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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 identified EC projects in the domain | percentage of the total number
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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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[]
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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
[ "identification", "of", "\n", "domains", "following", "the", "topic", "modelling", ",", "some", "do-", "\n", "mains", "in", "some", "countries", "failed", "to", "emerge", "due", "to", "\n", "being", "too", "small", ",", "in", "particular", ":", "Transportation", "in", "Ar-", "\n", "menia", ",", "Azerbaijan", ",", "Georgia", "and", "Moldova", ";", "Electric", "\n", "and", "electronic", "technologies", "in", "Azerbaijan", "and", "En-", "\n", "ergy", "in", "Georgia", ".", "\n", "The", "first", "graphic", "presents", "data", "for", "publications", ".", "\n", "As", "already", "presented", "at", "the", "beginning", "of", "the", "cur-", "\n", "rent", "chapter", ",", "large", "science", "-", "oriented", "S&T", "domains", "\n", "dominated", "the", "EaP", "and", "the", "countries", ",", "notably", "\n", "Fundamental", "physics", "and", "mathematics", "and", "Na-", "\n", "notechnology", "and", "materials", ",", "particularly", "in", "Arme-", "\n", "nia", "and", "Georgia", ".", "Health", "and", "wellbeing", "also", "ranks", "\n", "highly", "in", "all", "countries", ",", "with", "small", "differences", "in", "\n", "rank", ".", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy", "has", "a", "high", "rank", "in", "most", "countries", ",", "al-", "\n", "though", "the", "share", "of", "publications", "in", "these", "domains", "\n", "varies", "notably", ".", "\n", "The", "second", "graphic", "presents", "data", "for", "patents", ",", "al-", "\n", "beit", "the", "low", "number", "of", "patents", "compiled", "for", "some", "\n", "of", "the", "countries", "(", "notably", "Armenia", "and", "Azerbaijan", ")", "\n", "must", "be", "taken", "into", "account", ".", "As", "expected", ",", "Mechan-", "\n", "ical", "engineering", "and", "heavy", "machinery", "dominates", "\n", "the", "EaP", ",", "with", "a", "relevant", "weight", "for", "Health", "and", "\n", "wellbeing", "(", "Azerbaijan", ",", "Moldova", ",", "Ukraine", ")", ",", "Electric", "\n", "and", "electronic", "technologies", "(", "peaking", "in", "Armenia", ")", ",", "\n", "Nanotechnology", "and", "materials", "and", "Agrifood", "(", "no-", "\n", "tably", "in", "Armenia", ",", "Georgia", ",", "Moldova", "and", "Ukraine", ")", ".", "\n", "Finally", ",", "the", "third", "graphic", "presents", "data", "for", "EC", "\n", "R&I", "projects", ".", "As", "noted", "earlier", ",", "the", "distribution", "of", "\n", "S&T", "domains", "across", "EC", "projects", "is", "not", "only", "af", "-", "fected", "by", "the", "underlying", "specialisation", "and", "initi-", "\n", "ative", "of", "EaP", "actors", ",", "but", "also", "by", "the", "orientation", "\n", "of", "the", "programmes", "(", "7th", "Framework", "Programme", "\n", "and", "Horizon", "2020", ")", "as", "well", "as", "the", "differences", "in", "\n", "accessing", "the", "funding", "by", "the", "EaP", "countries", ".", "Due", "\n", "to", "the", "nature", "of", "the", "programmes", "and", "the", "coop-", "\n", "eration", "framework", "between", "the", "EU", "and", "the", "EaP", "\n", "countries", ",", "Governance", ",", "culture", ",", "education", "and", "\n", "the", "economy", "is", "by", "far", "the", "largest", "S&T", "domain", "\n", "in", "projects", ".", "Some", "differences", "then", "emerge", ",", "with", "\n", "Nanotechnology", "and", "materials", "having", "consider-", "\n", "able", "weight", "in", "Ukraine", ";", "Environmental", "sciences", "in", "\n", "Azerbaijan", ",", "Georgia", ",", "Moldova", "and", "Ukraine", ";", "and", "ICT", "\n", "and", "computer", "science", "across", "the", "board", "(", "peaking", "\n", "in", "Armenia", "and", "Georgia", ")", ".", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation167", "168", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Figure", "3.20", ".", "Top", "7", "identified", "domains", "in", "each", "EaP", "country", "in", "publications", "(", "number", "of", "identified", "publications", "in", "the", "\n", "domain", "|", "percentage", "of", "the", "total", "number", "of", "publications", "analysed", "in", "the", "country", ")", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "(", "29", "067", "|", "23.12", "%", ")", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "(", "1", "265", "|", "14.56", "%", ")", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "(", "682", "|", "12.4%)Chemistry", "and" ]
[]
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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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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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 cCBˆβsCiCk cCB⎣ αsCiCk pforcˆ⊔F1CF2⊓ (1) WTPsCiCk cCBˆβsCiCk cCBEαsCiCk pforcˆ⊔F1CF2⊓‰1ŠSetting 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 cCGˆβsCiCk cCG⎣ αsCiCk pforcˆ⊔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 cCGHˆ( βsCiCk cCGβsCiCk cCH)⎣ αsCiCk pforcˆ⊔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 cCGHD0forcˆ⊔F1CF2⊓(4). The equivalent hypothesis for branded products leads to the following specification: H1a,B: WTPsCIcCk cCBD0 for cˆ⊔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 ˆ{Hungary, Lithuanian, Romania} and W ˆ{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 AM 10 19 30 8 33 313 AZ 10 7 16 4 15 164 BY 19 7 13 9 83 497 GE 30 16 13 7 33 534 MD 8 4 9 7 36 151 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- ration is between Ukraine and Armenia. The inten- 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 AZ BY GE MD UA Other AM 94 1 412 1 663 6 876 3 162 AZ 94 6 9 3 35 648 BY 1 412 6 1 377 10 673 2 619 GE 1 663 9 1 377 9 872 2 500 MD 6 3 10 9
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Other organic chemicals 522 Inorganic chemical elements, oxides and halogen salts X X X 523 Salts and peroxysalts, of inorganic acids and metals X 524 Other inorganic chemicals; organic and inorganic compounds of precious metals 525 Radioactive and associated materials X 531 Synthetic organic colouring matter and colour lakes, and preparations based thereon 532 Dyeing and tanning extracts, and synthetic tanning materials 533 Pigments, paints, varnishes and related materials X X X 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 19 12 3 8 65 64 18 26 41 23 51 52 541 Medicinal and pharmaceutical products, other than medicaments of group 542 X 542 Medicaments (including veterinary medicaments) X X X X 551 Essential oils, perfume and flavour materials X 553 Perfumery, cosmetic or toilet preparations (excluding soaps) X X X X 554 Soap, cleansing and polishing preparations X X 562 Fertilizers (other than those of group 272) X X 571 Polymers of ethylene, in primary forms X X 572 Polymers of styrene, in primary forms 573 