name Current Current CurrentEmerg-
ingEmerg-
ingEmerg-
ingCurrent Current CurrentEmerg-
ingEmerg-
ingEmerg-
ingCurrent Current CurrentEmerg-
ingEmerg-
ingEmerg-
ing
34 52 28 61 64 40 31 29 15 50 47 21 55 40 35 83 57 34
7 Mining of metal ores
7.1 Mining of iron ores X X X
7.2 Mining of non-ferrous metal ores X X X X X X
8 Other mining and quarrying
8.1 Quarrying of stone, sand and clay X X X X
8.9 Mining and quarrying n.e.c.
9 Mining support service activities
9.1 Support activities for petroleum and natural gas extraction
9.9 Support activities for other mining and quarrying
C MANUFACTURING
10 Manufacture of food products
10.1 Processing and preserving of meat and production of meat products X X X X
10.2 Processing and preserving of fish, crustaceans and molluscs X X X
10.3 Processing and preserving of fruit and vegetables X X X X X X
10.4 Manufacture of vegetable and animal oils and fats X X X X X
10.5 Manufacture of dairy products X X X X X X
10.6 Manufacture of grain mill products, starches and starch products X X X X X
10.7 Manufacture of bakery and farinaceous products X X X
10.8 Manufacture of other food products X X X X X
10.9 Manufacture of prepared animal feeds X X X X
11 Manufacture of beverages X X X X
12 Manufacture of tobacco products X X X X
13 Manufacture of textiles
13.1 Preparation and spinning of textile fibres
13.2 Weaving of textiles
13.3 Finishing of textiles
13.9 Manufacture of other textiles X X X X X
14 Manufacture of wearing apparel
14.1 Manufacture of wearing apparel, except fur apparel X X X X X X X X
14.2 Manufacture of articles of fur
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation273 274
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
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Fundamental physics and
mathematics
Chemistry and chemical engineering
Health and wellbeing
Nanotechnology and materials
ICT and computer science
Environmental sciences and
industries
Governance, culture, education and
the economy
Energy
Mechanical engineering and heavy
machinery
Agrifood
Optics and photonics
Biotechnology
Baku State University 546 381 73 308 75 105 86 27 28 44 86 33
Azerbaijan Mational Academy of
Sciences742 196 64 61 162 81 75 92 68 55 7 27
Azerbaijan State Oil and Industry
University256 80 20 75 74 84 34 41 14 10 16 9
Azerbaijan Medical University 8 28 481 20 13 15 15 3 3 46 5 51
Azerbaijan Technical University 83 48 1 69 50 4 23 9 29 2 10 2
State Oil Company of the Azerbaijan
Republic48 23 1 13 32 93 13 20 20 1 0 1
Azerbaijan State University of
Economics32 11 13 31 18 4 38 12 10 2 5 4
National Nuclear Research Center 109 10 1 34 1 7 0 4 1 2 2 1
National Aviation Academy of
Azerbaijan12 6 4 37 19 16 3 1 7 0 18 0
Azerbaijan State Advanced Training
Institute for Doctors named after A.
Aliyev1 1 77 2 2 1 1 0 0 0 0 0Figure 3.41. Top actors in Azerbaijan by number of records (all types), across all domainsAzerbaijan
Scientific production in Azerbaijan is heavily con-
centrated in a few institutions, notably Baku State
University, the National Academy of Sciences and
a few specialised universities.
The most active public actor, by far, is the State Oil
Company of the Azerbaijan Republic, also known
as SOCAR, followed by several medical centres and ministries making up the rest of the ranking.
Private actors, which account for a very low num-
ber of records, are mostly linked to scientific and
technical services, mining and the energy industry.
200
Part 3 Analysis of scientific and technological potential
AZERBAIJAN
Top 10 actors classified as ‘Public sector (excluding higher education and research institutions)’
NameNo of
recordsMain S&T domains
State Oil Company of the Azerbaijan
Republic209Environmental sciences and industries; Fundamental physics and
mathematics; ICT and computer science
Ministry of Health 38 Health and wellbeing; Agrifood; Biotechnology
National Centre of Ophthalmology 35Health and wellbeing; Environmental sciences and industries;
Governance, culture, education and the economy
Azerbaijan Scientific Research
and Design-Prospecting Power
Engineering Institute27Energy; ICT and computer science; Fundamental physics and
mathematics
Azerbaijan National Aerospace
Agency22Environmental sciences and industries; Nanotechnology | [
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] | [] |
up of successful
ventures . The EU should become as attractive for inventors as other leading regions for innovation. The report
recommends a number of measures to support the transition from invention to commercialisation in Europe. First,
to overcome bureaucratic barriers in universities and research institutions to managing intellectual property rights
with their researchers, a new blueprint for fair and transparent royalty sharing is recommended. Second, to lower
application costs for young companies and to offer uniform protection of intellectual property, it is proposed to adopt
the Unitary Patent in all EU Member States. Third, the EU should carry out a thorough impact assessment of the effect
of digital and other regulation on small companies, with the aim of excluding SMEs from regulations that only large
companies are able to comply with. Finally, the EU should support rapid growth within the European market by giving
innovative start-ups the opportunity to adopt a new EU-wide legal statute (the “Innovative European Company”).
This status would provide companies with a single digital identity valid throughout the EU and recognised by all
Member States. These companies would have access to harmonised legislation concerning corporate law and insol -
vency, as well as a few key aspects of labour law and taxation, to be made progressively more ambitious, and they
would be entitled to establish subsidiaries across the EU without incorporating separately in each Member State.
A better financing environment for disruptive innovation, start-ups and scale-ups is needed as barriers
to growth within the European markets are removed [see the chapters on innovation, and investment] . While
high-growth companies can typically obtain finance from international investors, there are good reasons to further
develop the financing ecosystem within Europe. Very early-stage innovation would benefit from a deeper pool of
angel investors. Ensuring sufficient local capital to fund scale-ups would concentrate the spillovers of innovation
within Europe. Increasing the appeal of European stock markets for IPOs would improve funding options for founders,
encouraging more start-up activity in the EU. To generate a significant increase in equity and debt funding available
to start-ups and scale-up, the report proposes the following measures. First, expanding incentives for business
33THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2“angels” and seed capital investors. Second, assessing whether further changes to capital requirements under
Solvency II are warranted, which establishes capital adequacy rules for insurance companies, and issuing guide -
lines for | [
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] | [] |
and data source size independent
(% change for 2015-2018 over previous period 2011-2014)
Change in
share of
publicationsChange in share
of patents Change, weighted average of
publications and patents
Nanotechnology and materials -13.27% -3.39% -11.40%
Health and wellbeing 30.28% 0.03% 18.24%
Fundamental physics and mathematics -18.34% -6.19% -17.38%
Mechanical engineering and heavy
machinery18.18% 3.70% 7.01%
ICT and computer science 27.42% 17.74% 25.03%
Biotechnology -15.17% 2.87% -8.53%
Governance, culture, education and the
economy44.99% 20.29% 44.27%
Environmental sciences and industries 8.60% -11.43% 3.85%
Electric and electronic technologies -4.88% -10.54% -8.00%
Energy 10.66% -21.14% -5.87%
Chemistry and chemical engineering -27.29% 15.53% -17.43%
Optics and photonics -25.31% -11.38% -22.61%
Agrifood 25.21% 5.89% 12.24%
Transportation 63.04% -2.89% 32.63%Table 3.6. Temporal evolution of S&T domains in the Eastern Partnership region
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation159
scheme for patents. These native classifications
are exploited here to further characterise the iden-
tified S&T specialisation domains.
Table 3.7 and Table 3.8 present the most rele-
vant native taxonomies in the original data sourc-
es associated with the documents appearing in
the respective S&T domain. These tables provide
a deeper understanding of the most relevant and
distinctive scientific disciplines or technological areas for each domain. Table 3.7 shows, for each
domain, the bibliometric categories that appear
relatively more frequently in each domain with re-
spect to the average in terms of the subject fields
adopted by Scopus to categorise scientific publica-
tions. Table 3.8 instead reports the top IPC patent
classes (by number of records) that categorise the
patent families included in the different domains.
Top publication subject fields in the S&T specialisation domains
Domain ASJC Description No recordsRelative
freq.
Agrifood1106 Food science 184 260.38%
1108 Horticulture 71 179.75%
1102 Agronomy and crop science 194 173.99%
1110 Plant science 229 146.95%
2716 Genetics (clinical) 84 137.70%
Biotechnology1600 General chemistry 2 047 329.32%
1606 Physical and theoretical chemistry 1 037 311.41%
1500 General chemical engineering 968 297.50%
2304 Environmental chemistry 346 184.78%
1303 Biochemistry 976 183.80%
Chemistry
and chemical
engineering1606 Physical and theoretical chemistry 1 319 396.10%
1600 General chemistry 2 209 355.38%
1604 Inorganic chemistry 867 350.30%
1500 General chemical engineering 797 244.95%
1605 Organic chemistry 1 862 211.95%
Electric and
electronic
technologies2102 Energy engineering and power technology 824 190.61%
2104 Nuclear energy and engineering 514 188.69%
2208 Electrical and electronic engineering 2 196 159.04%
1705 Computer networks and communications 601 115.24%
| [
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] | [] |
having to snap shots for the police to find. {What I would like to address is the mindset
that generally surrounds Chrome OS users. To me this is analogous to saying that Apple
does“hate their Windows”, or that HP does“hate their Macs” as if http://twitter.com/)
(and that quote is from two years ago), that anyone who covers smartphones and tablets
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MITTEE ON THE STUDY OF THE AMERICAN SECURITY AGENDA, {America’s fu-
ture has been mapped out in carved stone. Metro Atlanta’s last US congressman, Bill Posey,
was a inextricable integral element of the Citadel project as it became another metaphor for
Atlanta’s transformation from an industry backwater into the finance and information hub of
the nation’s capital. Meanwhile, Cobb County – Atlanta’s geode of change – is home to some
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Truth Raters p1.0 k40 p0.96 Truth Raters p1.0 k40 p0.96
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Exidentia at Eurnari, is an upcoming Cryptopia event which is currently still in devel-
opment. Be a part of the first live stream of this year’s event on 15-16 January 2016!
{Since the release of v1.22, Exidentia has received a fair amount of user feedback. This
event takes place in the underwater Cryptopia they have built. During this event, you
will learn about the ocean and areas around it, and be reached by a treasure hunter that
helps you explore the different areas. {There will be six different levels in this event
that you will become acquainted with: thought Polar Lava, Ocean Seared Cones and
Celestine Floors, Sea Damaged Aerie Bricks, coast Puddle (congipit stopping at red
water), Shaikh Swamp and Bugmite. At rotating points, you will learn how to access
various types of creaturesEver since the opening of the North American College of Art Education in 1990, the demand | [
"\n",
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for EU Eastern Partnership countries (EaP), aimed
to strengthen evidence-informed research and
innovation policymaking and the development of
Smart Specialisation Strategies. The study pre-
sents and tests various analytical approaches,
methods and tools, some of which are new and
experimental, with the purpose of better under-
standing national and EaP-wide, place-based in-
novation capacities, emerging specialisations and
supporting ongoing national analytical exercises.
As this report is based on available international
data, it is important that the countries analysed
continue the development of Smart Specialisation
through their own efforts, discussing the results of
this study and adding the relevant national data
sources and other useful information.
Economic and innovation (E&I) specialisation do-
mains have been identified for each EaP country
by using the multidimensional analysis and in-
dicators based on: economic and manufacturing
strengths, innovativeness and IP intensity, ex-
ports and the presence of start-ups and venture
capital-backed companies as well as cluster or-
ganisations. In this respect, Part 2 of this report
offers a wealth of information addressing differ-
ent questions that can emerge at different stages
for priority-setting, the identification of topics and
niches within priorities, actor identification and
mobilisation and for the identification of areas of
potential knowledge-based cooperation through-
out EaP countries.
Similarly, scientific and technological specialisa-
tion domains, emerging from the text contained
in S&T records (namely, scientific publications,
patents and research and innovation projects
funded by the European Commission), have been
identified for the whole EaP, providing a general
overview of the key areas of activity in the re-
gion – as reported in Part 3. These domains have
been characterised at national level, and a set of
critical mass, specialisation and excellence indica-
tors have been computed for each country, which
can be utilised in priority-setting and to organ-
ise the entrepreneurial discovery process (EDP).
In this sense, the report is complemented by an
interactive digital tool facilitating the exploration of national and EaP S&T networks and the iden-
tification of key actors in the knowledge, public
and private sectors. Furthermore, patterns of in-
tra-EaP and extra-EaP international collaborations
are also analysed, both globally and for each S&T
domain. Again, the analysis has been carried out
by exploiting international data sources, whereof
the local actors fostering the knowledge econo-
my in the 6 EaP countries can have relatively low
visibility. The S&T results presented in the current
work should be considered as an educated | [
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Home… Horizon Europe Democratising and making sense out of heterogeneous scholarly content
Democratising and making sense out of
heterogeneous scholarly content
Reporting
SciLake
Grant agreement ID: 101058573
DOI
10.3030/101058573
EC signature date
28 November 2022Funded under
Research infrastructures
Coordinated byProject Information
Start date
1 January 2023End date
31 December 2025Total cost
€ 4 809 450,00
EU contribution
€ 4 809 449,00
ATHINA-EREVNITIKO KENTRO
KAINOTOMIAS STIS
TECHNOLOGIES TIS
PLIROFORIAS, TON
EPIKOINONION KAI TIS GNOSIS
Greece
Periodic Reporting for period 1 - SciLake (Democratising
and making sense out of heterogeneous scholarly
content)
SciLake is a 3-year project that aims to leverage Science Knowledge Graphs (SKGs) as the
foundation to establish the concept of the Scientific Lake: a research ecosystem designed to facilitate
the creation, integration, and querying of SKGs. This ecosystem includes tools capable of extracting
knowledge from unstructured data, enhancing SKG interoperability, supporting knowledgeReporting period: 2023-01-01 to 2024-06-30
Summary of the context and overall objectives of the project ⌄
1 of 4
transformation, unifying and simplifying SKGs querying, and accelerating graph processing and
analysis.
SciLake sets the following objectives:
- Overcome the underlying heterogeneity of scholarly content and address domain-specific and cross-
disciplinary information needs
- Democratize scholarly content facilitating the content acquisition and the creation, interlinking, and
management of community-based SKGs and related services
- Facilitate the identification of research trends and of valuable research objects of different types
considering various aspects of research impact
- Facilitate the assessment of the reproducibility and replicability/repeatability of research works.
- Customize, test, and demonstrate the developed services in real-world pilots
- Leverage & further enrich EOSC services landscape
The progress towards these objectives during the RP1 is elaborated in the Technical Report (Part B).
SciLake’s key results include:
- A customisable Scientific Lake service, built on SKG technologies, which includes a suite of
components designed to streamline the process of scientific knowledge acquisition, management,
and navigation
- An SKG Interoperability Framework, built upon and extending existing standards, to standardize the
way SKG contents are exposed to the developers of added-value services
- A customisable Smart Impact-driven Knowledge Discovery service, which leverages the Scientific
Lake service to significantly enhance the ability of researchers to navigate the vast landscape of
scientific outputs in the domains of interest
- A customisable Smart Reproducibility Assistance service, which leverages the Scientific Lake
service to enrich the contained SKGs with valuable information for the relationships between research
| [
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1.3
1
0.75
0.50.25 0.5 1 2 4
0.60 0.80 1.00 2.00
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation175
Armenia
Table 3.9 and Figure 3.24 showcase the num-
ber of records per S&T specialisation domain in
Armenia. Fundamental physics and mathematics
is the domain with the most records (with a to-
tal of 4 262), followed by Health and wellbeing
(1 436), Nanotechnology and materials (1 326),
Governance, culture, education and the economy
(731) and Chemistry and chemical engineering
(632). The first one accounts for almost half the
total number of records (45%). It must be noted,
however, that the number of patents obtained for
Armenia is rather small, jeopardising any analysis
and interpretation.
Consequently, publications account for the vast
majority of records in all domains, ranging from
90% to 99% of the total records in most cases,
as shown in Figure 3.24. The only exceptions are
Electric and electronic technologies (23%) and Me-
chanical engineering and heavy machinery (47%),
where the number of patents is higher than the
number of publications.
Following the trend in the EaP, EC projects in Ar-
menia are highly concentrated in the domain of Governance, culture, education and the economy
due to the nature of these projects. There is, how-
ever, also some concentration in the domain of ICT
and computer science.
The growth rate of publications in recent years, in
terms of the compound annual growth rate, is also
shown. Of the top 5 domains in terms of critical
mass, Health and wellbeing (+7.5%), Governance,
culture, education and the economy (+9.5) and
Chemistry and chemical engineering (+2.6) have
a growing trend, while Fundamental physics and
mathematics (-0.6%) and Nanotechnology and
materials (-1.6%) show a decreasing trend. This is
particularly noteworthy for these last two domains,
as it signals that the number of publications in the
coming years may continue to decrease and these
domains may become less relevant. The rest of
the domains, barring a couple of exceptions, all
have a positive growth rate for publications. ICT
and computer science and Environmental sciences
and industries present the highest growth in publi-
cations, a proof of thematic dynamism.
