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Steel Europe TotalEnergies Uber Vodafone Volvo Wolt ZF @levelsio TRADE AND BUSINESS ASSOCIATIONS A4E - Airlines for Europe ACEA - European Automobile Manufacturers’ Association ACI - Airports Council International Europe ADRA - CLAIRE - Confederation of Laboratories for Artificial Intelligence Research in Europe Affordable Medicines Europe AFME - Association for Financial Markets in Europe APPLiA - Home Appliance Europe ARM - Alliance for Regenerative Medicine ASD Aerospace, Security and Defence Industries Association of Europe ASD Eurospace Bio Based Industries Consortium Cefic - European Chemical Industry Council CER - Community of European Railways and Infrastructure Managers CLECAT - European Association for Forwarding, Transport, Logistics and Customs Services CLEPA - European Association of Automotive Suppliers Confcommercio COCIR - European Coordination Committee of the Radiological, Electromedical and Healthcare IT Industry Digital SME alliance EARTO - European Association of Research and Technology Organisations EASE - European Association for Storage of Energy EBF - European Banking Federation EBIC - European Banking Industry Committee 03 THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | ACKNOWLEDGMENTECSA - European Community Shipowners’ Association ECTA - European Competitive Telecommunications Association EFA - European FinTech Association EFAMA - European fund and asset management industry EFPIA - European Federation of Pharmaceutical Industries EFR - European Financial Services Round Table EFSA - European Forum of Securities Associations EHPA - European Heat Pumps Association ERT - European Round Table of Industrialists ESPO - European Sea Ports Organisation ETNO - European Telecommunications Network Operators’ Association EUCOPE - European Confederation of Pharmaceutical Entrepreneurs EUROBAT - Association of European Automotive and Industrial Battery Manufacturers EUROFER - The European Steel Association EUROMETAUX Euromines - European Association of Mining Industries, Metal Ores & Industrial Minerals EuropaBio European Aluminium European council of young farmers - CEJA European Entrepreneurs CEA-PME European Venture Fund Investors Network EUTA - European Tech Alliance EU-ASE European Alliance to Save Energy France Industrie FSE - Federation of European Securities Exchanges GSMA Hydrogen Europe IE - Invest Europe International Lithium Association IOGP Europe - International Association of Oil & Gas Producers Medicines for Europe MedTech Europe METI Micromobility for Europe nucleareurope - Forum Atomique Européen Plastics Europe Platform for Electromobility SEA Europe - Shipyards’ and Maritime Equipment Association of Europe SGI EuropeSMEunited SME4SPACE SolarPower Europe The Guild of European Research Intensive Universities UNIFE VCI - Verband der Chemischen Industrie WindEurope Young European Enterprises Syndicate for Space ZEP - Zero Emission Platform PROFESSIONAL CONSULTANCIES Arthur D Little BCG
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[]
Article VIP-expressing interneurons in the anterior insular cortex contribute to sensory processing to regulateadaptive behavior Graphical abstract Highlights daIC VIP+ INs receive inputs from a wide variety of sensory- related brain regions daIC VIP+ INs respond to diverse sensory stimuli dInhibition of aIC VIP+ INs reduces fear memory retrieval andsocial interaction daIC VIP+ INs are functionally heterogeneous and displaycoding instabilityAuthors Arnau Ramos-Prats, Enrica Paradiso,Federico Castaldi, ..., Heide Ho ¨rtnagl, Georg Go ¨bel, Francesco Ferraguti Correspondence [email protected] In brief The mechanisms underlying the responses of specific subclasses ofinterneurons to sensory stimuli withpotential importance for behavioraladaptations is largely unknown. Ramos-Prats et al. identify VIP+ interneurons(INs) in the anterior insular cortex (aIC) asmediators of sensory processing andadaptive behaviors, such as socialpreference and fear learning. Ramos-Prats et al., 2022, Cell Reports 39, 110893 May 31, 2022 ª2022 The Author(s). https://doi.org/10.1016/j.celrep.2022.110893 ll Article VIP-expressing interneurons in the anterior insular cortex contribute to sensoryprocessing to regulate adaptive behavior Arnau Ramos-Prats,1Enrica Paradiso,1Federico Castaldi,1Maryam Sadeghi,2Mohd Yaqub Mir,1,3Heide Ho ¨rtnagl,1 Georg Go ¨bel,2and Francesco Ferraguti1,4,* 1Department of Pharmacology, Medical University of Innsbruck, 6020 Innsbruck, Austria 2Department for Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, 6020 Innsbruck, Austria 3Szenta ´gothai Doctoral School of Neuroscience, Semmelweis University, 1121 Budapest, Hungary 4Lead contact *Correspondence: [email protected] https://doi.org/10.1016/j.celrep.2022.110893 SUMMARY Adaptive behavior critically depends on the detection of behaviorally relevant stimuli. The anterior insular cor- tex (aIC) has long been proposed as a key player in the representation and integration of sensory stimuli, andimplicated in a wide variety of cognitive and emotional functions. However, to date, little is known about thecontribution of aIC interneurons to sensory processing. By using a combination of whole-brain connectivitytracing, imaging of neural calcium dynamics, and optogenetic modulation in freely moving mice acrossdifferent experimental paradigms, such as fear conditioning and social preference, we describe here a rolefor aIC vasoactive intestinal polypeptide-expressing (VIP+) interneurons in mediating adaptive behaviors.Our findings enlighten the contribution of aIC VIP+ interneurons to sensory processing, showing that they are anatomically connected to a wide range of sensory-related brain areas and critically respond to behav- iorally relevant stimuli independent of task and modality. INTRODUCTION One of the evolutionary advantages associated with the devel- opment of complex cortical circuitry is the ability to discriminate specific stimuli from a stream of ascending sensory information and to adapt to their repeated presentation to enable flexiblebehavioral responses. Behavioral relevance is attributed based on previous experiences, temporary need state, and disposition
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had the same composition. In each session, a maximum of 20 participants entered the lab and sat in an assigned location. To avoid interactions, participants were separated from each other by panels. Participants started by tasting the three versions of Danone Activia Strawberry yogurt, alternating each tasting with a sip of water and a bite of toasted bread. They then used a tablet to report their satisfaction with each version on a scale of 0 (‘Very unsat - isfied with the product ’) to 10 (‘Very satisfied with the product ’) (sensory preference score) and ranked them on a scale from 1 (‘Best ’) to 3 (‘Worst ’) Fig. 2.Stylized example of cards for the absence (left) and presence (right) of the ‘made for’ claim conditions 170 gr bag of crisps. Table 2 Attributes and levels used for the construction of the choice set. Attributes Levels Price Six price levels per product (based on actual prices for the studied products found in the markets studied) – See details in Appendix C Nutritional information and list of ingredientsThree levels (based on the actual information of the products marketed in the respective countries) Brand Brand logo/Generic brand (product specific brand) Source: Authors ’ elaboration Fig. 3.Protocol for the sensorial evaluation laboratory experiment.D.M. Federica et al. Food Policy 131 (2025) 102803 5 (order score). Next, participants were shown (on the device) the list of ingredients, the nutritional facts, and the price of each of the three versions. Based on this information, they selected the product version they would buy. This exercise was then repeated for the second product. Round two in Frame 1 was identical as round one, except for the in- clusion of the ‘made for’ claim. Similarly, round two in Frame 2 was identical to round one, except for the inclusion of the brand name. A pair of coloured stickers was associated with each product version to track changes in individual choices between rounds. 3.2. Econometric approach The key objective of experiment 1 is to assess which composition of the same product consumers prefer. The key objective of experiment 1 is to assess which version of the same product consumers prefer. We model consumer choice in the presence of DFQ as a discrete-choice variable and adopt a random utility approach. We conceptualize our model at the version level and assume that consumer r from country s…s ˆ1C⋯CS† evaluating k…kˆ1C⋯CK†product
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[]
electron drift time in each side of the chamber: rt=¯tL/¯tH (1) where L(H) refers to the light (heavy) fission fragment. This quantity is closely related to the one defined in Ref. [ 8]t o estimate the nuclear charge split of the fission fragments, and was also mentioned in a more recent paper [ 50]. How- ever, as illustrated in Fig. 11, this ratio depends not only on the nuclear charge of the fragments but also on their kinetic energy. Hence, we construct the following charge calibration func- tion for fission events with TKE >120 MeV: ˆZL,k=ZCN/2−rt−/angbracketleft rt/angbracketrightsym,k ak×(TKE−/angbracketleftTKE/angbracketright)+bk(2) where ZCN/2 and /angbracketleftrt/angbracketrightsymare the nuclear charge and mean drift-time ratio of the fragments, respectively, when thenucleus undergoes symmetric fission, i.e., A L=AH=126 in the case of252Cf(sf). It is generally admitted that for such a fragmentation both fission fragments have the same nuclear charge, that is ZL=ZH=ZCN/2[51]./angbracketleftTKE/angbracketrightis the aver- age post-neutron TKE, that is 181.6(7) MeV from our mea- surement. The index kdenotes the chamber side to which the light fragment is emitted (source or backing). The/angbracketleftrt/angbracketrightsymparameters of the calibration were obtained from symmetric fission, that is: AH−AL/lessorequalslant0.1u . T h e remaining aand bparameters of the calibration function were obtained from four isomers with high yields:95 38Sr,10843Tc, 123Eur. Phys. J. A (2025) 61:5 Page 9 of 12 5 Fig. 11 Evolution of the electron drift-time ratio rtas a function of TKE gated on isomers, depending on the side where the light fragmentswere detected. Thick lines are the profiles of each distribution, i.e.,/angbracketleftr t|TKE/angbracketright Fig. 12 Distribution of (rt−/angbracketleft rt/angbracketrightsym)/(ZL−ZCN/2)vs TKE in both chamber sides, from95 38Sr,10843Tc,134 52Te and13654Xe. Red points corre- spond to the profile along TKE of these distributions: /angbracketleft/Delta1rt//Delta1 Z|TKE/angbracketright, while the fit result is represented by a magenta line 134 52Te and13654Xe. These isomers were selected by gating on γ-γcoincidences. For134 52Te and13654Xe, the complementary light fragments are Pd ( ZL=46) and Ru ( ZL=44), respec- tively. The resulting (rt−/angbracketleft rt/angbracketrightsym)/(ZL−ZCN/2)distribu- tions are shown in Fig. 12, together with the associated linear functions f(TKE)=a×(TKE−/angbracketleftTKE/angbracketright)+bused to fit them. Corresponding calibration parameters aand bextracted from these fits are given in Table 4. Using the calibration parameters listed in Table 4,t h e nuclear charge ˆZLof the light fission fragments could be extracted from the VESPA IC. This charge calibration wasvalidated using the isomers identified in this work, as repre- sented in Fig. 13. From these figures, we can notice that
[ "electron", "drift", "time", "in", "each", "side", "of", "\n", "the", "chamber", ":", "\n", "rt=¯tL/¯tH", "(", "1", ")", "\n", "where", "L(H", ")", "refers", "to", "the", "light", "(", "heavy", ")", "fission", "fragment", ".", "This", "\n", "quantity", "is", "closely", "related", "to", "the", "one", "defined", "in", "Ref", ".", "[", "8]t", "o", "\n", "estimate", "the", "nuclear", "charge", "split", "of", "the", "fission", "fragments", ",", "\n", "and", "was", "also", "mentioned", "in", "a", "more", "recent", "paper", "[", "50", "]", ".", "How-", "\n", "ever", ",", "as", "illustrated", "in", "Fig", ".", "11", ",", "this", "ratio", "depends", "not", "only", "on", "\n", "the", "nuclear", "charge", "of", "the", "fragments", "but", "also", "on", "their", "kinetic", "\n", "energy", ".", "\n", "Hence", ",", "we", "construct", "the", "following", "charge", "calibration", "func-", "\n", "tion", "for", "fission", "events", "with", "TKE", ">", "120", "MeV", ":", "\n", "ˆZL", ",", "k", "=", "ZCN/2−rt−/angbracketleft", "rt", "/", "angbracketrightsym", ",", "k", "\n", "ak×(TKE−/angbracketleftTKE", "/", "angbracketright)+bk(2", ")", "\n", "where", "ZCN/2", "and", "/angbracketleftrt", "/", "angbracketrightsymare", "the", "nuclear", "charge", "and", "mean", "\n", "drift", "-", "time", "ratio", "of", "the", "fragments", ",", "respectively", ",", "when", "thenucleus", "undergoes", "symmetric", "fission", ",", "i.e.", ",", "A", "\n", "L", "=", "AH=126", "\n", "in", "the", "case", "of252Cf(sf", ")", ".", "It", "is", "generally", "admitted", "that", "for", "such", "a", "\n", "fragmentation", "both", "fission", "fragments", "have", "the", "same", "nuclear", "\n", "charge", ",", "that", "is", "ZL", "=", "ZH", "=", "ZCN/2[51]./angbracketleftTKE", "/", "angbracketrightis", "the", "aver-", "\n", "age", "post", "-", "neutron", "TKE", ",", "that", "is", "181.6(7", ")", "MeV", "from", "our", "mea-", "\n", "surement", ".", "The", "index", "kdenotes", "the", "chamber", "side", "to", "which", "the", "\n", "light", "fragment", "is", "emitted", "(", "source", "or", "backing", ")", ".", "\n", "The", "/", "angbracketleftrt", "/", "angbracketrightsymparameters", "of", "the", "calibration", "were", "obtained", "\n", "from", "symmetric", "fission", ",", "that", "is", ":", "AH−AL", "/", "lessorequalslant0.1u", ".", "T", "h", "e", "\n", "remaining", "aand", "bparameters", "of", "the", "calibration", "function", "were", "\n", "obtained", "from", "four", "isomers", "with", "high", "yields:95", "\n", "38Sr,10843Tc", ",", "\n", "123Eur", ".", "Phys", ".", "J.", "A", " ", "(", "2025", ")", "61:5", "Page", "9", "of", "12", " ", "5", "\n", "Fig", ".", "11", "Evolution", "of", "the", "electron", "drift", "-", "time", "ratio", "rtas", "a", "function", "of", "\n", "TKE", "gated", "on", "isomers", ",", "depending", "on", "the", "side", "where", "the", "light", "fragmentswere", "detected", ".", "Thick", "lines", "are", "the", "profiles", "of", "each", "distribution", ",", "i.e.", ",/angbracketleftr", "\n", "t|TKE", "/", "angbracketright", "\n", "Fig", ".", "12", "Distribution", "of", "(", "rt−/angbracketleft", "rt", "/", "angbracketrightsym)/(ZL−ZCN/2)vs", "TKE", "in", "both", "\n", "chamber", "sides", ",", "from95", "\n", "38Sr,10843Tc,134", "\n", "52Te", "and13654Xe", ".", "Red", "points", "corre-", "\n", "spond", "to", "the", "profile", "along", "TKE", "of", "these", "distributions", ":", "/angbracketleft", "/", "Delta1rt//Delta1", "Z|TKE", "/", "angbracketright", ",", "\n", "while", "the", "fit", "result", "is", "represented", "by", "a", "magenta", "line", "\n", "134", "\n", "52Te", "and13654Xe", ".", "These", "isomers", "were", "selected", "by", "gating", "on", "\n", "γ", "-", "γcoincidences", ".", "For134", "\n", "52Te", "and13654Xe", ",", "the", "complementary", "\n", "light", "fragments", "are", "Pd", "(", "ZL=46", ")", "and", "Ru", "(", "ZL=44", ")", ",", "respec-", "\n", "tively", ".", "The", "resulting", "(", "rt−/angbracketleft", "rt", "/", "angbracketrightsym)/(ZL−ZCN/2)distribu-", "\n", "tions", "are", "shown", "in", "Fig", ".", "12", ",", "together", "with", "the", "associated", "linear", "\n", "functions", "f(TKE)=a×(TKE−/angbracketleftTKE", "/", "angbracketright)+bused", "to", "fit", "them", ".", "\n", "Corresponding", "calibration", "parameters", "aand", "bextracted", "from", "\n", "these", "fits", "are", "given", "in", "Table", "4", ".", "\n", "Using", "the", "calibration", "parameters", "listed", "in", "Table", "4,t", "h", "e", "\n", "nuclear", "charge", "ˆZLof", "the", "light", "fission", "fragments", "could", "be", "\n", "extracted", "from", "the", "VESPA", "IC", ".", "This", "charge", "calibration", "wasvalidated", "using", "the", "isomers", "identified", "in", "this", "work", ",", "as", "repre-", "\n", "sented", "in", "Fig", ".", "13", ".", "From", "these", "figures", ",", "we", "can", "notice", "that" ]
[]