Polymers of vinyl chloride or of other halogenated olefins, in primary forms 574Polyacetals, other polyethers and epoxide resins, in primary forms; polycarbonates, alkyd resins, polyallyl esters and other polyesters, in primary forms X X 575 Other plastics, in primary forms X X 579 Waste, parings and scrap, of plastics 581 Tubes, pipes and hoses, and fittings therefor, of plastics 582 Plates, sheets, film, foil and strip, of plastics X 583Monofilament of which any cross-sectional dimension exceeds 1 mm, rods, sticks and profile shapes, whether or not surface-worked but not otherwise worked, of plastics 591Insecticides, rodenticides, fungicides, herbicides, anti-sprouting products and plant-growth regulators, disinfectants and similar products, put up in forms or packings for retail sale or as preparations or articles (e.g., sulphur-treated bands, wicks and candles, and fly-papers) X X 592 Starches, inulin and wheat gluten; albuminoidal substances; glues X X X 593 Explosives and pyrotechnic products 597Prepared additives for mineral oils and the like; prepared liquids for hydraulic transmission; anti-freezing preparations and prepared de-icing fluids; lubricating preparations X 598 Miscellaneous chemical products, n.e.s. X X 599 Residual products of the chemical or allied industries, n.e.s.; municipal waste; sewage sludge; other wastes 6 Manufactured goods classified chiefly by material 600 Complete industrial plant appropriate
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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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"S&T", "\n", "specialisation", "domains", "..................................................................................................................................", "6", "\n", "Figure", "IIb", ".", "The", "most", "relevant", "S&T", "specialisation", "domains", "by", "EaP", "countries", ".....................", "8", "\n", "Figure", "III", ".", "Example", "of", "one", "such", "interactive", "visualisation", "tool", ",", "depicting", "the", "main", "\n", "analysed", "actors", "and", "collaboration", "networks", "in", "the", "Eastern", "Partnership", "...........................", "22", "\n", "Figure", "IV", ".", "Number", "of", "publications", "and", "EC", "projects", "in", "collaboration", "between", "EaP", "actors", "\n", "in", "different", "countries", "..................................................................................................................................", "23", "\n", "Figure", "V.", "Number", "of", "publications", "and", "EC", "projects", "in", "collaboration", "between", "EaP", "actors", "\n", "in", "different", "countries", "..................................................................................................................................", "24", "\n", "Figure", "VI", ".", "Number", "of", "publications", "and", "EC", "projects", "in", "collaboration", "between", "EaP", "actors", "\n", "and", "partners", "outside", "of", "the", "EaP", "............................................................................................................", "25", "\n", "Figure", "1.1", ".", "Summary", "scheme", "of", "the", "methodological", "steps", "leading", "to", "the", "selection", "\n", "and", "definition", "of", "a", "list", "of", "specialisation", "domains", "for", "each", "country", "and", "the", "potential", "\n", "cooperation", "areas", "for", "the", "whole", "region", "and", "with", "international", "partners", "..........................", "31", "\n", "Figure", "2.1", ".", "Importance", "of", "Agriculture", "in", "EaP", "countries", ".............................................................", "40", "\n", "Figure", "2.2", ".", "Distribution", "of", "employment", "in", "Manufacturing", "for", "five" ]
[]
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
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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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[]
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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[]
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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[]
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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 Joint Research Centre
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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 that is enough.” We have argued for the general principle that learning meaning requires more than form. How much form can be observed is not relevant to our point; the octopus can observe A and B for as long as he wants, and the quantity of training data inx5
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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. Brief description/Description succincte 1 Research dataset on political campaign finance in Eastern Europe (2000-2020). Valorisation/Valorization Patent, creation of a start-up, prototype, audiovisual production, artistic creation etc./Brevet, création d’entreprise, prototype, production audiovisuelle, création artistique etc. 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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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 REFERENCES Alvarez, R.P., Kirlic, N., Misaki, M., Bodurka, J., Rhudy, J.L., Paulus, M.P., and Drevets, W.C. (2015). Increased anterior insula activity in anxious individuals islinked to diminished perceived control. Transl. Psychiatry 5, E591. https://doi. org/10.1038/tp.2015.84 . Anastasiades, P.G., Collins, D.P., and Carter, A.G. (2021). Mediodorsal and ventromedial thalamus engage distinct L1 circuits in the prefrontal cortex.Neuron 109, 314–330.e4. https://doi.org/10.1016/j.neuron.2020.10.031E4 . Ayzenshtat, I., Karnani, M.M., Jackson, J., and Yuste, R. (2016). Cortical con- trol of spatial resolution by VIP +interneurons. J. Neurosci. 36, 11498–11509. https://doi.org/10.1523/jneurosci.1920-16.2016 . Bariselli, S., Tzanoulinou, S., Glangetas, C., Pre ´vost-Solie ´, C., Pucci, L., Viguie ´, J., Bezzi, P., O’connor, E.C., Georges, F., L €uscher, C., and Bellone, C. (2016). Shank3 controls maturation of social reward circuits in the vta. Nat. Neurosci.19, 926–934. https://doi.org/10.1038/nn.4319 . Batista-Brito, R., Vinck, M., Ferguson, K.A., Chang, J.T., Laubender, D., Lur, G., Mossner, J.M., Hernandez, V.G., Ramakrishnan, C., Deisseroth, K., et al.(2017). Developmental dysfunction of vip interneurons impairs cortical circuits.Neuron 95, 884–895. https://doi.org/10.1016/j.neuron.2017.07.034 . Beer, A.L., Plank, T., Meyer, G., and Greenlee, M.W. (2013). Combined diffusion-weighted and functional magnetic resonance imaging reveals A tem-poral-occipital network involved in auditory-visual object processing. Front.Integr. Neurosci. 7,5 .https://doi.org/10.3389/fnint.2013.00005 . Carta, I., Chen, C.H., Schott, A.L., Dorizan, S., and Khodakhah, K. (2019). Cerebellar modulation of the reward circuitry and social behavior. Science 363, Eaav0581. https://doi.org/10.1126/science.aav0581 .