Finally, as the Figure 3.25 and Figure 3.26 show,
Armenia’s publications are specialised in Funda-
Publications
(critical mass | CAGR)PatentsEC
projectsTotal
Fundamental physics and mathematics 4 200 -0.6% 58 3 4 261
Health and wellbeing 1 411 7.5% 22 4 | [
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"23",
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] | [] |
NACE Innovation – Patents
23.9Man. of abrasive products and
non-metallic mineral productsX 24 Manufacture of basic metals X 14 Manufacture of wearing apparel
24.1Manufacture of basic iron and
steel and of ferro-alloysX 28 Manufacture of machinery and equipment n.e.c. X 24 Manufacture of basic metals
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation137 138
Part 2 Analysis of economic and innovation potential
24.2Man. of tubes, pipes, hollow
profiles and related fittingsX 29Manufacture of motor vehicles, trailers and semi-
trailersX 25.1 Manufacture of structural metal products
24.3Manufacture of other products of
first processing of steelX 30 Manufacture of other transport equipment X 25.3 Manufacture of steam generators
24.4Manufacture of basic precious and
other non-ferrous metalsX 25.9 Man. of other fabricated metal products
25.1Manufacture of structural metal
products X SITC Goods exports C E 26.4 Manufacture of consumer electronics
25.6Treatment and coating of metals;
machiningX X 1 Meat and meat preparations X 26.5 Man. of instruments and appliances for measuring
25.9Manufacture of other fabricated
metal productsX 4 Cereals and cereal preparations X X 27.3 Manufacture of wiring and wiring devices
27.1Manufacture of electric motors,
generators, etc.X 5 Vegetables and fruit X 27.9 Manufacture of other electrical equipment
28.1Manufacture of general-purpose
machineryX 6 Sugars, sugar preparations and honey X 28.4 Man. of metal forming machinery and machine tools
28.3Manufacture of agricultural and
forestry machineryX X 8Feeding stuff for animals (not including unmilled
cereals)X X 32.5 Manufacture of medical and dental instruments and supplies
28.9Manufacture of other special-
purpose machineryX 9 Miscellaneous edible products and preparations X X
29.1 Manufacture of motor vehicles X 11 Beverages X NACE Innovation – VC & start-ups
29.3Manufacture of parts and
accessories for motor vehiclesX 22 Oil-seeds and oleaginous fruits X X J62, J63 Software
30.2Manufacture of railway
locomotives and rolling stockX 24 Cork and wood X Professional services
30.3Manufacture of air and spacecraft
and related machineryX 27Crude fertilizers, other than those of division 56,
and crude mineralsX X C26 Hardware
33.1Repair of fabricated metal
products, machinery and
equipment X 32 Coal, coke and briquettes X G47, M73 Sales and marketing
35.1Electric power generation,
transmission and distributionX 42Fixed vegetable fats and oils, crude, refined or
fractionatedX X G46, G47 Commerce and shopping
35.3 Steam and air conditioning supply X X 52 Inorganic chemicals X X
41.1 Development of building projects X 53 Dyeing, tanning and colouring materials X Clusters
41.2Construction of residential and
| [
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"X",
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"\n",
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"X",
"32.5",
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" ",
"9",
"Miscellaneous",
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"X",
"X",
" \n",
"29.1",
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"X",
" ",
"11",
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" ",
"X",
"NACE",
"Innovation",
"–",
"VC",
"&",
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"-",
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"\n",
"29.3Manufacture",
"of",
"parts",
"and",
"\n",
"accessories",
"for",
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" ",
"22",
"Oil",
"-",
"seeds",
"and",
"oleaginous",
"fruits",
"X",
"X",
"J62",
",",
"J63",
"Software",
"\n",
"30.2Manufacture",
"of",
"railway",
"\n",
"locomotives",
"and",
"rolling",
"stockX",
" ",
"24",
"Cork",
"and",
"wood",
"X",
" ",
"Professional",
"services",
"\n",
"30.3Manufacture",
"of",
"air",
"and",
"spacecraft",
"\n",
"and",
"related",
"machineryX",
" ",
"27Crude",
"fertilizers",
",",
"other",
"than",
"those",
"of",
"division",
"56",
",",
"\n",
"and",
"crude",
"mineralsX",
"X",
"C26",
"Hardware",
"\n",
"33.1Repair",
"of",
"fabricated",
"metal",
"\n",
"products",
",",
"machinery",
"and",
"\n",
"equipment",
"X",
"32",
"Coal",
",",
"coke",
"and",
"briquettes",
"X",
" ",
"G47",
",",
"M73",
"Sales",
"and",
"marketing",
"\n",
"35.1Electric",
"power",
"generation",
",",
"\n",
"transmission",
"and",
"distributionX",
" ",
"42Fixed",
"vegetable",
"fats",
"and",
"oils",
",",
"crude",
",",
"refined",
"or",
"\n",
"fractionatedX",
"X",
"G46",
",",
"G47",
"Commerce",
"and",
"shopping",
"\n",
"35.3",
"Steam",
"and",
"air",
"conditioning",
"supply",
"X",
"X",
"52",
"Inorganic",
"chemicals",
"X",
"X",
" \n",
"41.1",
"Development",
"of",
"building",
"projects",
" ",
"X",
"53",
"Dyeing",
",",
"tanning",
"and",
"colouring",
"materials",
" ",
"X",
" ",
"Clusters",
"\n",
"41.2Construction",
"of",
"residential",
"and",
"\n"
] | [] |
projects in collaboration between EaP
actors and partners outside of the EaP .......................................................................................... 217
Figure 3.62. Keyword cloud for Fundamental physics and mathematics in Armenia 220
Figure 3.63. Keyword cloud for Agrifood in Armenia ............................................................... 220
Figure 3.64. Keyword cloud for Nanotechnology and materials in Armenia ................. 220
Figure 3.65. Keyword cloud for Health and wellbeing in Armenia ..................................... 220
Figure 3.66. Keyword cloud for Chemistry and chemical engineering in Azerbaijan 222
Figure 3.67. Keyword cloud for Energy in Azerbaijan .............................................................. 222
Figure 3.68. Keyword cloud for Mechanical engineering and heavy machinery in
Azerbaijan ..................................................................................................................................................... 222
Figure 3.69. Keyword cloud for Health and wellbeing in Azerbaijan ................................ 222
Figure 3.70. Keyword cloud for Environmental sciences and industries in Georgia .. 224
Figure 3.71. Keyword cloud for Agrifood in Georgia ................................................................ 224
Figure 3.72. Keyword cloud for Health and wellbeing in Georgia ...................................... 224
Figure 3.73. Keyword cloud for ICT and computer science in Georgia ............................. 224
Figure 3.74. Keyword cloud for Health and wellbeing in Moldova ..................................... 226
Figure 3.75. Keyword cloud for Nanotechnology and materials in Moldova ................ 226
Figure 3.76. Keyword cloud for Electric and electronic technologies in Moldova ....... 226
262
List of figures and tables
Figure 3.77. Keyword cloud for Mechanical engineering and heavy machinery in
Moldova .......................................................................................................................................................... 226
Figure 3.78. Keyword cloud for Health and wellbeing in Ukraine ...................................... 228
Figure 3.79. Keyword cloud for Energy in Ukraine .................................................................... 228
Figure 3.80. Keyword cloud for Biotechnology in Ukraine ..................................................... 228
Figure 3.81. Keyword cloud for Transportation in Ukraine ................................................... 228
Figure 3.82. Keyword cloud for Mechanical engineering and heavy machinery in
Ukraine ............................................................................................................................................................ 228
Figure 3.83. Keyword cloud for Nanotechnology and materials in Ukraine .................. 228
Figure 4.1. Summary schema of the methodological steps leading to the selection and
definition of a list of EIST specialisation domains for each country and the potential
cooperation areas for the whole region and with international partners. ...................... 232
Figure 4.2. Keyword cloud for the S&T domain Agrifood in Armenia ............................... 237
Figure 4.3. Keyword cloud for the S&T domain Electric and electronic technologies in
Armenia .......................................................................................................................................................... 237
Figure 4.4. Keyword cloud for the S&T domain ICT and computer science in Armenia 237
Figure 4.5. Keyword cloud for the S&T domain Nanotechnology and materials in
Armenia .......................................................................................................................................................... 237
Figure 4.6. Keyword cloud for the S&T domain Agrifood in Azerbaijan .......................... 239
Figure 4.7. Keyword cloud for the S&T domain | [
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the study and
provided significant input through reviews and guidance. All au-
thors approved the final publication.
Competing interests. The contact author has declared that none of
the authors has any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims made in the text, pub-
lished maps, institutional affiliations, or any other geographical rep-
resentation in this paper. While Copernicus Publications makes ev-
ery effort to include appropriate place names, the final responsibility
lies with the authors.
Special issue statement. This article is part of the special issue
“Methodological innovations for the analysis and management of
compound risk and multi-risk, including climate-related and geo-
physical hazards (NHESS/ESD/ESSD/GC/HESS inter-journal SI)”.
It is not associated with a conference.
Review statement. This paper was edited by Robert Sakic Trogrlic
and reviewed by two anonymous referees.References
Becker, B. J.: Combining significance levels, in: The Handbook
of Research Synthesis, edited by: Cooper, H. and Hedges, L.
V ., Russell Sage Foundation, New York, 15–230, ISBN 978-
0871542267, 1994.
Borenstein, M., Hedges, L. V ., Higgins, J. P. T., and Roth-
stein, H. R.: Front Matter, in: Introduction to Meta-
Analysis, John Wiley & Sons, Ltd, Chichester, UK,
https://doi.org/10.1002/9780470743386, 2009.
Camus, P., Haigh, I. D., Nasr, A. A., Wahl, T., Darby, S. E.,
and Nicholls, R. J.: Regional analysis of multivariate com-
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21-2021-2021, 2021.
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Cover, T. M. and Hart, P. E.: Nearest Neighbor Pattern Classifica-
tion, IEEE T. Inform. Theory, IT-13, 21–27, 1967. | [
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- Potential for knowledge-based economic cooperation243
Ukraine
For Ukraine, the following concordances between
E&I and S&T domains could be identified:
■in the cluster ‘Food Processing and Manufac-
turing’, the S&T domain ‘Agrifood’ could be
aligned with the ‘Manufacture of food prod-
ucts’ E&I domain. The concordance was pro-
duced by both publications and patents;
■in the cluster ‘Wood Products’, a concordance
could be observed between the S&T domain
‘Nanotechnology and materials’ and the ‘Man-
ufacture of wood and wood products’ E&I
domain. The concordance is triggered by pub-
lications alone. In this case also, a review of
the semantic content of the ‘Nanotechnology
and materials’ S&T domain suggests that this
domain is primarily concerned with research
on alloys and inorganic materials in general;
therefore, it was decided to manually discard
this observed concordance;
■in the ‘Metalworking Technology’ cluster, a
concordance could be produced between the
‘Nanotechnology and materials’ S&T domain
and the respective E&I domain. The concord-
ance was again produced by publications only,
but in this case its soundness was indeed con-
firmed;
■for the cluster ‘Information Technology and
Analytical Instruments’, a concordance be-
tween the E&I domain ‘Manufacture of com-
puter, electronic and optical products’ and the
S&T domains ‘Electric and electronic tech-
nologies’, ‘Energy’, ‘Fundamental physics and
mathematics’, ‘ICT and computer science’
and ‘Optics and photonics’ was observed. All
of these concordances are produced by both
patents and publications. All of these S&T do-
mains seemed to indeed fit into the cluster
once their textual content was analysed;
■for the cluster ‘Production Technology and
Heavy Machinery’, a concordance between the
‘Manufacture of machinery and equipment’
E&I domain and the S&T domains ‘Agrifood’,
‘Energy’, ‘Fundamental physics and mathe-matics’, ‘Environmental sciences and indus-
tries’ and ‘Mechanical engineering and heavy
machinery’ could be observed. All of these
concordances were produced by both publi-
cations and patents and their soundness was
confirmed by reviewing the semantic content
of the S&T domains (for instance, for the spe-
cific case of Agrifood, the concordance was
brokered by the agricultural machinery niche),
except for the case of ‘Fundamental physics
and mathematics’, which was eventually man-
ually discarded from the mapping;
■for the cluster ‘Automotive’, a concordance
between the ‘Transportation’ S&T domain and
the ‘Manufacture of motor vehicles, trailers
and semi-trailers’ E&I domain could be pro-
duced. The alignment was, in this case, pro-
duced by both patents and publications and
was evidently deemed appropriate.
| [
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] | [] |
has
access to form. But language is used for communi-
cation about the speakers’ actual (physical, social,
and mental) world, and so the reasoning behind
producing meaningful responses must connect the
meanings of perceived inputs to information about
that world. This in turn means that for a human
or a machine to learn a language, they must solve
what Harnad (1990) calls the symbol grounding
problem . Harnad encapsulates this by pointing to
the impossibility for a non-speaker of Chinese to
learn the meanings of Chinese words from Chinese
dictionary definitions alone.
Our purpose here is to look more deeply into
why meaning can’t be learned from linguistic form
alone, even in the context of modern hardware and
techniques for scaling connectionist models to the
point where they can take in vast amounts of data.
We argue that, independently of whether passing
the Turing test would mean a system is intelligent,
a system that is trained only on form would fail
a sufficiently sensitive test, because it lacks the
ability to connect its utterances to the world.
4 The octopus test
In order to illustrate the challenges in attempting
to learn meaning from form alone, we propose a
concrete scenario. Say that A and B, both fluent
speakers of English, are independently stranded ontwo uninhabited islands. They soon discover that
previous visitors to these islands have left behind
telegraphs and that they can communicate with
each other via an underwater cable. A and B start
happily typing messages to each other.
Meanwhile, O, a hyper-intelligent deep-sea oc-
topus who is unable to visit or observe the two
islands, discovers a way to tap into the underwa-
ter cable and listen in on A and B’s conversations.
O knows nothing about English initially, but is
very good at detecting statistical patterns. Over
time, O learns to predict with great accuracy how
B will respond to each of A’s utterances. O also
observes that certain words tend to occur in similar
contexts, and perhaps learns to generalize across
lexical patterns by hypothesizing that they can be
used somewhat interchangeably. Nonetheless, O
has never observed these objects, and thus would
not be able to pick out the referent of a word when
presented with a set of (physical) alternatives.
At some point, O starts feeling lonely. He cuts
the underwater cable and inserts himself into the
conversation, by pretending to be B and | [
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] | [
{
"end": 368,
"label": "CITATION-REFEERENCE",
"start": 355
}
] |
milk products other than butter or
cheese X 0.3%
025Eggs, birds', and egg yolks, fresh, dried or otherwise
preserved, sweetened or not; egg albuminX 0.3%
041 Wheat (including spelt) and meslin, unmilled X 5.4% X 5.4%
043 Barley, unmilled X 1.5%
044 Maize (not including sweet corn), unmilled X 6.9%
046 Meal and flour of wheat and flour of meslin X 0.2%
048Cereal preparations and preparations of flour or starch of
fruits or vegetablesX 0.5% X 0.5%
054Vegetables, fresh, chilled, frozen or simply preserved
(including dried leguminous vegetables); roots, tubers and
other edible vegetable products, n.e.s., fresh or dried X 0.4%
056 Vegetables, roots and tubers, prepared or preserved, n.e.s. X 0.2%
057 Fruit and nuts (not including oil nuts), fresh or dried X 0.3%
061 Sugars, molasses and honey X 0.6%
062 Sugar confectionery X 0.3% X 0.3%
081 Feeding stuff for animals (not including unmilled cereals) X 2.2% X 2.2%
091 Margarine and shortening X 0.2% X 0.2%
098 Edible products and preparations, n.e.s. X 0.5% X 0.5%Table 2.20. Goods export specialisation for Ukraine
76
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%
1 Beverages and tobacco
111 Non-alcoholic beverages, n.e.s. X 0.1%
112 Alcoholic beverages X 0.4%
2 Crude materials, inedible, except fuels
222Oil-seeds and oleaginous fruits of a kind used for the
extraction of ‘soft’ fixed vegetable oils (excluding flours
and meals)X 3.6% X 3.6%
245 Fuel wood (excluding wood waste) and wood charcoal X 0.2%
248 Wood, simply worked, and railway sleepers of wood X 0.9% X 0.9%
278 Other crude minerals X 0.7%
281 Iron ore and concentrates X 5.7% X 5.7%
282Ferrous waste and scrap; remelting scrap ingots of iron or
steelX 0.3%
285 Aluminium ores and concentrates (including alumina) X 1.0% X 1.0%
287 Ores and concentrates of base metals, n.e.s. X 0.3%
3 Mineral fuels, lubricants and related materials
321 Coal, whether or not pulverized, but not agglomerated X 0.4%
4 Animal and vegetable oils, fats and waxes
421Fixed vegetable fats and oils, ‘soft’, crude, refined or
fractionatedX 8.3% X 8.3%
5 Chemicals and related products, n.e.s.
511Hydrocarbons, n.e.s., and their halogenated, sulphonated,
nitrated or nitrosated derivativesX 0.2%
522 Inorganic chemical elements, oxides and halogen salts X 0.7%
525 Radioactive and associated materials X 0.3%
533 Pigments, paints, varnishes and related materials X 0.3%
553Perfumery, cosmetic | [
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"025Eggs",
",",
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",",
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"yolks",
",",
"fresh",
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"\n",
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"X",
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"X",
"0.2",
"%",
" \n",
"048Cereal",
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"preparations",
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"flour",
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"starch",
"of",
"\n",
"fruits",
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"vegetablesX",
"0.5",
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"X",
"0.5",
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"\n",
"054Vegetables",
",",
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"chilled",
",",
"frozen",
"or",
"simply",
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"\n",
"(",
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"roots",
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",",
"n.e.s",
".",
",",
"fresh",
"or",
"dried",
" ",
"X",
"0.4",
"%",
"\n",
"056",
"Vegetables",
",",
"roots",
"and",
"tubers",
",",
"prepared",
"or",
"preserved",
",",
"n.e.s",
".",
" ",
"X",
"0.2",
"%",
"\n",
"057",
"Fruit",
"and",
"nuts",
"(",
"not",
"including",
"oil",
"nuts",
")",
",",
"fresh",
"or",
"dried",
" ",
"X",
"0.3",
"%",
"\n",
"061",
"Sugars",
",",
"molasses",
"and",
"honey",
" ",
"X",
"0.6",
"%",
"\n",
"062",
"Sugar",
"confectionery",
"X",
"0.3",
"%",
"X",
"0.3",
"%",
"\n",
"081",
"Feeding",
"stuff",
"for",
"animals",
"(",
"not",
"including",
"unmilled",
"cereals",
")",
"X",
"2.2",
"%",
"X",
"2.2",
"%",
"\n",
"091",
"Margarine",
"and",
"shortening",
"X",
"0.2",
"%",
"X",
"0.2",
"%",
"\n",
"098",
"Edible",
"products",
"and",
"preparations",
",",
"n.e.s",
".",
"X",
"0.5",
"%",
"X",
"0.5%Table",
"2.20",
".",
"Goods",
"export",
"specialisation",
"for",
"Ukraine",
"\n",
"76",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"SITC",
"Goods",
"nameCurrent",
"\n",
"strength%",
"share",
"\n",
"of",
"\n",
"exportsEmerging",
"\n",
"strength%",
"share",
"\n",
"of",
"\n",
"exports",
"\n",
"51",
"69.6",
"%",
"52",
"47.0",
"%",
"\n",
"1",
"Beverages",
"and",
"tobacco",
" \n",
"111",
"Non",
"-",
"alcoholic",
"beverages",
",",
"n.e.s",
".",
" ",
"X",
"0.1",
"%",
"\n",
"112",
"Alcoholic",
"beverages",
" ",
"X",
"0.4",
"%",
"\n",
"2",
"Crude",
"materials",
",",
"inedible",
",",
"except",
"fuels",
" \n",
"222Oil",
"-",
"seeds",
"and",
"oleaginous",
"fruits",
"of",
"a",
"kind",
"used",
"for",
"the",
"\n",
"extraction",
"of",
"‘",
"soft",
"’",
"fixed",
"vegetable",
"oils",
"(",
"excluding",
"flours",
"\n",
"and",
"meals)X",
"3.6",
"%",
"X",
"3.6",
"%",
"\n",
"245",
"Fuel",
"wood",
"(",
"excluding",
"wood",
"waste",
")",
"and",
"wood",
"charcoal",
"X",
"0.2",
"%",
" \n",
"248",
"Wood",
",",
"simply",
"worked",
",",
"and",
"railway",
"sleepers",
"of",
"wood",
"X",
"0.9",
"%",
"X",
"0.9",
"%",
"\n",
"278",
"Other",
"crude",
"minerals",
"X",
"0.7",
"%",
" \n",
"281",
"Iron",
"ore",
"and",
"concentrates",
"X",
"5.7",
"%",
"X",
"5.7",
"%",
"\n",
"282Ferrous",
"waste",
"and",
"scrap",
";",
"remelting",
"scrap",
"ingots",
"of",
"iron",
"or",
"\n",
"steelX",
"0.3",
"%",
" \n",
"285",
"Aluminium",
"ores",
"and",
"concentrates",
"(",
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"X",
"1.0",
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",",
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"0.3",
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"3",
"Mineral",
"fuels",
",",
"lubricants",
"and",
"related",
"materials",
" \n",
"321",
"Coal",
",",
"whether",
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"not",
"pulverized",
",",
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"not",
"agglomerated",
"X",
"0.4",
"%",
" \n",
"4",
"Animal",
"and",
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"oils",
",",
"fats",
"and",
"waxes",
" \n",
"421Fixed",
"vegetable",
"fats",
"and",
"oils",
",",
"‘",
"soft",
"’",
",",
"crude",
",",
"refined",
"or",
"\n",
"fractionatedX",
"8.3",
"%",
"X",
"8.3",
"%",
"\n",
"5",
"Chemicals",
"and",
"related",
"products",
",",
"n.e.s",
".",
" \n",
"511Hydrocarbons",
",",
"n.e.s",
".",
",",
"and",
"their",
"halogenated",
",",
"sulphonated",
",",
"\n",
"nitrated",
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"0.2",
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" \n",
"522",
"Inorganic",
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"elements",
",",
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"and",
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"salts",
"X",
"0.7",
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" \n",
"525",
"Radioactive",
"and",
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"X",
"0.3",
"%",
" \n",
"533",
"Pigments",
",",
"paints",
",",
"varnishes",
"and",
"related",
"materials",
" ",
"X",
"0.3",
"%",
"\n",
"553Perfumery",
",",
"cosmetic"
] | [] |
in specific S&T domains or
related sets of domains.
The presence of public actors depends on the
public-sector structure of each country, since hos-
pitals and medical facilities, ministries and minis-
terial institutes and state companies (notably in
Azerbaijan and Ukraine) can be observed with a
differing presence depending on the country.
With regard to private for-profit companies, their
presence in the international S&T data sources is,
for the most part, rather small. In all countries,
there is a relevant presence of scientific, applied
research and technical companies, as well as ICT
companies. Beyond those, some clear national
champions and small and medium highly special-
ised companies in specific sectors can be found.
EaP regional collaboration
In publications, Armenia and Georgia present con-
sistent scientific collaboration with one another.
Ukraine also presents a high level of collaboration
with these two countries. Conversely, Azerbaijan
and Moldova are currently minor scientific part-
ners of the rest of the EaP countries, only present-
Figure III. Example of one such interactive visualisation tool, depicting the main analysed actors and collaboration
networks in the Eastern Partnership
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation23
ing a moderate collaboration with Ukraine. It must
be noted, however, that the scientific collaboration
between EaP countries is mainly driven by very in-
tense collaboration in physics (within the Funda-
mental physics and mathematics domain), which
concentrates by far the largest number of co-pub-
lications due to the countries’ co-participation in
large, high-energy and astronomy endeavours. At
a great distance, Health and wellbeing; Govern-
ance, culture, education and the economy; Nano-
technology and materials; Optics and photonics;
ICT and computer science; and Environmental
sciences and industries present some regional sci-
entific collaboration.
In EC-funded projects, Ukraine collaborates most
intensively with Georgia and Moldova. Armenia
and Moldova also have a high level of collabo-
ration. Azerbaijan remains slightly more isolated,
also due to the lower number of projects overall.
This collaboration intensity is certainly a positive
result of the EaP countries’ participation in H2020,
particularly since a significant number of these
collaborations are concentrated in the domain
Governance, culture, education and the economy.