to printing X 23.7Cutting, shaping and finishing of stone X 21 Pharmaceuticals, medicinal chemicals, etc. X NACE Innovation – Patents 24.1Manufacture of basic iron and steel and of ferro-alloys X 25 Manufacture of fabricated metal products X 12 Manufacture of tobacco products 25.9Manufacture of other fabricated metal products X 15 Manufacture of leather and related products 35.1Electric power generation, transmission and distribution X SITC Goods exports C E 23 Manufacture of other non-metallic mineral products 41.2Construction of residential and non-residential buildingsX 0 Live animals other than animals of division 03 X 23.1 Manufacture of glass and glass products 42.2 Construction of utility projects X 7Coffee, tea, cocoa, spices, and manufactures thereofX 23.4 Man. of other porcelain and ceramic products 42.9Construction of other civil engineering projects X 9 Miscellaneous edible products and preparations X 24 Manufacture of basic metals 43.1 Demolition and site preparation X 11 Beverages X X 25.5 Forging, pressing, stamping and roll-forming of metal 43.2Electrical, plumbing and other construction installation act. X 28 Metalliferous ores and metal scrap X X 25.6 Treatment and coating of metals; machining 43.9Other specialised construction activities X 33Petroleum, petroleum products and related materialsX 25.7 Manufacture of cutlery, tools, and general hardware 45.1 Sale of motor vehicles X 35 Electric current X 27.5 Manufacture of domestic appliances 47.1Retail sale in non-specialised stores X 41 Animal oils and fats X 29.1 Manufacture of motor vehicles 47.5Retail sale of other household equipment in specialised storesX X 52 Inorganic chemicals X 31 Manufacture of furniture 47.6Retail sale of cultural and recreation goods in spec. storesX 54 Medicinal and pharmaceutical products X X 49.3 Other passenger land transport X 55Essential oils and resinoids and perfume materials; toilet, etc.X NACE Innovation – VC & start-ups 49.4Freight transport by road and removal services X 59 Chemical materials and products X 62, 63 Software Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation129 130 Part 2 Analysis of economic and innovation potential 53.2 Other postal and courier activities X 62 Rubber manufactures X X 62, 64 Financial services 55.1 Hotels and similar accommodation X X 64Paper, paperboard and articles of paper pulp, of paper or of paperboardX 64 Lending and investments 56.1Restaurants and mobile food service activitiesX 66 Non-metallic mineral manufactures X 53, 55, 79 Travel and tourism 56.3 Beverage serving activities X 67 Iron and steel X 61, 63 Internet services 58.1Publishing of books, periodicals and
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[]
contributions of the different institutions or individuals to each identified topic. In addition, if the production year of each doc- ument is known, TM can be used to track the time evolution of each topic to detect emerging or declining research topics.Focus – Topic modelling 34 Part 1 Introduction and methodology 3. Key constraints and limita- tions As explained in the previous section, the analyses carried out in this work are based on internation- ally available data, which was retrieved from a se- ries of consolidated sources. The added value of this approach lies in the definition and application of international standards for data collection and aggregation, which are applied throughout the EaP countries. This makes it possible to draw compar- isons between EaP countries in an educated fash- ion, so that transversal views are attainable across the EaP region. Additionally, with regard to scientif- ic and technological potential, the presence of data in international databases signals the existence of internationally relevant local scientific and techno- logical activities and capacities, which are essential for propelling place-based knowledge economies. For instance, scientific publications in well-estab- lished journals covered by international databases signal the good quality of the respective scientific works which, in turn, pinpoint specific niches of ex- cellence within some scientific domains of interest. An analogous reflection can be achieved with re- spect to the research projects funded by the Eu- ropean Commission through the FP7 and H2020 Framework Programmes: indeed, funded projects in the EaP countries met the highly demanding se- lection criteria of the evaluation schemes of eligible proposals. This, again, is a symptom of the excel- lence of the beneficiaries of the granted projects. Nevertheless, the analysis of the EIST potential hereby drawn crucially depends on the availability of comparable data for each of the six EaP coun- tries. Specific limitations affect both the economic and innovation and scientific and technological po- tential, as further explained below. Despite the advantages discussed above, it is of course understood that, for a more accurate view of the local ecosystems and to move further to- wards the EDP, the results of this study should be supplemented by national-level analyses with complementary local data sources and insight from experts, which are, however, beyond the scope of this project.3.1 Constraints of the economic and innovation (E&I) analysis The analysis of the economic and innovation po- tential crucially depends on the availability of
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[]
(33 %), Germany, Italy, and Austria (each >10 %). (ii) Urbanization levels. Moreover, from Fig. 6a, we ob- serve that the number of urban LAUs (based on 2018 URAU definitions and on correspondence to LAUs) is much smaller than the number of rural ones (26.3 % or 6585 and 73.7 % or 19 200, respectively). Nevertheless, the urban population ex- posed to multi-hazards totals 54 % (46.8 million) compared with the rural administrative areas at 46 % (40.1 million) (Fig. 6c). Based on the urbanization degree, 15 countries in Europe have a higher share of population exposed to multi-hazards in rural areas compared to urban areas: Sweden and Norway (100 %); Croatia, Cyprus, Portugal, and Slovakia (between 70 %–90 %); and Hungary, Spain, Belgium, Slovenia, Roma- nia, and Switzerland (between 50 %–70 %). In the remaining countries like the Netherlands and Austria ( <20 %); Poland, Germany, and Greece (20 %–40 %); and Ireland, the United Kingdom, France, Denmark, the Czech Republic, and Bul- garia (40 %–50 %), the share of population exposed to multi- hazards in rural areas is lower compared to urban areas. This suggests that individuals living in regions with higher GDP and greater population density (characterized by high- income and high-middle-income levels and urban areas, which comprise approximately 12 % of European adminis- trative regions) are more exposed to multi-hazards compared to those residing in regions with lower GDP and lower popu- lation density (typically low-income and low-middle-income areas and rural regions) (54 % of LAUs). By considering the degree of urbanization only, people are more exposed to multi-hazards if they live in either high-income urban areas (compared with low-income urban areas) or low-income ru- ral areas (compared with high-income rural areas) (Fig. 6c). 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 295 Figure 5. Regions (LAUs) with population exposed to multi-hazards by significance level (a); sum of population exposed to multi-hazards assessed at the NUTS3 level (only hotspot regions with >90 % confidence interval) (b); number of administrative areas exposed to multi- hazards by confidence interval and number of hazards (c); population exposed to multi-hazards by confidence interval and number of haz- ards (d). As can be seen in Fig. 7, exploring the differences between various income classes, we find that as countries and their regions get richer, they are more
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[ { "end": 2005, "label": "CITATION-SPAN", "start": 1815 } ]
both in terms of the available data (which seem to be relatively skewed towards the technological sectors) and in terms of the dominance of Ukraine in terms of re- cords, compared to the other five countries. Never- theless, the analysis does highlight some relevant information about the innovation potential fuelled by the start-ups and venture capital-backed com- panies in the EaP countries. In particular, several synergies and complementa- rities between different EaP countries are primar- ily found as follows: ■the Software industry is particularly devel- oped in the region, appearing at the top of the rankings (both in terms of company and employment specialisation) for all countries, except Azerbaijan; ■the Software industries in these countries are also mainly active in the Information Technol- ogy, Mobile and Apps sectors. One exception is Georgia, for which a substantial amount of Software companies are related to Financial Services and Payments – two sectors in which the country is the leader in the region accord- ing to all specialisation indices; ■Armenia is predominantly specialised in ‘tech’ sectors; it shares strength in Mobile with Azer- baijan, as both countries feature a relevant number of industries that develop mobile software and apps; ■Ukraine covers sectors which find no corre- spondence in other countries. Table 2.54 presents the recommended selection of industry groups, based on the analysed Crunch- base data on start-ups and venture capital-backed companies. Although no official mapping of the Crunchbase Industry Groups to NACE exists, the above industry groups can be qualitatively led back to the NACE sectors presented in Table 2.55.3.6. Cluster organisations Cluster organisations are relevant intermediary structures which foster industrial, research and in- novation cooperation. Although the number of iden- tified clusters in EaP countries is small, it provides a high-quality view of the formalisation, dynamism and collaboration of the value chains in the various EaP STI ecosystems and can be useful venues sup- porting entrepreneurial discovery process. The existence of cluster or cluster-like structures in the EaP countries can be verified, for instance, by the following two sources: 1. the European Cluster Collaboration Platform 2. the report Review of the state of development of clusters in EaP countries. The European Cluster Collaboration Platform (ECCP) is a service facility aiming to provide dy- namic mapping of over 1 000 profiled cluster organisations worldwide. The facility offers a database on regional, national, international and sectoral clusters and their networks, including relevant geographic,
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[]
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"Biophysics", "and", "Biomolecular", "Structure", ".", "30", ":", "23–65", ".", "doi:10.1146", "/", "annurev.biophys.30.1.23", ".", "PMID", "11340051", ".", "Archived", "(", "PDF", ")", "from", "the", "original", "on", "22", "January", "2020", ".", "Retrieved", "11", "November", "2019", ".", "\n ", "Capaldi", "RA", ",", "Aggeler", "R", "(", "March", "2002", ")", ".", "\"", "Mechanism", "of", "the", "F(1)F(0)-type", "ATP", "synthase", ",", "a", "biological", "rotary", "motor", "\"", ".", "Trends", "in", "Biochemical", "Sciences", ".", "27", "(", "3", "):", "154–60", ".", "doi:10.1016", "/", "S0968", "-", "0004(01)02051", "-", "5", ".", "PMID", "11893513", ".", "\n ", "Friedrich", "B", ",", "Schwartz", "E", "(", "1993", ")", ".", "\"", "Molecular", "biology", "of", "hydrogen", "utilization", "in", "aerobic", "chemolithotrophs", "\"", ".", "Annual", "Review", "of", "Microbiology", ".", "47", ":", "351–83", ".", "doi:10.1146", "/", "annurev.mi.47.100193.002031", ".", "PMID", "8257102", ".", "\n ", "Weber", "KA", ",", "Achenbach", "LA", ",", "Coates", "JD", "(", "October", "2006", ")", ".", "\"", "Microorganisms", "pumping", "iron", ":", "anaerobic", "microbial", "iron", "oxidation", "and", "reduction", "\"", ".", "Nature", "Reviews", ".", "Microbiology", ".", "4", "(", "10", "):", "752–64", ".", "doi:10.1038", "/", "nrmicro1490", ".", "PMID", "16980937", ".", "S2CID", "8528196", ".", "Archived", "from", "the", "original", "on", "2", "May", "2019", ".", "Retrieved", "6", "October", "2019", ".", "\n ", "Jetten", "MS", ",", "Strous", "M", ",", "van", "de", "Pas", "-", "Schoonen", "KT", ",", "Schalk", "J", ",", "van", "Dongen", "UG", ",", "van", "de", "Graaf", "AA", ",", "et", "al", ".", "(", "December", "1998", ")", ".", "\"", "The", "anaerobic", "oxidation", "of", "ammonium", "\"", ".", "FEMS", "Microbiology", "Reviews", ".", "22", "(", "5", "):", "421–37", ".", "doi:10.1111", "/", "j.1574", "-", "6976.1998.tb00379.x", ".", "PMID", "9990725", ".", "\n ", "Simon", "J", "(", "August", "2002", ")", ".", "\"", "Enzymology", "and", "bioenergetics", "of", "respiratory", "nitrite", "ammonification", "\"", ".", "FEMS", "Microbiology", "Reviews", ".", "26", "(", "3", "):", "285–309", ".", "doi:10.1111", "/", "j.1574", "-", "6976.2002.tb00616.x", ".", "PMID", "12165429", ".", "\n ", "Conrad", "R", "(", "December", "1996", ")", ".", "\"", "Soil", "microorganisms", "as", "controllers", "of", "atmospheric", "trace", "gases", "(", "H2", ",", "CO", ",", "CH4", ",", "OCS", ",", "N2O", ",", "and", "NO", ")", "\"", ".", "Microbiological", "Reviews", ".", "60", "(", "4", "):", "609–40", ".", "doi:10.1128", "/", "MMBR.60.4.609", "-", "640.1996", ".", "PMC", "239458", ".", "PMID", "8987358", ".", "\n ", "Barea", "JM", ",", "Pozo", "MJ", ",", "Azcón", "R", ",", "Azcón", "-", "Aguilar", "C", "(", "July", "2005", ")", ".", "\"", "Microbial", "co", "-", "operation", "in", "the", "rhizosphere", "\"", ".", "Journal", "of", "Experimental", "Botany", ".", "56", "(", "417", 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Mining4EBPH Supporting Evidence-based Public Health Interventions using Text Mining SLiM Pilot study of the utility of text mining and machine learning tools to accelerate systematic review and meta-analysis of findings of in vivo research Publications Detecting machine-generated text P. Przybyła, N. Duran-Silva, S. Egea-Gómez, “I've Seen Things You Machines Wouldn't Believe: Measuring Content Predictability to Identify Automatically-Generated Text,” in Proceedings of the 5th Workshop on Iberian Languages Evaluation Forum (IberLEF 2023), Jaén, Spain, 2023. [bib][paper][code] P. Przybyła, “Detecting Bot Accounts on Twitter by Measuring Message Predictability,” in Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, 2019. [bib][paper][code] Credibility and misinformation P. Przybyła, A. Shvets, H. Saggion, “Verifying the Robustness of Automatic Credibility Assessment,” Natural Language Processing, 2024. [bib][paper][code] A. Barrón-Cedeño, F. Alam, J. M. Struß, P. Nakov, T. Chakraborty, T. Elsayed, P. Przybyła, T. Caselli, G. Da San Martino, F. Haouari, M. Hasanain, C. Li, J. Piskorski, F. Ruggeri, X. Song, R. Suwaileh, “Overview of the CLEF-2024 CheckThat! Lab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness,” in Proceedings of the Fifteenth International Conference of the CLEF Association (CLEF 2024), Grenoble, France, 2024. [bib][paper][preprint][event] P. Przybyła, E. McGill, H. Saggion, “Know Thine Enemy: Adaptive Attacks on Misinformation Detection Using Reinforcement Learning,” in Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, Bangkok, Thailand, 2024. [bib][paper][code] P. Przybyła, B. Wu, A. Shvets, Y. Mu, K. C. Sheang, X. Song, H. Saggion, “Overview of the CLEF-2024 CheckThat! Lab Task 6 on Robustness of Credibility Assessment with Adversarial Examples (InCrediblAE),” in Working Notes of CLEF 2024 - Conference and Labs of the Evaluation Forum, Grenoble, France, 2024. [bib][paper][event][code] A. Barrón-Cedeño, F. Alam, T. Chakraborty, T. Elsayed, P. Nakov, P. Przybyła, J. M. Struß, F. Haouari, M. Hasanain, F. Ruggeri, X. Song, R. Suwaileh, “The CLEF-2024 CheckThat! Lab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness,” in Proceedings of the 46th European Conference on Information Retrieval (ECIR 2024), Glasgow, UK, 2024. [bib][paper][preprint][event] P. Przybyła, K. Kaczyński, “Where Does It End? Long Named Entity Recognition for Propaganda Detection and Beyond,” in Proceedings of the Workshop on NLP applied to Misinformation co-located with 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), Jaén, Spain, 2023. [bib][paper][code] P. Przybyła, H. Saggion, “ERINIA: Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions,” in Proceedings of the Workshop on NLP applied to Misinformation co-located with 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), Jaén, Spain, 2023.