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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
[ "and", "communication", "technologies", ",", "Industrial", "\n", "manufacturing", "and", "processes", ",", "Green", "energy", "and", "\n", "environmental", "services", ",", "Medicine", "and", "health", ",", "Bi-", "\n", "otechnology", ",", "Pharmaceuticals", "and", "Food", "and", "ag-", "\n", "riculture", ".", "\n", "6", "As", "presented", "previously", ",", "other", "niches", "were", "also", "identified", "\n", "for", "more", "than", "one", "country", "(", "financial", "services", ",", "information", "\n", "technology", ",", "lending", "and", "investments", ",", "travel", "and", "tourism", ")", ",", "\n", "but", "these", "are", "already", "directly", "covered", "by", "the", "preceding", "\n", "analysis", "based", "on", "full", "economic", "and", "innovation", "mapping", "\n", "–", "NACE", "codes.2", ".", "Scientific", "and", "technologi-", "\n", "cal", "(", "S&T", ")", "potential", "in", "the", "Eastern", "\n", "Partnership", "countries", "\n", "Part", "3", "of", "this", "report", "presents", "the", "analysis", "of", "the", "\n", "scientific", "and", "technological", "(", "S&T", ")", "specialisations", "\n", "in", "the", "Eastern", "Partnership", "countries", ".", "This", "exercise", "\n", "is", "carried", "out", "by", "extracting", "and", "further", "analys-", "\n", "ing", "the", "textual", "content", "of", "scientific", "publications", ",", "\n", "research", "projects", "funded", "by", "the", "European", "Com-", "\n", "mission", "and", "patents", "(", "as", "indexed", "by", "the", "Europe-", "\n", "an", "Patent", "Office", ")", "produced", "by", "each", "of", "the", "six", "EaP", "\n", "countries", ".", "The", "analyses", "are", "performed", "by", "means", "\n", "of", "topic", "modelling", ",", "an", "algorithmic", "approach", "that", "\n", "automatically", "extracts", "groups", "of", "co", "-", "occurring", "key-", "\n", "words", "(", "the", "topics", ")", "from", "large", "textual", "corpora", ",", "and", "\n", "which", "allows", "emerging", "thematic", "specialisations", "\n", "across", "data", "sources", "to", "be", "identified", "in", "a", "transver-", "\n", "sal", "fashion", ".", "\n", "The", "objectives", "of", "these", "analyses", "are", "the", "follow-", "\n", "ing", ":", "to", "identify", "a", "set", "of", "scientific", "and", "technological", "\n", "(", "S&T", ")", "specialisation", "domains", "in", "terms", "of", "emerg-", "\n", "ing", "topics", ";", "to", "further", "characterise", "these", "domains", "\n", "for", "each", "EaP", "country", "and", "for", "the", "EaP", "as", "a", "whole", ";", "\n", "and", "to", "identify", "key", "local", "and", "international", "actors", "\n", "involved", "in", "these", "domains", "and", "their", "collaboration", "\n", "networks", ".", "\n", "Identification", "and", "characterisation", "of", "\n", "S&T", "specialisation", "domains", "across", "the", "\n", "EaP", "\n", "Table", "I", "presents", "the", "14", "labelled", "topic", "groups", "ob-", "\n", "tained", "from", "the", "topic", "modelling", "process", ",", "described", "\n", "in", "detail", "in", "Part", "3", ".", "These", "initial", "‘", "topic", "groups", "’", "are", "\n", "the", "S&T", "specialisation", "domains", ",", "which", "are", "char-", "\n", "acterised", "and", "further", "defined", "throughout", "the", "\n", "analysis", ".", "\n", "Overall", ",", "the", "EaP", "science", "and", "technology", "system", "\n", "is", "most", "active", "in", "experimental", "and", "fundamen-", "\n", "tal", "sciences", "(", "physics", ",", "mathematics", ",", "chemistry", ")", "\n", "and", "their", "technical", "applications", "(", "materials", ",", "me-", "\n", "chanical", "engineering", ",", "ICT", "and", "computer", "science", ",", "\n", "biotechnology", ",", "chemical", "engineering", ")", ",", "including", "\n", "emerging", "areas", "such", "as", "nanotechnology", "and", "op-", "\n", "tics", "and", "photonics", ".", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation13", "\n", "In", "any", "case", ",", "fundamental", "and", "applied", "fields", "do", "\n", "have", "direct", "and", "indirect", "impacts", "on", "the", "techno-", "\n", "logical", "development", "and", "innovativeness", "of", "sec-", "\n", "tors", ",", "as", "is", "further", "explored", "in", "Part", "4", "of", "this", "report", ".", "\n", "Furthermore", ",", "the", "identified", "S&T", "domains", "are", "not", "\n", "hermetic", ":", "since", "S&T", "records", "are", "analysed", "and", "\n", "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", "is", "performed", "to", "obtain", "the", "final", "indicator", ".", "\n", "In", "line", "with", "what", "was", "done", "for", "the", "SI", ",", "the", "average", "\n", "61", "https://en.wikipedia.org/wiki/Citation_impact", ".", "\n", "174", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "number", "of", "citations", "per", "publication", "for", "the", "EaP", "re-", "\n", "gion", "within", "each", "bibliometric", "category", "is", "used", "as", "a", "\n", "baseline", "to", "compute", "the", "NCI", ".", "\n", "It", "is", "important", "to", "consider", "that", "Ukraine", "produces", "\n", "most", "of", "the", "records", "in", "the", "Eastern", "Partnership", "and", "\n", "its", "publications", "therefore", "have", "a", "greater", "impact", "\n", "and", "are", "cited", "more", "often", "(", "proportionately", ")", ".", "There-", "\n", "fore", ",", "most", "of", "the", "computed", "NCI", "indicators", "for", "\n", "Ukraine", "are", "higher", "than", "1", ".", "This", "is", "considered", "when", "\n", "analysing", "the", "results", "in", "the", "following", "section", ".", "\n", "4.2", "Results", "by", "country", "\n", "For", "each", "of", "the", "six", "countries", ",", "results", "are", "present-", "\n", "ed", "and", "discussed", "in", "the", "following", "pages", ".", "Firstly", ",", "\n", "the", "bare", "numbers", "(", "i.e.", "critical", "mass", ")", "of", "records", "are", "\n", "presented", "per", "S&T", "specialisation", "domain", ",", "both", "in", "\n", "the", "form", "of", "a", "table", "and", "a", "stacked", "bar", "chart", ".", "Those", "\n", "domains", "with", "the", "highest", "number", "of", "records", "are", "\n", "highlighted", ",", "and", "special", "consideration", "is", "given", "to", "\n", "their", "evolution", "in", "recent", "years", ",", "as", "captured", "by", "the", "\n", "compound", "annual", "growth", "rate", "(", "CAGR", ")", ".", "The", "dis-", "\n", "tribution", "of", "records", "(", "by", "type", ")", "per", "domain", "is", "also", "\n", "discussed", ".", "\n", "The", "citation", "impact", "(", "for", "publications", ")", "and", "the", "\n", "specialisation", "index", "(", "both", "for", "publications", "and", "patents", ")", "are", "then", "presented", "for", "each", "domain", "for", "\n", "individual", "countries62", ".", "For", "publications", ",", "a", "two", "-", "di-", "\n", "mensional", "graph", "is", "provided", ":", "the", "graph", "shows", "the", "\n", "specialisation", "index", "on", "the", "x", "-", "axis", "and", "the", "normal-", "\n", "ised", "citation", "impact", "on", "the", "y", "-", "axis", ".", "In", "each", "graph", ",", "\n", "EaP", "countries", "feature", "a", "higher", "specialisation", "and", "\n", "higher", "citation", "impact", "for", "domains", "scattered", "to-", "\n", "wards", "the", "right", "and", "the", "top", "parts", "of", "the", "graph", ",", "\n", "respectively", ".", "In", "the", "case", "of", "patents", ",", "a", "simple", "bar", "\n", "chart", "is", "featured", ";", "each", "bar", "represents", "the", "speciali-", "\n", "sation", "index", "of", "a", "domain", ",", "and", "special", "consideration", "\n", "is", "given", "to", "those", "domains", "with", "an", "SI", "higher", "than", "\n", "1", ",", "which", "signals", "a", "relative", "specialisation", "of", "the", "\n", "country", "in", "that", "particular", "domain", ".", "These", "graphs", ",", "\n", "elaborated", "for", "each", "of", "the", "six", "EaP", "countries", ",", "are", "\n", "exemplified", "in", "the", "following", "pages", ".", "\n", "62", "As", "per", "how", "the", "specialisation", "index", "has", "been", "defined", ",", "it", "\n", "would", "make", "no", "sense", "to", "present", "its", "results", "for", "the", "Eastern", "\n", "Partnership", "region", "as", "a", "whole", ".", "\n", "Figure", "3.23", ".", "Example", "of", "the", "figures", "used", "to", "present", "the", "citation", "impact", "and", "specialisation", "index", "for", "publications", "(", "left", ")", "\n", "and", "the", "specialisation", "index", "for", "patents", "(", "right", ")", "\n", "Specialisation", "index", "Specialisation", "indexNo", "pubs", ".", "\n", "100", "\n", "500", "\n", "1", "000Normalised", "citation", "impact2", "\n" ]