Lastly, some domains which present a strong bi-
lateral collaboration between EaP countries in scientific publications are ICT and computer sci-
ence, Biotechnology, Fundamental physics and
mathematics and Nanotechnology and materials.
As shown in the figure below, the geometries of
the scientific collaboration | [
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",",
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"\n",
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",",
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"-",
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"and",
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".",
"At",
"\n",
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",",
"Health",
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";",
"Govern-",
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"Optics",
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";",
"\n",
"ICT",
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";",
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"Environmental",
"\n",
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"and",
"industries",
"present",
"some",
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"sci-",
"\n",
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"collaboration",
".",
"\n",
"In",
"EC",
"-",
"funded",
"projects",
",",
"Ukraine",
"collaborates",
"most",
"\n",
"intensively",
"with",
"Georgia",
"and",
"Moldova",
".",
"Armenia",
"\n",
"and",
"Moldova",
"also",
"have",
"a",
"high",
"level",
"of",
"collabo-",
"\n",
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".",
"Azerbaijan",
"remains",
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"more",
"isolated",
",",
"\n",
"also",
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"to",
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"lower",
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"overall",
".",
"\n",
"This",
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"is",
"certainly",
"a",
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"\n",
"result",
"of",
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"EaP",
"countries",
"’",
"participation",
"in",
"H2020",
",",
"\n",
"particularly",
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"a",
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"number",
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"these",
"\n",
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"the",
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"\n",
"Governance",
",",
"culture",
",",
"education",
"and",
"the",
"economy",
".",
"\n",
"Lastly",
",",
"some",
"domains",
"which",
"present",
"a",
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"bi-",
"\n",
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"EaP",
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"are",
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"sci-",
"\n",
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",",
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",",
"Fundamental",
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"and",
"\n",
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"and",
"Nanotechnology",
"and",
"materials",
".",
"\n",
"As",
"shown",
"in",
"the",
"figure",
"below",
",",
"the",
"geometries",
"of",
"\n",
"the",
"scientific",
"collaboration"
] | [] |
Eastern Partnership region ... 158
Table 3.7. The Scopus subject fields that appear more frequently within each domain
in comparison with the average publications ............................................................................... 159
Table 3.8. Top IPC symbols per number of records associated with the patents classified
within each domain, at subclass level ............................................................................................. 162
Table 3.9. Number of records per S&T specialisation domain in Armenia ..................... 175
Table 3.10. Temporal evolution of Armenia’s S&T domains ................................................. 178
Table 3.11. Number of records per S&T specialisation domain in Azerbaijan ............. 179
Table 3.12. Temporal evolution of Azerbaijan’s S&T domains ............................................ 182
Table 3.13. Number of records per S&T specialisation domain in Georgia ................... 183
Table 3.14. Temporal evolution of Georgia’s S&T domains .................................................. 186
Table 3.15. Number of records per S&T specialisation domain in Moldova .................. 187
Table 3.16. Temporal evolution of Moldova’s S&T domains ................................................. 190
Table 3.17. Number of records per S&T specialisation domain in Ukraine ................... 191
Table 3.18. Temporal evolution of Ukraine’s S&T domains .................................................. 194
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation267
Table 3.19. Top private actors in Armenia by number of records, across all domains . 198
Table 3.20. Top private actors in Armenia by number of records, across all domains . 198
Table 3.21. Top public actors in Azerbaijan by number of records, across all
domains ............................................................................................................................................ 200
Table 3.22. Top private actors in Azerbaijan by number of records, across all
domains .................................................................................................................................................. 200
Table 3.23. Top public actors in Georgia by number of records, across all domains .... 202
Table 3.24. Top private actors in Georgia by number of records, across all domains .. 202
Table 3.25. Top public actors in Moldova by number of records, across all domains ... 204
Table 3.26. Top private actors in Moldova by number of records, across all domains 204
Table 3.27. Top public actors in Ukraine by number of records, across all domains ..... 206
Table 3.28. Top private actors in Ukraine by number of records, across all domains .. 206
Table 3.29. Selected S&T specialisation domains in Armenia ............................................. 219
Table 3.30. Selected S&T specialisation domains in Azerbaijan ......................................... 221
Table 3.31. Selected S&T specialisation domains in Georgia .............................................. 223
Table 3.32. Selected S&T specialisation domains in Moldova ............................................. 225
Table 3.33. Selected S&T specialisation domains in Ukraine ............................................... 227
Table 4.1. Means that could be exploited to derive concordances between S&T and E&I
domains. | [
"Eastern",
"Partnership",
"region",
"...",
"158",
"\n",
"Table",
"3.7",
".",
"The",
"Scopus",
"subject",
"fields",
"that",
"appear",
"more",
"frequently",
"within",
"each",
"domain",
"\n",
"in",
"comparison",
"with",
"the",
"average",
"publications",
"...............................................................................",
"159",
"\n",
"Table",
"3.8",
".",
"Top",
"IPC",
"symbols",
"per",
"number",
"of",
"records",
"associated",
"with",
"the",
"patents",
"classified",
"\n",
"within",
"each",
"domain",
",",
"at",
"subclass",
"level",
".............................................................................................",
"162",
"\n",
"Table",
"3.9",
".",
"Number",
"of",
"records",
"per",
"S&T",
"specialisation",
"domain",
"in",
"Armenia",
".....................",
"175",
"\n",
"Table",
"3.10",
".",
"Temporal",
"evolution",
"of",
"Armenia",
"’s",
"S&T",
"domains",
".................................................",
"178",
"\n",
"Table",
"3.11",
".",
"Number",
"of",
"records",
"per",
"S&T",
"specialisation",
"domain",
"in",
"Azerbaijan",
".............",
"179",
"\n",
"Table",
"3.12",
".",
"Temporal",
"evolution",
"of",
"Azerbaijan",
"’s",
"S&T",
"domains",
"............................................",
"182",
"\n",
"Table",
"3.13",
".",
"Number",
"of",
"records",
"per",
"S&T",
"specialisation",
"domain",
"in",
"Georgia",
"...................",
"183",
"\n",
"Table",
"3.14",
".",
"Temporal",
"evolution",
"of",
"Georgia",
"’s",
"S&T",
"domains",
"..................................................",
"186",
"\n",
"Table",
"3.15",
".",
"Number",
"of",
"records",
"per",
"S&T",
"specialisation",
"domain",
"in",
"Moldova",
"..................",
"187",
"\n",
"Table",
"3.16",
".",
"Temporal",
"evolution",
"of",
"Moldova",
"’s",
"S&T",
"domains",
".................................................",
"190",
"\n",
"Table",
"3.17",
".",
"Number",
"of",
"records",
"per",
"S&T",
"specialisation",
"domain",
"in",
"Ukraine",
"...................",
"191",
"\n",
"Table",
"3.18",
".",
"Temporal",
"evolution",
"of",
"Ukraine",
"’s",
"S&T",
"domains",
"..................................................",
"194",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation267",
"\n",
"Table",
"3.19",
".",
"Top",
"private",
"actors",
"in",
"Armenia",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
".",
"198",
"\n",
"Table",
"3.20",
".",
"Top",
"private",
"actors",
"in",
"Armenia",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
".",
"198",
"\n",
"Table",
"3.21",
".",
"Top",
"public",
"actors",
"in",
"Azerbaijan",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"\n",
"domains",
"............................................................................................................................................",
"200",
"\n",
"Table",
"3.22",
".",
"Top",
"private",
"actors",
"in",
"Azerbaijan",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"\n",
"domains",
"..................................................................................................................................................",
"200",
"\n",
"Table",
"3.23",
".",
"Top",
"public",
"actors",
"in",
"Georgia",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
"....",
"202",
"\n",
"Table",
"3.24",
".",
"Top",
"private",
"actors",
"in",
"Georgia",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
"..",
"202",
"\n",
"Table",
"3.25",
".",
"Top",
"public",
"actors",
"in",
"Moldova",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
"...",
"204",
"\n",
"Table",
"3.26",
".",
"Top",
"private",
"actors",
"in",
"Moldova",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
"204",
"\n",
"Table",
"3.27",
".",
"Top",
"public",
"actors",
"in",
"Ukraine",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
".....",
"206",
"\n",
"Table",
"3.28",
".",
"Top",
"private",
"actors",
"in",
"Ukraine",
"by",
"number",
"of",
"records",
",",
"across",
"all",
"domains",
"..",
"206",
"\n",
"Table",
"3.29",
".",
"Selected",
"S&T",
"specialisation",
"domains",
"in",
"Armenia",
".............................................",
"219",
"\n",
"Table",
"3.30",
".",
"Selected",
"S&T",
"specialisation",
"domains",
"in",
"Azerbaijan",
".........................................",
"221",
"\n",
"Table",
"3.31",
".",
"Selected",
"S&T",
"specialisation",
"domains",
"in",
"Georgia",
"..............................................",
"223",
"\n",
"Table",
"3.32",
".",
"Selected",
"S&T",
"specialisation",
"domains",
"in",
"Moldova",
".............................................",
"225",
"\n",
"Table",
"3.33",
".",
"Selected",
"S&T",
"specialisation",
"domains",
"in",
"Ukraine",
"...............................................",
"227",
"\n",
"Table",
"4.1",
".",
"Means",
"that",
"could",
"be",
"exploited",
"to",
"derive",
"concordances",
"between",
"S&T",
"and",
"E&I",
"\n",
"domains",
".",
" "
] | [] |
C, (d) D, (e) rated capacity, manufacturer, size, and (f) average charging voltage
profile.
The selected portable NiMH batteries exhibit different internal resistances ranging
from 2 mΩ up to 130 mΩ (see Figure 4). Increasing the size of the batteries tends to corre-
late 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
Figure 3. NiMH battery charge profile at 0.1 C and 16 h of different battery manufacturers of ( a) AAA,
(b) AA, ( c) C, ( d) D, ( e) rated capacity, manufacturer, size, and ( f) average charging voltage profile.
The difference in resistance and specific energy of batteries of the same size can also be
related to the internal construction of the batteries. Figure 5 shows the X-ray tomography
scan of the cross-sectional view of two AAA NiMH batteries with different rated capacities
(see Figure 5a NiMH 900 mAh and Figure 5b NiMH 1000 mAh). The reconstructed CT data
are evaluated by the VGSTUDIO MAX 3.4 software.
There are different design approaches for NiMH batteries depending on the manu-
facturer and application. In the case of the AAA NiMH of 900 mAh (see Figure 5a), the
thickness of the electrode ensemble is small (0.1–0.2 mm); this allows for a high number of
windings inside the battery casing (23 turns). The high number of windings is considered a
method to increase both cell mechanical stability and cycle life [ 34]. In the other case, the
AAA NiMH of 1000 mAh (see Figure 5b), the ensemble of separator and electrodes has a
thickness of 0.5–0.7 mm and is wound four times. The electrodes with higher thickness in
this case are of the felt structure type, with an increase in surface area that could increase
the capacity of a battery [35].Batteries 2025 ,11, 30 8 of 20
Batteries 2025 ,11, 0 8 of | [
"C",
",",
" ",
"(",
"d",
")",
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] | [] |
allowances for
EIIs if implementation is ineffective.
To capitalise on the decarbonisation push, Europe should refocus its support for clean tech manufac -
turing, focusing on technologies where it either has a lead or where there is a strategic case for developing
domestic capacity [see the chapter on clean technologies] . The next Multiannual Financial Framework (MFF)
should streamline the number of funds devoted to the manufacturing of clean tech, concentrating on technologies
where the EU has an advantage and strong potential for growth – such as the opportunity presented by batteries.
Support under the EU budget should offer companies a single point of entry with a uniform application procedure
and awarding conditions, and should feature support for both capital expenditure and operational expenditure. To
attract more private sector funding to clean tech, and especially towards innovative companies, dedicated financing
schemes should be developed employing the same financing strategies discussed in chapter 2. At the national
level, to ensure predictable demand for the EU clean tech industry and to offset trade distorting policies abroad,
the report recommends introducing an explicit minimum quota for the local production of selected products and
components in public procurement and in CfD auctions and other forms of local production offtake. This quota
should be combined with criteria established at EU level for orienting local production to the most innovative and
sustainable solutions. The approach could be supported by the creation of joint ventures or cooperation agreements
51THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3for knowledge transfer and sharing between EU and non-EU companies. For “infant industries”, it is recommended
that Member States plan upcoming auctions and public procurement procedures to act as a “launch customer” for
new technologies.
Trade policy will be fundamental to combine decarbonisation with competitiveness, securing supply chains,
growing new markets and offsetting state-sponsored competition . As supply chains for some clean technolo -
gies are highly concentrated, the EU has win-win opportunities to strategically partner with other regions in targeted
steps of clean technology supply chains. Like-minded neighbouring regions with access to low-cost renewable
energy sources and raw materials could help Europe accomplish its energy and climate goals in an affordable
manner while widening the diversification of supplies. At the same time, the EU should leverage its strong position
in clean tech and pursue opportunities to invest in other countries to widen the deployment market for technologies
the | [
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] | [] |
The
laboratory is in Petten, NL, and for this research, we used the following equipment:
• Three Maccor battery cyclers with the following capabilities:
➢ Series 4000 with 32 channels rated 20 A @ −2/+8 V , and 32 T-Type thermocou-
ple inputs.
➢ Series 4000 M with 48 channels rated 5 A @ −2/+8 V , and 32 T-Type thermo-
couple inputs.
➢ Series 4000 M with 16 channels rated 25 A @ 0–18 V , and 16 T-Type thermocou-
ple inputs.
• Three BIA MTH 4.46 units (see Figure 1a), each composed of four independent and
identical temperature chambers with a temperature control range between −40◦C
and 85◦C. Each chamber has a volume of 46 l. The temperature deviation in the centerBatteries 2025 ,11, 30 4 of 20
of the working space is ±0.5◦C, the temperature homogeneity in space relative to the
set value is ±1.5◦C.
• Two independent and identical Vötsch VCS3 7060-5 climate chambers (see Figure 1b),
with a temperature control range between −55◦C and 155◦C and a humidity range
of 10% to 98% RH. Each chamber has a volume of 600 l. The temperature deviation in
the center of working space is ±0.5◦C.
• Hioki BT3562 battery testers for initial voltage and internal resistance measurements
(3 mΩto 3000 Ω/60 V DC).
• Nanotom S X-ray computed tomography system (GE Sensing & Inspection Technolo-
gies, phoenix X-ray, Wunstorf, Germany). X-ray tube with a maximum output power
of 15 W and a maximum voltage of 180 kV , in combination with a 2D detector with a
dynamic range of 850:1 which consists of 2304 ×2304 pixels
Batteries 2025, 11, x FOR PEER REVIEW 4 of 21
Duracell AAA 900 NiMH
Tronic AAA 1000 NiMH
GP AAA 650 NiMH
Duracell AAA 750 NiMH
Duracell C 3000 NiMH
GP C 3000 NiMH
Energizer C 2500 NiMH
Energizer D 2500 NiMH
Duracell D 3000 NiMH
Phillips D 3000 NiMH
Energizer 9V 175 NiMH
Agfaphoto AA 2300 NiMH
Ansmann C 4500 NiMH
RS pro D 10000 NiMH
Agfaphoto AAA 900 NiMH
Varta C 3000 NiMH
GP D 5700 NiMH
Duracell 9V 170 NiMH
Energizer AAA 700 NiMH
Energizer AA N/A Alkaline primary
Duracell 9V N/A Alkaline primary
Varta AAA N/A Alkaline primary
Duracell Plus C N/A Alkaline primary
Energizer D N/A Alkaline primary
The testing of the batteries is performed at the BESTEST Lab (see Figure 1). The la-
boratory is | [
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and chemical engineering
(492). The first one accounts for almost one quar-
ter of the total number of records (23%).
Publications represent the biggest share of re-
cords in almost all domains, ranging from 60% to
99% of the total records in most cases, as shown
inFigure 3.33. Electric and electronic technologies
(56%), ICT and computer science (55%), Biotech-
nology (50%), Agrifood (40%) and Mechanical
engineering and heavy machinery (21%) are the
domains which have a high share of patents.
Due to their nature, as with other countries, EC
projects in Moldova are highly concentrated in the
domain of Governance, culture, education and the
economy. ICT and computer science and Environ-
mental sciences and industries also present a rel-atively high number of projects, but this is quite
frequent given the orientation of the funding pro-
grammes.
The growth rate of publications in recent years, in
terms of the compound annual growth rate, is also
shown. Out of the top 5 domains in terms of crit-
ical mass, Nanotechnology and materials (-4.8%)
shows a decreasing trend. This is particularly note-
worthy, as it signals that the number of publications
in subsequent years may continue to decrease and
this domain may become less relevant.
Finally, as Figure 3.34 and Figure 3.35 show, Mol-
dova’s publications are highly specialised in Elec-
tric and electronic technologies (with an SI of 2.5),
as well as Mechanical engineering and heavy ma-
chinery (1.6), Nanotechnology and materials (1.6),
Energy (1.4) and Governance, culture, education
and the economy (1.3), amongst others.
Moldova presents few domains with a high-
er-than-average citation impact vs the EaP region,
notably Nanotechnology and materials (with an
NCI of 1.5), but also Health and wellbeing (1.1)
and Chemistry and chemical engineering (1.1).
Thus, both Nanotechnology and materials and
Publications
(critical mass | CAGR)PatentsEC
projectsTotal
Nanotechnology and materials 1 168 -4.8% 127 8 1 303
Health and wellbeing 622 22.2% 284 10 916
Mechanical engineering and heavy
machinery160 15.2% 600 1 761
Governance, culture, education and the
economy507 13.2% 3 62 572
Chemistry and chemical engineering 416 2.5% 75 1 492
Fundamental physics and mathematics 449 -3.7% 18 1 468
Electric and electronic technologies 219 13.8% 175 1 395
Biotechnology 192 6.2% 190 3 385
Agrifood 136 -2.0% 206 7 349
Environmental sciences and industries 256 6.3% 12 15 283
Energy 132 12.1% 66 6 204
ICT and computer science 102 -10.5% 77 19 198
| [
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] | [] |
international
frameworks, such as the Hyogo Framework for Action (UN-
ISDR, 2005) and the Sendai Framework for Disaster Risk
Reduction 2015–2030, have endorsed the multi-hazard ap-
proach for disaster risk reduction. Additionally, a “needs and
gaps” analysis conducted as part of the preparation of the
European Commission Staff Working Document “Overview
of natural and man-made disaster risks the European Union
may face” identified a significant knowledge and data gap in
multi-hazard assessments (EUR-Lex, 2014; European Com-
mission, 2017, 2020).
It is now well recognized in the research community
that for an adequate understanding of disaster risk potential
within a region, it is essential to move from a single-hazard to
a multi-hazard approach (Marzocchi et al., 2009; Kappes et
al., 2012a; Gill and Malamud, 2014; Tilloy et al., 2019; Ward
et al., 2022). Multi-hazard interrelations can lead to a com-
bined impact that is different from the sum of each hazards’
impacts taken separately. To assess the potential hazards and
risks to which a region is exposed, some studies have em-
ployed an approach that combines independent analysis of
individual hazards (Granger et al., 1999; van Westen et al.,
Published by Copernicus Publications on behalf of the European Geosciences Union.288 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level
2002; Greiving et al., 2006; Grünthal et al., 2006; Marzoc-
chi et al., 2012; Forzieri et al., 2016). This approach, often
referred to as “multi-layer hazards”, superposes natural haz-
ards over a region. Conversely, other studies explicitly con-
sidered hazard interrelations (Tarvainen et al., 2006; Han et
al., 2007; De Pippo et al., 2008; Kappes et al., 2010, 2012b;
van Westen et al., 2014; Liu et al., 2016; Sadegh et al., 2018;
Gill et al., 2020; Claassen et al., 2023; Lee et al., 2024). How-
ever, these assessments are often based on case studies within
limited spatial extension, addressing a limited number of per-
ils or hazards and a limited number of sectors (Ciurean et al.,
2018; Tilloy et al., 2019).
In this context, our study aligns with the first definition of
multi-layer hazards, as we examine the combined exposure
levels of several natural hazards over a region, recognizing
that hazard interrelations can result in an impact distinct from
the sum of individual hazard impacts. This is exemplified by
events such as the Portugal wildfires and flash floods of Oc-
tober 2017, where both hazards occurred in | [
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assessed following the procedure in standard IEC 61951-
2, clause 7.10. Prior to testing, the NiMH batteries are stored for 8 months in their original
package in a temperature chamber at 21 °C ± 2 °C before doing the following test:
1. The batteries are discharged at a 0.2 C rate until they reach a cut-off voltage of 1 V.
2. The batteries are then charged for 16 h at a 0.1 C rate.
Figure 8. Cont .Batteries 2025 ,11, 30 12 of 20
Batteries 2025, 11, x FOR PEER REVIEW 13 of 21
Figure 8. NiMH charge (capacity) retention analysis according to IEC 61951-2 with the pre-charged
test for (a) AAA Duracell, (b) AA Agfaphoto, (c) C Ansmann, (d) D Ansmann, and (e) 9V Energizer
batteries.