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science and innovation policies. The relative absence of non-technological research and innova- tion activities (i.e. textiles, design or experi- ence-based industries or tourism), which can play an important role in Smart Specialisation Strategies, and the lower propensity to pub- lish, protect intellectual property or participate in EU projects of lower-technology and tradi- tional sectors has to be taken into account. A more holistic view of the EaP’s capacities and opportunities for innovation must be consid- ered with experts, measured using other indi- cators, and explored in the EDP. 2. Uneven number of records across the analysed data sources which overrepre- sents scientific outputs. The relative num- ber or production of records in the different data sources (publications, patents, projects) is very different, with a much larger number of publications than the other two. To avoid manipulation of the raw numbers, no normal- isation has been performed to control for this disparity. Nevertheless, this must be consid- ered when interpreting the results and conclu- sion of the analysis, particularly when there is a specific interest in technological innovation and the role of companies and other non-ac-ademic actors. Nevertheless, throughout the report, information is also presented by type of data source to allow for a more balanced view of the specialisations. 3. Bias in the coverage of EaP scientific publications in local journals, local lan- guages and some disciplines. Scopus, the bibliometric database used in this report, in- dexes a fairly large selection of international journals, but its coverage of EaP-based pub- lishers is relatively small. Therefore, science and innovation fields that tend to publish in local journals and/or local languages (typically within the domains of law, social sciences and the humanities) are not covered as extensive- ly as those fields that publish in international journals. 4. Low number of records hindering a deep- er characterisation of some specialisa- tion domains. The low number of records in some domains and/or countries may prevent a richer/finer characterisation of some S&T do- mains and could provide unreliable indicators both in terms of S&T specialisations and in their concordance with the E&I specialisation domains coming from the economic and inno- vation analysis. 5. Uneven representation of institutional typologies, overrepresentation of aca- demic actors and underrepresentation of companies, NGOs, governments, etc., developing innovation or applying tech- nology. As a consequence of the above lim- itations, it can be expected that companies, NGOs, governments, etc. are under-repre-
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[]
that is con- sidered significantly more human-like to human raters than the text produced by using the untrun-cated distribution. Table 4 gives several examples where human raters and our BERT-based discriminators dis- agreed. When raters incorrectly labeled human- written text as machine-generated, often the ex- cerpts contained formatting failures introduced when the HTML was stripped out. In the mid- dle two examples, topic drift and falsehoods such as Atlanta being the “information hub of the na- tion’s capital” allowed humans to correctly detect the generated content. However, in the bottom two examples, the high level of fluency left human raters fooled. Overall we find that human raters—even “ex- pert” trained ones—have consistently worse ac- curacy than automatic discriminators for all de- coding methods and excerpt lengths. In our ex- periments, randomly-selected pairs of raters agree with each other on a mere 59% of excerpts on average. (In comparison, raters and discrimina- tors agree on 61% to 70% of excerpts depending on the discriminator considered). We surmise that the gap between human and machine performance will only grow as researchers inevitably train big- ger, better detection models on larger amounts oftraining data. While improved detection models are inevitible, it is unclear how to go about im- proving human performance. GLTR proposes pro- viding visual aids to humans to improve their per- formance at detecting generated-text, but it is un- likely that their histogram-based color-coding will continue to be effective as generative methods get better at producing high-quality text that lacks sta- tistical anomalies. 8 Conclusion In this work, we study the behavior of auto- mated discriminators and their ability to iden- tify machine-generated and human-written texts. We train these discriminators on balanced bi- nary classification datasets where all machine- generated excerpts are drawn from the same gener- ative model but with different decoding strategies. We find that, in general, discriminators transfer poorly between decoding strategies, but that train- ing on a mix of data from methods can help. We also show the rate at which discriminator accuracy increases as excerpts are lengthened. We further study the ability of expert human raters to perform the same task. We find that rater accuracy varies wildly, but has a median of 74%, which is less than the accuracy of our best- performing discriminator. Most interestingly, we find that human raters and discriminators make de- cisions based on different qualities, with humans more easily noticing
[ " ", "that", "is", "con-", "\n", "sidered", "significantly", "more", "human", "-", "like", "to", "human", "\n", "raters", "than", "the", "text", "produced", "by", "using", "the", "untrun", "-", "cated", "distribution", ".", "\n", "Table", "4", "gives", "several", "examples", "where", "human", "\n", "raters", "and", "our", "BERT", "-", "based", "discriminators", "dis-", "\n", "agreed", ".", "When", "raters", "incorrectly", "labeled", "human-", "\n", "written", "text", "as", "machine", "-", "generated", ",", "often", "the", "ex-", "\n", "cerpts", "contained", "formatting", "failures", "introduced", "\n", "when", "the", "HTML", "was", "stripped", "out", ".", "In", "the", "mid-", "\n", "dle", "two", "examples", ",", "topic", "drift", "and", "falsehoods", "such", "\n", "as", "Atlanta", "being", "the", "“", "information", "hub", "of", "the", "na-", "\n", "tion", "’s", "capital", "”", "allowed", "humans", "to", "correctly", "detect", "\n", "the", "generated", "content", ".", "However", ",", "in", "the", "bottom", "\n", "two", "examples", ",", "the", "high", "level", "of", "fluency", "left", "human", "\n", "raters", "fooled", ".", "\n", "Overall", "we", "find", "that", "human", "raters", "—", "even", "“", "ex-", "\n", "pert", "”", "trained", "ones", "—", "have", "consistently", "worse", "ac-", "\n", "curacy", "than", "automatic", "discriminators", "for", "all", "de-", "\n", "coding", "methods", "and", "excerpt", "lengths", ".", "In", "our", "ex-", "\n", "periments", ",", "randomly", "-", "selected", "pairs", "of", "raters", "agree", "\n", "with", "each", "other", "on", "a", "mere", "59", "%", "of", "excerpts", "on", "\n", "average", ".", "(", "In", "comparison", ",", "raters", "and", "discrimina-", "\n", "tors", "agree", "on", "61", "%", "to", "70", "%", "of", "excerpts", "depending", "\n", "on", "the", "discriminator", "considered", ")", ".", "We", "surmise", "that", "\n", "the", "gap", "between", "human", "and", "machine", "performance", "\n", "will", "only", "grow", "as", "researchers", "inevitably", "train", "big-", "\n", "ger", ",", "better", "detection", "models", "on", "larger", "amounts", "oftraining", "data", ".", "While", "improved", "detection", "models", "\n", "are", "inevitible", ",", "it", "is", "unclear", "how", "to", "go", "about", "im-", "\n", "proving", "human", "performance", ".", "GLTR", "proposes", "pro-", "\n", "viding", "visual", "aids", "to", "humans", "to", "improve", "their", "per-", "\n", "formance", "at", "detecting", "generated", "-", "text", ",", "but", "it", "is", "un-", "\n", "likely", "that", "their", "histogram", "-", "based", "color", "-", "coding", "will", "\n", "continue", "to", "be", "effective", "as", "generative", "methods", "get", "\n", "better", "at", "producing", "high", "-", "quality", "text", "that", "lacks", "sta-", "\n", "tistical", "anomalies", ".", "\n", "8", "Conclusion", "\n", "In", "this", "work", ",", "we", "study", "the", "behavior", "of", "auto-", "\n", "mated", "discriminators", "and", "their", "ability", "to", "iden-", "\n", "tify", "machine", "-", "generated", "and", "human", "-", "written", "texts", ".", "\n", "We", "train", "these", "discriminators", "on", "balanced", "bi-", "\n", "nary", "classification", "datasets", "where", "all", "machine-", "\n", "generated", "excerpts", "are", "drawn", "from", "the", "same", "gener-", "\n", "ative", "model", "but", "with", "different", "decoding", "strategies", ".", "\n", "We", "find", "that", ",", "in", "general", ",", "discriminators", "transfer", "\n", "poorly", "between", "decoding", "strategies", ",", "but", "that", "train-", "\n", "ing", "on", "a", "mix", "of", "data", "from", "methods", "can", "help", ".", "We", "\n", "also", "show", "the", "rate", "at", "which", "discriminator", "accuracy", "\n", "increases", "as", "excerpts", "are", "lengthened", ".", "\n", "We", "further", "study", "the", "ability", "of", "expert", "human", "\n", "raters", "to", "perform", "the", "same", "task", ".", "We", "find", "that", "\n", "rater", "accuracy", "varies", "wildly", ",", "but", "has", "a", "median", "of", "\n", "74", "%", ",", "which", "is", "less", "than", "the", "accuracy", "of", "our", "best-", "\n", "performing", "discriminator", ".", "Most", "interestingly", ",", "we", "\n", "find", "that", "human", "raters", "and", "discriminators", "make", "de-", "\n", "cisions", "based", "on", "different", "qualities", ",", "with", "humans", "\n", "more", "easily", "noticing" ]
[]
60% of the records. Alternatively, Figure 3.19 presents the distribu- tion of analysed records in the form of weighted percentages by type of record. This means that percentages are calculated within each domain relative to the total number of records per data source. This figure, complementing Figure 3.18, allows to better observe the distribution patterns of patents and EC projects, which are smaller, in volume terms, than publications. As presented in Figure 3.19, Governance, culture, education and the economy is primarily composed of EC R&I projects due to the nature of the Eu- ropean research and innovation framework pro- grammes, which support capacity building as well as transnational cooperation projects, typically oriented to research, education, economic devel- opment, education as well as some priority areas such as ICT, energy, transportation and the envi- ronment. Thus, although R&I projects are not the ideal source for identifying purely emerging spe- cialisations, they do allow relevant internationally connected actors in the EaP to be identified, espe- cially in the areas above, as well as, again, gaug- ing the strength of the hard sciences in the EaP. It also enables different relative specialisations to be explored throughout the EaP countries. 58 Noting that the total volume of analysed scientific publi- cations is higher than that of patents. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation155 Agrifood Biotechnology Chemistry and chemical engineering Electric and electronic technologies Energy Environmental sciences and industries Fundamental physics and mathematics Governance, culture, education and the economy Health and wellbeing ICT and computer science Mechanical engineering and heavy machinery Nanotechnology and materials Optics and photonics Transportation Agrifood 964 315 65 149 1 347 138 196 722 133 615 236 45 52 Biotechnology 964 3 277 178 325 465 342 110 2 687 138 212 1 656 109 45 Chemistry and chemical engineering315 3 277 111 314 694 308 40 682 88 143 1 809 91 9 Electric and electronic technologies65 178 111 3 102 148 660 121 217 1 008 1 213 1 004 856 240 Energy 149 325 314 3 102 666 2 470 288 257 504 2 135 799 185 326 Environmental sciences and industries1 347 465 694 148 666 512 1 128 754 592 693 542 101 113 Fundamental physics and mathematics138 342 308 660 2 470 512 520 414 1 164 1 139 2 115 930 258 Governance, culture, education and the
[ "60", "%", "of", "the", "\n", "records", ".", "\n", "Alternatively", ",", "Figure", "3.19", "presents", "the", "distribu-", "\n", "tion", "of", "analysed", "records", "in", "the", "form", "of", "weighted", "\n", "percentages", "by", "type", "of", "record", ".", "This", "means", "that", "\n", "percentages", "are", "calculated", "within", "each", "domain", "\n", "relative", "to", "the", "total", "number", "of", "records", "per", "data", "\n", "source", ".", "This", "figure", ",", "complementing", "Figure", "3.18", ",", "\n", "allows", "to", "better", "observe", "the", "distribution", "patterns", "\n", "of", "patents", "and", "EC", "projects", ",", "which", "are", "smaller", ",", "in", "\n", "volume", "terms", ",", "than", "publications", ".", "\n", "As", "presented", "in", "Figure", "3.19", ",", "Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", "is", "primarily", "composed", "\n", "of", "EC", "R&I", "projects", "due", "to", "the", "nature", "of", "the", "Eu-", "\n", "ropean", "research", "and", "innovation", "framework", "pro-", "\n", "grammes", ",", "which", "support", "capacity", "building", "as", "well", "\n", "as", "transnational", "cooperation", "projects", ",", "typically", "\n", "oriented", "to", "research", ",", "education", ",", "economic", "devel-", "\n", "opment", ",", "education", "as", "well", "as", "some", "priority", "areas", "\n", "such", "as", "ICT", ",", "energy", ",", "transportation", "and", "the", "envi-", "\n", "ronment", ".", "Thus", ",", "although", "R&I", "projects", "are", "not", "the", "\n", "ideal", "source", "for", "identifying", "purely", "emerging", "spe-", "\n", "cialisations", ",", "they", "do", "allow", "relevant", "internationally", "\n", "connected", "actors", "in", "the", "EaP", "to", "be", "identified", ",", "espe-", "\n", "cially", "in", "the", "areas", "above", ",", "as", "well", "as", ",", "again", ",", "gaug-", "\n", "ing", "the", "strength", "of", "the", "hard", "sciences", "in", "the", "EaP.", "\n", "It", "also", "enables", "different", "relative", "specialisations", "to", "\n", "be", "explored", "throughout", "the", "EaP", "countries", ".", "\n", "58", "Noting", "that", "the", "total", "volume", "of", "analysed", "scientific", "publi-", "\n", "cations", "is", "higher", "than", "that", "of", "patents", ".", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation155", "\n", "Agrifood", "\n", "Biotechnology", "\n", "Chemistry", "and", "chemical", "\n", "engineering", "\n", "Electric", "and", "electronic", "\n", "technologies", "\n", "Energy", "\n", "Environmental", "sciences", "and", "\n", "industries", "\n", "Fundamental", "physics", "and", "\n", "mathematics", "\n", "Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", "\n", "Health", "and", "wellbeing", "\n", "ICT", "and", "computer", "science", "\n", "Mechanical", "engineering", "and", "\n", "heavy", "machinery", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "Optics", "and", "photonics", "\n", "Transportation", "\n", "Agrifood", "964", "315", "65", "149", "1", "347", "138", "196", "722", "133", "615", "236", "45", "52", "\n", "Biotechnology", "964", "3", "277", "178", "325", "465", "342", "110", "2", "687", "138", "212", "1", "656", "109", "45", "\n", "Chemistry", "and", "chemical", "\n", "engineering315", "3", "277", "111", "314", "694", "308", "40", "682", "88", "143", "1", "809", "91", "9", "\n", "Electric", "and", "electronic", "\n", "technologies65", "178", "111", "3", "102", "148", "660", "121", "217", "1", "008", "1", "213", "1", "004", "856", "240", "\n", "Energy", "149", "325", "314", "3", "102", "666", "2", "470", "288", "257", "504", "2", "135", "799", "185", "326", "\n", "Environmental", "sciences", "and", "\n", "industries1", "347", "465", "694", "148", "666", "512", "1", "128", "754", "592", "693", "542", "101", "113", "\n", "Fundamental", "physics", "and", "\n", "mathematics138", "342", "308", "660", "2", "470", "512", "520", "414", "1", "164", "1", "139", "2", "115", "930", "258", "\n", "Governance", ",", "culture", ",", "\n", "education", "and", "the" ]
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in other economic areas, notably digital sectors. EIST domains, where a direct concordance between E&I and S&T has been found, present adequate environments for knowledge exchange and the co-development of strategies, technologies and niches by the quadruple helix, particularly between academic actors and private companies. Here, shared resources and venues, such as technolog- ical platforms or clusters, collaborative R&D, sub- sidised contract research or innovation vouchers would find a fertile landscape. Finally, it must be noted that, in some cases, data unavailability or sparsity has reduced the poten- tial of the analysis for some countries and some domains of specialisation, and must lead to a cau- tious interpretation and apprehension of the re- sults. Furthermore, the current analysis contained in the ‘Analysis of context – country specific con- ditions’ phase of the above-mentioned S3 Frame- work must be complemented with quantitative analyses based on additional and national data sources, as well as expert and qualitative insight into the EDP framework.Overview of economic, innovation, scientific and technological specialisations 1. Economic and innovation (E&I) potential in the Eastern Partnership countries Part 2 of this study presents the results of the mapping of the economic and innovation potential of the Eastern Partnership countries, which results in the identification of a set of domains of eco- nomic and innovation specialisation summarised below. This is achieved by looking at relevant eco- nomic variables – such as employment, turnover and average wages – as well as their growth dy- namics. Similar indicators have been quantified for the manufacturing sector and for the export of goods and services. Additionally, a critical mass of venture capital-backed companies per industry group and per respective class for patent, trade- mark, and industrial design applications has been measured. Lastly, innovation has been quantified by means of the results of the innovation surveys carried out by the World Bank Enterprise Survey. E&I specialisations for Armenia The economic and innovation analysis for Arme- nia leads to the identification of the following E&I specialisation domains: ■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 per- formance in related patents and specialised performance in related goods exports; 10 Overview of economic, innovation, scientific and technological specialisations ■Travel and tourism
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[]
the VESPA ionization chamber. The post-neutron mass distribu-tion of fission fragments in the spontaneous fission of 252Cf is repre- sented in green The half-life associated with these identified isomers was extracted by fitting their time distribution with respect to fission with an exponential decay function. This commonly- used method was well suited to simple cases, where an iso-meric state decays by emitting at least two γ-rays in cas- cade, with a half-life larger than a few nanoseconds (due to the 35 ns threshold). It was also necessary that the nucleusstudied by this method did not contain consecutive isomers having similar half-lives (e.g., 97Sr), which would bias the measurements. Hence, we developed a complementary analysis proce- dure to study more complicated level schemes having con- secutive isomeric states, i.e., an isomer populated by another higher-lying isomeric state. In this second approach, FF- γ coincidences of previously identified isomers were gated on fragment mass, γ-ray energies, and time. The isomeric state on which these gates were applied is referred to as themain isomer. Then, all γ-rays from the same fission event and detected before this gated transition were recorded. This method could highlight a populating isomer, that is an iso- meric state whose decay fed the main isomer. It could also beused to pinpoint intermediate isomers, which are underlying isomeric states at one stage of the γcascade, populated by the main isomer. In particular, this method could be used toextract very short-lived isomers (with half-lives of a few ns) fed by longer living isomers. These two cases are depicted in Fig. 6a) and b), respec- tively. The half-life of the two consecutive isomers in play was estimated by constructing the time distribution of the populating γ-ray and the distribution of the time differ- ence/Delta1tbetween these two γ-rays for the populating and 123Eur. Phys. J. A (2025) 61:5 Page 5 of 12 5 Fig. 6 Schematic principle of the ’multiple isomers analysis’ for con- secutive isomeric states. The isomer to which energy gates are appliedis represented in green, while populating γ-rays from prompt and iso- meric decays are represented in red and blue, respectively Fig. 7 Chart of the identified nuclei having one or more isomeric state(s) whose half-life was measured in this work with VESPA underlying isomeric states, respectively. In the following, this method will be referred to as the ’multiple isomers analysis’. Through this analysis of the VESPA data, obtained from