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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", "the", "data", "sources", "and", "thus", "in", "the", "\n", "results", "of", "the", "analysis", ",", "while", "universities", "and", "\n", "research", "organisations", "feature", "in", "particular", ".", "\n", "Additional", "care", "must", "be", "taken", "when", "analysing", "\n", "results", "and", "interpreting", "conclusions", "related", "to", "\n", "priority", "-", "setting", "and", "the", "market", "or", "society", "-", "ori-", "\n", "ented", "innovation", "and", "application", "capacity", "of", "\n", "the", "preliminary", "priority", "domains", ".", "In", "Chapter", "5", "\n", "‘", "Identification", "of", "the", "main", "actors", "and", "collabo-", "\n", "ration", "patterns", "within", "the", "S&T", "specialisation", "\n", "domains", "’", ",", "private", "companies", "have", "been", "high-", "\n", "36", "\n ", "Part", "1", "Introduction", "and", "methodology", "\n", "lighted", "to", "facilitate", "an", "interpretation", "of", "the", "\n", "specialisations", "of", "the", "private", "sector", ".", "\n", "6", ".", "Large", "number", "of", "individual", "patent", "appli-", "\n", "cants", "jeopardising", "a", "representative", "char-", "\n", "acterisation", "of", "patenting", "activity", "by", "EaP", "\n", "organisations", ".", "The", "six", "EaP", "countries", "present", "\n", "a", "very", "large", "number", "of", "individual", "persons", "as", "\n", "applicants", "in", "the", "patent", "data", "source", ",", "amount-", "\n", "ing", "to", "40.7", "%", ".", "That", "is", ",", "slightly", "less", "than", "60", "%", "\n", "of", "EaP", "patents", "can", "be", "directly", "connected", "to", "\n", "an", "organisation", "(", "academic", "institutions", ",", "com-", "\n", "panies", "or", "some", "other", "organisation", ")", ".", "There", "may", "\n", "certainly", "be", "a", "number", "of", "individual", "inventors", "\n", "and", "patent", "agents", ",", "but", "there", "are", "also", "cases", "\n", "where", "university", "staff", ",", "for", "instance", ",", "apply", "for", "\n", "patents", "as", "individuals", ".", "EaP", "legislation", "as", "well", "\n", "as", "the", "internal", "regulations", "and", "organisation", "of", "\n", "academic", "institutions", "and", "R&D", "grants", "could", "\n", "facilitate", "and", "incentivise", "institutional", "patent", "\n", "applications", ",", "always", "guaranteeing", "the", "intel-", "\n", "lectual", "and", "economic", "rights", "of", "the", "inventors", ".", "\n", "7", ".", "Relative", "weight", "of", "Ukraine", "in", "trans", "-", "EaP", "\n", "analyses", ".", "From", "each", "data", "source", ",", "the", "num-", "\n", "ber", "of", "records", "available", "for", "Ukraine", "outnum-", "\n", "bers", "those", "for", "the", "rest", "of", "the", "EaP", "countries", "\n", "combined", ".", "This", "means", "that", "statistics", "and", "\n", "record", "figures", "for", "the", "whole", "EaP", "region", "are", "\n", "highly", "congruent", "with", "those", "of", "Ukraine", ";", "as", "a", "\n", "consequence", ",", "the", "analyses", "carried", "out", "at", "en-", "\n", "tire", "-", "EaP", "regional", "level", "are", "inevitably", "skewed", "\n", "towards", "the", "Ukrainian", "profile", ".", "Indicators", "of", "\n", "critical", "mass", "and", "the", "internal", "distribution", "of", "\n", "S&T", "domains", "within", "each", "country", "complement", "\n", "specialisation", "indicators", "to", "offer", "a", "more", "com-", "\n", "prehensive", "view", "of", "national", "specialisation", "ar-", "\n", "eas", "and", "niches", ".", "\n", "8", ".", "Computation", "of", "specialisation", "and", "excel-", "\n", "lence", "indicators", "against", "the", "EaP", "aggre-", "\n", "gate", ".", "Due", "to", "the", "size", "of", "the", "data", "sources", "and", "\n", "the", "methodological", "approach", ",", "specialisation", "\n", "and", "excellence", "indicators", "for", "each", "country", "\n", "have", "been", "computed", "against", "the", "aggregate", "\n", "S&T", "activity", "of", "the", "six", "EaP", "countries", ".", "This", "\n", "provides", "a", "notion", "of", "relative", "specialisation", "\n", "and", "excellence", ",", "relevant", "in", "terms", "of", "Eastern", "\n", "Partnership", "initiatives", "and", "useful", "as", "a", "gener", "-", "al", "contextualisation", ",", "but", "incapable", "of", "offering", "\n", "insight", "supporting", "differentiation", "versus", "Euro-", "\n", "pean", ",", "Asian", "or", "global", "partners", "and", "competi-", "\n" ]
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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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"of", "other", "non", "-", "metallic", "mineral", "productsGeneral", "Chemical", "Engineering", ";", "Surfaces", ",", "Coatings", "\n", "and", "Films", ";", "Materials", "Chemistry", ";", "Mechanics", "of", "\n", "Materials", ";", "Process", "Chemistry", "and", "Technology", "\n", "25Manufacture", "of", "fabricated", "metal", "products", ",", "except", "\n", "machinery", "and", "equipmentElectronic", ",", "Optical", "and", "Magnetic", "Materials", ";", "Mechanics", "\n", "of", "Materials", "\n", "26Manufacture", "of", "computer", ",", "electronic", "and", "optical", "\n", "productsElectrical", "and", "Electronic", "Engineering", ";", "Electronic", ",", "\n", "Optical", "and", "Magnetic", "Materials", "\n", "27", "Manufacture", "of", "electrical", "equipmentElectrical", "and", "Electronic", "Engineering", ";", "Instrumentation", ";", "\n", "Electronic", ",", "Optical", "and", "Magnetic", "Materials", ";", "Control", "\n", "and", "Systems", "Engineering", "\n", "28", "Manufacture", "of", "machinery", "and", "equipment", "n.e.c", ".", "Instrumentation", ";", "Mechanical", "Engineering", "\n", "29Manufacture", "of", "motor", "vehicles", ",", "trailers", "and", "semi-", "\n", "trailersInstrumentation", "\n", "33", "Repair", "and", "installation", "of", "machinery", "and", "equipmentInstrumentation", ";", "Electronic", ",", "Optical", "and", "Magnetic", "\n", "Materials", ";", "Mechanical", "Engineering", "\n", "61", "TelecommunicationsInformation", "Systems", ";", "Computer", "Networks", "and", "\n", "Communications", ";", "Computer", "Science", "Applications", ";", "\n", "General", "Computer", "Science", ";", "Modelling", "and", "Simulation", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation347", "\n", "UKRAINE", "\n", "Concordances", "between", "NACE", "sectors", "and", "the", "intersection", "of", "ASJC", "subject", "fields", "&", "S&T", "domains", "\n", "NACE", "sector", "ASJC", "Scopus", "subject", "field", "\n", "16Manufacture", "of", "wood", "and", "of", "products", "of", "wood", "and", "\n", "cork", ",", "except", "furniture", ";", "manufacture", "of", "articles", "of", "\n", "straw", "and", "plaiting", "materialsMaterials", "Chemistry", ";", "Mechanics", "of", "Materials", "\n", "18", "Printing", "and", "reproduction", "of", "recorded", "media", "Materials", "Chemistry", "\n", "19", "Manufacture", "of", "coke", "and", "refined", "petroleum", "productsEnergy", "Engineering", "and", "Power", "Technology", ";", "General", "\n", "Chemical", "Engineering", ";", "Materials", "Chemistry", ";", "Catalysis", "\n", "20", "Manufacture", "of", "chemicals", "and", "chemical", "productsBiochemistry", ";", "Drug", "Discovery", ";", "General", "Chemical", "\n", "Engineering", ";", "Materials", "Chemistry", ";", "Catalysis", "\n", "23", "Manufacture", "of", "other", "non", "-", "metallic", "mineral", "productsGeneral", "Chemical", "Engineering", ";", "Materials", "Chemistry", ";", "\n", "Mechanics", "of", "Materials", ";", "Catalysis", "\n", "25Manufacture", "of", "fabricated" ]