5. Charge (Capacity) Recovery of Portable NiMH Batteries
The charge recovery test is assessed following the procedure in standard IEC 61951-
2, clause 7.10. Prior to testing, the NiMH batteries are stored for 8 months in their original
package in a temperature chamber at 21 °C ± 2 °C before doing the following test:
1. The batteries are discharged at a 0.2 C rate until they reach a cut-off voltage of 1 V.
2. The batteries are then charged for 16 h at a 0.1 C rate.
Figure 8. NiMH charge (capacity) retention analysis according to IEC 61951-2 with the pre-charged
test for ( a) AAA Duracell, ( b) AA Agfaphoto, ( c) C Ansmann, ( d) D Ansmann, and ( e) 9V Ener-
gizer batteries.
5. Charge (Capacity) Recovery of Portable NiMH Batteries
The charge recovery test is assessed following the procedure in standard IEC 61951-2,
clause 7.10. Prior to testing, the NiMH batteries are stored for 8 months in their original
package in a temperature chamber at 21◦C±2◦C before doing the following test:
1. The batteries are discharged at a 0.2 C rate until they reach a cut-off voltage of 1 V .
2. The batteries are then charged for 16 h at a 0.1 C rate.
3. Finally, the batteries are discharged again at a 0.2 C rate, and the duration of this
discharge was measured.
A battery is considered to pass the test if the discharge is longer than 4 h before
reaching the cut-off voltage, i.e., if they have at least 80% of their initial capacity left
after storage.
Figure 9a,b show the discharge profiles of AAA and | [
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E&I specialisations for Azerbaijan
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specialisations:
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cluster and specialised performance in related
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20) based on an economic specialisation, and
specialised performance in related patents;
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E&I specialisations for Georgia
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E&I specialisations for Moldova
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specialised performance in related goods ex-
ports;
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the
linguistic form which is very useful in applications.
We have offered some thoughts on how to main-
tain a healthy, but not exaggerated, optimism with
respect to research that builds upon these LMs. In
particular, this paper can be seen as a call for pre-
cise language use when talking about the success
of current models and for humility in dealing with
natural language. With this we hope to encourage
a top-down perspective on our field which we think
will help us select the right hill to climb towards
human-analogous NLU.
Acknowledgments. This paper benefitted from
many inspiring and often spirited discussions.
Without implying any agreement with the con-
tents as presented, we thank Sam Bowman, Vera
Demberg, Lucia Donatelli, Jason Eisner, Jonas
Groschwitz, Kristen Howell, Angie McMillan-
Major, Joakim Nivre, Stephan Oepen, Ellie Pavlick,
Benjamin Roth, Dan Roth, Asad Sayeed, Hinrich
Sch¨utze, Nina Tahmasebi, and Olga Zamaraeva.
This paper originated in a Twitter mega-thread that
was neatly summarized by Thomas Wolf (2018).
We also thank the ACL reviewers and the partic-
ipants of the Toulouse Workshop on Formal and
Distributional Semantics (2015) and *SEM 2016
for their insightful and constructive thoughts.5194References
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer
Lavi, and Yoav Goldberg. 2017. Fine-grained anal-
ysis of sentence embeddings using auxiliary predic-
tion tasks. In Proceedings of ICLR .
Dare A. Baldwin. 1995. Understanding the link be-
tween joint attention and language. In Chris Moore
and Philip J. Dunham, editors, Joint Attention: Its
Origins and Role in Development , pages 131–158.
Psychology Press.
Andrei Barbu, David Mayo, Julian Alverio, William
Luo, Christopher Wang, Dan Gutfreund, Josh Tenen-
baum, and Boris Katz. 2019. ObjectNet: A large-
scale bias-controlled dataset for pushing the lim-
its of object recognition models. In H. Wallach,
H. Larochelle, A. Beygelzimer, F. d’Alch ´e Buc,
E. Fox, and R. Garnett, editors, Advances in Neu-
ral Information Processing Systems 32 , pages 9453–
9463. Curran Associates, Inc.
Marco Baroni, Raffaella Bernardi, Roberto Zamparelli,
et al. 2014. Frege in space: A program for composi-
tional distributional semantics. Linguistic Issues in
Language Technology , 9(6):5–110.
Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob
Andreas, Yoshua Bengio, Joyce Chai, Mirella Lap-
ata, Angeliki Lazaridou, Jonathan May, Aleksandr
Nisnevich, Nicolas Pinto, and Joseph Turian. 2020.
Experience grounds language. ArXiv preprint.
Ned Block. 1981. Psychologism and behaviorism. The
Philosophical Review , 90(1):5–43.
Samuel R. Bowman, Gabor Angeli, Christopher Potts,
and Christopher D. Manning. 2015. A large anno-
tated corpus for learning natural language inference.
InProceedings of the 2015 Conference on Empiri-
cal Methods in Natural Language Processing , pages
632–642, Lisbon, Portugal. Association for Compu-
tational Linguistics. | [
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until reaching a voltage of ~1.25 V after approximately 1 h. This is fol-
lowed by a plateau where the discharge voltage is relatively stable. After 4.5–5.5 h, the
voltage drop accelerates again until the cut-off voltage of 1 V is reached. The batteries have
different final cut-off times (from 4.5 h to 5.8 h) depending on the manufacturer and size.
It should be noted that the capacity declared by the manufacturer and not the actual ca-
pacity was used to determine the C rate (Figure 4); this can influence discharge duration.
Figure 5. X-ray tomography scan cross-sectional view of AAA NiMH battery: ( a) GP 900 mAh and
(b) Tronic 1000 mAh rated capacity.Batteries 2025 ,11, 30 9 of 20
3.2. Discharge Analysis of Portable NiMH Batteries
The discharge is performed by following the procedure in standard IEC 61951-2.
Figure 6 shows the discharge profile of different sizes of portable NiMH batteries. The
batteries are discharged at a rate of 0.2 C until a cut-off voltage of 1 V (no resting period
between charging and discharging is used). The discharge voltage profiles of the different
NiMH batteries with sizes AAA, AA, C, and D are similar (see Figure 6a–d). The starting
discharge voltage is between 1.38 V and 1.45 V , depending on the brand and battery size.
The voltages drop until reaching a voltage of ~1.25 V after approximately 1 h. This is
followed by a plateau where the discharge voltage is relatively stable. After 4.5–5.5 h, the
voltage drop accelerates again until the cut-off voltage of 1 V is reached. The batteries have
different final cut-off times (from 4.5 h to 5.8 h) depending on the manufacturer and size. It
should be noted that the capacity declared by the manufacturer and not the actual capacity
was used to determine the C rate (Figure 4); this can influence discharge duration.
Batteries 2025, 11, x FOR PEER REVIEW 10 of 21
Figure 6. NiMH battery discharge profiles at 0.2 C and a cut-off of 1.0 V for different battery manu-
facturers of (a) AA, (b) AAA, (c) C, (d) D, and (e) average SOC.
Figure 6e shows the average voltage profile as a function of SOC. The starting aver-
age voltage at 100% SOC is 1.45 V. During the discharge, the voltage decreases quickly to
1.28 V, which corresponds to 80% SOC. Then the voltage drops more slowly while | [
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] | [] |
of the S&T specialisa-
tions
Moldova presents a rather diversified S&T pan-
orama.
Its most highlighted S&T specialisation domains
are the following:
■Health and wellbeing presents a nota-
ble critical mass, specialisation and citation
impact in publications, as well as a relevant
number of patents and EC projects. The ‘Nico-
lae Testemitanu’ State University of Medicine
and Pharmacy is the leading academic institu-
tion in the domain, accounting for more than
one third of the country’s total produced re-
cords on this topic;
■Nanotechnology and materials presents a
notable critical mass, specialisation and cita-
tion impact in publications, as well as a rel-
MOLDOVA Critical mass Specialisation Excellence Summary
S&T domain Pubs. Pat. Pubs. Pat. NCI*EC
projects*Total
Agrifood 2
Biotechnology 2
Chemistry and chemical
engineering2
Electric and electronic
technologies4
Energy 1
Environmental sciences and
industries2
Fundamental physics and
mathematics1
Governance, culture, education
and the economy3
Health and wellbeing 5
ICT and computer science 1
Mechanical engineering and
heavy machinery3
Nanotechnology and materials 4
Optics and photonics 0
*NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable 3.32. Selected S&T specialisation domains in Moldovaevant number of EC projects. In this country,
the domain correlates highly with Optics and
photonics, although produced records in both
domains have been declining significantly in
recent years;
■Electric and electronic technologies pre-
sents a notable critical mass in patents, as
well as a high specialisation in both publica-
tions and patents. Its publication citation im-
pact is one of the highest in the country, but
still below 1.0;
■Mechanical engineering and heavy ma-
chinery presents a notable critical mass in
patents, as well as a high specialisation in both
publications and patents. In Moldova, this do-
main presents a very high co-occurrence with
Energy.
The following clouds present the most relevant
keywords for these highlighted S&T domains.
Figure 3.74. Keyword cloud for Health and wellbeing in Moldova
Figure 3.76. Keyword cloud for Electric and electronic
technologies in Moldova
Figure 3.75. Keyword cloud for Nanotechnology and materials
in Moldova
Figure 3.77. Keyword cloud for Mechanical engineering and
heavy machinery in Moldova
226 Part 3 Analysis of scientific and technological potential
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation227
UKRAINE Critical mass Specialisation Excellence Summary
S&T domain Pubs. Pat. Pubs. Pat. NCI*EC
projects*Total
Agrifood 0
Biotechnology 3
Chemistry and chemical
engineering0
Electric and electronic
technologies2
Energy 4
Environmental sciences and
industries1
| [
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] | [] |
versions chooses his/her preferred
and least preferred alternative l
l1C⋯CLto maximize his/her utility
under the presence and absence of the ‘made for’ claim for each product
k∃K, in any of the six choice situation t∃T.
Each version k has a design vector of discrete variables xj indicating
the different attribute levels (e.g., country-specific list of ingredients and
nutritional facts, brand) and continuous variables (e.g., price), yielding a
multinomial logit model (McFadden 1973). The unobserved error term
is assumed to be Gumbel-distributed, following standard discrete-choice
modeling assumptions. The model also includes an outside option,
representing the choice not to purchase any product version. Following
Marley and Louviere (2005) , we exploit information on individuals ’
choices of best and worst product alternatives. With the branded do-
mestic version as the reference, we estimate the WTPs for:
1. Generic foreign versions over generic domestic product versions
(H1 aG);
2. Branded-foreign over branded-domestic product versions (H1 aB).
By analyzing the signs of WTPs in H1a,G and H1a,B, we estimate the
preferences of Eastern-country consumers for domestic versions of
generic (H1 c,G) and branded (H1 c,B) products versus Western-country
versions and vice versa.
The impact of a potential policy requiring a claim (e.g., ‘made for’)
on consumer choice is measured as the difference in WTP premia for
branded domestic versions between the presence and absence of the
‘made for’ claim, for both the generic (H2 a,G) and branded (H2 a,B)
product versions. To assess whether this policy intervention differs be-
tween Western and Eastern consumers, we analyze how the WTP for
generic (H2 a,G) and branded (H2 a,B) product versions changes across the
two groups of countries.
Finally, to investigate the brand-name implications empirically, we
examine the proportion of consumers whose WTP for different product
versions varies in the presence of the ‘made for’ claim. More specifically,
we examine the number of cases where the preference for domestic
branded versions exceeds that for domestic generic versions and
whether these preferences change across different regimes. For the
econometric details, see the Appendix B.
For the two sensorial evaluation laboratory lab experiments we use a
pairwise comparison test of consumer taste preferences for the different
product versions. For each of the twelve product/version combinations,
we apply a mean difference t-test for the different hypotheses based on
the sensory and the purchase choice of each participant. We construct a
sensory preference score (SPS) capturing the organoleptic preferences
| [
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media Materials Chemistry
19 Manufacture of coke and refined petroleum productsEnergy Engineering and Power Technology; Materials
Chemistry
20 Manufacture of chemicals and chemical productsBiotechnology; Applied Microbiology and
Biotechnology; Drug Discovery; Pharmacology;
Materials ChemistryAZERBAIJAN
Concordances between NACE sectors and the intersection of ASJC subject fields & S&T domains
NACE sector ASJC Scopus subject field
26Manufacture of computer, electronic and optical
productsElectrical and Electronic Engineering; Electronic,
Optical and Magnetic Materials
27 Manufacture of electrical equipmentElectrical and Electronic Engineering; Instrumentation;
Electronic, Optical and Magnetic Materials; Control
and Systems Engineering
28 Manufacture of machinery and equipment n.e.c. Instrumentation; Mechanical Engineering
29Manufacture of motor vehicles, trailers and semi-
trailersFuel Technology; Instrumentation
33 Repair and installation of machinery and equipmentInstrumentation; Electronic, Optical and Magnetic
Materials; Mechanical Engineering
61 TelecommunicationsInformation Systems; Computer Networks and
Communications; Computer Science Applications;
General Computer Science; Modelling And Simulation
62Computer programming, consultancy and related
activitiesArtificial Intelligence
63 Information service activities Artificial Intelligence
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation345
GEORGIA
Concordances between NACE sectors and the intersection of ASJC subject fields & S&T domains
NACE sector ASJC Scopus subject field
23 Manufacture of other non-metallic mineral products Materials Chemistry; Mechanics of Materials
25Manufacture of fabricated metal products, except
machinery and equipmentElectronic, Optical and Magnetic Materials; Mechanics
of Materials
26Manufacture of computer, electronic and optical
productsElectrical and Electronic Engineering; Electronic,
Optical and Magnetic Materials
27 Manufacture of electrical equipmentElectrical and Electronic Engineering; Instrumentation;
Electronic, Optical and Magnetic Materials
28 Manufacture of machinery and equipment n.e.c. Instrumentation; Mechanical Engineering
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33 Repair and installation of machinery and equipmentInstrumentation; Electronic, Optical and Magnetic
Materials; Mechanical Engineering
53 Postal and courier activities Transportation
61 TelecommunicationsInformation Systems; Computer Networks and
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Signal Processing; General Computer Science;
Transportation
62Computer programming, consultancy and related
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63 Information service activities Artificial Intelligence
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Technology
14 Manufacture of wearing apparelSurfaces, Coatings and Films; Materials Chemistry;
Mechanics of Materials; Process Chemistry and
Technology
15 Manufacture of leather and related productsSurfaces, Coatings and Films; Materials Chemistry;
Mechanics of Materials; Process Chemistry and
Technology
16Manufacture of wood and of products of wood and
cork, except | [
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raw materials] . In the short term, the EU needs to imple -
ment the Critical Raw Materials Act (CRMA) rapidly and fully. The report recommends complementing this Act with
a comprehensive strategy covering all stages of the critical mineral supply chain, from extraction to processing
to recycling. To strengthen Europe’s position at the procurement stage, it is proposed to create a dedicated EU
Critical Raw Material Platform. The platform would leverage Europe’s market power by aggregating demand for
the joint purchasing of critical materials (following the model used in South Korea and Japan) and coordinating
the negotiation of joint purchases with producer countries. It would also help lower “insurance costs” for Member
States by managing future strategic stockpiles at the EU level, going beyond the soft request for national stockpiles
57THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 4included in the CRMA. In parallel, it is recommended that the EU further develops its “resource diplomacy” for
CRMs. Proposals include upgrading the Global Gateway – which promotes investment in third countries – to focus
on the EU’s strategic needs and developing joint strategies with other buyers from strategically aligned countries,
for example through a G7+ Critical Raw Materials Club (including Japan, South Korea and Australia). The EU should
also carefully explore the potential of environmentally-sustainable deep sea mining: estimates suggest that the sea
bed holds large multiples of the known land-based reserves for example for copper, titanium, manganese, cobalt,
nickel and rare earth elementsvii.
The EU must also harness the potential of domestic resources through mining, recycling and innovation
in alternative materials . Unlike fossil fuels, the EU has deposits of some critical raw materials, such as lithium in
Portugal. Accelerating the opening of domestic mines could enable the EU to meet its entire demand for some
critical minerals. The CRMA already calls on Member States to implement shorter permitting timeframes for “Strategic
Projects”: 27 months for extraction permits and 15 months for processing, compared with processes that take three
to five times as long today. However, the report recommends additional actions to accelerate the pace of permitting,
for example increasing administrative capacity by mandating pre-defined staff resources to be allocated to Strategic
Projects. At the same time, materials found in retired EVs, windmills and other goods represent a further supply that
could be tapped through recycling. The EU could potentially meet more than half to three quarters | [
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Milka Choco Cookies, 36 % of
German participants assigned the top rank to the Hungarian version
while only 29 % ranked the domestic version highest. In Hungary,
participants mostly preferred the domestic version (35 %), followed by
the German (34 %) and Lithuanian versions (31 %) Fig. 5. In both
countries (Germany and Hungary), 11 % of respondents ranked the
domestic versions of both products highest based on taste across the two
frames.
The descriptive statistics for ratings, presented in Fig. 6, show that
German participants ’ ratings are lower than those of Hungarian partic -
ipants (MDanoneDE 6.03 vs MDanoneHU 6.46 and MMilkaDE
5.80 vs MMilkaHU 6.74). In both countries, participants tend to rate
the taste of versions from other countries higher than the domestic
Table 3
Percentage of consumers preferring domestic versions to foreign ones.
Frame 1 Frame 2
R1 R2 R1 R2
DE – preference for domestic version (%
consumers)17.25 23 27 25.25
HU – preference for domestic version (%
consumers)28.5 23.5 27 28.25
Source: Authors ’ elaboration
Fig. 4.Percentage of cases where hypotheses are confirmed under generic and
branded conditions. Note: The bars represent the percentage of cases con-
firming hypotheses H1a and H2a for generic (G) and branded products (B).
Hypotheses H3a and H3b, represented by the differences between these bars,
illustrate the number of cases in which differences between domestic and
foreign products disappeared in the presence of the brand names.D.M. Federica et al. Food Policy 131 (2025) 102803
7 version, except for yogurt in the German sample. Responses to the rating
and ranking questions are consistent for the different versions.
In Frame 1, German consumers were more likely to choose domestic
versions (23 %) after being informed about the destination market (23
%) compared to before (17.25 %), whereas Hungarian consumers
showed the opposite trend (23.5 % after vs. 28.5 % before) (Table 3). In
Frame 2, awareness of brand names and DFQ information increased the
preference for domestic versions among Hungarian consumers. How-
ever, in this country, the share of consumers choosing domestic versions
was lower in the absence of a brand name (23.5 %) than in its presence
(28.25 %). Conversely, German consumers ’ preference for domestic
versions decreased by 2.25 % with brand and DFQ information
(Table 3).
The results of test-based inference across 3,200 product pair ratings
indicate that consumers perceived domestic versions to be as good as
| [
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X X X X
25Manufacture of fabricated metal products,
except machinery and equipment X X X X X
26Manufacture of computer, electronic and
optical productsX X X X X X
27 Manufacture of electrical equipment X X
28Manufacture of machinery and equipment
n.e.c. X X X X X X X X X X
29Manufacture of motor vehicles, trailers and
semi-trailers X X X X
30 Manufacture of other transport equipment X X X X
31 Furniture X X X X X X X
32 Other manufacturing X X X X X X X
33Repair and installation of machinery and
equipment X X X X X X X
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation301 302
Annexes
Annex 3. Results of the
mapping analysis for
goods exportsAn ‘X’ in a green-coloured cell shows whether an
export category passed the selection criterion.
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
0 Food and live animals
001 Live animals other than animals of division 03 X X X
011 Meat of bovine animals, fresh, chilled or frozen X X X
012Other meat and edible meat offal, fresh, chilled or frozen (except meat and meat offal unfit or unsuitable for
human consumption) X X
016 Meat and edible meat offal, salted, in brine, dried or smoked; edible flours and meals of meat or meat offal
017 Meat and edible meat offal, prepared or preserved, n.e.s. X
022 Milk and cream and milk products other than butter or cheese X X X
023 Butter and other fats and oils derived from milk X
024 Cheese and curd X X X
025 Eggs, birds', and egg yolks, fresh, dried or otherwise preserved, sweetened or not; egg albumin X
034 Fish, fresh (live or dead), chilled or frozen X
035Fish, dried, salted or in brine; smoked fish (whether or not cooked before or during the smoking process); flours,
meals and pellets of fish, fit for human consumption X
036Crustaceans, molluscs and aquatic invertebrates, whether in shell or not, fresh (live or dead), chilled, frozen, dried,
salted or in brine; crustaceans, in shell, cooked by steaming or boiling in water, whether or not chilled, frozen, dried,
salted or in brine; flours, meals and pellets of crustaceans or | [
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that this is
particularly aligned with the ‘Analytical In-
struments’ side of the industrial spectrum be-
cause of the development and application of
advanced sensors;
■in the ‘Food Processing and Manufacturing’
cluster, the Agrifood S&T domain could be
aligned with the food and beverages manu-
facturing E&I domains exclusively via patents.