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[]
"*Lan A, Kotler D, Kronfeld-Schor N, Stukalin Y, Einat H (Jan 2022; Online ahead of print) Changes in sleep patterns of college students in Israel during COVID-19 lockdown, a sleep diaries study. Sleep and Biological Rhythms *Bilu C, Frolinger-Ashkenazy T, Einat H, Zimmet P, Bishko Y, Halperin D, Kronfeld-Schor N (Nov 2021; Online ahead of print) Effects of photoperiod and diet on BDNF daily rhythms in diurnal sand rats. Behavioural Brain Research 418 113666 *Oved S, Mofaz M, Lan A, Einat H, Kronfeld-Schor N, Yamin D, Shmueli E (June 2021) Differential effects of COVID-19 lockdowns on well-being: interaction between age, gender and chronotype. Journal of the Royal Society Interface 18(179):20210078 *Kazavchinsky L, Dahan S, Einat H (Nov 2020) Exploring test batteries for affective- and anxiety-like behaviors in female and male ICR and black Swiss mice. Acta Neuropsychiatrica. 32:293-302 *Stukalin Y, Lan A, Einat H (2020) Revisiting the validity of the mouse tail suspension test: Systematic review and meta-analysis of the effects of prototypic antidepressants. Neuroscience and Biobehavioral Reviews. 112:39-47 " "• Petrache, A.L., Khan, A.A., Nicholson, M.W., Monaco, A., Kuta-Siejkowska, M., Haider, S., ...Ali, A. (2020). Selective modulation of α5 GABAA receptors exacerbates aberrant inhibition at key hippocampal neuronal circuits in APP mouse model of Alzheimer’s disease. Front. Cell. Neurosci., https://doi.org/10.3389 • Petrache, A.L., Rajulawalla, A., Shi, A., Wetzel, A., Saito, T., Saido, T.C., ...Ali, A. (2019). Aberrant Excitatory-Inhibitory Synaptic Mechanisms in Entorhinal Cortex Microcircuits during the Pathogenesis of Alzheimer’s disease. Cerebral Cortex, doi:10.1093/cercor/bhz016 • Shi, A., Petrache, A.L., Shi, J., Ali, A. (2019). Preserved Calretinin interneurons in an app model of Alzheimer’s Disease disrupts hippocampal inhibition via up-regulated P2Y1 purinoreceptors. Cerebral Cortex, doi:10.1093/cercor/bhz165 • Khan, A., Shekh-Ahmad, T., Khalilova, A., Walker, M., Ali, A.B. (2018). Cannabidiol exerts antiepileptic effects by restoring hippocampal interneuron functions in a temporal lobe epilepsy model. British Journal of Pharmacology, • Nicholson, M.W., Sweeney, A., Pekle, E., Alam, S., Ali, A.B., Duchen, M., Jovanovic, J.N. (2018). Diazepam-induced loss of inhibitory synapses mediated by PLCδ/ Ca²⁺/calcineurin signalling downstream of GABAA receptors. Molecular Psychiatry, doi:10.1038/s41380-018-0100-y " "Craig, L., Hoo, Z. L., Yan, T. Z., Wardlaw, J. and Quinn, T. J. (2022) Prevalence of dementia in ischaemic or mixed stroke populations: systematic review and meta-analysis. Journal of Neurology, Neurosurgery and Psychiatry, 93(2), pp. 180-187. (doi: 10.1136/jnnp-2020-325796) (PMID:34782389) Burton, J. K. et al. (2021) Non-pharmacological interventions for preventing delirium in hospitalised non-ICU patients. Cochrane Database of Systematic Reviews, 2021(11), CD013307. (doi: 10.1002/14651858.CD013307.pub3) (PMID:34826144)
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and materials; ICT and computer science Scientific Center of Surgery 21 Health and wellbeing; ICT and computer science; Agrifood Central Hospital of Oil Workers 19Health and wellbeing; Governance, culture, education and the economy; Chemistry and chemical engineering Ministry of Agriculture 13Agrifood; Health and wellbeing; Fundamental physics and mathematics Republican Anti-Plague Station 13 Health and wellbeing; Agrifood; Biotechnology Central Customs Hospital 11 Health and wellbeing; Agrifood; BiotechnologyTable 3.21. Top public actors in Azerbaijan by number of records, across all domains AZERBAIJAN Top actors classified as ‘Private company, for-profit’ NameNo of recordsMain S&T domains Kiber Ltd. Company 6Fundamental physics and mathematics; Environmental sciences and industries; ICT and computer science Azerbaijan International Mining Company3Environmental sciences and industries; Governance, culture, education and the economy Signature Science, LLC 3 Health and wellbeing MALAXIT CO 3 Nanotechnology and materials Anglo Asian Mining PLC 2 Chemistry and chemical engineering; Agrifood; Biotechnology Bahar Energy Operating Company 2Chemistry and chemical engineering; Environmental sciences and industries Baku Steel Company 2Chemistry and chemical engineering; Energy; Environmental sciences and industries R.I.T.A. LLC 2Governance, culture, education and the economy; ICT and computer science Milotek Pty LTD 1 Mechanical engineering and heavy machineryTable 3.22. Top private actors in Azerbaijan by number of records, across all domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation201 Georgia Ivane Javakhishvili Tbilisi State University domi- nates scientific production in the country, followed at a distance by the Georgian Technical University and the Ilia State University. Some specialised insti- tutions have secondary roles in selected domains. Most of the top public actors in Georgia are med- ical and health centres, with the National Center for Disease Control and Public Health accounting for the highest number of records. The most active private actor is the Electromagnetic Consulting and Software company (EMCoS), which works closely with providers in the automotive, aerospace and naval industries. Many of the other private actors are in the Health and wellbeing domain.Fundamental physics and mathematics Environmental sciences and industries Health and wellbeing Nanotechnology and materials Governance, culture, education and the economy Optics and photonics ICT and computer science Chemistry and chemical engineering Agrifood Biotechnology Electric and electronic technologies Mechanical engineering and heavy machinery Tbilisi State University 2 625 313 344 410 286 294 109 155 56 29 39 15 Georgian Technical University 1 095 108 57 193 93 78 89 61 25 7 20 27 Ilia State University 643 300 291
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[]
the mapping exercise described in the previous section. Specif- ically, an S&T domain is assigned to a cluster if: 1. at least 3 IPC classes and/or Scopus ASJC fields produced a mapping to NACE sectors within the cluster; or 2. for Clusters with no assigned S&T domains via method 1 above, a concordance was allowed if at least 2 IPC classes and/or Scopus ASJC fields produced a mapping to NACE sectors within the cluster, but only if critical mass or specialisation was observed for the S&T do- main, in the respective EaP country. Therefore, by definition, each of the clusters fea- ture one or more E&I domains identified in Part 2 and, if a concordance has been identified by the methodology presented above, also features one or more S&T domains identified in Part 3. Although some E&I domains identified in Part 2, such as Hospitality and Tourism or Tobacco, can- not be mapped onto S&T domains via the current exercise, concordances could be found within most of the E&I and S&T domains. Additionally, some S&T domains may be relevant for other economic sectors which were not found to be potential E&I specialisation domains in Part 2; these additional areas of application are discussed in Part 3. The following tables present the clusters for all EaP countries and show the identified concord- ances between E&I and S&T domains for each country. 73 Ketels, C., Protsiv, S., Methodology and Findings Report for a Cluster Mapping of Related Sectors, Center for Strategy and Competitiveness – Stockholm School of Economics, October 2014. 236 Part 4 Identification of concordances between the economic, innovation, scientific and technological potentials Armenia For Armenia, the following concordances between E&I and S&T domains were identified: ■in the ‘Communications Equipment and Ser- vices’ cluster, the S&T domain ‘ICT computer science’ could be aligned with the E&I domain ‘Telecommunications’ exclusively via publica- tions; ■in the ‘Information Technology and Analytical Instruments’ cluster, the S&T domains ‘Electric and electronic technologies’ and ‘Nanotech- nology and materials’ could be mapped with the E&I domain ‘Manufacture of computer, electronic and optical products’ via both pat- ents and publications. Notably, the weight of patents in producing the concordance is much higher for the ‘Electric and electronic technol- ogies’ S&T domain than for ‘Nanotechnology and materials’ (where, conversely, publica- tions are mainly driving the concordance). By inspecting the semantic content of the latter domain, it is possible to observe
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in the other domains is quite diverse, with Ukraine being a relevant link in most networks (due to its volume of activity and diversified research). Extra-EaP international collaboration The Russian Federation was (before the unjustified agression on Ukraine) the largest scientific partner of EaP institutions, followed by Germany, Poland and the United States of America. At some dis- tance, other European countries follow: France, the UK, Italy, Spain and Switzerland can be observed as recurrent partners. A considerable share of these co-publications are the result of great endeavours in physics, mainly classified under the Fundamen- tal physics and mathematics domain. China is the only Asian country appearing on the list. In EC-funded projects, the top European partners identified in scientific publications are repeated, complemented by Belgium and the Netherlands, very active countries in Horizon 2020, and Greece and Romania, which are geographically closer to the EaP countries.Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia 130 1 471 1 756 42 980 Azerbaijan 130 49 73 26 138 Belarus 1 471 49 1 440 83 1 268 Georgia 1 756 73 1 440 58 1 058 Moldova 42 26 83 58 202 Ukraine 980 138 1 268 1 058 202 Publications Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia 10 21 26 19 21 Azerbaijan 10 8 11 8 11 Belarus 21 8 20 17 33 Georgia 26 11 20 23 32 Moldova 19 8 17 23 25 Ukraine 21 11 33 32 25 EC projectsFigure IV. Number of publications and EC projects in collaboration between EaP actors in different countries Colour indicates the relative distribution of documents, computed row-wise. 24 Overview of economic, innovation, scientific and technological specialisations Figure V. Number of publications and EC projects in collaboration between EaP actors in different countries Agrifood BiotechnologyChemistry and chemical engineeringElectric and electronic technologies BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AMUA BY MD GE AM AZUAEnergyFundamental physics and mathematicsEnvironmental sciences and industriesGovernance, culture, education and the economy BY MD AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA BY MD GE AM AZUA Health and wellbeingMechanical engineering and heavy machinery ICT and computer scienceNanotechnology and materials BY MD GE AM AZUA BY MD GE AM AZUA Transportation Optics and photonics Strong collaboration BY MD GE AM AZUA BY UA BY MD GE AM AZUA Intermediate collaborationColour indicates the relative
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[see Figure 3] . While rising bankruptcies in China suggest that the economy is entering a phase of industrial consolidation, overcapacities are likely to persist, especially given ongoing weaknesses in household consumption and high saving rates. Moreover, in response to perceived unfair competition, an increasing number of countries are raising tariff and non-tariff barriers against China, which will re-direct Chinese overcapacity towards the EU market. In May, the US announced significant hikes in tariffs against a range of products. 40THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3FIGURE 3 EU trade balance by partner country EUR billion Source: Eurostat, 2024. Europe must confront some fundamental choices about how to pursue its decarbonisation path while preserving the competitive position of its industry . Black-and-white solutions are unlikely to be successful in the European context. Emulating the US approach of systematically shutting out Chinese technology would likely set back the energy transition and therefore impose higher costs on the EU economy. It would also be more costly for Europe to trigger reciprocal tariffs: more than a third of the EU’s manufacturing GDP is absorbed outside the EU, compared with only around a fifth for the USv. However, a laissez-faire approach is also unlikely to succeed in Europe given the threat it could pose to employment, productivity and economic security. According to ECB simulations, if the Chinese EV industry were to follow a similar trajectory of subsidies to that applied in the solar PV industry, EU domestic production of EVs would decline by 70% and EU producers’ global market share would fall by 30 percentage pointsvi. The automotive industry alone employs, directly and indirectly, almost 14 million Euro - peans. Given Europe’s strong position in clean tech innovation, it could also lose the possibility to benefit from the future productivity gains this sector will bring. Without some foothold in EIIs, Europe’s economic security could be undermined, for example via lower food security (lack of fertilisers and pesticides) and less autonomy for the defence sector. Most importantly, the “European Green Deal” was premised on the creation of new green jobs, so its political sustainability could be endangered if decarbonisation leads instead to de-industrialisation in Europe – including of industries that can support the green transition. Europe will need to deploy a mixed strategy that combines different policy tools and approaches for different industries . Four different broad cases can be distinguished. First,
[ " ", "[", "see", "Figure", "3", "]", ".", "While", "rising", "bankruptcies", "in", "\n", "China", "suggest", "that", "the", "economy", "is", "entering", "a", "phase", "of", "industrial", "consolidation", ",", "overcapacities", "are", "likely", "to", "persist", ",", "\n", "especially", "given", "ongoing", "weaknesses", "in", "household", "consumption", "and", "high", "saving", "rates", ".", "Moreover", ",", "in", "response", "to", "\n", "perceived", "unfair", "competition", ",", "an", "increasing", "number", "of", "countries", "are", "raising", "tariff", "and", "non", "-", "tariff", "barriers", "against", "\n", "China", ",", "which", "will", "re", "-", "direct", "Chinese", "overcapacity", "towards", "the", "EU", "market", ".", "In", "May", ",", "the", "US", "announced", "significant", "hikes", "\n", "in", "tariffs", "against", "a", "range", "of", "products", ".", "\n", "40THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "CHAPTER", "3FIGURE", "3", "\n", "EU", "trade", "balance", "by", "partner", "country", " \n", "EUR", "billion", "\n", "Source", ":", "Eurostat", ",", "2024", ".", "\n", "Europe", "must", "confront", "some", "fundamental", "choices", "about", "how", "to", "pursue", "its", "decarbonisation", "path", "while", "\n", "preserving", "the", "competitive", "position", "of", "its", "industry", ".", "Black", "-", "and", "-", "white", "solutions", "are", "unlikely", "to", "be", "successful", "in", "\n", "the", "European", "context", ".", "Emulating", "the", "US", "approach", "of", "systematically", "shutting", "out", "Chinese", "technology", "would", "likely", "\n", "set", "back", "the", "energy", "transition", "and", "therefore", "impose", "higher", "costs", "on", "the", "EU", "economy", ".", "It", "would", "also", "be", "more", "costly", "\n", "for", "Europe", "to", "trigger", "reciprocal", "tariffs", ":", "more", "than", "a", "third", "of", "the", "EU", "’s", "manufacturing", "GDP", "is", "absorbed", "outside", "the", "\n", "EU", ",", "compared", "with", "only", "around", "a", "fifth", "for", "the", "USv", ".", "However", ",", "a", "laissez", "-", "faire", "approach", "is", "also", "unlikely", "to", "succeed", "\n", "in", "Europe", "given", "the", "threat", "it", "could", "pose", "to", "employment", ",", "productivity", "and", "economic", "security", ".", "According", "to", "ECB", "\n", "simulations", ",", "if", "the", "Chinese", "EV", "industry", "were", "to", "follow", "a", "similar", "trajectory", "of", "subsidies", "to", "that", "applied", "in", "the", "solar", "PV", "\n", "industry", ",", "EU", "domestic", "production", "of", "EVs", "would", "decline", "by", "70", "%", "and", "EU", "producers", "’", "global", "market", "share", "would", "fall", "\n", "by", "30", "percentage", "pointsvi", ".", "The", "automotive", "industry", "alone", "employs", ",", "directly", "and", "indirectly", ",", "almost", "14", "million", "Euro", "-", "\n", "peans", ".", "Given", "Europe", "’s", "strong", "position", "in", "clean", "tech", "innovation", ",", "it", "could", "also", "lose", "the", "possibility", "to", "benefit", "from", "the", "\n", "future", "productivity", "gains", "this", "sector", "will", "bring", ".", "Without", "some", "foothold", "in", "EIIs", ",", "Europe", "’s", "economic", "security", "could", "be", "\n", "undermined", ",", "for", "example", "via", "lower", "food", "security", "(", "lack", "of", "fertilisers", "and", "pesticides", ")", "and", "less", "autonomy", "for", "the", "defence", "\n", "sector", ".", "Most", "importantly", ",", "the", "“", "European", "Green", "Deal", "”", "was", "premised", "on", "the", "creation", "of", "new", "green", "jobs", ",", "so", "its", "political", "\n", "sustainability", "could", "be", "endangered", "if", "decarbonisation", "leads", "instead", "to", "de", "-", "industrialisation", "in", "Europe", "–", "including", "of", "\n", "industries", "that", "can", "support", "the", "green", "transition", ".", "\n", "Europe", "will", "need", "to", "deploy", "a", "mixed", "strategy", "that", "combines", "different", "policy", "tools", "and", "approaches", "for", "different", "\n", "industries", ".", "Four", "different", "broad", "cases", "can", "be", "distinguished", ".", "First", "," ]
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laws, from the Commission’s proposal to the signing of the adopted act – and before new laws are even implemented across Member States. The objective of this report is to lay out a new industrial strategy for Europe to overcome these barriers. We identify the root causes of the EU’s weakening position in key strategic sectors and lay out a series of proposals to restore the EU’s competitive strength. For each sector we analyse, we identify priority proposals for the short and medium term. In other words, these proposals are not intended to be aspirations: most of them are designed to be implemented quickly and to make a tangible difference to the EU’s prospects. In many areas, the EU can achieve a lot by taking a large number of smaller steps, but doing so in a coordinated way that aligns all policies behind the common goal. In other areas, a small number of larger steps are needed – dele - gating tasks to the EU level that can only be performed there. In still other areas, the EU should step back, applying the subsidiarity principle more rigorously and reducing the regulatory burden it imposes on EU companies. 08 THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | FOREWORDA key question that arises is how the EU should finance the massive investments needs that transforming the economy will entail. We present simulations in this report to address this question. Two key conclusions can be drawn for the EU. First, while Europe must advance with its Capital Markets Union, the private sector will not be able to bear the lion’s share of financing investment without public sector support. Second, the more willing the EU is to reform itself to generate an increase in productivity, the more fiscal space will increase, and the easier it will be for the public sector to provide this support. 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 procurement 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. This report is