[]
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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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
[ " ", "up", "of", "successful", "\n", "ventures", ".", "The", "EU", "should", "become", "as", "attractive", "for", "inventors", "as", "other", "leading", "regions", "for", "innovation", ".", "The", "report", "\n", "recommends", "a", "number", "of", "measures", "to", "support", "the", "transition", "from", "invention", "to", "commercialisation", "in", "Europe", ".", "First", ",", "\n", "to", "overcome", "bureaucratic", "barriers", "in", "universities", "and", "research", "institutions", "to", "managing", "intellectual", "property", "rights", "\n", "with", "their", "researchers", ",", "a", "new", "blueprint", "for", "fair", "and", "transparent", "royalty", "sharing", "is", "recommended", ".", "Second", ",", "to", "lower", "\n", "application", "costs", "for", "young", "companies", "and", "to", "offer", "uniform", "protection", "of", "intellectual", "property", ",", "it", "is", "proposed", "to", "adopt", "\n", "the", "Unitary", "Patent", "in", "all", "EU", "Member", "States", ".", "Third", ",", "the", "EU", "should", "carry", "out", "a", "thorough", "impact", "assessment", "of", "the", "effect", "\n", "of", "digital", "and", "other", "regulation", "on", "small", "companies", ",", "with", "the", "aim", "of", "excluding", "SMEs", "from", "regulations", "that", "only", "large", "\n", "companies", "are", "able", "to", "comply", "with", ".", "Finally", ",", "the", "EU", "should", "support", "rapid", "growth", "within", "the", "European", "market", "by", "giving", "\n", "innovative", "start", "-", "ups", "the", "opportunity", "to", "adopt", "a", "new", "EU", "-", "wide", "legal", "statute", "(", "the", "“", "Innovative", "European", "Company", "”", ")", ".", "\n", "This", "status", "would", "provide", "companies", "with", "a", "single", "digital", "identity", "valid", "throughout", "the", "EU", "and", "recognised", "by", "all", "\n", "Member", "States", ".", "These", "companies", "would", "have", "access", "to", "harmonised", "legislation", "concerning", "corporate", "law", "and", "insol", "-", "\n", "vency", ",", "as", "well", "as", "a", "few", "key", "aspects", "of", "labour", "law", "and", "taxation", ",", "to", "be", "made", "progressively", "more", "ambitious", ",", "and", "they", "\n", "would", "be", "entitled", "to", "establish", "subsidiaries", "across", "the", "EU", "without", "incorporating", "separately", "in", "each", "Member", "State", ".", "\n", "A", "better", "financing", "environment", "for", "disruptive", "innovation", ",", "start", "-", "ups", "and", "scale", "-", "ups", "is", "needed", "as", "barriers", "\n", "to", "growth", "within", "the", "European", "markets", "are", "removed", " ", "[", "see", "the", "chapters", "on", "innovation", ",", "and", "investment", "]", ".", "While", "\n", "high", "-", "growth", "companies", "can", "typically", "obtain", "finance", "from", "international", "investors", ",", "there", "are", "good", "reasons", "to", "further", "\n", "develop", "the", "financing", "ecosystem", "within", "Europe", ".", "Very", "early", "-", "stage", "innovation", "would", "benefit", "from", "a", "deeper", "pool", "of", "\n", "angel", "investors", ".", "Ensuring", "sufficient", "local", "capital", "to", "fund", "scale", "-", "ups", "would", "concentrate", "the", "spillovers", "of", "innovation", "\n", "within", "Europe", ".", "Increasing", "the", "appeal", "of", "European", "stock", "markets", "for", "IPOs", "would", "improve", "funding", "options", "for", "founders", ",", "\n", "encouraging", "more", "start", "-", "up", "activity", "in", "the", "EU", ".", "To", "generate", "a", "significant", "increase", "in", "equity", "and", "debt", "funding", "available", "\n", "to", "start", "-", "ups", "and", "scale", "-", "up", ",", "the", "report", "proposes", "the", "following", "measures", ".", "First", ",", "expanding", "incentives", "for", "business", "\n", "33THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "CHAPTER", "2“angels", "”", "and", "seed", "capital", "investors", ".", "Second", ",", "assessing", "whether", "further", "changes", "to", "capital", "requirements", "under", "\n", "Solvency", "II", "are", "warranted", ",", "which", "establishes", "capital", "adequacy", "rules", "for", "insurance", "companies", ",", "and", "issuing", "guide", "-", "\n", "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%
[ "and", "data", "source", "size", "independent", "\n", "(", "%", "change", "for", "2015", "-", "2018", "over", "previous", "period", "2011", "-", "2014", ")", "\n", "Change", "in", "\n", "share", "of", "\n", "publicationsChange", "in", "share", "\n", "of", "patents", "Change", ",", "weighted", "average", "of", "\n", "publications", "and", "patents", "\n", "Nanotechnology", "and", "materials", "-13.27", "%", "-3.39", "%", "-11.40", "%", "\n", "Health", "and", "wellbeing", "30.28", "%", "0.03", "%", "18.24", "%", "\n", "Fundamental", "physics", "and", "mathematics", "-18.34", "%", "-6.19", "%", "-17.38", "%", "\n", "Mechanical", "engineering", "and", "heavy", "\n", "machinery18.18", "%", "3.70", "%", "7.01", "%", "\n", "ICT", "and", "computer", "science", "27.42", "%", "17.74", "%", "25.03", "%", "\n", "Biotechnology", "-15.17", "%", "2.87", "%", "-8.53", "%", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "\n", "economy44.99", "%", "20.29", "%", "44.27", "%", "\n", "Environmental", "sciences", "and", "industries", "8.60", "%", "-11.43", "%", "3.85", "%", "\n", "Electric", "and", "electronic", "technologies", "-4.88", "%", "-10.54", "%", "-8.00", "%", "\n", "Energy", "10.66", "%", "-21.14", "%", "-5.87", "%", "\n", "Chemistry", "and", "chemical", "engineering", "-27.29", "%", "15.53", "%", "-17.43", "%", "\n", "Optics", "and", "photonics", "-25.31", "%", "-11.38", "%", "-22.61", "%", "\n", "Agrifood", "25.21", "%", "5.89", "%", "12.24", "%", "\n", "Transportation", "63.04", "%", "-2.89", "%", "32.63%Table", "3.6", ".", "Temporal", "evolution", "of", "S&T", "domains", "in", "the", "Eastern", "Partnership", "region", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation159", "\n", "scheme", "for", "patents", ".", "These", "native", "classifications", "\n", "are", "exploited", "here", "to", "further", "characterise", "the", "iden-", "\n", "tified", "S&T", "specialisation", "domains", ".", "\n", "Table", "3.7", "and", "Table", "3.8", "present", "the", "most", "rele-", "\n", "vant", "native", "taxonomies", "in", "the", "original", "data", "sourc-", "\n", "es", "associated", "with", "the", "documents", "appearing", "in", "\n", "the", "respective", "S&T", "domain", ".", "These", "tables", "provide", "\n", "a", "deeper", "understanding", "of", "the", "most", "relevant", "and", "\n", "distinctive", "scientific", "disciplines", "or", "technological", "areas", "for", "each", "domain", ".", "Table", "3.7", "shows", ",", "for", "each", "\n", "domain", ",", "the", "bibliometric", "categories", "that", "appear", "\n", "relatively", "more", "frequently", "in", "each", "domain", "with", "re-", "\n", "spect", "to", "the", "average", "in", "terms", "of", "the", "subject", "fields", "\n", "adopted", "by", "Scopus", "to", "categorise", "scientific", "publica-", "\n", "tions", ".", "Table", "3.8", "instead", "reports", "the", "top", "IPC", "patent", "\n", "classes", "(", "by", "number", "of", "records", ")", "that", "categorise", "the", "\n", "patent", "families", "included", "in", "the", "different", "domains", ".", "\n", "Top", "publication", "subject", "fields", "in", "the", "S&T", "specialisation", "domains", "\n", "Domain", "ASJC", "Description", "No", "recordsRelative", "\n", "freq", ".", "\n", "Agrifood1106", "Food", "science", "184", "260.38", "%", "\n", "1108", "Horticulture", "71", "179.75", "%", "\n", "1102", "Agronomy", "and", "crop", "science", "194", "173.99", "%", "\n", "1110", "Plant", "science", "229", "146.95", "%", "\n", "2716", "Genetics", "(", "clinical", ")", "84", "137.70", "%", "\n", "Biotechnology1600", "General", "chemistry", "2", "047", "329.32", "%", "\n", "1606", "Physical", "and", "theoretical", "chemistry", "1", "037", "311.41", "%", "\n", "1500", "General", "chemical", "engineering", "968", "297.50", "%", "\n", "2304", "Environmental", "chemistry", "346", "184.78", "%", "\n", "1303", "Biochemistry", "976", "183.80", "%", "\n", "Chemistry", "\n", "and", "chemical", "\n", "engineering1606", "Physical", "and", "theoretical", "chemistry", "1", "319", "396.10", "%", "\n", "1600", "General", "chemistry", "2", "209", "355.38", "%", "\n", "1604", "Inorganic", "chemistry", "867", "350.30", "%", "\n", "1500", "General", "chemical", "engineering", "797", "244.95", "%", "\n", "1605", "Organic", "chemistry", "1", "862", "211.95", "%", "\n", "Electric", "and", "\n", "electronic", "\n", "technologies2102", "Energy", "engineering", "and", "power", "technology", "824", "190.61", "%", "\n", "2104", "Nuclear", "energy", "and", "engineering", "514", "188.69", "%", "\n", "2208", "Electrical", "and", "electronic", "engineering", "2", "196", "159.04", "%", "\n", "1705", "Computer", "networks", "and", "communications", "601", "115.24", "%", "\n" ]
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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 from a “PC” perspective is just jealous. {Chrome OS is for browsing the web, PC processors can do stronger things in that regard, Windows is a juggernaut on those fronts. This is how I see it. Yes, it can be slow. And yes, you need a fast CPUFOR ALABAMA, GOOD WEEKS {AND A TOUR OF CAIRO {THE ALABAMA COM- 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 of the largest industrial parks in the South, a regional cultural center, a 100-year-old manufac- turing town and a potent symbol of the former city’s cherished Georgian past. The gentry still live there, the defunct industrial landscapes carry the names of Truth Raters p1.0 k40 p0.96 Truth Raters p1.0 k40 p0.96 M H - - M M H - M - 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
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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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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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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- pound coastal flooding potential around Europe and envi- rons: sensitivity analysis and spatial patterns, Nat. Hazards Earth Syst. Sci., 21, 2021–2040, https://doi.org/10.5194/nhess- 21-2021-2021, 2021. Charlton, M. and Kemp, K. K.: Encyclopaedia of Geographic Infor- mation Science, SAGE Publications, Inc., 289–290, ISBN 978- 1-4129-1313-3, 2008. Chen, Z.: Is the weighted z-test the best method for combining prob- abilities from independent tests?, J. Evolution. Biol., 24, 926– 930, 2011. Ciurean, R., Gill, J. C., Reeves, H., O’Grady, S. K., Donald, K., and Aldridge, T.: Review of multi-hazards research and risk assess- ments, British Geological Survey Engineering Geology & Infras- tructure Programme, Open Report OR/18/057, British Geologi- cal Survey, UK, https://nora.nerc.ac.uk/id/eprint/524399 (last ac- cess: 17 January 2025), 2018. Claassen, J. N., Ward, P. J., Daniell, J., Koks, E. E., Tiggeloven, T., and de Ruiter, M. C.: A new method to compile global multi-hazard event sets, Sci. Rep.-UK, 13, 1–14, https://doi.org/10.1038/s41598-023-40400-5, 2023. Cousineau, D. and Chartier, S.: Outliers Detection and Treatment: A Review, International Journal of Psychological Research, 3, 58–67, 2010. Cutter, S. L., Ash, K. D., and Emrich, C. T.: The geographies of community disaster resilience, Global Environ. Change, 29, 65– 77, https://doi.org/10.1016/j.gloenvcha.2014.08.005, 2014. 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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[]
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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[]
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.
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[]
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
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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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[]
(NACE 53, 55) based on specialised performance in venture capital (VC) and start-ups, and specialised perfor- mance 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 The economic and innovation analysis for Azerbai- jan leads to the identification of the following E&I specialisations: ■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 The economic and innovation analysis for Geor- gia leads to the identification of 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 The economic and innovation analysis for Moldo- va leads to the identification of the following E&I specialisations: ■Food & beverages (NACE 10, 11) based on an economic specialisation, specialised per- formance in venture capital and start-ups and specialised performance in related goods ex- ports; ■Textiles & wearing apparel (NACE 13, 14) based on an economic specialisation, and spe- cialised performance in related goods exports; ■Leather and related products
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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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"Semantics", "(", "2015", ")", "and", "*", "SEM", "2016", "\n", "for", "their", "insightful", "and", "constructive", "thoughts.5194References", "\n", "Yossi", "Adi", ",", "Einat", "Kermany", ",", "Yonatan", "Belinkov", ",", "Ofer", "\n", "Lavi", ",", "and", "Yoav", "Goldberg", ".", "2017", ".", "Fine", "-", "grained", "anal-", "\n", "ysis", "of", "sentence", "embeddings", "using", "auxiliary", "predic-", "\n", "tion", "tasks", ".", "In", "Proceedings", "of", "ICLR", ".", "\n", "Dare", "A.", "Baldwin", ".", "1995", ".", "Understanding", "the", "link", "be-", "\n", "tween", "joint", "attention", "and", "language", ".", "In", "Chris", "Moore", "\n", "and", "Philip", "J.", "Dunham", ",", "editors", ",", "Joint", "Attention", ":", "Its", "\n", "Origins", "and", "Role", "in", "Development", ",", "pages", "131–158", ".", "\n", "Psychology", "Press", ".", "\n", "Andrei", "Barbu", ",", "David", "Mayo", ",", "Julian", "Alverio", ",", "William", "\n", "Luo", ",", "Christopher", "Wang", ",", "Dan", "Gutfreund", ",", "Josh", "Tenen-", "\n", "baum", ",", "and", "Boris", "Katz", ".", "2019", ".", "ObjectNet", ":", "A", "large-", "\n", "scale", "bias", "-", "controlled", "dataset", "for", "pushing", "the", "lim-", "\n", "its", "of", "object", "recognition", "models", ".", "In", "H.", "Wallach", ",", "\n", "H.", "Larochelle", ",", "A.", "Beygelzimer", ",", "F.", "d’Alch", "´", "e", "Buc", ",", "\n", "E.", "Fox", ",", "and", "R.", "Garnett", ",", "editors", ",", "Advances", "in", "Neu-", "\n", "ral", "Information", "Processing", "Systems", "32", ",", "pages", "9453", "–", "\n", "9463", ".", "Curran", "Associates", ",", "Inc.", "\n", "Marco", "Baroni", ",", "Raffaella", "Bernardi", ",", "Roberto", "Zamparelli", ",", "\n", "et", "al", ".", "2014", ".", "Frege", "in", "space", ":", "A", "program", "for", "composi-", "\n", "tional", "distributional", "semantics", ".", "Linguistic", "Issues", "in", "\n", "Language", "Technology", ",", "9(6):5–110", ".", "\n", "Yonatan", "Bisk", ",", 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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…lˆ1C⋯CL†to 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 29Manufacture of motor vehicles, trailers and semi- trailersInstrumentation 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 Communications; Computer Science Applications; Signal Processing; General Computer Science; Transportation 62Computer programming, consultancy and related activitiesArtificial Intelligence 63 Information service activities Artificial Intelligence MOLDOVA 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 textilesSurfaces, Coatings and Films; Materials Chemistry; Mechanics of Materials; Process Chemistry and 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 ˆ6.03 vs MDanoneHU ˆ6.46 and MMilkaDE ˆ 5.80 vs MMilkaHU ˆ6.74). 