The most relevant keywords for these Armenian
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.
ARMENIA
Concordance between E&I analysis and S&T analysis
Economic clusterE&I domains
(NACE sectors)S&T domains
Food Processing and
Manufacturing 10 Manufacture of food products
11 Manufacture of beverages• Agrifood
Tobacco 12 Manufacture of tobacco products
Information Technology and
Analytical Instruments26 Manufacture of computer,
electronic and optical products• Electric and electronic technologies
• Nanotechnology and materials
Postal and Courier Activities 53 Postal and courier activities
Hospitality and Tourism 55 Accommodation
Communications Equipment and
Services61 Telecommunications • ICT and computer science
Computer Programming and
Information Services62 Computer programming,
consultancy and related activities
63 Information service activitiesTable 4.2. Combined EIST specialisation domains in Armenia
Figure 4.2. Keyword cloud for the S&T domain Agrifood in
Armenia
Figure 4.4. Keyword cloud for the S&T domain ICT and computer
science in Armenia
Figure 4.3. Keyword cloud for the S&T domain Electric and
electronic technologies in Armenia
Figure 4.5. Keyword cloud for the S&T domain Nanotechnology
and materials in Armenia
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation 237
238
Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials
Azerbaijan
For Azerbaijan, the following concordances be-
tween E&I and S&T domains were identified:
■for the ‘Oil and Gas Production and Transpor-
tation’ cluster, the S&T domains ‘Chemistry
and chemical engineering’, ‘Energy’ and ‘Na-
notechnology and materials’ could be aligned
with the E&I domain ‘Manufacture of coke and
refined petroleum products’. For all of these
S&T domains, the alignment is obtained based
on publication data exclusively. Notably, the
most relevant Scopus ASJC subject fields that
produce the mapping, namely Fuel Technolo-
gy, Energy Engineering and Power Technology
are recurrent throughout the S&T domains;
■for the ‘Chemical Products’ cluster, the S&T
domains ‘Chemistry and chemical engineer-
ing’ and ‘Nanotechnology and materials’ could
again be matched with the E&I domains. In
this case, further concordances could be also
found with the ‘Agrifood’ and ‘Biotechnology’
| [
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"53",
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"activities",
"\n",
"Hospitality",
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"55",
"Accommodation",
"\n",
"Communications",
"Equipment",
"and",
"\n",
"Services61",
"Telecommunications",
"•",
"ICT",
"and",
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"\n",
"Computer",
"Programming",
"and",
"\n",
"Information",
"Services62",
"Computer",
"programming",
",",
"\n",
"consultancy",
"and",
"related",
"activities",
"\n",
"63",
"Information",
"service",
"activitiesTable",
"4.2",
".",
"Combined",
"EIST",
"specialisation",
"domains",
"in",
"Armenia",
"\n",
"Figure",
"4.2",
".",
"Keyword",
"cloud",
"for",
"the",
"S&T",
"domain",
"Agrifood",
"in",
"\n",
"Armenia",
"\n",
"Figure",
"4.4",
".",
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the examples in the training set
impacts our ability to automatically classify ex-
cerpts of that same length as either human-written
or machine-generated.
4 Dataset Methodology
All of our generated text samples are drawn from
GPT-2, a state-of-the-art Transformer-based gen-
erative language model that was trained on text
from popular web pages (Radford et al., 2019).
While we use the GPT-2 L ARGE model with
774M parameters, we found that similar trends
to those reported here hold in experiments withsmaller language models.
Given an autoregressive language model that
defines a probability distribution over the next to-
ken given the previous tokens in a sequence, a
decoding strategy generates text by deciding how
to output a token at each step based on the pre-
dicted distributions. Perhaps the most straightfor-
ward decoding strategy is to randomly choose a to-
ken with probability proportional to its likelihood.
A challenge with the random sampling approach
is that these probability distributions often contain
a long tail of vocabulary items that are individu-
ally low-probability but cumulatively comprise a
substantial amount of probability mass. Holtzman
et al. (2020) observe that choosing tokens from
this tail often leads to incoherent generations.
Top-ksampling, nucleus sampling, and (in the
extreme) beam search have all been proposed to
heuristically promote samples with higher per-
token likelihoods. Top- kand nucleus sampling
both do so by setting the likelihood of tokens in
the tail of the distribution to zero. Top- krestricts
the distribution to all but the kmost likely tokens,
wherekis a constant (Fan et al., 2018). Nucleus
sampling, also called top- p, truncates the distribu-
tion at each decoding step tto thekt-most-likely
next tokens such that the cumulative likelihood of
these tokens is no greater than a constant p(Holtz-
man et al., 2020).
We thus consider three different decoding strat-
egy settings:
Sample from the untruncated distribution
Top-k, choosingk=40 (Radford et al., 2019).
Nucleus sampling (aka top- p), choosing
p=0.96 (Zellers et al., 2019).
In addition, we form “negative” examples of
human-written text by taking excerpts of web text
that come from the same distribution as GPT-2’s
training data.2By picking text that resembles
GPT-2’s train set, we ensure that our classifiers
can’t simply take advantage of stylistic differences
between the human-written text corpus and the
kind of text GPT-2 was trained to generate.
For each decoding method, we construct a train-
ing dataset by pairing 250,000 generated samples
with 250,000 | [
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Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation305 306
Annexes
ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE
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Metabolism
Article
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Read
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Featured article
From Wikipedia, the free encyclopedia
"Cellular metabolism" redirects here. For the journal, see Cell Metabolism.
For calories burned at rest, see Basal metabolic rate. For other uses, see Metabolism (disambiguation).
Simplified view of cellular metabolism
Structure of adenosine triphosphate (ATP), a central intermediate in energy metabolism
Part of a series on
Biochemistry
Chemistry of life
IndexOutlineHistory
Key components
List of biochemists
Biomolecule families
Chemical synthesis
Biochemistry fields
Glossaries
Category
vte
Metabolism (/məˈtæbəlɪzəm/, from Greek: μεταβολή metabolē, "change") is the set of life-sustaining chemical reactions in organisms. The three main functions of metabolism are: the conversion of the energy in food to energy available to run cellular processes; the conversion of food to building blocks of proteins, lipids, nucleic acids, and some carbohydrates; and the elimination of metabolic wastes. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. The word metabolism can also refer to the sum of all chemical reactions that occur in living organisms, including digestion and the transportation of substances into and between different cells, in which case the above described set of reactions within the cells is called intermediary (or intermediate) metabolism.
Metabolic reactions may be categorized as catabolic—the breaking down of compounds (for example, of glucose to pyruvate by cellular respiration); or anabolic—the building up (synthesis) of compounds (such as proteins, carbohydrates, lipids, and nucleic acids). Usually, catabolism releases energy, and anabolism consumes energy.
The chemical reactions of metabolism are organized into metabolic pathways, in which one chemical is transformed through a series of steps into another chemical, each step being facilitated by a specific enzyme. Enzymes are crucial to metabolism because they allow organisms to drive desirable reactions that require energy and will not occur by themselves, by coupling them to spontaneous reactions that release energy. Enzymes act as catalysts—they allow a reaction to proceed more rapidly—and they also allow the regulation of the rate of a metabolic reaction, for example in response to changes in the cell's environment or to signals from other cells.
The metabolic system of a particular organism determines which substances it will find nutritious and which poisonous. For example, some prokaryotes use hydrogen sulfide as a nutrient, yet this gas is poisonous to animals.[1] The basal metabolic rate | [
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specific test might depend on
the battery size. Moreover, we observed that the application test from primary batteries
(measured in duration) could have a direct relation with the NiMH battery (measured inBatteries 2025 ,11, 30 17 of 20
capacity). Furthermore, conversion values for duration and capacity can be calculated, e.g.,
AAA 850 mAh NiMH can have a duration of 221 min, and AA 2100 mAh NiMH can have
a duration of 419 min for the “toy” application test. These values might provide a realistic
picture of the use of portable NiMH batteries in specific applications as observed in the toy
test for primary batteries.
Batteries 2025, 11, x FOR PEER REVIEW 18 of 21
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)).
Figure 11. Comparison of portable NiMH batteries with primary batteries (alkaline) using the “toy”
application test from IEC 60086-2, including the minimum average duration value, average dura-
tion, and measured capacity for (a) AAA, (b) AA, (c) C, (d) D, and (e) 9V batteries.
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 specific test might depend on
the battery size. Moreover, we observed that the application test from primary batteries
(measured in duration) could have a direct relation with the NiMH battery (measured in
capacity). Furthermore, conversion values for duration and capacity can be calculated,
e.g., AAA 850 mAh NiMH can have a duration of 221 min, and AA 2100 mAh NiMH can
have a duration of 419 min for the “toy” application test. These values might provide a
Figure 11. Comparison of portable NiMH batteries with primary batteries (alkaline) using the “toy”
application test from IEC 60086-2, including the minimum average duration value, average duration,
and measured capacity for ( a) AAA, ( b) AA, ( c) C, ( d) D, and ( e) 9V batteries.
9. | [
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paradigm for specific suppression of aIC VIP+ IN activity during interactions with a novel conspecific on day 2 of testing, during the first 5 min
of the test.(H) Time spent in the social interaction zone differed between ArchT- and GFP-injected animals. While GFP-injected mice showed decreased social int eraction
time during the last 5 min of the test, ArchT-injected animals did not (two-way ANOVA, main effect group: p = 0.08; main effect time: p = 0.75; interactio n effect:
p = 0.004; Bonferroni multiple comparisons test, OFF GFP versus ON GFP, p = 0.04; OFF ArchT versus ON ArchT, p = 0.11).
(I) Social interaction ratios in ArchT-injected animals during periods of ON (first 5 min) laser-mediated inhibition were lower than in OFF periods co mpared with
GFP-injected animals (two-way ANOVA, main effect group: p = 0.74; main effect time: p = 0.23; interaction effect: p = 0.006; Bonferroni multiple compa risons test,
OFF GFP versus ON GFP, p = 0.42; OFF ArchT versus ON ArchT, p = 0.01).(J) Fear conditioning paradigm for closed-loop suppression of aIC VIP+ IN activity during CS-US pairings (left panel). Fear retrieval was performed in a different
context presenting five times the same CS (right panel).(K) Optogenetic inhibition of VIP+ aIC IN activity during fear conditioning does not affect associative learning as measured by freezing responses t o CS 1–5 (two-
way ANOVA, main effect group: p = 0.33; main effect time: p = 0.0001; interaction effect: p = 0.2117).(L) Optogenetic inhibition of aIC VIP+ IN activity during fear conditioning reduces the strength of associative learning as measured by freezing res ponses to CS-R
1–5 on retrieval testing (two-way ANOVA, main effect group: p = 0.001; main effect time: p = 0.001; interaction effect: p = 0.03; Bonferroni multiple co mparisons
test, Pre-CS-R GFP versus Pre-CS-R ArchT, p = 0.99; CS-R GFP versus CS-R ArchT, p = 0.001).Data are shown as mean ±SEM. Details of statistical analyses are provided in Table S1 .
8Cell Reports 39, 110893, May 31, 2022Articlell
OPEN ACCESSthe coding specificity of the different functional populations of
aIC VIP+ INs, but rather results from a mechanism of repetition
suppression ( Henson and Rugg, 2003 ;Zweynert et al., 2011 ).
We further examined whether individual aIC VIP+ INs main-
tained their coding specificity across days. To this aim, we
registered their activity in the two consecutive days of socialpreference testing ( Figure 7 | [
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services X 2.8%
10.1 Audiovisual and related services X 0.3% X 0.3%
10.2 Other personal, cultural, and recreational services X 0.9% X 0.9%
11 Government services, n.e.c. X 1.9%Table 2.22. Services export specialisation for Armenia
82
Part 2 Analysis of economic and innovation potential
Mapping of services export specialisa-
tions – results for Azerbaijan
Results of the export mapping for Azerbaijan
are shown in Table 2.23. The 2 services catego-
ries with current strength represent 28% of the
total exports for 2011-2018. Specialised exports
in Business travel account for almost 17% of the
total exports, and those in Other business services
for 11%. The 3 services categories with emerging
strength represent, at 65%, a much higher share
of the total exports due to large export shares for
Transportation and Personal travel.Mapping of services export specialisa-
tions – results for Georgia
Results of the export mapping for Georgia are
shown in Table 2.24. The 2 services categories
with current strength represent 24% of the to-
tal exports for 2011-2018. Specialised exports
in Business travel account for 21% of the total
exports. The 6 services categories with emerging
strength represent almost 69% of the total ex-
ports, with high export shares for Business travel
and Personal travel.
Current
strength% share
of
exportsEmerging
strength% share
of
exports
2 24.2% 6 68.7%
1.1 Sea transport X 4.9%
2.1 Business travel X 21.0% X 21.0%
2.2 Personal travel X 39.3%
3.1 Postal and courier services X 0.1%
10.1 Audiovisual and related services X 0.2%
11 Government services, n.e.c. X 3.2% X 3.2%Table 2.24. Services export specialisation for GeorgiaCurrent
strength% share
of
exportsEmerging
strength% share
of
exports
2 28.0% 3 65.3%
1 Transportation X 24.6%
2.1 Business travel X 16.9%
2.2 Personal travel X 40.3%
5 Insurance services X 0.4%
9 Other business services X 11.1% Table 2.23. Services export specialisation for Azerbaijan
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation83
Mapping of services export specialisa-
tions – results for Moldova
Results of the export mapping for Moldova are
shown in Table 2.25. The 6 services categories
with current strength represent 74.5% of the total
exports for 2011-2018. Specialised exports in Per-
sonal travel account for almost 13% of the total exports, and those in Telecommunication services
for 10%. The 7 services categories with emerging
strength represent 36% of the total exports, with
Air transport and Other business services account-
ing | [
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highest
number of records in Armenia were attributed
to the National Academy of Sciences of Armenia
and A.I. Alikhanyan National Science Laboratory,
highly specialised in fundamental physics and
mathematics. In Ukraine – National Academy of
Sciences of Ukraine and Taras Shevchenko Nation-
al University of Kyiv had major shares of scientif-
ic production. In Azerbaijan and Georgia, it was
leading universities – Baku State University and
Tbilisi State University. In Moldova, this was the
Academy of Sciences of Moldova.
The bilateral collaboration relationships between
EaP countries in terms of the co-authorship of
scientific publications are the most intensive and
strong in ICT and computer science, Biotechnology,
Fundamental physics and mathematics and Nano-
technology and materials. The least collaboration
has been recorded in the Transportation domain,
while Optics and photonics, Health and wellbeing,
Environmental sciences and industries, Electric
and electronic technologies and Energy has inter-
mediate collaboration.
The most relevant S&T specialisation domains
were identified based on critical mass, speciali-
sation and excellence. For Armenia, they are as
follows: Fundamental physics and mathemat-
ics, Agrifood, Nanotechnology and materials and
Health and wellbeing. Azerbaijan has the follow-
ing: Chemistry and chemical engineering, Energy,
Mechanical engineering and heavy machinery and
Health and wellbeing. Georgia has the following:
Environmental sciences and industries, Agrifood,
Health and wellbeing and ICT and computer sci-
ence. For Moldova, this is as follows: Health and
wellbeing, Nanotechnology and materials, Electric
and electronic technologies and Mechanical en-
gineering and heavy machinery. Ukraine, having
the largest numbers of publication and patents,
has the following: Health and wellbeing, Energy,
Biotechnology, Transportation, Mechanical engi-
neering and Nanotechnology and materials.The final part of identifying concordances between
the economic, innovation, scientific and technolog-
ical potentials was to establish a list of clusters
of economic activities, with matching E&I domains
(expressed as NACE codes) and their correspond-
ing S&T domains.
For Armenia, the following three concordances
were identified: in the ‘Communications Equipment
and Services’ cluster, the S&T domain ICT Comput-
er Science could be aligned with the E&I domain
Telecommunications exclusively via publications.
In the ‘Information Technology and Analytical In-
struments’ cluster, the S&T domains Electric and
electronic technologies and Nanotechnology and
materials could be mapped with the E&I domain
Manufacture of computer, electronic and optical
products via both patents and publications. In the
‘Food Processing and Manufacturing’ cluster, the
Agrifood S&T domain could be aligned with the
food and beverages manufacturing E&I domains
exclusively via | [
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were randomly assignedto the experimental groups. Animals were kept in a temperature-controlled room with a 12/12 h light/dark cycle with access to food
and water ad libitum. All behavioral experiments were conducted during the light cycle.
METHOD DETAILS
Surgical procedures
Anesthesia was induced with a combination of intraperitoneally injected Ketamine (80 mg/kg; Ketasol, AniMedica) and Xylazine
(5 mg/kg; Xylasol, Animedica) and maintained with 2% Sevofluran (SEVOrane). The head was then fixed on a stereotactic frame
(Model 1900; Kopf Instruments) and ophthalmic ointment was applied to the eyes to avoid drying. Postoperative pain medicationincluded administration of meloxicam (Metacam, Boehringer Ingelheim; 1 mg/kg subcutaneously).
At the end of the experimental procedures, mice were deeply anesthetized with thiopental sodium (150 mg/kg, i.p.) and transcar-
dially perfused with a fixative made of 4% paraformaldehyde, 15% picric acid in 0.1 M phosphate-buffer (PB), pH 7.2–7.4. Brains
were cut into 50 mm thick coronal sections using a vibratome (Leica Microsystems VT1000S, Vienna, Austria).
Mono-trans-synaptic rabies tracing
VIP-ires-cre mice (N = 7) were unilaterally injected with a 1:1 mixture of AAV1.syn.FLEX.splitTVA-EGFP-tTA (1.7 310^13 GC/mL,
diluted 1:50, Addgene #100798) and AAV1.TREtight.mTagBFP2-B19G (3.2 310^13 GC/mL, Addgene #100799) into the aIC (coor-
dinates from bregma: AP: +2.00 mm; ML: ±2.75 mm; DV: 3.4 mm) in a volume of 300 nL. Mice were allowed to recover for 1 week
before the injection of the rabies virus RV.EnvA.dG.mCherry (300 nL, produced in house; Viral Vector Core, Salk Institute) using the
same coordinates. One week following the RV injection in the aIC, mice were transcardially perfused as described above.Anterograde tracing
VIP-ires-cre mice (N = 2) were unilaterally injected with a AAV5.CamKIIa-hChR2(HI34R)-mCherry (5.1 310^12 GC/mL, Addgene
#26975) into the MD (coordinates from bregma: AP: /C01.58mm; ML: +0.44 mm; DV: /C03.20 mm) in a volume of 300 nL. Mice were al-
lowed to recover for 3 weeks before perfusion to ensure adequate viral transduction.Deep-brain Ca
2+imaging
VIP-ires-cre mice (N = 7) were unilaterally injected with 300 nL AAV2/9.CAG.flex.GCaMP6s (Addgene, #100842) into the aIC using a
glass pipette (tip diameter /C2430mm) connected to a Picospritzer III microinjection system (Parker Hannifin Corporation) at the
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3.3 mm. Immediately after the AAV injection, a GRIN lens (0.6 37.3 mm GLP-0673, Inscopix) was lowered into the aIC as previously
described using a custom-built lens holder, and fixed to the skull using ultraviolet light-curable glue (Loctite 4305, | [
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that are
both coherent and also helpful in the real world.
Here, we provide two more constrained thought ex-
periments, to focus more narrowly on the problem
of learning the meaning relation, for both natural
languages and programming languages.
Because programming languages are designed to
be unambiguous and relatively insensitive to execu-
tion context, the distinction between standing and
speaker meaning is less important than for natural
languages. A Java program e, when compiled and
executed on the Java Virtual Machine, can be inter-
preted as a function iwhich maps program inputs
to program outputs. We take the meaning relation
JEIof Java to contain all such pairs (e; i).
Java Imagine that we were to train an LM on all
of the well-formed Java code published on Github.
The input is only the code. It is not paired with
bytecode, nor a compiler, nor sample inputs and
outputs for any specific program. We can use any
type of LM we like and train it for as long as we
like. We then ask the model to execute a sample
program, and expect correct program output.
English As as second example, imagine train-
ing an LM (again, of any type) on English text,
again with no associated independent indications
of speaker intent. The system is also given access
to a very large collection of unlabeled photos, but
without any connection between the text and the
photos. For the text data, the training task is purely
one of predicting form. For the image data, the
training task could be anything, so long as it only
involves the images. At test time, we present the
model with inputs consisting of an utterance and
a photograph, like How many dogs in the picture
are jumping? orKim saw this picture and said
“What a cute dog!” What is cute? and the photos5190
Figure 1: Photo stimuli 1 (L) and 2 (R)
in Figure 1, where the appropriate answers are a
number or a region of the photo, respectively.