[ "laws", ",", "from", "the", "Commission", "’s", "\n", "proposal", "to", "the", "signing", "of", "the", "adopted", "act", "–", "and", "before", "new", "laws", "are", "even", "implemented", "across", "Member", "States", ".", "\n", "The", "objective", "of", "this", "report", "is", "to", "lay", "out", "a", "new", "industrial", "strategy", "for", "Europe", "to", "overcome", "these", "barriers", ".", "\n", "We", "identify", "the", "root", "causes", "of", "the", "EU", "’s", "weakening", "position", "in", "key", "strategic", "sectors", "and", "lay", "out", "a", "series", "of", "proposals", "\n", "to", "restore", "the", "EU", "’s", "competitive", "strength", ".", "For", "each", "sector", "we", "analyse", ",", "we", "identify", "priority", "proposals", "for", "the", "short", "and", "\n", "medium", "term", ".", "In", "other", "words", ",", "these", "proposals", "are", "not", "intended", "to", "be", "aspirations", ":", "most", "of", "them", "are", "designed", "to", "be", "\n", "implemented", "quickly", "and", "to", "make", "a", "tangible", "difference", "to", "the", "EU", "’s", "prospects", ".", "\n", "In", "many", "areas", ",", "the", "EU", "can", "achieve", "a", "lot", "by", "taking", "a", "large", "number", "of", "smaller", "steps", ",", "but", "doing", "so", "in", "a", "coordinated", "way", "\n", "that", "aligns", "all", "policies", "behind", "the", "common", "goal", ".", "In", "other", "areas", ",", "a", "small", "number", "of", "larger", "steps", "are", "needed", "–", "dele", "-", "\n", "gating", "tasks", "to", "the", "EU", "level", "that", "can", "only", "be", "performed", "there", ".", "In", "still", "other", "areas", ",", "the", "EU", "should", "step", "back", ",", "applying", "\n", "the", "subsidiarity", "principle", "more", "rigorously", "and", "reducing", "the", "regulatory", "burden", "it", "imposes", "on", "EU", "companies", ".", "\n", "08", "\n", "THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "FOREWORDA", "key", "question", "that", "arises", "is", "how", "the", "EU", "should", "finance", "the", "massive", "investments", "needs", "that", "transforming", "the", "\n", "economy", "will", "entail", ".", "We", "present", "simulations", "in", "this", "report", "to", "address", "this", "question", ".", "Two", "key", "conclusions", "can", "be", "\n", "drawn", "for", "the", "EU", ".", "\n", "First", ",", "while", "Europe", "must", "advance", "with", "its", "Capital", "Markets", "Union", ",", "the", "private", "sector", "will", "not", "be", "able", "to", "bear", "the", "lion", "’s", "\n", "share", "of", "financing", "investment", "without", "public", "sector", "support", ".", "Second", ",", "the", "more", "willing", "the", "EU", "is", "to", "reform", "itself", "to", "\n", "generate", "an", "increase", "in", "productivity", ",", "the", "more", "fiscal", "space", "will", "increase", ",", "and", "the", "easier", "it", "will", "be", "for", "the", "public", "sector", "\n", "to", "provide", "this", "support", ".", "\n", "This", "connection", "underscores", "why", "raising", "productivity", "is", "fundamental", ".", "It", "also", "has", "implications", "for", "the", "issuance", "of", "\n", "common", "safe", "assets", ".", "To", "maximise", "productivity", ",", "some", "joint", "funding", "for", "investment", "in", "key", "European", "public", "goods", ",", "\n", "such", "as", "breakthrough", "innovation", ",", "will", "be", "necessary", ".", "\n", "At", "the", "same", "time", ",", "there", "are", "other", "public", "goods", "identified", "in", "this", "report", "–", "such", "as", "defence", "procurement", "or", "cross-", "\n", "border", "grids", "–", "that", "will", "be", "undersupplied", "without", "common", "action", ".", "If", "the", "political", "and", "institutional", "conditions", "are", "met", ",", "\n", "these", "projects", "would", "also", "call", "for", "common", "funding", ".", "\n", "This", "report", "is" ]
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business support activities 82.1 Office administrative and support activities 82.2 Activities of call centres X X 82.3 Organisation of conventions and trade shows 82.9 Business support service activities n.e.c. X X X X X OPUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIAL SECURITY 84 Public administration and defence; compulsory social security 84.1Administration of the State and the economic and social policy of the community X X 84.2 Provision of services to the community as a whole 84.3 Compulsory social security activities P EDUCATION 85 Education 85.1 Pre-primary education X X X 85.2 Primary education 85.3 Secondary education X X X X X 85.4 Higher education X X X X X 85.5 Other education X X X X X 85.6 Educational support activities Q HUMAN HEALTH AND SOCIAL WORK ACTIVITIES 86 Human health activities 86.1 Hospital activities X X X X X X 86.2 Medical and dental practice activities X X X X X 86.9 Other human health activities X X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation293 294 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 87 Residential care activities 87.1 Residential nursing care activities 87.2Residential care activities for mental retardation, mental health and substance abuse 87.3 Residential care activities for the elderly and disabled 87.9 Other residential care activities 88 Social work activities without accommodation 88.1Social work activities without accommodation for the elderly and disabledX X X 88.9 Other social work activities without accommodation X X X R ARTS, ENTERTAINMENT AND RECREATION 90 Creative, arts and entertainment activities X X X X X 91 Libraries, archives, museums and other cultural activities X X X 92 Gambling and betting activities X X X X X X 93 Sports activities and amusement and recreation activities 93.1 Sports activities X X X X X 93.2 Amusement and recreation activities X X X X S OTHER SERVICE ACTIVITIES 94 Activities of membership organisations 94.1Activities of business, employers and professional membership organisationsX X X 94.2
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[]
relatively close succession (Figueiredo et al., 2021), both affecting the same buildings and infrastructure. Similarly, the floods and subse- quent dam failure in the Czech Republic during the summer of 2002 had a devastating impact, causing significant damage to buildings, infrastructure, and agricultural land and exacer- bating the destruction by affecting structures already weak- ened by the floods (Da ˇnhelka, 2004). Research on multi-hazard analysis has underscored several critical gaps that request attention for more effective multi- hazard assessments. These gaps include the following: –data quality (e.g. incomplete, outdated) that hampers accurate multi-hazard assessments (Cutter et al., 2014; Gentile et al., 2022), –the complexity of hazard interrelations (Gill and Mala- mud, 2016; Lee et al., 2024), –temporal dynamics (Fuchs and Thaler, 2018; De Ange- lis et al., 2022), –the varying vulnerabilities across hazards (Saaty, 1987; UNISDR, 2004). Additionally, there has been limited attention given to uncertainty and sensitivity analyses in multi-hazard assess- ments (Haasnoot et al., 2013; Camus et al., 2021). Most multi-hazard assessments tend to overlook the implications of climate change (IPCC, 2012; Gallina et al., 2016; Ghan- bari et al., 2021). The effective communication of multi- hazard risks to stakeholders also remains a challenge (Dallo et al., 2020; De Fino et al., 2023). The Risk Data Hub (RDH) platform of the Disaster Risk Management Knowledge Centre (DRMKC) aims to address these multiple challenges. The platform serves as a central hub for accessing and sharing curated, European-wide risk data and methodologies, providing essential support for dis- aster risk management (DRM) and climate change adaptation(CCA) actions at both the national and the subnational level (European Civil Protection Knowledge Network, 2021; Eu- ropean Commission, 2021). Within the RDH development, we propose a methodology that is accessible, scalable, and replicable even at the subnational and local level for the iden- tification of regions exposed to multi-hazards. The multi-hazard methodological approach is the main goal of this study, focused on addressing four major chal- lenges: 1. identification of regions with significant multi-hazard potential, 2. identification of exposure relationships between assets and multiple hazards, 3. quantification of multi-hazard exposure, 4. transferability of the method. These challenges are further constrained by the wide scale of our analysis (European coverage); the alignment to a com- mon hazard definition; and practical implementation on the online web platform, the RDH. This study addresses Challenge 1 by introducing a novel methodology that identifies, at a pan-European scale, regions (local
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cells. Whole slide imaging was performed with a 6 channel LED Panoramic /C226250 Flash III scanner (3DHistech) equipped with a 16-bit 4.2 MP camera and a 20x objective yielding a final res- olution of 0.242 mm per pixel. 2-D images from the digital Allen Brain Atlas ( Sunkin et al., 2013 ) were registered by means of linear local transformations to the coronal histological sections using a set of manually chosen landmarks (40 ±10 per slice). Presynaptic neurons were quantified using a semi-automatic procedure. Briefly, labeled cells were automatically detected based on signal intensity and size and assigned to brain structures, as delineated in the reference atlas, according to their coordinates using a custom-written Py-thon script. Subsequently, an expert experimenter, through visual inspection of the processed images, manually corrected for pos- itive- and false-negative cells. Data are reported as cell counts normalized to the total cell counts per animal (percent of total input). Areas containing less than 1% of the total sum of inputs and the aIC itself were excluded. Anatomical abbreviations of IC subdivisions used for figure display included: agranular insula (AI), agranular insular cortex, ventral (AIV), agranular insular cortex, dorsal (AID), dysgranular insular cortex (DI), granular insular cortex (GI), agranular insular cortex, pos- terior (AIP). Anatomical abbreviations of presynaptic brain areas used for figure display included: ventral orbital cortex (VO), lateralorbital cortex (LO), primary motor cortex (M1), dorsolateral orbital cortex (DLO), cecondary motor cortex (M2), piriform cortex (Pir),somatosensory cortex (SS), caudate putamen (CPu), globus pallidus (GP), ventral pallidum (VP), anterior amygdaloid area (AA), ante- rior cortical amygdaloid nucleus (ACo), basolateral amygdala (BLA), mediodorsal thalamic nucleus (MD), anteromedial thalamic nu- cleus (AM), submedius thalamic nucleus (Sub), perirhinal cortex (PRh), posterolateral cortical amygdaloid area (PLCo), posteriorthalamic nuclei (Po), parafascicular thalamic nucleus (PaF), rubrospinal tract (RS), paraventricular thalamic nucleus (PV), intermedio- dorsal thalamic nucleus (IM), mediodorsal thalamic nucleus, central part (MDC), mediodorsal thalamic nucleus, medial part (MDM), mediodorsal thalamic nucleus, lateral part (MDL), ventrolateral thalamic nucleus (VL), central medial thalamic nucleus (CM), prelimbiccortex (PrL), claustrum (CL), medial orbital cortex (MO). QUANTIFICATION AND STATISTICAL ANALYSISCa 2+data analysis Basic processing of Ca2+imaging videos was performed using the MATLAB (MathWorks) pipeline developed by Corder and col- leagues ( Corder et al., 2019 ) available at https://github.com/bahanonu/calciumImagingAnalysis . Briefly, imaging frames were first spatially down-sampled in both x and y lateral spatial dimensions using 4 34 bilinear interpola- tion. Following this, imaging frames were normalized
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SciDetect Method The next approach [21] was invented to detect documents automatically generated using the software SCIGen, MathGen, PropGen and PhysGen. For this, the distancesbetween a text and others (inter-textual distances) are computed. Then these distancesare used to determine which texts, within a large set, are closer to each other and maythus be grouped together. Inter-textual distance depends on four factors: genre, author,subject and epoch. As the authors don ’t provide any numerical results of the method ’s work, we have implemented the method using the source Java code provided by SciDetect developers. 3 Choosing a Method Every arti ficial content is generated for a speci fic purpose. Here we focus only on fake scienti fic paper for academic publishing or increase in the percentage of originality of the article. It is important to recognize the aim of fake content for its subsequent detection. Various generation strategies require different approaches to find it. For example, algorithms for detecting word salad are clearly possible and are not particularly dif ficult to implement. A statistical approach based on Zipf ’s law of word frequency has potential in detecting simple word salad, as do grammar checking and the use of naturallanguage processing. Statistical Markovian analysis, where short phrases are used todetermine if they can occur in normal English sentences, is another statistical approach that would be effective against completely random phrasing but might be fooled by dissociated press methods. Combining linguistic and statistical features can improvethe result of experiment. By contrast, texts generated with stochastic language modelsappear much harder to detect. One also needs to estimate the data capacity. Text corpuses are taken depending on the aim of the experiment and capabilities of getting them. Like a generation strategy,every data capacity needs different approach. For instance, small trainings samplespermit to use such indexes as Jaccard or Dice [ 5] to count the similarity measure or distance between documents. For big datasets, one can use some linguistics features424 D. Beresnevaand variations of Support Vector Machine and Decision Trees algorithms. Table 1 summarizes results of described methods. The numerical results are provided by theauthors of the articles, except the last one. 4 Conclusion This work presents the results of a systematic review of arti ficial content detection methods. About a hundred articles were considered for this review; perhaps one-sixthof them met our selection criteria. All the presented methods give good result inpractice, but it makes
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[]
other preparations for making beverages Class 33 – Alcoholic beverages (except beers) Class 34 –Tobacco; smokers’ articles; matches SERVICES Class 35 – Advertising; business management; business administration; office functions Class 36 – Insurance; financial affairs; monetary affairs; real estate affairs Class 37 – Building construction; repair; installa- tion services 326 Annexes Class 38 – Telecommunications Class 39 – Transport; packaging and storage of goods; travel arrangement Class 40 – Treatment of materials Class 41 – Education; providing of training; enter- tainment; sporting and cultural activities Class 42 – Scientific and technological services and research and design relating thereto; indus- trial analysis and research services; design and development of computer hardware and software Class 43 – Services for providing food and drink; temporary accommodation Class 44 – Medical services; veterinary services; hygienic and beauty care for human beings or ani- mals; agriculture, horticulture and forestry services Class 45 – Legal services; security services for the physical protection of tangible property and indi- viduals; personal and social services rendered by others to meet the needs of individuals Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation327 Annex 6. Locarno Classification for industrial designs Class 1 – Foodstuffs Class 2 – Articles of clothing and haberdashery Class 3 – Travel goods, cases, parasols and per- sonal belongings, not elsewhere specified Class 4 – Brushware Class 5 – Textile piece goods, artificial and natural sheet material Class 6 – Furnishing Class 7 – Household goods, not elsewhere spec- ified Class 8 – Tools and hardware Class 9 – Packages and containers for the trans- port or handling of goods Class 10 – Clocks and watches and other meas- uring instruments, checking and signalling instru- ments Class 11 – Articles of adornment Class 12 – Means of transport or hoisting Class 13 – Equipment for production, distribution or transformation of electricity Class 14 – Recording, telecommunication or data processing equipment Class 15 – Machines, not elsewhere specified Class 16 – Photographic, cinematographic and op- tical apparatus Class 17 – Musical instruments Class 18 – Printing and office machinery Class 19 – Stationery and office equipment, art- ists’ and teaching materials Class 20 – Sales and advertising equipment, signs Class 21 – Games, toys, tents and sports goodsClass 22 – Arms, pyrotechnic articles, articles for hunting, fishing and pest killing Class 23 – Fluid distribution equipment, sanitary, heating, ventilation and air-conditioning equip-
[ "other", "preparations", "for", "\n", "making", "beverages", "\n", "Class", "33", "–", "Alcoholic", "beverages", "(", "except", "beers", ")", "\n", "Class", "34", "–", "Tobacco", ";", "smokers", "’", "articles", ";", "matches", "\n", "SERVICES", "\n", "Class", "35", "–", "Advertising", ";", "business", "management", ";", "\n", "business", "administration", ";", "office", "functions", "\n", "Class", "36", "–", "Insurance", ";", "financial", "affairs", ";", "monetary", "\n", "affairs", ";", "real", "estate", "affairs", "\n", "Class", "37", "–", "Building", "construction", ";", "repair", ";", "installa-", "\n", "tion", "services", "\n", "326", "\n", "Annexes", "\n", "Class", "38", "–", "Telecommunications", "\n", "Class", "39", "–", "Transport", ";", "packaging", "and", "storage", "of", "\n", "goods", ";", "travel", "arrangement", "\n", "Class", "40", "–", "Treatment", "of", "materials", "\n", "Class", "41", "–", "Education", ";", "providing", "of", "training", ";", "enter-", "\n", "tainment", ";", "sporting", "and", "cultural", "activities", "\n", "Class", "42", "–", "Scientific", "and", "technological", "services", "\n", "and", "research", "and", "design", "relating", "thereto", ";", "indus-", "\n", "trial", "analysis", "and", "research", "services", ";", "design", "and", "\n", "development", "of", "computer", "hardware", "and", "software", "\n", "Class", "43", "–", "Services", "for", "providing", "food", "and", "drink", ";", "\n", "temporary", "accommodation", "\n", "Class", "44", "–", "Medical", "services", ";", "veterinary", "services", ";", "\n", "hygienic", "and", "beauty", "care", "for", "human", "beings", "or", "ani-", "\n", "mals", ";", "agriculture", ",", "horticulture", "and", "forestry", "services", "\n", "Class", "45", "–", "Legal", "services", ";", "security", "services", "for", "the", "\n", "physical", "protection", "of", "tangible", "property", "and", "indi-", "\n", "viduals", ";", "personal", "and", "social", "services", "rendered", "by", "\n", "others", "to", "meet", "the", "needs", "of", "individuals", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation327", "\n", "Annex", "6", ".", "Locarno", "\n", "Classification", "for", "\n", "industrial", "designs", "\n", "Class", "1", "–", "Foodstuffs", "\n", "Class", "2", "–", "Articles", "of", "clothing", "and", "haberdashery", "\n", "Class", "3", "–", "Travel", "goods", ",", "cases", ",", "parasols", "and", "per-", "\n", "sonal", "belongings", ",", "not", "elsewhere", "specified", "\n", "Class", "4", "–", "Brushware", "\n", "Class", "5", "–", "Textile", "piece", "goods", ",", "artificial", "and", "natural", "\n", "sheet", "material", "\n", "Class", "6", "–", "Furnishing", "\n", "Class", "7", "–", "Household", "goods", ",", "not", "elsewhere", "spec-", "\n", "ified", "\n", "Class", "8", "–", "Tools", "and", "hardware", "\n", "Class", "9", "–", "Packages", "and", "containers", "for", "the", "trans-", "\n", "port", "or", "handling", "of", "goods", "\n", "Class", "10", "–", "Clocks", "and", "watches", "and", "other", "meas-", "\n", "uring", "instruments", ",", "checking", "and", "signalling", "instru-", "\n", "ments", "\n", "Class", "11", "–", "Articles", "of", "adornment", "\n", "Class", "12", "–", "Means", "of", "transport", "or", "hoisting", "\n", "Class", "13", "–", "Equipment", "for", "production", ",", "distribution", "\n", "or", "transformation", "of", "electricity", "\n", "Class", "14", "–", "Recording", ",", "telecommunication", "or", "data", "\n", "processing", "equipment", "\n", "Class", "15", "–", "Machines", ",", "not", "elsewhere", "specified", "\n", "Class", "16", "–", "Photographic", ",", "cinematographic", "and", "op-", "\n", "tical", "apparatus", "\n", "Class", "17", "–", "Musical", "instruments", "\n", "Class", "18", "–", "Printing", "and", "office", "machinery", "\n", "Class", "19", "–", "Stationery", "and", "office", "equipment", ",", "art-", "\n", "ists", "’", "and", "teaching", "materials", "\n", "Class", "20", "–", "Sales", "and", "advertising", "equipment", ",", "signs", "\n", "Class", "21", "–", "Games", ",", "toys", ",", "tents", "and", "sports", "goodsClass", "22", "–", "Arms", ",", "pyrotechnic", "articles", ",", "articles", "for", "\n", "hunting", ",", "fishing", "and", "pest", "killing", "\n", "Class", "23", "–", "Fluid", "distribution", "equipment", ",", "sanitary", ",", "\n", "heating", ",", "ventilation", "and", "air", "-", "conditioning", "equip-", "\n" ]