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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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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a kind used for the extraction of ‘soft’ fixed vegetable oils (excluding flours and meals) X X X X 223Oil-seeds and oleaginous fruits, whole or broken, of a kind used for the extraction of other fixed vegetable oils (including flours and meals of oil-seeds or oleaginous fruit, n.e.s.) 231Natural rubber, balata, gutta-percha, guayule, chicle and similar natural gums, in primary forms (including latex) or in plates, sheets or strip 232 Synthetic rubber; reclaimed rubber; waste, parings and scrap of unhardened rubber 244 Cork, natural, raw and waste (including natural cork in blocks or sheets) 245 Fuel wood (excluding wood waste) and wood charcoal X 246 Wood in chips or particles and wood waste X 247 Wood in the rough, whether or not stripped of bark or sapwood, or roughly squared X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation305 306 Annexes ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE SITC Goods name Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging 19 12 3 8 65 64 18 26 41 23 51 52 248 Wood, simply worked, and railway sleepers of wood X X X X 251 Pulp and waste paper 261 Silk 263 Cotton 264Jute and other textile bast fibres, n.e.s., raw or processed but not spun; tow and waste of these fibres (including yarn waste and garnetted stock) 265 Vegetable textile fibres (other than cotton and jute), raw or processed but not spun; waste of these fibres 266 Synthetic fibres suitable for spinning X 267 Other man-made fibres suitable for spinning; waste of man-made fibres 268 Wool and other animal hair (including wool tops) 269 Worn clothing and other worn textile articles; rags 271 Confidential trade of group 271 272 Fertilizers, crude, other than those of division 56 273 Stone, sand and gravel X X 274 Sulphur and unroasted iron pyrites 277 Natural abrasives, n.e.s. (including industrial diamonds) 278 Other crude minerals X X X 281 Iron ore and concentrates X X 282 Ferrous waste and scrap; remelting scrap ingots of iron or steel X 283 Copper ores and concentrates; copper mattes; cement copper X X X 284Nickel ores and concentrates; nickel mattes, nickel oxide sinters and other intermediate products of nickel metallurgy 285 Aluminium ores and concentrates (including alumina) X X 286 Uranium or thorium ores and concentrates 287 Ores and concentrates of base metals, n.e.s. X X 288 Non-ferrous
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Metabolism Article Talk Read Edit View history Tools Appearance hide Text Small Standard Large Width Standard Wide Color (beta) Automatic Light Dark 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 following coordinates from bregma: anterior–posterior (AP): +1.95 mm; medio–lateral (ML): +2.85 mm; dorso–ventral (DV): 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" ]
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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" ]
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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", "-", "related", "and", "other", "business", "services", ";", "waste", "treatment", "and", "de", "-", "pollution", ",", "\n", "agricultural", "and", "mining", "services", ";", "operating", "leasing", "services", ";", "and", "trade", "-", "related", "services", ".", "\n", "80", "\n ", "Part", "2", "Analysis", "of", "economic", "and", "innovation", "potential", "\n", "Data", "for", "8", "years", "have", "been", "used", "(", "2011", "-", "2018", ")", "for", "\n", "the", "mapping", "analysis", ",", "divided", "in", "three", "periods", "to", "\n", "measure", "changes", "over", "time", "similar", "to", "the", "eco-", "\n", "nomic", "mapping", "using", "Orbis", "data", ",", "i.e.", "2011", "-", "2014", ",", "\n", "2013", "-", "2016", "and", "2015", "-", "2018", ".", "\n", "Methodology", "\n", "Export", "specialisation", "is", "measured", "by", "comparing", "\n", "relative", "export", "shares", "for", "each", "country", "to", "the", "un-", "\n", "weighted", "average", "export", "shares", "of", "the", "EaP", "coun-", "\n", "tries", ":", "\n ", "■", "service", "categories", "with", "a", "current", "\n", "strength", ",", "including", "specialised", "service", "cate-", "\n", "gories", "with", "critical", "mass", ",", "where", "the", "degrees", "\n", "of", "specialisation", "for", "export", "volumes", "are", "above", "\n", "predefined", "thresholds", ";", "\n ", "■", "service", "categories", "with", "an", "emerging", "\n", "strength", ",", "including", "emerging", "service", "catego-", "\n", "ries", "with", "increasing", "degrees", "of", "specialisation", ",", "\n", "where", "the", "change", "in", "the", "degree", "of", "speciali-", "\n", "sation", "for", "export", "volumes", "is", "above", "predefined", "\n", "thresholds", ".", "\n", "For", "all", "service", "categories", ",", "the", "following", "indicators", "\n", "have", "been", "calculated", "for", "each", "EaP", "country", ":", "\n ", "■", "the", "degree", "of", "specialisation", "for", "export", "values", "\n", "for", "each", "year", "in", "the", "2011", "-", "2018", "period", ";", "■", "average", "relative", "share", "of", "export", "values", "for", "\n", "each", "year", "in", "the", "2011", "-", "2018", "period", ";", "\n ", "■", "rate", "of", "change", "in", "degree", "of", "specialisation", "for", "\n", "export", "values", "for", "three", "time", "periods", ":", "between", "\n", "2011", "and", "2014", ",", "between", "2013", "and", "2016", "and", "\n", "between", "2015", "and", "2018", ".", "\n", "Degrees", "of", "specialisation", "have", "been", "calculat-", "\n", "ed", "relative", "to", "the", "unweighted", "average", "of", "the", "six", "\n", "EaP", "countries", ".", "Specialised", "service", "categories", "\n", "with", "critical", "mass", "are", "identified", "as", "those", "service", "\n", "categories" ]
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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