Reflections In both cases, the tests are ridiculous.
It seems patently unfair to ask the model to per-
form them, given what it was trained on. But that
is precisely the point we are trying to make: a sys-
tem that has learned the meaning (semantics) of a
programming language knows how to execute code
in that language. And a system that has learned
the meaning of | [
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] | [] |
dioxide takes to the Calvin cycle, with C3 plants fixing CO2 directly, while C4 and CAM photosynthesis incorporate the CO2 into other compounds first, as adaptations to deal with intense sunlight and dry conditions.[66]
In photosynthetic prokaryotes the mechanisms of carbon fixation are more diverse. Here, carbon dioxide can be fixed by the Calvin–Benson cycle, a reversed citric acid cycle,[67] or the carboxylation of acetyl-CoA.[68][69] Prokaryotic chemoautotrophs also fix CO2 through the Calvin–Benson cycle, but use energy from inorganic compounds to drive the reaction.[70]
Carbohydrates and glycans
Further information: Gluconeogenesis, Glyoxylate cycle, Glycogenesis, and Glycosylation
In carbohydrate anabolism, simple organic acids can be converted into monosaccharides such as glucose and then used to assemble polysaccharides such as starch. The generation of glucose from compounds like pyruvate, lactate, glycerol, glycerate 3-phosphate and amino acids is called gluconeogenesis. Gluconeogenesis converts pyruvate to glucose-6-phosphate through a series of intermediates, many of which are shared with glycolysis.[43] However, this pathway is not simply glycolysis run in reverse, as several steps are catalyzed by non-glycolytic enzymes. This is important as it allows the formation and breakdown of glucose to be regulated separately, and prevents both pathways from running simultaneously in a futile cycle.[71][72]
Although fat is a common way of storing energy, in vertebrates such as humans the fatty acids in these stores cannot be converted to glucose through gluconeogenesis as these organisms cannot convert acetyl-CoA into pyruvate; plants do, but animals do not, have the necessary enzymatic machinery.[73] As a result, after long-term starvation, vertebrates need to produce ketone bodies from fatty acids to replace glucose in tissues such as the brain that cannot metabolize fatty acids.[74] In other organisms such as plants and bacteria, this metabolic problem is solved using the glyoxylate cycle, which bypasses the decarboxylation step in the citric acid cycle and allows the transformation of acetyl-CoA to oxaloacetate, where it can be used for the production of glucose.[73][75] Other than fat, glucose is stored in most tissues, as an energy resource available within the tissue through glycogenesis which was usually being used to maintained glucose level in blood.[76]
Polysaccharides and glycans are made by the sequential addition of monosaccharides by glycosyltransferase from a reactive sugar-phosphate donor such as uridine diphosphate glucose (UDP-Glc) to an acceptor hydroxyl group on the growing polysaccharide. As any of the hydroxyl groups on the ring of the substrate can be acceptors, the polysaccharides produced can | [
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] | [] |
2.
Steps 1 and 2 – quantified mapping of
S&T domains to E&I domains, via patents
In the work presented in Part 3, EaP patents were
semantically analysed, together with scientific
publications and FP7/H2020 projects, and classi-
fied into a list of 14 S&T domains. While domains
are transversal across patent classes, in this part
the intersection of any given domain with the re-
spective set of patent classes is exploited to map
S&T domains to NACE sectors, which were used in
Part 2 to characterise E&I domains. Specifically,
IPC-to-NACE concordance tables are used to map
patent records to NACE and to derive a quantita-
tive mapping of S&T domains to E&I.
234
Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials
To identify the relevant IPC classes for each S&T
domain, a careful, quantitative protocol was de-
veloped. In fact, patents linked in Part 2 to each
S&T domain could be categorised into several IPC
categories and each record could in turn be as-
sociated with more than one single IPC symbol72.
This led to a proliferation of patent classes within
single S&T domains, which needed to be filtered
out. Notably, the most frequent IPC symbols could
not readily be used because the multiple classi-
fication of single patent records typically mixes
highly specific and broader classes. As specific
symbols can be extremely narrow, when group-
ing patents within S&T domains it is found that
broad classification symbols (generally related to
the domain at hand but not distinctive of it) that
are shared across patents typically outnumber the
more specific and pertinent symbols. For this rea-
son, it was decided to rank IPC symbols by relative
frequency within each S&T domain rather than by
sheer numbers – i.e. by looking at which symbols
were more frequent with respect to the average
within S&T domains. These more frequent cate-
gories were in turn mapped to NACE by means of
concordance tables for each S&T domain.
In order to avoid the presence of NACE sectors
mapped to IPC classes that are ranked very low
in terms of the raw number of records, but ranked
highly in terms of relative frequency, a threshold
was fixed for setting a minimum number of pat-
ents to trigger a mapping from a given S&T domain
to a NACE sector via IPC symbols. The threshold
was defined as the mean plus standard | [
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] | [] |
of its metal
requirements for clean technologies in 2050 through local recyclingviii. It is therefore recommended to establish a
true Single Market for waste and circularity. Achieving this goal will require strengthening the secondary market for
critical raw materials waste, effectively enforcing existing legislation on waste collection and shipment to allow the
build-up of scale, and coordinating EU export controls on waste. Finally, boosting R&I for alternative materials or
processes will be crucial to substitute critical raw materials. For example, US tech companies have recently combined
federal research labs to use AI to develop a new material that could reduce the lithium content in batteries by 70%ix.
For strategic industries, the EU should pursue a coordinated EU strategy to bolster domestic production
capacity and to protect key network infrastructures [see the chapter on digital and advanced technologies] .
While EU ownership of large foundries may be unrealistic at this stage owing to the required investment levels,
Europe should maximise its joint efforts to strengthen innovation in semiconductors and its presence in the most
advanced chips segments. The report recommends launching a common strategy based around four elements.
First, funding for innovation and the establishment of testing labs near existing centres of excellence. Second,
providing grants or R&D tax incentives for “fabless” companies active in chips design and foundries in selected
strategic segments. Third, supporting the innovation potential of mainstream chips. Fourth, coordinating EU efforts
in back-end 3D advanced packaging, advanced materials and finishing processes. Total investments in industrial
deployment of around EUR 100 billion have been announced in the EU since the proposal for a European Chips Act,
mostly supported by Member States under State aid control. However, there is a risk that a fragmented approach
leads to weak coordination of priorities and demand requirements, lack of scale for domestic producers, and in turn
less ability to invest in more innovative semiconductor segments. It is therefore proposed to create a centralised
EU budgetary allocation dedicated to semiconductors supported by a new “fast-track” IPCEI. Use of this tool would
entail co-financing from the EU budget and shorter approval times for semiconductor projects. For telecoms, it is
recommended to strengthen security considerations in technology sourcing by favouring the use of EU trusted
vendors for spectrum assignment in all future tenders, and by promoting EU-based telecoms equipment providers
as strategic in trade negotiations.
58THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER | [
" ",
"of",
"its",
"metal",
"\n",
"requirements",
"for",
"clean",
"technologies",
"in",
"2050",
"through",
"local",
"recyclingviii",
".",
"It",
"is",
"therefore",
"recommended",
"to",
"establish",
"a",
"\n",
"true",
"Single",
"Market",
"for",
"waste",
"and",
"circularity",
".",
"Achieving",
"this",
"goal",
"will",
"require",
"strengthening",
"the",
"secondary",
"market",
"for",
"\n",
"critical",
"raw",
"materials",
"waste",
",",
"effectively",
"enforcing",
"existing",
"legislation",
"on",
"waste",
"collection",
"and",
"shipment",
"to",
"allow",
"the",
"\n",
"build",
"-",
"up",
"of",
"scale",
",",
"and",
"coordinating",
"EU",
"export",
"controls",
"on",
"waste",
".",
"Finally",
",",
"boosting",
"R&I",
"for",
"alternative",
"materials",
"or",
"\n",
"processes",
"will",
"be",
"crucial",
"to",
"substitute",
"critical",
"raw",
"materials",
".",
"For",
"example",
",",
"US",
"tech",
"companies",
"have",
"recently",
"combined",
"\n",
"federal",
"research",
"labs",
"to",
"use",
"AI",
"to",
"develop",
"a",
"new",
"material",
"that",
"could",
"reduce",
"the",
"lithium",
"content",
"in",
"batteries",
"by",
"70%ix",
".",
"\n",
"For",
"strategic",
"industries",
",",
"the",
"EU",
"should",
"pursue",
"a",
"coordinated",
"EU",
"strategy",
"to",
"bolster",
"domestic",
"production",
"\n",
"capacity",
"and",
"to",
"protect",
"key",
"network",
"infrastructures",
" ",
"[",
"see",
"the",
"chapter",
"on",
"digital",
"and",
"advanced",
"technologies",
"]",
".",
"\n",
"While",
"EU",
"ownership",
"of",
"large",
"foundries",
"may",
"be",
"unrealistic",
"at",
"this",
"stage",
"owing",
"to",
"the",
"required",
"investment",
"levels",
",",
"\n",
"Europe",
"should",
"maximise",
"its",
"joint",
"efforts",
"to",
"strengthen",
"innovation",
"in",
"semiconductors",
"and",
"its",
"presence",
"in",
"the",
"most",
"\n",
"advanced",
"chips",
"segments",
".",
"The",
"report",
"recommends",
"launching",
"a",
"common",
"strategy",
"based",
"around",
"four",
"elements",
".",
"\n",
"First",
",",
"funding",
"for",
"innovation",
"and",
"the",
"establishment",
"of",
"testing",
"labs",
"near",
"existing",
"centres",
"of",
"excellence",
".",
"Second",
",",
"\n",
"providing",
"grants",
"or",
"R&D",
"tax",
"incentives",
"for",
"“",
"fabless",
"”",
"companies",
"active",
"in",
"chips",
"design",
"and",
"foundries",
"in",
"selected",
"\n",
"strategic",
"segments",
".",
"Third",
",",
"supporting",
"the",
"innovation",
"potential",
"of",
"mainstream",
"chips",
".",
"Fourth",
",",
"coordinating",
"EU",
"efforts",
"\n",
"in",
"back",
"-",
"end",
"3D",
"advanced",
"packaging",
",",
"advanced",
"materials",
"and",
"finishing",
"processes",
".",
"Total",
"investments",
"in",
"industrial",
"\n",
"deployment",
"of",
"around",
"EUR",
"100",
"billion",
"have",
"been",
"announced",
"in",
"the",
"EU",
"since",
"the",
"proposal",
"for",
"a",
"European",
"Chips",
"Act",
",",
"\n",
"mostly",
"supported",
"by",
"Member",
"States",
"under",
"State",
"aid",
"control",
".",
"However",
",",
"there",
"is",
"a",
"risk",
"that",
"a",
"fragmented",
"approach",
"\n",
"leads",
"to",
"weak",
"coordination",
"of",
"priorities",
"and",
"demand",
"requirements",
",",
"lack",
"of",
"scale",
"for",
"domestic",
"producers",
",",
"and",
"in",
"turn",
"\n",
"less",
"ability",
"to",
"invest",
"in",
"more",
"innovative",
"semiconductor",
"segments",
".",
"It",
"is",
"therefore",
"proposed",
"to",
"create",
"a",
"centralised",
"\n",
"EU",
"budgetary",
"allocation",
"dedicated",
"to",
"semiconductors",
"supported",
"by",
"a",
"new",
"“",
"fast",
"-",
"track",
"”",
"IPCEI",
".",
"Use",
"of",
"this",
"tool",
"would",
"\n",
"entail",
"co",
"-",
"financing",
"from",
"the",
"EU",
"budget",
"and",
"shorter",
"approval",
"times",
"for",
"semiconductor",
"projects",
".",
"For",
"telecoms",
",",
"it",
"is",
"\n",
"recommended",
"to",
"strengthen",
"security",
"considerations",
"in",
"technology",
"sourcing",
"by",
"favouring",
"the",
"use",
"of",
"EU",
"trusted",
"\n",
"vendors",
"for",
"spectrum",
"assignment",
"in",
"all",
"future",
"tenders",
",",
"and",
"by",
"promoting",
"EU",
"-",
"based",
"telecoms",
"equipment",
"providers",
"\n",
"as",
"strategic",
"in",
"trade",
"negotiations",
".",
"\n",
"58THE",
"FUTURE",
"OF",
"EUROPEAN",
"COMPETITIVENESS",
" ",
"—",
"PART",
"A",
"|",
"CHAPTER"
] | [] |
1 437
Nanotechnology and materials 1 265 -1.6% 58 5 1 328
Governance, culture, education and the
economy680 9.5% 14 33 727
Chemistry and chemical engineering 595 2.6% 35 1 631
Optics and photonics 603 -2.5% 9 1 613
Environmental sciences and industries 482 16.3% 27 1 510
Biotechnology 442 4.0% 5 3 450
Agrifood 331 8.6% 62 2 395
ICT and computer science 351 18.6% 29 15 395
Electric and electronic technologies 30 2.6% 102 0 132
Mechanical engineering and heavy
machinery56 -5.6% 64 0 120
Energy 98 3.8% 8 5 111Table 3.9. Number of records per S&T specialisation domain in Armenia
176
Part 3 Analysis of scientific and technological potential
mental physics and mathematics (with an SI of
1.6), Optics and photonics (1.2), Agrifood (1.2) and
Biotechnology (1.1).
Overall, Armenian publications present a lower
normalised citation impact than the EaP average.
Four domains, however, stand out: Agrifood (with
an NCI of 1.3), Environmental sciences and indus-
tries (1.2), Fundamental physics and mathematics
(1.1) and Governance and culture (1.1).
Thus, in terms of scientific publications, Funda-
mental physics and mathematics is a domain in
which Armenia’s S&T ecosystem simultaneously
presents a high critical mass, relative specialisa-
tion and scientific impact.
In patents, Fundamental physics and mathemat-
ics (3.1), Electric and electronic technologies (2.4)
have an SI higher than 2, signalling a very high
specialisation, followed by Agrifood (1.5) and Na-
notechnology and materials(1.3).
It must be noted that patent specialisation indexes
for Armenian domains may suffer from the low
aggregate number of records, leading to vola-
tile indicators for single domains – where patent counts can be in single units or low tens, especially
for Optics and photonics, Biotechnology, Govern-
ance, culture, education and the economy, Chem-
istry and chemical engineering and Environmental
sciences and industries. Given this issue with data
richness, specialisation indexes for these do-
mains will not be considered in the summary
of the strengths of each S&T specialisation
domain for each EaP country at the end of the
document.
Finally, Table 3.10 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 period63. The small number of
records, particularly patents, gathered for Arme-
nia affects the temporal evolution indicators sig-
nificantly, and thus some domains have not been
considered.
63 See section ‘Temporal evolution of the S&T specialisation
domains’ for further | [
"1",
"437",
"\n",
"Nanotechnology",
"and",
"materials",
"1",
"265",
"-1.6",
"%",
"58",
"5",
"1",
"328",
"\n",
"Governance",
",",
"culture",
",",
"education",
"and",
"the",
"\n",
"economy680",
"9.5",
"%",
"14",
"33",
"727",
"\n",
"Chemistry",
"and",
"chemical",
"engineering",
"595",
"2.6",
"%",
"35",
"1",
"631",
"\n",
"Optics",
"and",
"photonics",
"603",
"-2.5",
"%",
"9",
"1",
"613",
"\n",
"Environmental",
"sciences",
"and",
"industries",
"482",
"16.3",
"%",
"27",
"1",
"510",
"\n",
"Biotechnology",
"442",
"4.0",
"%",
"5",
"3",
"450",
"\n",
"Agrifood",
"331",
"8.6",
"%",
"62",
"2",
"395",
"\n",
"ICT",
"and",
"computer",
"science",
"351",
"18.6",
"%",
"29",
"15",
"395",
"\n",
"Electric",
"and",
"electronic",
"technologies",
"30",
"2.6",
"%",
"102",
"0",
"132",
"\n",
"Mechanical",
"engineering",
"and",
"heavy",
"\n",
"machinery56",
"-5.6",
"%",
"64",
"0",
"120",
"\n",
"Energy",
"98",
"3.8",
"%",
"8",
"5",
"111Table",
"3.9",
".",
"Number",
"of",
"records",
"per",
"S&T",
"specialisation",
"domain",
"in",
"Armenia",
"\n",
"176",
"\n ",
"Part",
"3",
"Analysis",
"of",
"scientific",
"and",
"technological",
"potential",
"\n",
"mental",
"physics",
"and",
"mathematics",
"(",
"with",
"an",
"SI",
"of",
"\n",
"1.6",
")",
",",
"Optics",
"and",
"photonics",
"(",
"1.2",
")",
",",
"Agrifood",
"(",
"1.2",
")",
"and",
"\n",
"Biotechnology",
"(",
"1.1",
")",
".",
"\n",
"Overall",
",",
"Armenian",
"publications",
"present",
"a",
"lower",
"\n",
"normalised",
"citation",
"impact",
"than",
"the",
"EaP",
"average",
".",
"\n",
"Four",
"domains",
",",
"however",
",",
"stand",
"out",
":",
"Agrifood",
"(",
"with",
"\n",
"an",
"NCI",
"of",
"1.3",
")",
",",
"Environmental",
"sciences",
"and",
"indus-",
"\n",
"tries",
"(",
"1.2",
")",
",",
"Fundamental",
"physics",
"and",
"mathematics",
"\n",
"(",
"1.1",
")",
"and",
"Governance",
"and",
"culture",
"(",
"1.1",
")",
".",
"\n",
"Thus",
",",
"in",
"terms",
"of",
"scientific",
"publications",
",",
"Funda-",
"\n",
"mental",
"physics",
"and",
"mathematics",
"is",
"a",
"domain",
"in",
"\n",
"which",
"Armenia",
"’s",
"S&T",
"ecosystem",
"simultaneously",
"\n",
"presents",
"a",
"high",
"critical",
"mass",
",",
"relative",
"specialisa-",
"\n",
"tion",
"and",
"scientific",
"impact",
".",
"\n",
"In",
"patents",
",",
"Fundamental",
"physics",
"and",
"mathemat-",
"\n",
"ics",
"(",
"3.1",
")",
",",
"Electric",
"and",
"electronic",
"technologies",
"(",
"2.4",
")",
"\n",
"have",
"an",
"SI",
"higher",
"than",
"2",
",",
"signalling",
"a",
"very",
"high",
"\n",
"specialisation",
",",
"followed",
"by",
"Agrifood",
"(",
"1.5",
")",
"and",
"Na-",
"\n",
"notechnology",
"and",
"materials(1.3",
")",
".",
"\n",
"It",
"must",
"be",
"noted",
"that",
"patent",
"specialisation",
"indexes",
"\n",
"for",
"Armenian",
"domains",
"may",
"suffer",
"from",
"the",
"low",
"\n",
"aggregate",
"number",
"of",
"records",
",",
"leading",
"to",
"vola-",
"\n",
"tile",
"indicators",
"for",
"single",
"domains",
"–",
"where",
"patent",
"counts",
"can",
"be",
"in",
"single",
"units",
"or",
"low",
"tens",
",",
"especially",
"\n",
"for",
"Optics",
"and",
"photonics",
",",
"Biotechnology",
",",
"Govern-",
"\n",
"ance",
",",
"culture",
",",
"education",
"and",
"the",
"economy",
",",
"Chem-",
"\n",
"istry",
"and",
"chemical",
"engineering",
"and",
"Environmental",
"\n",
"sciences",
"and",
"industries",
".",
"Given",
"this",
"issue",
"with",
"data",
"\n",
"richness",
",",
"specialisation",
"indexes",
"for",
"these",
"do-",
"\n",
"mains",
"will",
"not",
"be",
"considered",
"in",
"the",
"summary",
"\n",
"of",
"the",
"strengths",
"of",
"each",
"S&T",
"specialisation",
"\n",
"domain",
"for",
"each",
"EaP",
"country",
"at",
"the",
"end",
"of",
"the",
"\n",
"document",
".",
"\n",
"Finally",
",",
"Table",
"3.10",
"presents",
"the",
"change",
"in",
"the",
"\n",
"share",
"of",
"each",
"domain",
"within",
"the",
"S&T",
"data",
"sourc-",
"\n",
"es",
",",
"comparing",
"the",
"2011",
"-",
"2014",
"period",
"to",
"the",
"more",
"\n",
"recent",
"2015",
"-",
"2018",
"period63",
".",
"The",
"small",
"number",
"of",
"\n",
"records",
",",
"particularly",
"patents",
",",
"gathered",
"for",
"Arme-",
"\n",
"nia",
"affects",
"the",
"temporal",
"evolution",
"indicators",
"sig-",
"\n",
"nificantly",
",",
"and",
"thus",
"some",
"domains",
"have",
"not",
"been",
"\n",
"considered",
".",
"\n",
"63",
"See",
"section",
"‘",
"Temporal",
"evolution",
"of",
"the",
"S&T",
"specialisation",
"\n",
"domains",
"’",
"for",
"further"
] | [] |
exports in transporta-
tion and travel dominate total services exports, as
shown in Figure 2.5. Exports of transportation ser-
vices account for more than 65% of Ukrainian ser-
vices exports. Exports of travel services account
for almost 60% of services exports in Armenia,
Azerbaijan and Georgia. Exports of communica-
tion services exceed 10% of the total exports in
Moldova, exports of construction services exceed
10% of the total exports in Armenia, exports of
computer and information services exceed 10% of
exports in Ukraine.