[]
44 19 Environmental sciences and industries 1 331 614 710 623 368 383 263 253 172 188 Fundamental physics and mathematics 5 398 4 606 3 907 4 495 3 734 3 438 3 608 3 123 3 028 2 832 Governance, culture, education and the economy 903 438 687 675 279 453 240 211 146 155 Health and wellbeing 1 731 1 414 1 249 2 126 986 1 465 925 707 366 599 ICT and computer science 678 496 823 466 383 201 189 137 281 121 Mechanical engineering and heavy machinery 388 182 319 96 67 60 55 30 55 23 Nanotechnology and materials 4 197 2 452 2 526 1 568 1 202 649 435 461 523 283 Optics and photonics 1 054 573 372 483 300 200 147 182 211 57 Transportation 70 67 104 42 15 24 8 9 23 2 PublicationsGermany France United Kingdom Spain Italy Belgium Greece Netherlands Poland Romania Agrifood 12 14 7 14 14 11 9 13 5 8 Biotechnology 12 10 13 9 7 5 5 6 10 4 Chemistry and chemical engineering 9 5 5 4 4 4 3 3 3 3 Electric and electronic technologies 6 4 5 3 5 4 4 1 Energy 40 23 22 24 25 28 17 14 15 19 Environmental sciences and industries 51 42 36 42 41 36 26 31 23 28 Fundamental physics and mathematics 7 6 6 5 5 2 2 2 4 3 Governance, culture, education and the economy 138 115 99 106 109 93 83 76 77 71 Health and wellbeing 34 26 29 27 21 16 12 17 14 10 ICT and computer science 37 33 37 39 34 29 29 24 22 22 Mechanical engineering and heavy machinery 3 1 4 1 2 2 1 2 2 Nanotechnology and materials 33 23 29 18 20 13 12 5 11 3 Optics and photonics 10 8 7 3 6 2 4 Transportation 17 10 13 11 13 11 8 7 8 5 EC projectsFigure 3.61. Number of publications and EC projects in collaboration between EaP actors and partners outside of the EaP Colour indicates the relative distribution, computed row-wise. 218 Part 3 Analysis of scientific and technological potential Extra-EaP international collaboration This last section of the chapter presents indicators of collaboration activity between EaP countries and external international partners. The Russian Federation was so far the
[ "44", "19", "\n", "Environmental", "sciences", "and", "industries", "1", "331", "614", "710", "623", "368", "383", "263", "253", "172", "188", "\n", "Fundamental", "physics", "and", "mathematics", "5", "398", "4", "606", "3", "907", "4", "495", "3", "734", "3", "438", "3", "608", "3", "123", "3", "028", "2", "832", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "903", "438", "687", "675", "279", "453", "240", "211", "146", "155", "\n", "Health", "and", "wellbeing", "1", "731", "1", "414", "1", "249", "2", "126", "986", "1", "465", "925", "707", "366", "599", "\n", "ICT", "and", "computer", "science", "678", "496", "823", "466", "383", "201", "189", "137", "281", "121", "\n", "Mechanical", "engineering", "and", "heavy", "machinery", "388", "182", "319", "96", "67", "60", "55", "30", "55", "23", "\n", "Nanotechnology", "and", "materials", "4", "197", "2", "452", "2", "526", "1", "568", "1", "202", "649", "435", "461", "523", "283", "\n", "Optics", "and", "photonics", "1", "054", "573", "372", "483", "300", "200", "147", "182", "211", "57", "\n", "Transportation", "70", "67", "104", "42", "15", "24", "8", "9", "23", "2", "\n", "PublicationsGermany", "\n", "France", "\n", "United", "\n", "Kingdom", "\n", "Spain", "\n", "Italy", "\n", "Belgium", "\n", "Greece", "\n", "Netherlands", "\n", "Poland", "\n", "Romania", "\n", "Agrifood", "12", "14", "7", "14", "14", "11", "9", "13", "5", "8", "\n", "Biotechnology", "12", "10", "13", "9", "7", "5", "5", "6", "10", "4", "\n", "Chemistry", "and", "chemical", "engineering", "9", "5", "5", "4", "4", "4", "3", "3", "3", "3", "\n", "Electric", "and", "electronic", "technologies", "6", "4", "5", "3", "5", "4", "4", "1", "\n", "Energy", "40", "23", "22", "24", "25", "28", "17", "14", "15", "19", "\n", "Environmental", "sciences", "and", "industries", "51", "42", "36", "42", "41", "36", "26", "31", "23", "28", "\n", "Fundamental", "physics", "and", "mathematics", "7", "6", "6", "5", "5", "2", "2", "2", "4", "3", "\n", "Governance", ",", "culture", ",", "education", "and", "the", "economy", "138", "115", "99", "106", "109", "93", "83", "76", "77", "71", "\n", "Health", "and", "wellbeing", "34", "26", "29", "27", "21", "16", "12", "17", "14", "10", "\n", "ICT", "and", "computer", "science", "37", "33", "37", "39", "34", "29", "29", "24", "22", "22", "\n", "Mechanical", "engineering", "and", "heavy", "machinery", "3", "1", "4", "1", "2", "2", "1", "2", "2", "\n", "Nanotechnology", "and", "materials", "33", "23", "29", "18", "20", "13", "12", "5", "11", "3", "\n", "Optics", "and", "photonics", "10", "8", "7", "3", "6", "2", "4", "\n", "Transportation", "17", "10", "13", "11", "13", "11", "8", "7", "8", "5", "\n", "EC", "projectsFigure", "3.61", ".", "Number", "of", "publications", "and", "EC", "projects", "in", "collaboration", "between", "EaP", "actors", "and", "partners", "outside", "of", "the", "\n", "EaP", "\n", "Colour", "indicates", "the", "relative", "distribution", ",", "computed", "row", "-", "wise", ".", "\n", "218", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Extra", "-", "EaP", "international", "collaboration", "\n", "This", "last", "section", "of", "the", "chapter", "presents", "indicators", "\n", "of", "collaboration", "activity", "between", "EaP", "countries", "\n", "and", "external", "international", "partners", ".", "\n", "The", "Russian", "Federation", "was", "so", "far", "the" ]
[]
engineering 187 196.84% 2200 General engineering 249 103.25% 2203 Automotive engineering 149 100.00% 162 Part 3 Analysis of scientific and technological potential Top IPC patent classes in the S&T specialisation domains Domain IPC Description No recordsRelative freq. Agrifood A23LFoods, foodstuffs, or non-alcoholic beverages, not covered by subclasses a23b - a23j; their preparation or treatment, cooking, modification of nutritive qualities, physical treatment1 075 810.10% Agrifood A01C Planting; Sowing; fertilising 533 298.32% Agrifood A01D Harvesting; mowing 492 372.73% Agrifood A21DTreatment, e.g. preservation, of flour or dough for baking, e.g. by addition of materials; baking; bakery products; preservation thereof384 298.45% Agrifood A23CDairy products, e.g. milk, butter or cheese; milk or cheese substitutes; making thereof312 480.00% Biotechnology A61K Preparations for medical, dental, or toilet purposes 1 679 368.06% Biotechnology A61PSpecific therapeutic activity of chemical compounds or medicinal preparations844 259.19% Biotechnology C07D Heterocyclic compounds 625 405.41% Biotechnology G01NInvestigating or analysing materials by determining their chemical or physical properties568 144.21% Biotechnology C12NMicroorganisms or enzymes; compositions thereof; propagating, preserving, or maintaining microorganisms; mutation or genetic engineering; culture media468 506.63% Chemistry and chemical engineeringA61K Preparations for medical, dental, or toilet purposes 295 64.67% Chemistry and chemical engineeringC01BNon-metallic elements; compounds thereof; {metalloids or compounds thereof not covered by subclass c01c}189 303.86% Chemistry and chemical engineeringC07D Heterocyclic compounds 184 119.35% Chemistry and chemical engineeringA61PSpecific therapeutic activity of chemical compounds or medicinal preparations182 55.89% Chemistry and chemical engineeringC07C Acyclic or carbocyclic compounds 159 198.75% Electric and electronic technologiesH03K Pulse technique 546 197.47% Electric and electronic technologiesG01RMeasuring electric variables; measuring magnetic variables533 392.87% Electric and electronic technologiesG01SRadio direction-finding; radio navigation; determining distance or velocity by use of radio waves; locating or presence-detecting by use of the reflection or reradiation of radio waves; analogous arrangements using other waves513 164.78%Table 3.8. Top IPC symbols per number of records associated with the patents classified within each domain, at subclass level Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation163 Domain IPC Description No recordsRelative freq. Electric and electronic technologiesG06F Electric digital data processing 445 342.31% Electric and electronic technologiesG01NInvestigating or analysing materials by determining their chemical or physical properties394 100.04% Energy F03D Wind motors 252 197.39% Energy H02K Dynamo-electric machines 235 190.03% Energy F24HFluid heaters, e.g. water or air heaters, having heat- generating means, e.g. heat pumps, in general172 154.26% Energy G01RMeasuring electric variables; measuring magnetic variables171 126.04% Energy H01LSemiconductor devices; electric solid state devices not otherwise provided for168 166.52% Environmental
[ "engineering", "187", "196.84", "%", "\n", "2200", "General", "engineering", "249", "103.25", "%", "\n", "2203", "Automotive", "engineering", "149", "100.00", "%", "\n", "162", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Top", "IPC", "patent", "classes", "in", "the", "S&T", "specialisation", "domains", "\n", "Domain", "IPC", "Description", "No", "recordsRelative", "\n", "freq", ".", "\n", "Agrifood", "A23LFoods", ",", "foodstuffs", ",", "or", "non", "-", "alcoholic", "beverages", ",", "not", "\n", "covered", "by", "subclasses", "a23b", "-", "a23j", ";", "their", "preparation", "or", "\n", "treatment", ",", "cooking", ",", "modification", "of", "nutritive", "qualities", ",", "\n", "physical", "treatment1", "075", "810.10", "%", "\n", "Agrifood", "A01C", "Planting", ";", "Sowing", ";", "fertilising", "533", "298.32", "%", "\n", "Agrifood", "A01D", "Harvesting", ";", "mowing", "492", "372.73", "%", "\n", "Agrifood", "A21DTreatment", ",", "e.g.", "preservation", ",", "of", "flour", "or", "dough", "for", "baking", ",", "\n", "e.g.", "by", "addition", "of", "materials", ";", "baking", ";", "bakery", "products", ";", "\n", "preservation", "thereof384", "298.45", "%", "\n", "Agrifood", "A23CDairy", "products", ",", "e.g.", "milk", ",", "butter", "or", "cheese", ";", "milk", "or", "cheese", "\n", "substitutes", ";", "making", "thereof312", "480.00", "%", "\n", "Biotechnology", "A61", "K", "Preparations", "for", "medical", ",", "dental", ",", "or", "toilet", "purposes", "1", "679", "368.06", "%", "\n", "Biotechnology", "A61PSpecific", "therapeutic", "activity", "of", "chemical", "compounds", "or", "\n", "medicinal", "preparations844", "259.19", "%", "\n", "Biotechnology", "C07D", "Heterocyclic", "compounds", "625", "405.41", "%", "\n", "Biotechnology", "G01NInvestigating", "or", "analysing", "materials", "by", "determining", "their", "\n", "chemical", "or", "physical", "properties568", "144.21", "%", "\n", "Biotechnology", "C12NMicroorganisms", "or", "enzymes", ";", "compositions", "thereof", ";", "\n", "propagating", ",", "preserving", ",", "or", "maintaining", "microorganisms", ";", "\n", "mutation", "or", "genetic", "engineering", ";", "culture", "media468", "506.63", "%", "\n", "Chemistry", "\n", "and", "chemical", "\n", "engineeringA61", "K", "Preparations", "for", "medical", ",", "dental", ",", "or", "toilet", "purposes", "295", "64.67", "%", "\n", "Chemistry", "\n", "and", "chemical", "\n", "engineeringC01BNon", "-", "metallic", "elements", ";", "compounds", "thereof", ";", "{", "metalloids", "\n", "or", "compounds", "thereof", "not", "covered", "by", "subclass", "c01c}189", "303.86", "%", "\n", "Chemistry", "\n", "and", "chemical", "\n", "engineeringC07D", "Heterocyclic", "compounds", "184", "119.35", "%", "\n", "Chemistry", "\n", "and", "chemical", "\n", "engineeringA61PSpecific", "therapeutic", "activity", "of", "chemical", "compounds", "or", "\n", "medicinal", "preparations182", "55.89", "%", "\n", "Chemistry", "\n", "and", "chemical", "\n", "engineeringC07C", "Acyclic", "or", "carbocyclic", "compounds", "159", "198.75", "%", "\n", "Electric", "and", "\n", "electronic", "\n", "technologiesH03", "K", "Pulse", "technique", "546", "197.47", "%", "\n", "Electric", "and", "\n", "electronic", "\n", "technologiesG01RMeasuring", "electric", "variables", ";", "measuring", "magnetic", "\n", "variables533", "392.87", "%", "\n", "Electric", "and", "\n", "electronic", "\n", "technologiesG01SRadio", "direction", "-", "finding", ";", "radio", "navigation", ";", "determining", "\n", "distance", "or", "velocity", "by", "use", "of", "radio", "waves", ";", "locating", "or", "\n", "presence", "-", "detecting", "by", "use", "of", "the", "reflection", "or", "reradiation", "\n", "of", "radio", "waves", ";", "analogous", "arrangements", "using", "other", "\n", "waves513", "164.78%Table", "3.8", ".", "Top", "IPC", "symbols", "per", "number", "of", "records", "associated", "with", "the", "patents", "classified", "within", "each", "domain", ",", "at", "\n", "subclass", "level", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation163", "\n", "Domain", "IPC", "Description", "No", "recordsRelative", "\n", "freq", ".", "\n", "Electric", "and", "\n", "electronic", "\n", "technologiesG06F", "Electric", "digital", "data", "processing", "445", "342.31", "%", "\n", "Electric", "and", "\n", "electronic", "\n", "technologiesG01NInvestigating", "or", "analysing", "materials", "by", "determining", "their", "\n", "chemical", "or", "physical", "properties394", "100.04", "%", "\n", "Energy", "F03D", "Wind", "motors", "252", "197.39", "%", "\n", "Energy", "H02", "K", "Dynamo", "-", "electric", "machines", "235", "190.03", "%", "\n", "Energy", "F24HFluid", "heaters", ",", "e.g.", "water", "or", "air", "heaters", ",", "having", "heat-", "\n", "generating", "means", ",", "e.g.", "heat", "pumps", ",", "in", "general172", "154.26", "%", "\n", "Energy", "G01RMeasuring", "electric", "variables", ";", "measuring", "magnetic", "\n", "variables171", "126.04", "%", "\n", "Energy", "H01LSemiconductor", "devices", ";", "electric", "solid", "state", "devices", "not", "\n", "otherwise", "provided", "for168", "166.52", "%", "\n", "Environmental", "\n" ]
[]
products, observed in both the DCE (experiment 1) and sensory laboratory experiment (experiment 2), challenges prevailing arguments that con- sumers inherently favor domestic food products tailored to their pref- erences. However, the divergence between ingredients and nutritional profiles in the products and countries examined, appear minimal. Since the selection of products was based on actual differences (Nes et al., 2023Annex 1 and 3), this raises the possibility that heightened con- sumer concern —likely fueled by media, advertising, and political dis- course —may have overstated the actual quality disparities (Z˘avadský and Hiadlovský, 2020).9Nevertheless, our results underscore the ne- cessity for food producers to engage in more extensive research into local consumer preferences and taste. Specifically, companies targeting markets in Eastern Europe, such as Romania, may benefit from adapting their product formulations to better match the preferences and tastes of foreign markets. This strategic alignment not only caters to consumer preferences but also builds brand loyalty in increasingly competitive markets. One of the most discussed aspects that so far was not investigated concerns the action company can undertake to avoid misleading con- sumers in the case of offering products with different versions in different markets. By assessing the effectiveness of a claim removing asymmetric information related to DFQ, we demonstrate that disclosing information on DFQ through the ‘made for’ claim boosts consumers trust and transparency without adverse marketing consequences. Indeed, the quality signal provided by brands remains almost unchanged in the absence of a ‘made for’ claim and intact in the presence of a ‘made for’ claim. Thus, contrary to some expectations (Sisto et al., 2019 ) using a claim to improve market transparency enables companies to leverage tailored products as a competitive advantage in diverse markets, while avoiding consumer reactance upon discovering that expectations on European-wide product homogeneity may not correspond to reality. 6.1. Implications for policy Makers Our study finds limited evidence that full harmonisation of product composition across the EU will improve consumer welfare. Overall, this finding suggests that the impact of DFQ on consumer purchase decisions and welfare may be minimal, reducing the urgency for widespread policy interventions. Yet, consumers may still perceive similar packaging and product branding as misleading, even when compositional differences are justified by legitimate factors like ingredient sourcing or national reg- ulations and fostering a regulatory environment that promotes trans - parency and supports informed consumer choices could be an effective way of preventing
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performed by relating an asset to a spe- cific hazard. There is also the possibility of relating an as- set to multiple hazards and having a multi-hazard assessment (of exposure or risk) on the single asset. This latter situation is the central aspect of the analysis presented in this study that considers the relation of a single asset (e.g. population or the residential built-up) to multiple hazards: landslides, coastal flooding, river flooding, earthquakes, wildfires, and https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025300 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level subsidence. Starting from this initial setting of the analysis, specific characteristics and limitations need to be presented. First, we show that the proposed methodology allows for the detection of the regions exposed to multi-hazards, dif- ferently, as a function of the typology of the assets. This is important as it directly reveals specific asset–threat relation- ships, valuable for the identification of the disaster risk man- agement pathways in multi-hazard assessment (Ward et al., 2022). Furthermore, our approach identifies LAUs prone to multi- hazards with a high level of significance. The meta-analysis approach adopted combines single-hazard hotspots with the objective to solve the problem of insignificant results and provides an objective statistical proof of the multi-hazard po- tential of a region. We support these results through a val- idation process that considers empirical data as explanatory variables. We highlight that the more significant multi-hazard clustering results in a stronger correlation relationship with the independent variables. We also demonstrate that the proposed methodology al- lows for detecting changing patterns of the population being exposed to multi-hazards by considering socioeconomic fac- tors. Our findings are in line with previous studies, highlight- ing an increasing gradient of multi-hazard risks from low- income countries towards higher-income countries and then a decrease as countries’ incomes increase (Koks et al., 2019). We also identified highly urbanized regions (urban areas) as a space of risk for multi-hazard occurrence (Hansjürgens and Antes, 2008) compared with the rural administrative units. Furthermore, we show the potential of this methodological approach in detecting the risk of multi-hazards associated with complex socioeconomic urban processes. We find that high population density is a good explanatory variable for the increase in risk of the metropolitan areas. However, this situation is particularly different in the case of high-income metropolitan areas, where the populations more exposed to multi-hazards live
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16.8 56.7 57.1 59.4 84.3 45.6 51.0 59.0 Recycling (33) -- 100.0 28.0 -- -- 77.2 68.4 -- 100.0 32.0 -- -- 11.8 47.9 -- 100.0 60.1 -- -- 77.2 79.1 Construction (section F) 43.6 3.5 29.0 28.7 34.2 31.5 28.4 17.8 0.0 19.3 6.0 4.0 3.2 8.4 59.5 3.5 40.9 28.7 38.0 32.2 33.8 Services of motor vehicles (45)22.2 39.2 20.6 4.1 44.2 41.2 28.6 0.0 7.5 27.9 0.5 0.0 40.4 12.7 22.2 44.4 35.3 4.1 44.2 56.0 34.4 Wholesale (46) 34.8 56.2 43.2 53.0 42.3 24.7 42.4 0.0 25.8 25.8 24.5 15.8 11.0 17.2 34.8 66.9 52.1 54.2 51.4 32.7 48.7 Retail (47) 35.8 14.9 23.0 48.3 29.4 30.0 30.3 18.4 3.9 22.2 18.5 10.7 8.5 13.7 39.4 15.8 32.4 49.2 36.7 33.0 34.4 Transport (Section H) 16.3 23.4 9.5 18.9 14.4 5.9 14.7 67.4 0.0 32.1 24.3 37.4 10.3 28.6 83.7 23.4 36.9 38.9 39.7 15.8 39.7 Hotel and restaurants (Section I)38.7 20.8 50.1 49.3 21.5 0.0 30.1 3.4 12.7 14.0 19.3 10.1 0.0 9.9 41.7 30.2 64.0 50.6 31.3 0.0 36.3 Information and communication (Section J)31.0 51.5 51.8 74.7 87.9 59.0 59.3 12.7 0.0 55.4 5.5 0.0 17.9 15.3 31.0 51.5 80.0 74.7 87.9 59.0 64.0 Total 35.5 22.8 39.9 42.9 36.7 32.9 35.1 12.6 8.8 26.6 16.9 14.7 13.3 15.5 40.6 27.5 50.9 45.4 45.1 37.3 41.1Table 2.28. Share of product, process and product and/or process innovators Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation89 90 Part 2 Analysis of economic and innovation potential Share of product innovators Share of process innovators Share of product and/or process innovators Name of industry Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia Azerbaijan Belarus Georgia Moldova Ukraine Armenia Azerbaijan Belarus Georgia Moldova Ukraine Food (10+11) 0.859 0.988 1.470 0.782 0.996 0.906 0.845 0.134 1.567 0.935 1.819 0.699 0.892 0.907 1.284 0.833 1.210 0.874 Tobacco (12) Textiles (13) 1.108 0.334 0.658 1.900 0.346 0.558 1.291 1.805 0.976 0.310 1.042 1.673 Garments (14) 0.702 0.884 1.270 1.144 1.583 1.338 0.000 1.079 0.692 0.894 1.253 1.160 Leather (15) 1.253 1.399 0.348 0.000 1.864 1.136 1.062 1.186 0.752 Wood (16) 1.049 0.000 2.152 0.710 1.124 0.965 0.000 0.465 1.176 0.000 3.834 0.526 0.643 0.251 1.321 0.435 2.758 0.592 Paper (17) 1.412 0.465 0.000 2.249 0.874 2.227 2.773 0.000 0.000 0.000 1.335 0.712 0.000 2.126 0.826 Publishing, printing and recorded media (18) 1.070 0.734 1.185 1.633 0.388 0.990 1.343