[ "for", "decarbonisation", "and", "competitiveness", ".", "\n", "If", "Europe", "’s", "ambitious", "climate", "targets", "are", "matched", "by", "a", "coherent", "plan", "to", "achieve", "them", ",", "decarbonisation", "will", "be", "an", "\n", "opportunity", "for", "Europe", ".", "But", "if", "we", "fail", "to", "coordinate", "our", "policies", ",", "there", "is", "a", "risk", "that", "decarbonisation", "could", "run", "contrary", "\n", "to", "competitiveness", "and", "growth", ".", "\n", "Even", "though", "energy", "prices", "have", "fallen", "considerably", "from", "their", "peaks", ",", "EU", "companies", "still", "face", "electricity", "prices", "\n", "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", "\n", "Europe", "’s", "lack", "of", "natural", "resources", ",", "but", "also", "by", "fundamental", "issues", "with", "our", "common", "energy", "market", ".", "Market", "rules", "\n", "prevent", "industries", "and", "households", "from", "capturing", "the", "full", "benefits", "of", "clean", "energy", "in", "their", "bills", ".", "High", "taxes", "and", "rents", "\n", "captured", "by", "financial", "traders", "raise", "energy", "costs", "for", "our", "economy", ".", "\n", "Over", "the", "medium", "term", ",", "decarbonisation", "will", "help", "shift", "power", "generation", "towards", "secure", ",", "low", "-", "cost", "clean", "energy", "\n", "sources", ".", "But", "fossil", "fuels", "will", "continue", "to", "play", "a", "central", "role", "in", "energy", "pricing", "at", "least", "for", "the", "remainder", "of", "this", "decade", ".", "\n", "Without", "a", "plan", "to", "transfer", "the", "benefits", "of", "decarbonisation", "to", "end", "-", "users", ",", "energy", "prices", "will", "continue", "to", "weigh", "on", "\n", "growth", ".", "\n", "06", "\n", "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", "\n", "technologies", "like", "wind", "turbines", ",", "electrolysers", "and", "low", "-", "carbon", "fuels", ",", "and", "more", "than", "one", "-", "fifth", "of", "clean", "and", "sustainable", "\n", "technologies", "worldwide", "are", "developed", "here", ".", "\n", "Yet", "it", "is", "not", "guaranteed", "that", "Europe", "will", "seize", "this", "opportunity", ".", "Chinese", "competition", "is", "becoming", "acute", "in", "industries", "\n", "like", "clean", "tech", "and", "electric", "vehicles", ",", "driven", "by", "a", "powerful", "combination", "of", "massive", "industrial", "policy", "and", "subsidises", ",", "\n", "rapid", "innovation", ",", "control", "of", "raw", "materials", "and", "ability", "to", "produce", "at", "continent", "-", "wide", "scale", ".", "\n", "The", "EU", "faces", "a", "possible", "trade", "-", "off", ".", "Increasing", "reliance", "on", "China", "may", "offer", "the", "cheapest", "and", "most", "efficient", "route", "\n", "to", "meeting", "our", "decarbonisation", "targets", ".", "But", "China", "’s", "state", "-", "sponsored", "competition", "also", "represents", "a", "threat", "to", "our", "\n", "productive", "clean", "tech", "and", "automotive", "industries", ".", "\n", "Decarbonisation", "must", "happen", "for", "the", "sake", "of", "our", "planet", ".", "But", "for", "it", "also", "to", "become", "a", "source", "of", "growth", "for", "Europe", ",", "\n", "we", "will", "need", "a", "joint", "plan", "spanning", "industries", "that", "produce", "energy", "and", "those", "that", "enable", "decarbonisation", "such", "as", "\n", "clean", "tech", "and", "automotives", ".", "\n", "The", "third", "area", "for", "action", "is", "increasing", "security", "and", "reducing", "dependencies", ".", "\n", "Security", "is", "a", "precondition", "for", "sustainable", "growth", ".", "Rising", "geopolitical", "risks", "can", "increase", "uncertainty", "and", "dampen", "\n", "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
[ "more", "granular", ",", "second", "-", "level", "NABS", "–", "further", "effort", "\n", "was", "devoted", "to", "remove", "redundant", "NACE", "sectors", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation235", "\n", "associated", "with", "a", "given", "Scopus", "field", ".", "As", "a", "con-", "\n", "crete", "example", "of", "this", "issue", ",", "the", "Scopus", "subject", "\n", "field", "‘", "Food", "Science", "’", "was", "mapped", "to", "NABS", "6.41", "\n", "‘", "Manufacture", "of", "food", "products", "’", ",", "part", "of", "the", "broad-", "\n", "er", "NABS06", "‘", "Industrial", "production", "and", "technology", "’", ".", "\n", "The", "concordance", "table", ",", "however", ",", "maps", "the", "entire", "\n", "NABS", "06", "to", "the", "entire", "Manufacturing", "NACE", "sector", ",", "\n", "so", "that", "by", "merely", "applying", "the", "concordance", ",", "NABS", "\n", "6.41", "‘", "Manufacture", "of", "food", "products", "’", "(", "and", ",", "in", "turn", ",", "\n", "the", "‘", "Food", "Science", "’", "science", "field", ")", "is", "mapped", "to", "any", "\n", "NACE", "Manufacturing", "sector", ",", "even", "outside", "the", "Food", "\n", "industry", ".", "Therefore", ",", "the", "final", "refinement", "effort", "\n", "was", ",", "in", "practice", ",", "focused", "on", "removing", "redundant", "\n", "and", "undesired", "NACE", "sectors", "from", "the", "mapping", ".", "\n", "As", "per", "the", "case", "of", "patents", ",", "publications", "can", "also", "\n", "be", "classified", "into", "more", "than", "one", "single", "taxon", "and", ",", "\n", "similar", "to", "that", "observed", "for", "patents", ",", "broad", "taxons", "\n", "can", "sometimes", "outnumber", "specific", "classifications", "\n", "when", "grouping", "per", "S&T", "domain", ".", "For", "this", "reason", ",", "\n", "to", "express", "S&T", "domains", "in", "terms", "of", "Scopus", "ASJC", "\n", "fields", ",", "it", "was", "decided", "to", "focus", "again", "on", "the", "rel-", "\n", "ative", "most", "frequent", "ASJC", "subject", "fields", "per", "S&T", "\n", "domain", ",", "rather", "than", "absolute", "numbers", ",", "to", "identify", "\n", "distinctive", "taxonomic", "areas", "to", "carry", "out", "mappings", "\n", "from", "S&T", "domains", "to", "NACE", "sectors", ".", "\n", "The", "final", "tables", "mapping", "Scopus", "subject", "fields", "to", "\n", "NACE", "for", "each", "EaP", "country", "are", "presented", "in", "Annex", "\n", "4", ".", "These", "mappings", "are", "complementary", "to", "those", "\n", "obtained", "via", "the", "patent", "classification", ",", "as", "both", "pro-", "\n", "vide", "relevant", "linkages", "between", "S&T", "domains", "and", "\n", "economic", "sectors", ".", "\n", "On", "expert", "assessment", ",", "the", "results", "of", "this", "mapping", "\n", "exercise", "as", "expressed", "by", "pairs", "of", "S&T", "domains", "\n", "and", "three", "-", "digit", "NACE", "codes", "seems", "satisfactory", ".", "\n", "The", "specificity", "and", "granularity", "of", "the", "subject", "field", "\n", "regarding", "NABS", "to", "NACE", "code", "concordances", "offers", "\n", "a", "highly", "compelling", "and", "descriptive", "list", "of", "NACE", "\n", "codes", "for", "each", "S&T", "domain", ".", "\n", "3", ".", "Results", "of", "the", "mapping", "ex-", "\n", "ercise", "\n", "The", "result", "of", "the", "current", "exercise", "provides", "a", "se-", "\n", "ries", "of", "economic", "clusters", "for", "every", "EaP", "country", ",", "\n", "each", "characterised", "by", "the", "respective", "E&I", "domains", "(", "identified", "in", "Part", "2", "via", "NACE", ")", "and", "S&T", "domains", "\n", "(", "identified", "in", "Part", "3", ")", ".", "The", "names", "of", "the", "clusters", "are", "\n", "inspired", "by", "the", "report", "Methodology", "and", "Findings", "\n", "Report", "for", "a", "Cluster", "Mapping", "of", "Related", "Sectors73", ".", "\n", "The", "clusters", "are", "defined", "based", "on", "groups", "of", "NACE", "\n", "sectors", ",", "while", "the", "presence", "of", "a", "given", "S&T", "domain", "\n", "in", "a", "specific", "cluster", "is", "produced", "by" ]
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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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[]
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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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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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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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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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