1_Transportation
2_Travel
3_Communications services
4_Construction services
5_Insurance services
6_Financial services
7_Computer and information services
8_Royalties and license fees
9_Other business services
10_Personal, cultural, and recreational services
11_Government services, n.i.e.Armenia
Azerbaijan
Belarus
Georgia
Moldova
Ukraine
EaP
countries0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Figure 2.5. Distribution of services exports (2011-2019) by EBOPS one-digit*
* The category ‘other business services’ includes the following: research and development services; professional and
management consulting services; technical, trade-related and other business services; waste treatment and de-pollution,
agricultural and mining services; operating leasing services; and trade-related services.
80
Part 2 Analysis of economic and innovation potential
Data for 8 years have been used (2011-2018) for
the mapping analysis, divided in three periods to
measure changes over time similar to the eco-
nomic mapping using Orbis data, i.e. 2011-2014,
2013-2016 and 2015-2018.
Methodology
Export specialisation is measured by comparing
relative export shares for each country to the un-
weighted average export shares of the EaP coun-
tries:
■service categories with a current
strength, including specialised service cate-
gories with critical mass, where the degrees
of specialisation for export volumes are above
predefined thresholds;
■service categories with an emerging
strength, including emerging service catego-
ries with increasing degrees of specialisation,
where the change in the degree of speciali-
sation for export volumes is above predefined
thresholds.
For all service categories, the following indicators
have been calculated for each EaP country:
■the degree of specialisation for export values
for each year in the 2011-2018 period; ■average relative share of export values for
each year in the 2011-2018 period;
■rate of change in degree of specialisation for
export values for three time periods: between
2011 and 2014, between 2013 and 2016 and
between 2015 and 2018.
Degrees of specialisation have been calculat-
ed relative to the unweighted average of the six
EaP countries. Specialised service categories
with critical mass are identified as those service
categories | [
"exports",
"in",
"transporta-",
"\n",
"tion",
"and",
"travel",
"dominate",
"total",
"services",
"exports",
",",
"as",
"\n",
"shown",
"in",
"Figure",
"2.5",
".",
"Exports",
"of",
"transportation",
"ser-",
"\n",
"vices",
"account",
"for",
"more",
"than",
"65",
"%",
"of",
"Ukrainian",
"ser-",
"\n",
"vices",
"exports",
".",
"Exports",
"of",
"travel",
"services",
"account",
"\n",
"for",
"almost",
"60",
"%",
"of",
"services",
"exports",
"in",
"Armenia",
",",
"\n",
"Azerbaijan",
"and",
"Georgia",
".",
"Exports",
"of",
"communica-",
"\n",
"tion",
"services",
"exceed",
"10",
"%",
"of",
"the",
"total",
"exports",
"in",
"\n",
"Moldova",
",",
"exports",
"of",
"construction",
"services",
"exceed",
"\n",
"10",
"%",
"of",
"the",
"total",
"exports",
"in",
"Armenia",
",",
"exports",
"of",
"\n",
"computer",
"and",
"information",
"services",
"exceed",
"10",
"%",
"of",
"\n",
"exports",
"in",
"Ukraine",
".",
"\n",
"1_Transportation",
"\n",
"2_Travel",
"\n",
"3_Communications",
"services",
"\n",
"4_Construction",
"services",
"\n",
"5_Insurance",
"services",
"\n",
"6_Financial",
"services",
"\n",
"7_Computer",
"and",
"information",
"services",
"\n",
"8_Royalties",
"and",
"license",
"fees",
"\n",
"9_Other",
"business",
"services",
"\n",
"10_Personal",
",",
"cultural",
",",
"and",
"recreational",
"services",
"\n",
"11_Government",
"services",
",",
"n.i.e",
".",
"Armenia",
"\n",
"Azerbaijan",
"\n",
"Belarus",
"\n",
"Georgia",
"\n",
"Moldova",
"\n",
"Ukraine",
"\n",
"EaP",
"\n",
"countries0",
"%",
"10",
"%",
"20",
"%",
"30",
"%",
"40",
"%",
"50",
"%",
"60",
"%",
"70",
"%",
"80",
"%",
"90",
"%",
"100%Figure",
"2.5",
".",
"Distribution",
"of",
"services",
"exports",
"(",
"2011",
"-",
"2019",
")",
"by",
"EBOPS",
"one",
"-",
"digit",
"*",
"\n",
"*",
"The",
"category",
"‘",
"other",
"business",
"services",
"’",
"includes",
"the",
"following",
":",
"research",
"and",
"development",
"services",
";",
"professional",
"and",
"\n",
"management",
"consulting",
"services",
";",
"technical",
",",
"trade",
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"\n",
"80",
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"2",
"Analysis",
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] | [] |
for decarbonisation and competitiveness.
If Europe’s ambitious climate targets are matched by a coherent plan to achieve them, decarbonisation will be an
opportunity for Europe. But if we fail to coordinate our policies, there is a risk that decarbonisation could run contrary
to competitiveness and growth.
Even though energy prices have fallen considerably from their peaks, EU companies still face electricity prices
that are 2-3 times those in the US. Natural gas prices paid are 4-5 times higher. This price gap is primarily driven by
Europe’s lack of natural resources, but also by fundamental issues with our common energy market. Market rules
prevent industries and households from capturing the full benefits of clean energy in their bills. High taxes and rents
captured by financial traders raise energy costs for our economy.
Over the medium term, decarbonisation will help shift power generation towards secure, low-cost clean energy
sources. But fossil fuels will continue to play a central role in energy pricing at least for the remainder of this decade.
Without a plan to transfer the benefits of decarbonisation to end-users, energy prices will continue to weigh on
growth.
06
THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | FOREWORDThe global decarbonisation drive is also a growth opportunity for EU industry. The EU is a world leader in clean
technologies like wind turbines, electrolysers and low-carbon fuels, and more than one-fifth of clean and sustainable
technologies worldwide are developed here.
Yet it is not guaranteed that Europe will seize this opportunity. Chinese competition is becoming acute in industries
like clean tech and electric vehicles, driven by a powerful combination of massive industrial policy and subsidises,
rapid innovation, control of raw materials and ability to produce at continent-wide scale.
The EU faces a possible trade-off. Increasing reliance on China may offer the cheapest and most efficient route
to meeting our decarbonisation targets. But China’s state-sponsored competition also represents a threat to our
productive clean tech and automotive industries.
Decarbonisation must happen for the sake of our planet. But for it also to become a source of growth for Europe,
we will need a joint plan spanning industries that produce energy and those that enable decarbonisation such as
clean tech and automotives.
The third area for action is increasing security and reducing dependencies.
Security is a precondition for sustainable growth. Rising geopolitical risks can increase uncertainty and dampen
investment, while major geopolitical shocks or | [
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] | [] |
more granular, second-level NABS – further effort
was devoted to remove redundant NACE sectors
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation235
associated with a given Scopus field. As a con-
crete example of this issue, the Scopus subject
field ‘Food Science’ was mapped to NABS 6.41
‘Manufacture of food products’, part of the broad-
er NABS06 ‘Industrial production and technology’.
The concordance table, however, maps the entire
NABS 06 to the entire Manufacturing NACE sector,
so that by merely applying the concordance, NABS
6.41 ‘Manufacture of food products’ (and, in turn,
the ‘Food Science’ science field) is mapped to any
NACE Manufacturing sector, even outside the Food
industry. Therefore, the final refinement effort
was, in practice, focused on removing redundant
and undesired NACE sectors from the mapping.
As per the case of patents, publications can also
be classified into more than one single taxon and,
similar to that observed for patents, broad taxons
can sometimes outnumber specific classifications
when grouping per S&T domain. For this reason,
to express S&T domains in terms of Scopus ASJC
fields, it was decided to focus again on the rel-
ative most frequent ASJC subject fields per S&T
domain, rather than absolute numbers, to identify
distinctive taxonomic areas to carry out mappings
from S&T domains to NACE sectors.
The final tables mapping Scopus subject fields to
NACE for each EaP country are presented in Annex
4. These mappings are complementary to those
obtained via the patent classification, as both pro-
vide relevant linkages between S&T domains and
economic sectors.
On expert assessment, the results of this mapping
exercise as expressed by pairs of S&T domains
and three-digit NACE codes seems satisfactory.
The specificity and granularity of the subject field
regarding NABS to NACE code concordances offers
a highly compelling and descriptive list of NACE
codes for each S&T domain.
3. Results of the mapping ex-
ercise
The result of the current exercise provides a se-
ries of economic clusters for every EaP country,
each characterised by the respective E&I domains (identified in Part 2 via NACE) and S&T domains
(identified in Part 3). The names of the clusters are
inspired by the report Methodology and Findings
Report for a Cluster Mapping of Related Sectors73.
The clusters are defined based on groups of NACE
sectors, while the presence of a given S&T domain
in a specific cluster is produced by | [
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"\n",
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"is",
"produced",
"by"
] | [] |
ta-
bles, adapted according to an ex post quality con-
trol of the alignment results.Since the objective is to find how S&T specialisa-
tion domains and excellence can be mobilised to
support knowledge-based economic transforma-
tion, the E&I classification, expressed in the form
of NACE codes, is the touchstone of the alignment.
As could be expected, some of the domains ex-
tracted from S&T sources directly align with E&I
specialisations, supporting knowledge-based eco-
nomic transformation, while others are discon-
nected from E&I domains as they either feature
basic science or may potentially contribute to the
emergence of new niches if science-business links
are established.
Step 3 offers direct evidence to answer re-
search Question 5 ‘Are there possible synergies/
concordances between the countries’ economic,
innovation, scientific and technological specialisa-
tions?’ and provides the results addressing the key
ambition of this report: ‘a list of potential Smart
Specialisation priority domains for each country
and the potential cooperation areas for the whole
region and with international partners’.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation33
Topic modelling (TM) is a domain of machine learning which aims at identifying the
topics handled by collections of texts. TM is a very useful tool for conducting an emerg-
ing analysis of research, technology and innovation ecosystems.
The topics identified by TM are sets of words frequently co-occurring, i.e. which reg-
ularly appear together in the analysed textual records (such as publication abstracts
or R&I project descriptions). TM is automated, but, because it is an unsupervised ma-
chine-learning technique, still requires partial human assessment (for instance, to link
intelligible labels to each extracted topic). Some solutions exist to fully automate the
process, but these solutions do not yet provide sufficiently clear results to label the
topics autonomously and transparently.
The advantages of TM over taxonomic classifications and keyword extraction are
that:
■the subjects come from the terms actually used by specialists (scientists, engi-
neers, technicians, policymakers, project managers, etc.);
■this procedure makes it possible to identify transverse concepts contributing to
several subjects;
■topics are related to one another through diverse strengths, allowing for partial
overlapping, vertical and horizontal relationships as well as the existence of core
or fundamental topics.
The usefulness of TM is clearer when used in parallel with the metadata associated
with the analysed documents, such as geographic or institutional information. It then
becomes possible to observe the respective | [
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fingers ES 1.65*** 0.25 ✕ ✓
Fish fingers SE 2.02*** 0.30 ✕ ✓
Crisps ES 1.27 0.96 ✕ ✕
Crisps SE 3.56*** 0.98 ✓ ✕
Source: Authors ’ elaboration
]The level of significance refers to H3a.
Table A3
Difference between the absence and presence of the ‘made for’ claim of the difference in WTP between generic foreign and generic domestic products.
Country Product Version Mean]Std Dev. H2aG H2bG
Western Countries
Germany Yogurt HU 0.39*** 0.06 ✓ ✓
(Euro) Yogurt LI 0.34*** 0.06 ✓ ✓
Spaghetti sauce HU 0.17*** 0.04 ✓ ✓
Spaghetti sauce LI 0.22*** 0.05 ✓ ✓
Cookies HU 0.14*** 0.05 ✓ ✓
Cookies LI 0.39*** 0.05 ✓ ✓
Spain Soft drink RO 0.02 0.05 ✕ ✕
(continued on next page)D.M. Federica et al. Food Policy 131 (2025) 102803
11 Table A3 (continued )
Country Product Version Mean]Std Dev. H2aG H2bG
(Euro) Soft drink SE 0.1*** 0.06 ✓
Fish fingers RO 0.7*** 0.18 ✓ ✓
Fish fingers SE 0.61*** 0.14 ✓
Crisps RO 0.58*** 0.09 ✓ ✓
Crisps SE 0.38*** 0.09 ✓
Sweden Soft drink ES 4.72*** 0.60 ✓
(Krona) Soft drink RO 7.34*** 0.76 ✓ ✓
Fish fingers ES 0.47 1.74 ✓
Fish fingers RO 7.66*** 1.79 ✓ ✓
Crisps ES 13.97*** 1.43 ✓
Crisps RO –22.63*** 1.57 ✓ ✓
Eastern Countries
Hungary Yogurt DE 14.13*** 12.77 ✕ ✓
(Forint) Yogurt LI 170.1*** 13.47 ✓
Spaghetti sauce DE 330.89*** 26.71 ✓ ✕
Spaghetti sauce LI 217.33*** 34.37 ✓
Cookies DE 31.11*** 13.84 ✓ ✓
Cookies LI 199.97*** 15.03 ✓
Lithuania Yogurt DE 0.01 0.09 ✕ ✕
(Euro) Yogurt HU 0.3*** 0.11 ✓
Spaghetti sauce DE 0.01 0.08 ✕ ✕
Spaghetti sauce HU 0.16** 0.09 ✓
Cookies DE 0.05 0.08 ✕ ✕
Cookies HU 0.23*** 0.08 ✓
Romania Soft drink ES 35.43 130.53 ✕ ✕
(Leu) Soft drink SE 8.99 40.26 ✕ ✕
Fish fingers ES 0.26 0.32 ✕ ✕
Fish fingers SE 0.54 0.38 ✕ ✕
Crisps ES 4.21* 2.32 ✕ ✓
Crisps SE 4.95** 2.34 ✕ ✓
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 | [
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8. C. Budtz-Jørgensen, H.-H. Knitter, C. Straede et al., A
twin ionization chamber for fission fragment detection. Nucl.Instr. Meth. A 258(2), 209–220 (1987). https://doi.org/10.1016/
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excited states of91Rb,93Rb, and95Rb. Phys. Rev. C 82(2), 024302
(2010). https://doi.org/10.1103/PhysRevC.82.024302
18. W. Urban, K. Sieja, G.S. Simpson et al., Isomeric levels in92Rb
and the structure of neutron-rich92,94Rb isotopes. Phys. Rev. C 85,
014329 (2012). https://doi.org/10.1103/PhysRevC.85.01432919. C.M. Baglin, Nuclear Data Sheets for A=92. Nucl. Data Sheets
113(10), 2187–2389 (2012). https://doi.org/10.1016/j.nds.2012.
10.001
20. I. Tsekhanovich, G.S. Simpson, W. Urban et al., Short-lived iso-
mers in94Rb. Phys. Rev. C 78, 011301 (2008). https://doi.org/10.
1103/PhysRevC.78.011301
21. M. Czerwi´ nski, T. Rza ˛ca-Urban, W. Urban et al., Neutron-proton
multiplets in the nucleus88Br. Phys. Rev. C 92, 014328 (2015).
https://doi.org/10.1103/PhysRevC.92.014328
22. J. Chen, B. Singh, Nuclear Data Sheets for A=98. Nucl. Data Sheets
164, 1–477 (2020). https://doi.org/10.1016/j.nds.2020.01.001
23. N. Nica, Nuclear Data Sheets for A=97. Nucl. Data Sheets 111(3),
525–716 (2010). https://doi.org/10.1016/j.nds.2010.03.001
24. W. Urban, J. Pinston, T. Rzaca-Urban et al., First observation of
theν9/2[404] orbital in the A ∼100 mass region. Eur. Phys. J. A
16(1), 11–15 (2003). https://doi.org/10.1140/epja/i2002-10104-y
25. F. Boulay, G.S. Simpson, Y . Ichikawa et al., g Factor of the
99Zr (7/2+) Isomer: Monopole Evolution in the Shape-Coexisting
Region. Phys. Rev. Lett. 124(11), 112501 (2020). https://doi.org/
10.1103/PhysRevLett.124.112501
26. A. Pfeil, K. Nomura, N. Gavrielov et al., Lifetime measure-
ments in99Nb and99Zr: Investigation of shape coexistence. Phys.
Rev. C 108, 034310 (2023). https://doi.org/10.1103/PhysRevC.
108.034310
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IV. Selected S&T specialisation domains in ArmeniaIn Armenia, Agrifood correlates highly with the
S&T domains Biotechnology and Health and
wellbeing, notably in the healthy sweetener
industry;
■owing to the country’s hard sciences strengths,
the domain Nanotechnology and materials
presents a notable critical mass and a rele-
vant number of EC projects, with an orienta-
tion to fundamental fields (such as Condensed
matter, or Electronic, optical and magnetic
materials);
■Health and wellbeing presents a large crit-
ical mass and specialisation in publications,
and important activity in EC projects. The sci-
entific publications cluster in the subject field
General medicine, followed at a distance by
Genetics, Public Health and Biochemistry.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation17
Azerbaijan – Summary of the strengths
of the S&T specialisations
Azerbaijan presents a rather diversified S&T pan-
orama, but in several S&T domains it is oriented
towards the oil and petrochemical industries from
a variety of disciplines and technologies. Its most
highlighted S&T domains are the following:
■Chemistry and chemical engineering
presents a notable critical mass in publica-
tions and patents, as well as a high scientif-
ic specialisation and citation impact. Highly
co-occurrent with Energy and Environmental
sciences and industries, Azeri chemistry and
chemical engineering is well-aligned with
the petrochemical industry, with particular
strengths in catalysis and synthesis processes
in organic chemistry;
■Energy presents a notable scientific and
technological specialisation, as well as a crit-
ical mass in patents. In accordance with the country’s Chemistry and chemical engineering
acumen, S&T activity in Energy is oriented to-
wards the oil and petrochemical industry;
■Mechanical engineering and heavy ma-
chinery presents a scientific and technologi-
cal specialisation, critical mass in patents and
a high scientific citation impact. This domain
frequently overlaps with Energy;
■Health and wellbeing presents a high crit-
ical mass and specialisation in both publica-
tions and patents. The scientific publications
cluster in the subject field ‘General medicine’,
followed at a distance by ‘Cardiology and car-
diovascular medicine’, and patents in the phar-
maceutical and medical devices classes (A61).