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[]
and Penn and Wuyang (2018) . In the statement, consumers were asked to imagine a real purchase situation where the three product al- ternatives are the only options available in their grocery store. 7Product information contains both the list of ingredients and the nutritional facts panel.D.M. Federica et al. Food Policy 131 (2025) 102803 3 one of the two blocks. Therefore, respondents completed 18 choice tasks (six per product). In each choice task, we showed them three different versions of six different product categories per group of countries. To prevent participants from making a holistic profile choice, we asked them to first indicate their preferred and then their least preferred al- ternatives (Marley and Louviere, 2005 ). Experiment II: Sensorial evaluation laboratory experiment. In this second experiment, we address one limitation of experiment 1, namely that consumers made choices based only on non-sensory in- formation. Experiment 2 is designed to gain further insight into con- sumer preferences based on taste and was implemented at the same time as experiment 1. More specifically, we examine the role of organoleptic properties in consumer purchase decisions in Germany and Hungary (where the issue of DFQ was most prominent) for three different product versions and whether these decisions might be affected by the ‘made for’ claim. This allows us to test whether preference for one version over another is linked to taste. Respondents were offered monetary compensation for their partici - pation. Incentive alignment instructions (Ding et al., 2005 ) informed respondents about the fixed endowment they would receive for taking part and the maximum amount they could spend in a real binding purchase. Participants were informed that they would taste and evaluate three different versions of a product.8It was also explained that the task would be repeated for two products as we had to exclude the product that required cooking for tasting (seasoning mix for Bolognese pasta sauce). In total, participants made four purchase decisions, though only one real purchase choice was carried out. The assignment of the pur- chased product, based on a random selection procedure, was revealed at the end of the experiment. The structure of experiment 2, undertaken in a dedicated laboratory, was similar to that of experiment 1 except for the following changes. First, this experiment included two information frames (see Fig. 3). In Frame 1, consumers were not informed about the DFQ practice (i.e. “Blind ” to the
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rate it highly. To speak of texts produced by language mod- els, we must first consider how these texts are generated. A neural language model encodes a probability distribution over the next word in a sequence given the previous words.1Adecod- ing strategy is an algorithm that generates se- quences from a language model by determining how words should get selected from this distribu- tion. The field has largely moved toward prob- abilistic decoding strategies that randomly sam- ple from the output distribution token-by-token. However, when many low-likelihood words cu- mulatively contain quite a bit of probability mass, choosing one of these words can lead to odd or contradictory phrases and semantic errors. Hu- mans are quick to notice these types of errors. For this reason, it has become common to mod- ify the language model’s output probability dis- tribution to increase the chance of sampling to- kens with high likelihood according to the lan- guage model. Top- krandom sampling, where low-likelihood words are restricted from being 1Often these ‘words” are actually subword character se- quences such as BPE tokens (Sennrich et al., 2016).arXiv:1911.00650v2 [cs.CL] 7 May 2020generated, is one such method. A language model that is only permitted to produce high-likelihood words is less likely to make a poor choice and cre- ate the type of mistakes that are easy for humans to detect. Since humans are not proficient at identi- fying when a model subtly favors some utterances more often than a human author would, they don’t notice the over-representation of high-likelihood words in the generated text. In contrast, automatic systems excel at identifying statistical anomalies and struggle to build deeper semantic understand- ing. Top-kin particular creates text that is easy for machines to detect but very hard for humans. Thus, we observe the general trend: as the num- ber of unlikely words available to be chosen is in- creased, humans get better at detecting fakes while automatic systems get worse . In this work, we study three popular random decoding strategies—top- k, nucleus, and temper- ature sampling—applied to GPT-2 (Radford et al., 2019). We draw a large number of excerpts gener- ated by each strategy and train a family of BERT- based (Devlin et al., 2019) binary classifiers to label text excerpts as human-written or machine- generated. We find large differences in human rater and classifier accuracy depending on the de- coding strategy employed and length of the
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■average relative share of the total output for each year in the 2012-2017 period, ■rate of change in degree of specialisation for output for two time periods – between 2012 and 2015, and between 2014 and 2017. Degrees of specialisation have been calculated relative to the unweighted average of Armenia, Azerbaijan, Georgia, Moldova and Ukraine. Spe- cialised industries with critical mass are iden- tified as those industries for which the degree of specialisation and relative size for both the number of employees and turnover are above the thresholds for at least 5 out of 6 years shown in the first two columns in Table 2.8. Emerging industries with increasing degrees of specialisa- tion and relative size are identified as those in- dustries for which the change in the degree of Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation57 specialisation for both the number of employ- ees and output are above the thresholds shown in the last column in Table 2.8. To identify current strengths for both the number of employees and output, the same minimum relative size of 0.1% has been used for each country, and for the degree of specialisation a minimum location quotient of 1.25 (which is lower than the 1.5 used in Section Current strengths Emerging strengths Degree of specialisationRelative size Change in degree of specialisation Armenia > 1.25 > 0.1% > 0 Azerbaijan > 1.25 > 0.1% > 0 Georgia > 1.25 > 0.1% > 0 Moldova > 1.25 > 0.1% > 0 Ukraine > 1.25 > 0.1% > 0Table 2.8. Thresholds used to identify economic specialisations in Manufacturing2.1). For changes over time, for both the number of employees and output, changes in both degrees of specialisation have to be positive for both time periods for all countries. Results for all five countries are shown in Annex 2. Next, we discuss the results for each of the coun- tries individually. 58 Part 2 Analysis of economic and innovation potential Mapping the economic potential in Manu- facturing – results for Armenia Results of the economic mapping for Armenia are shown in Table 2.9. In total, 3 manufacturing in- dustries have been identified as having a current strength and 5 manufacturing industries have been identified as having an emerging strength (these industries are highlighted in the two col- umns with an ‘X’ in a green-coloured cell).Industries with current strength include Bev- erages (110),
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Figure 3.66. Keyword cloud for Chemistry and chemical engineering in Azerbaijan Figure 3.68. Keyword cloud for Mechanical engineering and heavy machinery in Azerbaijan Figure 3.67. Keyword cloud for Energy in Azerbaijan Figure 3.69. Keyword cloud for Health and wellbeing in Azerbaijan 222 Part 3 Analysis of scientific and technological potential Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation223 6.4 Georgia – Summary of the strengths of the S&T specialisa- tions Georgia’s most highlighted S&T domains are the following: ■Environmental sciences and industries ticks all S&T indicators – on critical mass, spe- cialisation and excellence – for publications, patents and projects. It is a very clear spe- cialisation domain for Georgia, with particular relevance in Geology and Geotechnical engi- neering, as well as Environmental engineering and Chemistry; ■Agrifood presents a high specialisation in patents and publications, as well as a critical mass in patents and a relevant number of EU-funded R&I projects, with science orient- ed towards Horticulture, Genetics and Plant science; GEORGIA Critical mass Specialisation Excellence Summary S&T domain Pubs. Pat. Pubs. Pat. NCI*EC projects*Total Agrifood 4 Biotechnology 0 Chemistry and chemical engineering2 Electric and electronic technologies0 Environmental sciences and industries6 Fundamental physics and mathematics3 Governance, culture, education and the economy4 Health and wellbeing 3 ICT and computer science 3 Mechanical engineering and heavy machinery2 Nanotechnology and materials 2 Optics and photonics 1 *NCI = Normalised citation impact *EC projects = EU-funded R&I projectsTable 3.31. Selected S&T specialisation domains in Georgia ■Health and wellbeing presents a high crit- ical mass, specialisation and citation impact in publications, while no positive indicator emerges in relation to patents. It co-occurs frequently with the domain of Agrifood. Be- yond General medicine, research is related, in particular, to Infectious diseases and Immu- nology; and ■ICT and computer science presents a spe- cialisation in patents as well as highly cited publications and a relevant number of EC pro- jects. The following clouds present the most relevant keywords for these highlighted S&T domains. Figure 3.70. Keyword cloud for Environmental sciences and industries in Georgia Figure 3.72. Keyword cloud for Health and wellbeing in Georgia Figure 3.71. Keyword cloud for Agrifood in Georgia Figure 3.73. Keyword cloud for ICT and computer science in Georgia 224 Part 3 Analysis of scientific and technological potential Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation225 6.5 Moldova – Summary of the strengths
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[]
The report also indicates the evidence-informed areas for knowledge-based economic cooperation to support bilateral and region-wide initiatives. 2 Executive summary covery, resilience and reform: post 2020 Eastern Partnership priorities’4. The S3 Framework was developed at the request of EU Enlargement and Neighbourhood economies that voluntarily took on the development of Smart Specialisation Strategies in different administra- tive, institutional and political contexts. It oper- ationalises the guidance offered to EU Member States into a set of practical steps, but also adapts and adjusts the approach to suit non-EU countries. Starting from the first phase of 1 institution- al capacity building (Decision to start S3 Pro- cess) to the second 2 institutional capacity building (Analysis of the Strategic mandates), it continues with the mapping phases through steps 3diagnosis (Quantitative mapping) and 4 diagnosis (Qualitative mapping). The next steps include engaging in 5 stakeholder dialogue, developing the 6 institutional capacity for implementation and, finally, 7 drafting the strategy (see the figure below). This study aims to offer a solid basis for the Smart Specialisation process by offering an extensive quantitative analysis of national-level potential in the economy, innovation, science and technology based on available international data. This effort, amongst others, is part of the targeted support 4 SWD(2021) 186 final, 2.7.2021.EXECUTIVE SUMMARY Most Eastern Partnership countries have com- mitted to developing their Smart Specialisa- tion Strategies based on the Smart Specialisation Framework for EU Enlargement and Neighbour- hood Region (S3 Framework)2, developed by the JRC in cooperation with partner countries, inter- national experts and policy directorates of the European Commission. The application of this EU- made innovation policy concept will allow Eastern Partners to promote knowledge-based economic development and targeted research and innova- tion policies building on territorial specificities, unique potentials and emerging niches. This ef- fort has been recognised in the 2020 European Commission’s Joint Communication: ‘Eastern Partnership policy beyond 2020: Reinforcing Re- silience – an Eastern Partnership that delivers for all’3 and the Joint Staff Working Document ‘Re- 2 Matusiak, M., Kleibrink, A. (ed.), Supporting an Inno- vation Agenda for the Western Balkans – Tools and Methodologies, Publications Office of the European Un- ion, Luxembourg, 2018, ISBN 978-92-79-81870-7, doi:10.2760/48162, JRC111430. 3 https://eeas.europa.eu/sites/default/files/1_en_act_ part1_v6.pdf. INSTITUTIONAL CAPACITY FOR IMPLEMENTATIONFINAL S3 STRATEGYINSTITUTIONAL CAPACITY BUILDING (Decision to Start S3 Process)INSTITUTIONAL CAPACITY BUILDING (Analysis of Strategic Mandates)DIAGNOSIS (Quantitative mapping)DIAGNOSIS (Qualitative mapping) STAKEHOLDER DIALOGUE Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation3
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Heritage Sites but are projected to disappear from one-third of them by 2050. Glaciers are considered the abode of gods and spirits by Indigenous Peoples in Asia, Latin America, the Pacific and East Africa, and are the site of rituals and festive events that have been recognised in the UNESCO List of the Intangible Cultural Heritage of Humanity. The disappearance of glaciers would imply a substantial loss of other cultural and natural heritage and spiritual connection to the landscape and nature. Conclusion The International Year of Glaciers’ Preservation highlights the challenges posed to all countries by a shrinking mountain and global cryosphere. Governments need to be aware that vital assets to their economies and industries suchxzch action to reduce greenhouse gas emissions, governments and local authorities need to take the appropriate steps regarding adaptation. Changes in the amount of water and seasonal availability of water need to be studied and conveyed to the communities dependent on meltwater from glaciers and snowpack. Flood preparedness and early warning systems are also needed, both downstream from glaciers where glacial floods may originate and in low-lying areas threatened by sea level rise. Rapid decarbonisation is essential to preserving glaciers. The current rate of temperature increase is beyond limits of adaptation in many regions. By marking the International Year of Glaciers’ Preservation, the global community will both recognise the importance of glaciers and commit to taking the urgent steps needed to preserve them. The time to save our glaciers, and to save ourselves, is NOW. Credit: J. Kirkham
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[]
Upon replication of the same behav- ioral paradigm 24 h later, the temporal difference in activity be- tween social and non-social exploration remained stable ( Fig- ure 5 C). Similarly, while CS presentations elicited a similar activity in aIC VIP+ IN throughout fear acquisition, the activity of these INs decreased with successive US presentations (Figures 5 D and 5E). During fear retrieval, the initial CS presenta- tion induced a higher response compared with the one elicited during fear conditioning (Mann-Whitney, CS1 versus CS-R1, (I) Mean AUC of Zscored activity responses was significantly higher during interactions with a mouse compared with object interactions on day 1 (Wilcoxon signed rank, p = 0.0002, n = 88 cells).(J) Activity maps from all individual recorded aIC VIP+ INs on social preference test day 2 (n = 71 cells from N = 6 mice), sorted by time of peak activity du ring interactions with another conspecific mouse, averaged across all interactions with the conspecific (left panel) or object (right panel).(K) Mouse and object interaction responses averaged from all recorded aIC VIP+ INs across all interactions.(L) Mean AUC of Zscored activity responses was similar during interactions with the mouse or object on day 2 (Wilcoxon signed rank, p = 0.31, n=7 1c e l l s ) . Data are shown as mean + or ±SEM. Details of statistical analyses are provided in Table S1 . 6Cell Reports 39, 110893, May 31, 2022Articlell OPEN ACCESSFigure 4. aIC VIP+ IN activity is required for aversive learning and social preference (A) Schematic of the approach used for optogenetic loss-of-function experiments. (B) Example micrographs of injection and implantation sites of GFP-only (left) or ArchT-GFP (right) in the aIC of VIP-ires-cre mice. Scale bar, 200 mm. (C) Example of ArchT selective expression in aIC VIP+ INs. Scale bar, 20 mm. (D) Social preference paradigm for closed-loop light-induced suppression of aIC VIP+ IN activity during close interactions with a novel conspecific on day 1 of testing, during the second 5 min of the test. (legend continued on next page) Cell Reports 39, 110893, May 31, 2022 7Articlell OPEN ACCESSp = 0.025), which progressively diminished with consecutive pre- sentations of the CS, whereas the unexpected omission of the US led to a delayed increase in activity ( Figure 5 F). aIC VIP+ INs are functionally heterogeneous Our analyses of deep-brain Ca2+imaging showed that foot- shocks during fear conditioning and close interactions with
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[]
a mouse brain atlas ( Franklin, 2008 ). While counting neurons, sections were focused throughout their whole thickness to obtain the most accurate quantification.Immunofluorescence Immunofluorescent experiments were performed according to previously published procedures ( Ferraguti, 2004 ). Briefly, sections were first washed with TBS and then incubated in blocking solution made of 20% normal serum (as required) in 0.1% TBS-T, for1 h at room temperature. After blocking, sections were incubated with primary antibodies (see Table S2 ) diluted in 2% normal serum and 0.2% TBS-T for 72 h at 6 /C14C. Slices were then washed with TBS 3 times and then incubated overnight at 6/C14C with respective secondary antibody diluted in the same solution as used for the primary antibodies (see Table S2 ). Finally, sections were extensively washed with TBS, mounted on glass slides and coverslipped with Vectastain (Vector Laboratories) or ProLong Diamond (Thermo Fisher Scientific). Images were acquired using either an epifluorescence microscope (Axio Imager, Carl Zeiss, Oberkochen, Ger- many) and the Openlab software (Version 5.5.0) or an Airy Scan LSM980 laser scanning microscope (Carl Zeiss, Oberkochen, Ger-many) with a 40x/1.2 objective. Raw confocal images were channel dye separated and deconvolved using Huygens software (Sci- entific Volume Imaging, Hilversum, The Netherlands). Image processing was performed using the IMARIS 9.7.0 software (Oxford Instruments, Bitplane, Zurich, Switzerland). To confirm the sensitivity of the VIP-IHC analysis, we carried out double fluorescence experiments using sections (2/animal; N = 3 mice) obtained from VIP-ires-cre:Ai9 mice. In these slices, we visualized the endogenous fluorescence of the reporter molecule tdTo- mato expressed under the endogenous VIP promoter and VIP using an immune-complex composed of a rabbit primary antibody (Immunostar), as described previously, and an Alexa 488-conjugated donkey anti-rabbit secondary antibody (cat. no. A21206, Invi-trogen, 1:1,000). The sections were scanned using a LED Panoramic /C226250 Flash III scanner (3DHistech) equipped with a 16-bit Cell Reports 39, 110893, May 31, 2022 e4Articlell OPEN ACCESS4.2 MP camera and a 20x objective, and the quantification of the neurons colocalizing the two fluorescent signals was manually per- formed offline using CaseViewer (3DHistech). Image acquisition and data analysis For mono-trans-synaptic tracing, brains were embedded in 2% agarose in PBS and cut into coronal sections. Every third sectionfrom each brain was used for quantification of first order presynaptic neurons to aIC VIP + INs. Four sections before and after the injection site were not used for quantification as they contained the starter