AZERBAIJAN Critical mass Specialisation Excellence Summary
S&T domain Pubs. Pat. Pubs. Pat. NCI*EC
projects*Total
Agrifood 2
Biotechnology 2
Chemistry and chemical
engineering4
Energy 4
Environmental sciences and
industries1
Fundamental physics and
mathematics2
Governance, culture, education
and the economy3
Health and wellbeing 4
ICT and computer science 3
Mechanical engineering and
heavy machinery4
Nanotechnology and materials 1
Optics and photonics | [
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"NCI*EC",
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"\n",
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"\n",
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"1",
"\n",
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] | [] |
A. Massucci (SIRIS Academic)
Hugo Hollanders (Maastricht University)
Monika Matusiak, Ramojus Reimeris
(European Commission – Joint Research Centre)EDITORS
AUTHORSJRC TECHNICAL REPORT
Smart Specialisation in the
Eastern Partnership countries
Potential for knowledge-based
economic cooperation
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperationI
TABLE OF CONTENTS
ABSTRACT .................................................................................................. 1
EXECUTIVE SUMMARY ............................................................................. 2
Main results – The economic, innovation, scientific and technological
(EIST) specialisation domains of Eastern Partnership countries ..... 4
Overview of economic, innovation, scientific and technological spe-
cialisations ........................................................................................................... 9
1. Economic and innovation (E&I) potential in the Eastern Partnership coun-
tries ............................................................................................................................................ 9
2. Scientific and technological (S&T) potential in the Eastern Partnership
countries ................................................................................................................................. 12
Part 1. Introduction and methodology .................................................... 27
1. Introduction, study objectives and key requirements ....................................... 27
2. Methodological approach ............................................................................................... 27
3. Key constraints and limitations ................................................................................... 34
Part 2. Analysis of economic and innovation potential .................... 38
1. Introduction .......................................................................................................................... 38
2. Economic potential ............................................................................................................ 38
3. Innovation potential .......................................................................................................... 85
4. Specialisations resulting from the economic and innovation analysis ..119
5. Common E&I specialisations in the EaP region ................................................. 141
Part 3. Analysis of scientific and technological potential ............. 144
1. Introduction ....................................................................................................................... 144
2. Identification of the S&T specialisation domains in the Eastern Partner-
ship ........................................................................................................................................ 144
3. Characterisation of the S&T specialisation domains ..................................... 153
4. Critical mass, specialisation and excellence indicators in the S&T speciali-
sation domains ................................................................................................................. 173
5. Identification of the main actors and collaboration patterns within the S&T
specialisation domains ................................................................................................. 195
6. Summary of the strengths of each S&T specialisation domain for each EaP
country ................................................................................................................................. 218
II
Table of contents
Part 4. Identification of concordances between the economic, inno-
vation, scientific and technological potentials .................................. 230
1. Introduction ....................................................................................................................... 230
2. Methodology ...................................................................................................................... 231
3. Results of the mapping exercise .............................................................................. 235
4. Potential for EaP collaboration in combined EIST domains ......................... 246
Part 5. Discussion of results and final remarks ................................ 249
REFERENCES ........................................................................................ 254
LIST OF ABBREVIATIONS ................................................................... 256
LIST OF FIGURES ................................................................................. 258
LIST OF TABLES ................................................................................... 264
Annex 1. Results of the full economic mapping analysis for Georgia,
Moldova and Ukraine ................................................................................... 269
Annex 2. Results of the partial economic mapping analysis for Man-
ufacturing for five EaP countries ........................................................... 297
Annex 3. Results of the mapping analysis for goods exports ...... 301
Annex 4. Concordance between IPC and NACE ................................... 321
Annex 5. | [
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] | [] |
2 2 7
3 1 13 3 2 36
EC projectsAM
AZ
BY
GE
MD
UA
Other
AM 34 19 19 699
AZ 12 9 2 22 284
BY 34 12 15 33 328 2880
GE 19 9 15 3 32 348
MD 2 33 3 61 863
UA 19 22 328 32 61 9 112
PublicationsFigure 3.58. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Nanotechnology and materials’ domain
Colour indicates the relative distribution of documents, computed row-wise.
216
Part 3 Analysis of scientific and technological potential
Regional collaboration in Optics and pho-
tonics
Similar trends can be observed in the case of Op-
tics and photonics. Most collaborations are with
external partners. There are few EC projects in col-
laboration within the EaP.Regional collaboration in Transportation
In terms of Transportation publications, there
are very few. The highest number of collaborations
are Ukraine and Belarus with external partners.
AM
AZ
BY
GE
MD
UA
Other
1 1 1
1 1 4 12
1 2
1 4 7
EC projectsAM
AZ
BY
GE
MD
UA
Other
AM 2 17 6 1 6 296
AZ 2 3 3 1 2 45
BY 17 3 5 10 63 826
GE 6 3 5 1 10 257
MD 1 1 10 1 10 70
UA 6 2 63 10 10 1 988
PublicationsFigure 3.59. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Optics and photonics’ domain
Colour indicates the relative distribution of documents, computed row-wise.
AM
AZ
BY
GE
MD
UA
Other
3
17
EC projectsAM
AZ
BY
GE
MD
UA
Other
AM
AZ
BY 10 66
GE
MD
UA 10 505
PublicationsFigure 3.60. Number of publications and EC projects in collaboration between EaP actors in different countries, in the
‘Transportation’ domain
Colour indicates the relative distribution of documents, computed row-wise.
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation217
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 | [
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"17",
"6",
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"296",
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"\n",
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"\n",
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"17",
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"\n",
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"\n",
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"\n",
"Other",
"\n",
"AM",
"\n",
"AZ",
"\n",
"BY",
"10",
"66",
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"GE",
"\n",
"MD",
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"UA",
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"\n",
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"\n",
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"140",
"127",
"150",
"105",
"84",
"113",
"66",
"42",
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"966",
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"146",
"159",
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"186",
"633",
"399",
"356",
"287",
"116",
"143",
"119",
"86",
"54",
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"304",
"173",
"248",
"182",
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] | [] |
venture capital
and start-ups, and specialised performance in
related services exports.;
■information and communication (NACE
26, 61-63) based on specialised performance
in related patents, specialised performance in
venture capital and start-ups, the identifica-
tion of an information and communication
technologies cluster and specialised perfor-
mance in related services exports.
E&I specialisations for Azerbaijan
Summary table S.2 for Azerbaijan combines
the results of the various economic and innova-
tion mappings. Different colours have been used
to identify commonalities matching descriptions
of industry names, export categories and cluster
names, where possible. The economic and inno-
vation analysis shows the following E&I speciali-
sations:
■coke and refined petroleum products
(NACE 19) based on an economic specialisa-
tion, specialised performance in related pat-
ents, the identification of a petrochemicals
cluster and specialised performance in related
goods exports;
■chemicals and related activities (NACE
20) based on an economic specialisation, and
specialised performance in related patents;
■repair and installation of machinery and
equipment (NACE 33) based on an economic
specialisation, and specialised performance in
related patents;
■computer programming, consultancy and
related activities (NACE 62) based on spe-
cialised performance in related patents, and
specialised performance in related services
exports;
■financial services (NACE 64) based on spe-
cialised performance in related patents.E&I specialisations for Georgia
Summary table S.3 for Georgia combines the re-
sults of the various economic and innovation map-
pings. Different colours have been used to identify
commonalities matching descriptions of indus-
try names, export categories and cluster names,
where possible. The economic and innovation
analysis shows the following E&I specialisations:
■food and beverages (NACE 10, 11) based
on an economic specialisation, and specialised
performance in related goods exports;
■publishing, printing and recorded media
(NACE 18) based on an economic specialisa-
tion and an innovation specialisation;
■fabricated metal products, except ma-
chinery and equipment (NACE 25) based
on an economic specialisation, the identifica-
tion of an industrial manufacturing and pro-
cesses cluster and specialised performance in
related goods exports;
■tourism and travel (NACE 55, 56) based on
an economic specialisation, an innovation spe-
cialisation, specialised performance in related
goods exports and specialised performance in
related services exports;
■financial service activities (NACE 62, 64)
based on an economic specialisation, special-
ised performance in venture capital and start-
ups and specialised performance in related
services exports.
E&I specialisations for Moldova
Summary table S.4 for Moldova combines the re-
sults of the various economic and innovation map-
pings. | [
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(Marketing Science Review. (2023). "The Power of Personalization in Email Campaigns," Journal of Marketing Science, 35(1), 22-34).
Key Challenges in Digital Transformation for Enterprises
(Direct Citations to Scholarly Works)
Adoption of Cloud Technologies
– Tech Strategies Journal (2023) reports that cloud adoption leads to a 30% reduction in IT costs for businesses, while enhancing scalability (Tech Strategies Journal. (2023). "Cloud Technology and Its Impact on Business Operations," Journal of Cloud Computing, 11(2), 42-56).
– However, integrating cloud services with existing IT systems remains a challenge for many enterprises, as highlighted by Global IT Review (2022) (Global IT Review. (2022). "Challenges in Cloud Integration for Enterprises," Journal of IT Strategy, 10(4), 118-129).
Digital Skills Gap
– Deloitte (2023) states that upskilling employees in digital competencies is crucial for future competitiveness, with businesses investing in training programs experiencing a 25% increase in productivity (Deloitte. (2023). "The Digital Skills Gap in the Workforce," Journal of Business and Technology, 20(3), 64-78).
– Similarly, the World Economic Forum (2022) reports that 40% of companies struggle to find qualified digital talent, hindering digital transformation efforts (World Economic Forum. (2022). "Bridging the Digital Skills Gap," Global Employment Trends, 34(1), 19-30).
Cybersecurity Concerns
– According to TechWorld (2023), cybersecurity threats grow with digital transformation, requiring enhanced security protocols (TechWorld. (2023). "Cybersecurity Challenges in Digital Transformation," Cybersecurity and Technology Journal, 17(2), 51-65).
– Security Today (2021) highlights that organizations with strong cybersecurity frameworks see a 50% reduction in data breaches (Security Today. (2021). "Mitigating Cybersecurity Risks in Digital Business," Journal of Cybersecurity, 12(3), 72-85).
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NiMH batteries.
Figure 7. NiMH voltage and current profiles for rated capacity analysis according to IEC 619512, for
(a) AAA GP, (b) AA Duracell, (c) C Energizer, (d) D Duracell and (e) 9V Energizer battery.
The AAA and AA NiMH battery voltage and current profiles are presented in Figure
7a,b. As expected, the AAA and AA batteries have similar voltage and current shape pro-
files. In both cases, the batteries do not reach the manufacturer’s rated capacity in the first
cycle. However, in the second cycle, both batteries reached the rated capacity of 650 mAh
for AAA and 2500 mAh for AA, with a discharge time longer than 5 h (thus meeting the
requirement in IEC 61951-2). Similar behavior is observed for the C and D NiMH batteries
(see Figure 7c,d). Also in this case, both batteries reached the manufacturer-rated capacity
after the second cycle. The 9V battery consists of 7 cells connected in series. The voltage
Figure 7. NiMH voltage and current profiles for rated capacity analysis according to IEC 619512, for
(a) AAA GP , ( b) AA Duracell, ( c) C Energizer, ( d) D Duracell and ( e) 9V Energizer battery.
The AAA and AA NiMH battery voltage and current profiles are presented in
Figure 7a,b . As expected, the AAA and AA batteries have similar voltage and current
shape profiles. In both cases, the batteries do not reach the manufacturer’s rated capacity
in the first cycle. However, in the second cycle, both batteries reached the rated capacity ofBatteries 2025 ,11, 30 11 of 20
650 mAh for AAA and 2500 mAh for AA, with a discharge time longer than 5 h (thus meet-
ing the requirement in IEC 61951-2). Similar behavior is observed for the C and D NiMH
batteries (see Figure 7c,d). Also in this case, both batteries reached the manufacturer-rated
capacity after the second cycle. The 9V battery consists of 7 cells connected in series. The
voltage profile rises continuously during charging from 7 V to 10 V (Figure 7e); during
discharge, the battery reaches the rated capacity in the second charge/discharge cycle. It is
observed that the charging and discharging currents tend to have small changes in the 9V
battery; this may be due to the connections between the cells that create resistance for the
current to be fully constant. However, the fluctuation of the current is 1% to 3%, which, to
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] | [] |
abroad or modernisation by other means,
such as technology purchasing or advanced ser-
vices.
Highlighted S&T domains for which a specific con-
cordance with E&I domains was not found would
benefit most from ‘push’ policies, such as deep
tech entrepreneurship, or the internationalisation
of transfer strategies (notably IP and contract re-
search). In parallel, several of the highlighted S&T
domains belong to the hard and formal sciences
and produce versatile profiles with strong skills in
modelling, computation or experimentation. These
human resources can certainly be absorbed and
support the modernisation of existing economic
sectors as well as the growth of fast-moving nich-
es, notably in the digital field.
Finally, EIST domains where a direct concord-
ance was found would benefit the most from
demand-side policies connecting companies with
the knowledge sector, such as subsidised contract
research or innovation vouchers, from investment
in technological platforms and from networking
instruments such as clusters.
It is natural that the E&I and S&T landscape in an-
alysed countries will change, but likely that these
changes will not happen quickly, especially in the
domain of research. Identified niches (potential
priority domains) may grow and mature; new nich-
es or a new combination from the existing niches
may appear. For identification and understanding
of such new domains, a completely new analy-
sis or an addition to this one will be required. In
any case, this study will still be a strong point of
departure for many years to come. The detailed
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation253
methodology and data used are presented in the
annexes, which in time can be supplemented with
new data for new analyses and questions.
The results of the analysis can inform meaningful
discussions in all circles of quadruple-helix par-
ticipants, potentially inspiring further thoughts on
better and more impactful research and innova-
tion policies.
254
References
REFERENCES
Concordancia de actividades de la CNAE con la clasificación de locarno, http://www.
oepm.es/export/sites/oepm/comun/documentos_relacionados/varios_todas_
modalidades/Concordancia_CNAE_LOCARNO.pdf
Cordis, https://cordis.europa.eu/home_en.html
Crunchbase, https://www.crunchbase.com
European Cluster Collaboration Platform, https://www.clustercollaboration.eu/clus-
ter-mapping
European Patent Office, EPO worldwide bibliographic data (DOCDB), https://www.
epo.org/searching-for-patents/data/bulk-data-sets/docdb.html
Griffiths, T. L. and Steyvers, M., Finding scientific topics, Proceedings of the National
Academy of Sciences, USA, 101,5228-5235, 2004
Joint communication to the European Parliament, the European Council, the Council,
the European Economic and Social Committee and the Committee of the Regions -
Eastern Partnership policy beyond 2020, Brussels 18/03/20
Joint Staff Working Document. Eastern Partnership policy beyond 2020. Brussels,
18.3.2020 JOIN (2020)
| [
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"\n",
"References",
"\n",
"REFERENCES",
"\n",
"Concordancia",
"de",
"actividades",
"de",
"la",
"CNAE",
"con",
"la",
"clasificación",
"de",
"locarno",
",",
"http://www",
".",
"\n",
"oepm.es/export/sites/oepm/comun/documentos_relacionados/varios_todas",
"_",
"\n",
"modalidades",
"/",
"Concordancia_CNAE_LOCARNO.pdf",
"\n",
"Cordis",
",",
"https://cordis.europa.eu/home_en.html",
"\n",
"Crunchbase",
",",
"https://www.crunchbase.com",
"\n",
"European",
"Cluster",
"Collaboration",
"Platform",
",",
"https://www.clustercollaboration.eu/clus-",
"\n",
"ter",
"-",
"mapping",
"\n",
"European",
"Patent",
"Office",
",",
"EPO",
"worldwide",
"bibliographic",
"data",
"(",
"DOCDB",
")",
",",
"https://www",
".",
"\n",
"epo.org/searching-for-patents/data/bulk-data-sets/docdb.html",
"\n",
"Griffiths",
",",
"T.",
"L.",
"and",
"Steyvers",
",",
"M.",
",",
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"scientific",
"topics",
",",
"Proceedings",
"of",
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"National",
"\n",
"Academy",
"of",
"Sciences",
",",
"USA",
",",
"101,5228",
"-",
"5235",
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"Joint",
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"European",
"Parliament",
",",
"the",
"European",
"Council",
",",
"the",
"Council",
",",
"\n",
"the",
"European",
"Economic",
"and",
"Social",
"Committee",
"and",
"the",
"Committee",
"of",
"the",
"Regions",
"-",
"\n",
"Eastern",
"Partnership",
"policy",
"beyond",
"2020",
",",
"Brussels",
"18/03/20",
"\n",
"Joint",
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",",
"\n",
"18.3.2020",
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from 13.1 to 14.6 cm from the252Cf source. A schematic
view of this setup is depicted in Fig. 1, and the position of
the LaBr 3(Ce) detector is reported in Table 1.
As described in Ref. [ 1], the LaBr 3(Ce) detectors were
energy-calibrated from 80 keV to 9 MeV using standard
sources. The energy resolution of the detectors was also
obtained from this calibration data. For Eγ=661.7 keV, the
relative Full Width at Half Maximum (FWHM) lied between
2.7 % and 3 %, as indicated in Table 1.
In addition to their good energy resolution, the LaBr 3(Ce)
detectors also have excellent timing properties. γ-γcoinci-
dence measurements were performed on various calibration
sources, as depicted in Fig. 2. The time resolution (FWHM)
associated with these coincidence spectra was about 0.4 ns forthe
60Co (1173 keV and 1332 keV). It worsened at lower ener-
gies, e.g., for the152Eu coincidence (344 keV and 799 keV)
where the FWHM became 0.7 ns. In Fig. 2,t h e133Ba time
spectrum (80 keV/356 keV coincidence) demonstrates how
the good time resolution of the detectors makes them well
suited for half-life determination at the nanosecond scale.
Along with these γ-ray detectors, the setup contained an
ionization chamber similar to the one described in Ref. [ 7],
which served two purposes. First, it gave the fission trigger,
i.e., the time stamp corresponding to the detection of a fissionevent. This trigger, combined with the timing information
from the scintillators, was used to sort the γevents relative
to fission, which enabled us to distinguish prompt transitionsfrom isomeric level decays. As can be observed in Fig. 3,w e
observed three main sources of γ-rays following fission: the
γ-rays coming from the decay of the isomers, but also the
prompt γ-rays (prompt peak), and neutrons. This last con-Fig. 1 Schematic representation of the VESPA setup for isomers stud-
ies, consisting in five 2/prime/prime×2/prime/primeLaBr 3(Ce) detectors placed around a twin
Frisch-grid ionization chamber. B(resp. S) indicates the backing side
(resp. source side) of the IC
Fig. 2 Coincident γ-γtime spectra between any LaBr 3(Ce) com-
bination from standard sources:60Co (1173 keV, 1332 keV),152Eu
(344 keV, 799 keV) and133Ba (80 keV, 356 keV)
123Eur. Phys. J. A (2025) 61:5 Page 3 of 12 5
Table 1 Characteristics of the LaBr 3(Ce) detectors used in the VESPA
setup. The position of the detectors is defined by the distance rbetween
the source (at the | [
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"1. Beggiato S, Tomasini MC, Cassano T, Ferraro L. Chronic Oral Palmitoylethanolamide Administration Rescues Cognitive Deficit and Reduces Neuroinflammation, Oxidative Stress, and Glutamate Levels in A Transgenic Murine Model of Alzheimer's Disease. J Clin Med. 2020 Feb 5;9(2):428.
2. Beggiato S, Ieraci A, Tomasini MC, Schwarcz R, Ferraro L. Prenatal THC exposure raises kynurenic acid levels in the prefrontal cortex of adult rats. Prog Neuropsychopharmacol Biol Psychiatry. 2020 Feb 4;100:109883. doi: 10.1016/j.pnpbp.2020.109883.
3. Borroto-Escuela DO, Narváez M, Romero-Fernández W, Pinton L, Wydra K, Filip M, Beggiato S, Tanganelli S, Ferraro L, Fuxe K. Acute Cocaine Enhances Dopamine D2R Recognition and Signaling and Counteracts D2R Internalization in Sigma1R-D2R Heteroreceptor Complexes. Mol Neurobiol. 2019 Oct;56(10):7045-7055. doi: 10.1007/s12035-019-1580-8.
4. Secci ME, Mascia P, Sagheddu C, Beggiato S, Melis M, Borelli AC, Tomasini MC, Panlilio LV, Schindler CW, Tanda G, Ferré S, Bradberry CW, Ferraro L, Pistis M, Goldberg SR, Schwarcz R, Justinova Z. Astrocytic Mechanisms Involving Kynurenic Acid Control Δ9-Tetrahydrocannabinol-Induced Increases in Glutamate Release in Brain Reward-Processing Areas. Mol Neurobiol. 2019 May;56(5):3563-3575. doi: 10.1007/s12035-018-1319-y.
5. Borelli AC, Beggiato S, Ferraro L, Tanganelli S, Antonelli T, Tomasinia MC. Palmitoylethanolamide blunts Aβ42-induced astrocyte activation and improves neuronal survival in primary mouse cortical astrocyte-neuron co-cultures. J Alzheimers Dis. 2018;61(1):389-399. doi: 10.3233/JAD-170699.
"
"El Marroun, H., Klapwijk, E. T. Koevoets, M., Brouwer, R. M., Peters, S., van ’t Ent, D., Boomsma, D. I., Muetzel, R., Crone, E. A., Hulshoff Pol, H. E., & Franken, I. H. A. (in press). Alcohol use and brain morphology in adolescence: a longitudinal study in three different cohorts. European Journal of Neuroscience.
Lutz, M.C., Kok, R., Verveer, I., Malbec, M., Koot, S., van Lier, P., Franken, I.H.A. (in press). Diminished error-related negativity and error positivity in children and adults with externalizing problems and disorders: A meta-analysis on error processing. Journal of Psychiatry and Neuroscience.
Verveer, I., van der Veen, F.M., Shahbabaie, A., Remmerswaal, D., & Franken, I. H. A. (in press). Multi-session electrical neuromodulation effects on craving, relapse and cognitive functions in cocaine use disorder: A randomized, sham-controlled tDCS study. Drug and Alcohol Dependence.
Lee, R. S. C., Hoppenbrouwers, S., & Franken, I.H.A. (2019). A Systematic Meta-Review of Impulsivity and Compulsivity in Addictive Behaviors. Neuropsychology Review, 29(1), 14-26.
Niemantsverdriet, M. B. A., Slotema, C. W., van der Veen, F. M., van der Gaag, M., Sommer, I. E. C., Deen, M., & Franken, I. H. A. (2019). Sensory processing deficiencies in patients with borderline personality disorder who experience auditory verbal hallucinations. Psychiatry Research, 281, 112545. " | [
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