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impact on output. However, if the strategy and reforms outlined in this report are implemented in parallel, the investment push should be accompanied by a significant increase in EU total factor productivity (TFP). A sizable increase in TFP will improve the government budget surplus, significantly reducing the transitional costs of imple - menting the plan, provided that the additional revenue is not fully spent on other purposes. For example, a 2% increase in the level of TFP within ten years could already be sufficient to cover up to one third of the fiscal spending (investment subsidies and government investment) required to implement the plan. The 2% TFP increase can be considered modest given the current 20% gap TFP levels between the EU and the US. THE ROOT CAUSES OF LOW INVESTMENT FINANCING IN EUROPE A key reason for less efficient financial intermediation in Europe is that capital markets remain fragmented and flows of savings into capital markets are lower . While the Commission has introduced several measures to build a Capital Markets Union (CMU), three main fault lines remain. First, the EU lacks a single securities market regulator and a single rulebook for all aspects of trading and there is still high variation in supervisory practices and interpretations of regulations. Second, the post-trade environment for clearing and settlement in Europe is far less 01. Productive investment is defined as gross fixed capital formation minus residential investment. 63THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 5unified than in the US. Third, despite the recent progress made on withholding tax, tax and insolvency regimes across Member States remain substantially unaligned. EU capital markets are also undersupplied with long-term capital relative to other major economies, owing largely to the underdevelopment of pension funds. In 2022, the level of pension assets in the EU was only 32% of GDP while in the US total assets amounted to 142% of GDP and in the UK to 100%. This difference reflects the fact that most European households’ pension wealth takes the form of claims on public pay-as-you-go social security systems. EU pension assets are highly concentrated in a handful of Member States with more developed private pension systems. The combined share of the Netherlands, Denmark and Sweden in EU pension assets amounts to 62% of the EU total. The mirror image is that the EU relies excessively on bank financing, which is less well-suited
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[]
machinery and apparatus, n.e.s. X 0.2% 781Motor cars and other motor vehicles principally designed for the transport of persons (other than motor vehicles for the transport of ten or more persons, including the driver), including station-wagons and racing carsX 15.4% 791Railway vehicles (including hovertrains) and associated equipment X 0.5% 8 Miscellaneous manufactured articles 843Men’s or boys’ coats, capes, jackets, suits, blazers, trousers, shorts, shirts, underwear, nightwear and similar articles of textile fabrics, knitted or crocheted (other than those of subgroup 845.2)X 0.3% X 0.3% 872Instruments and appliances, n.e.s., for medical, surgical, dental or veterinary purposesX 0.3% 874Measuring, checking, analysing and controlling instruments and apparatus, n.e.s. X 0.2% 893 Articles, n.e.s., of plastics X 0.3% 9Commodities and transactions not classified elsewhere in the SITC Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation71 Mapping of goods export specialisations – results for Moldova Results of the export mapping for Moldova are shown in Table 2.19. The 41 goods categories with current strength represent almost 79% of the total exports for 2012-2019. Specialised exports in Food and live animals (SITC 0) account for 17% of the total exports, those in Machinery and trans- port equipment (SITC 7) for almost 14% and those in Miscellaneous manufactured articles (SITC 8) for more than 21%. In addition, Alcoholic beverag- es (SITC 112) and Oil seeds and oleaginous fruits (SITC 222) are specialised goods categories each accounting for around 8% of the total exports.The 23 goods categories with emerging strength represent almost 34% of the total exports. Export specialisations are primarily increasing in Food and live animals (SITC 0) and Crude materials, in- edible, except fuels (SITC 2). Moldova’s export specialisation suggests an over- all specialisation in both low value-added activities related to food production – putting the country at risk of the negative effects of falling food prices at global level – and in machinery and other man- ufactured articles. SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 41 78.7% 23 33.8% 0 Food and live animals 001 Live animals other than animals of division 03 X 0.4% 041 Wheat (including spelt) and meslin, unmilled X 3.2% 043 Barley, unmilled X 0.7% X 0.7% 044 Maize (not including sweet corn), unmilled X 2.6% 048Cereal preparations and preparations of flour or starch of fruits or vegetablesX 0.7% 056 Vegetables, roots and tubers, prepared or preserved, n.e.s. X 0.6% 057 Fruit
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exposed to unfair competition from abroad and/or facing more exacting decarbonisation targets than their international compet - itors – including applying tariffs and other trade measures where warranted. 42THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3The root cause of high energy prices Structural causes are at the heart of the energy price gap and may be exacerbated by both old and new challenges [see the chapter on energy] . The price differential vis-à-vis the US is primarily driven by Europe’s lack of natural resources, as well as by Europe’s limited collective bargaining power despite being the world’s largest buyer of natural gas. However, the gap is also caused by fundamental issues with the EU’s energy market. Infrastructure investment is slow and suboptimal, both for renewables and grids. Market rules prevent industries and households from capturing the full benefits of clean energy in their bills. Financial and behavioural aspects of derivative markets have driven higher price volatility. Higher energy taxation than other parts of the world adds a tax wedge to prices. Moreover, while these structural issues have been exacerbated by the energy crisis of the past two years, future crises may bring them to the fore again. Tensions in gas markets are expected to ease thanks to new global supply capacity coming online, but the EU energy system will have to cope with electrification and new security of supply needs. The EU is the largest global gas and LNG importer, yet its potential collective bargaining power is not being sufficiently leveraged and relies excessively on spot prices, threatening Europe with more volatile natural gas prices01. This lack of leverage is notable especially in the case of pipeline gas, where the possibility of rerouting gas flows is more limited as shown by the latest unsuccessful efforts by Russia. During the 2022 crisis, for example, intra-EU competition for natural gas between actors willing to pay high prices contributed to an excessive and unnecessary rise in prices. In response, the EU introduced a coordination mechanism to aggregate and match demand with competitive supply offers (AggregateEU), but there is no obligation for joint purchasing on the platform. At the same time, although natural gas prices have fallen considerably from their peaks during the energy crisis, the EU faces an increasingly volatile outlook. With the loss of access to Russian pipeline gas, 42% of EU gas imports arrived as LNG in 2023,
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[]
(Rajpurkar et al., 2016) or SNLI (Bowman et al., 2015), which do not rep- resent questions that any particular person really wanted to ask about a text, but the somewhat un- natural communicative situation of crowdsourcing work. If a system does better on such a task than the inter-annotator agreement,9the task probably has statistical artifacts that do not represent meaning. In the vision community, Barbu et al. (2019) offer a novel dataset which explicitly tries to achieve a more realistic distribution of task data; it would be interesting to explore similar ideas for language. Third, value and support the work of carefully creating new tasks (see also Heinzerling, 2019). For example, the DROP reading comprehension benchmark (Dua et al., 2019) seeks to create more stringent tests of understanding by creating ques- tions that require the system to integrate informa- tion from different parts of a paragraph via simple arithmetic or similar operations.10 Fourth, evaluate models of meaning across tasks. (Standing) meaning is task-independent, so a sys- tem that captures meaning should do well on mul- tiple tasks. Efforts like SuperGLUE (Wang et al., 2019) seem like a good step in this direction. Finally, perform thorough analysis of both errors and successes. As McCoy et al. (2019) and Niven and Kao (2019) have shown, systems that find suc- cess with large pretrained LMs do not necessarily do so because the LMs have learned “meaning”. 9https://rajpurkar.github.io/SQuAD-explorer/ 10See Appendix B for an exploration of what GPT-2 does with arithmetic.Analyses which start from an attitude of healthy skepticism (“too good to be true”) and probing tasks which try to identify what the model actually learned can be good ways to find out whether the system performs well for the right reasons. 9 Some possible counterarguments In discussing the main thesis of this paper with various colleagues over the past 18 months, we have observed recurring counterarguments. In this section, we address those counterarguments, plus a few more that might arise. “But ‘meaning’ doesn’t mean what you say it means.” Defining “meaning” is notoriously hard. For the purposes of this paper, we chose a working definition which is as general as we could make it, capturing the crucial point that meaning is based on the link between linguistic form and something that is not language. “Meaning” cannot simply be the relation between form and some kind of “deep syntax”, e.g. semantic dependency graphs (Oepen et al., 2015);
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technologies. With planned investment in the EU more than tripling in 2023, the IEA projects that the EU could meet its domestic demand for batteries by 2030. This capacity growth will increase Europe’s strategic resilience and benefit adjacent sectors such as automotives by shortening supply chains. However, many of these projects are at this stage still announcements, and actual development will depend on supporting policies from permitting to financing. In addition, roughly half of the announced investment is from non-EU companies and, in most cases, projects are not taking place in the form of joint ventures. As a result, the EU may be missing an opportunity to combine openness to inward FDI with the development of critical know-how among European manufacturers. 47THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3The challenges of asymmetric decarbonisation “Hard-to-abate” industries are suffering not only from high energy prices, but also from lack of public support to reach decarbonisation targets and investment in sustainable fuels [see the chapters on energy-intensive industries, and transport] . Despite the massive investment needs facing Energy Intensive Industries (EIIs), and the challenging business case for investment in “hard-to-abate” sectors, there is limited public support for the transition in Europe. Only a residual share of current ETS resources is earmarked to EIIs, with priority given to residential effi - ciency, renewables development or, recently, lowering energy bills. While EIIs in other regions face neither the same decarbonisation targets nor require similar investments, they benefit from more generous state support. China, for example, provides over 90% of the global USD 70 billion subsidies in the aluminium sector, as well as large subsidies for steel. Decarbonisation is also a competitive disadvantage for the “hardest-to-abate” parts of the transport sector (aviation and maritime). Extra-EU flights and sea journeys are partly excluded from the ETS, meaning the prices of these journeys do not yet reflect their climate impact. Consequently, there is a risk of carbon leakage and business diversion from transport hubs in the EU to those in the EU’s neighbourhood, unless effective solutions for ensuring a level playing field are found at the international level. At the same time, although low-carbon fuels will be critical for the decarbonisation of these industries, ramping up the marginal production capacity that exists today is chal - lenging. In particular, the EU needs to start building a supply chain for alternative fuels, or the costs of
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[]
....................................................................................................................................................... 233 Table 4.2. Combined EIST specialisation domains in Armenia ............................................. 236 Table 4.3. Combined EIST specialisation domains in Azerbaijan ........................................ 238 Table 4.4. Combined EIST specialisation domains in Georgia .............................................. 240 Table 4.5. Combined EIST specialisation domains in Moldova ............................................. 241 Table 4.6. Combined EIST specialisation domains in Ukraine .............................................. 244 Table 4.7. Pairs of economic clusters and S&T domains that can be identified in at least two countries ............................................................................................................................................... 246 268 Annexes Annexes Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation269 270 Annexes Annex 1. Results of the full economic mapping analysis for Georgia, Moldova and UkraineAn ‘X’ in a yellow-coloured cell shows whether an industry passed an individual criterion, either for the number of employees (or employment) and turnover. An ‘X’ in a green-coloured cell shows whether an industry passed the criteria for both the number of employees (or employment) and turnover. 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 A AGRICULTURE, FORESTRY AND FISHING 1 Crop and animal production, hunting and related service activities 1.1 Growing of non-perennial crops X X X X X 1.2 Growing of perennial crops X X X X X X X 1.3 Plant propagation 1.4 Animal production X X X X X X 1.5 Mixed farming X X 1.6 Support activities to agriculture and post-harvest crop activities X X X X 1.7 Hunting, trapping and related service activities 2 Forestry and logging 2.1 Silviculture and other forestry activities X X X X 2.2 Logging X X 2.3 Gathering of wild growing non-wood products 2.4 Support services to forestry X X 3 Fishing and aquaculture 3.1 Fishing 3.2 Aquaculture B MINING AND QUARRYING 5 Mining of coal and lignite 5.1 Mining of hard coal X X X 5.2 Mining of lignite 6 Extraction of crude petroleum and natural gas 6.1 Extraction of crude petroleum X X X X 6.2 Extraction of natural gas X X X Smart Specialisation in the Eastern Partnership countries -
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[]
for art education in America has grown steadily, and in recent years we have seen the rise of students that pursue art education not in the classroom but at art academies. This year saw another 50 percent increase in the number of art academies in the United States offering courses – with an additional 10 percent of students in 2017 taking art. {Some major changes have occurred in recent years with regard to the art curriculum and the way students learn, and we will explore each of these in coming months as we look at the various forms of art education. There is no one-size-fits-all approach for this or any other field of study, and students who begin a course in art education may change their plans based on what they see that course, including what lessons they have completed and the resources available, to create meaningful experiences of artistic creation. {One important area Table 4: Some 192-token examples where at least two expert raters agreed with each other, but were not in agree- ment with the automatic discriminators. The first row shows examples where the ground-truth was human-written, the second shows machine-generated examples where the corresponding discriminator guessed incorrectly, and the third shows machine-generated examples where the discriminator was correct, but raters got it wrong. ter a time. However, it is worth noting that our best raters achieved accuracy of 85% or higher, sug- gesting that it is possible for humans to do very well at this task. Further investigation is needed into how educational background, comfort with English, participation in more extensive training, and other factors can impact rater performance. To break up the accuracies by sampling method in a way that is comparable to the results shown for the automatic discriminators, we pair each machine-generated example with a randomly se- lected one of webtext to create a balanced dataset for each sampling strategy. Performance is shown in Figure 3a. Top- kproduces the text that is hard- est for raters to correctly distinguish, but as shown in Section 7, it is the easiest for our automatic de- tection systems. Samples from untruncated ran- dom sampling and nucleus sampling with p=0.96 are equivalently difficult for raters to classify as machine-generated. Our human evaluation results suggest that much lower p-values than the 0.92 to 0.98 range proposed in Zellers et al. (2019) might be necessary in order to generate text
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a new entity, rather than through mergers, acquisitions or spinoffs from established firms. 24THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2FIGURE 3 Venture capital investment by development stage USD billion, 2023 Source: Pitchbook data. Accessed 20 November, 2023. Integrating AI ‘vertically’ into European industry will be a critical factor in unlocking higher productivity [see the Boxes on AI use cases in the thematic chapters] . Quantitative estimates of the effects of AI on aggregate produc - tivity are still uncertainii. However, there are already clear signs that AI will revolutionise several industries in which Europe specialises and will be crucial for EU companies ’ ability to remain leaders in their sector. For example, AI will radically change the pharma sector via so-called “combination products” – therapeutic and diagnostic products combining drugs, devices and biological components – which integrate medicine delivery systems with AI algo - rithms and process feedback data in real time. Gains of USD 60-110 billion per year are estimated from the use cases of AI in the pharma and medical device industries. AI will likewise transform the automotive sector, as AI-powered (generative) algorithms enhance vehicle design by optimising structures and components, improve performance and reduce material use, and optimise supply chains by predicting demand and streamlining logistics operations. AI is expected to reduce inventories in the automotive sector, accelerate the time to market from R&I and increase labour productivity. AI uptake in freight and passenger transport will enable increasingly automated functions to deliver safety and quality, navigation and route optimisation, predictive maintenance and fuel or power reduction. The energy sector is already heavily deploying AI, with more than 50 use cases today ranging from grid maintenance to load forecasting. Large gains are however still available: estimates of the market value for future AI applications in the sector reach USD 13 billion. Although technology is crucial to protect Europe’s social model, AI could also undermine it without a strong focus on skills . AI is already a source of anxiety for European workers: almost 70% of respondents in a recent survey favoured government restrictions on AI to protect jobsiii. The impact of AI in Europe has so far been labour-enhancing rather than labour-replacing: there is a positive association between AI exposure and the sector-occupation employ - ment shareiv. However, this association may be transitory as businesses are still in the early stage of understanding how
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[]
test H1a, H1c, H2a, H2b, and H3. The second approach is a sensorial evaluation laboratory experiment to evaluate differences in organoleptic rankings of different product versions to test H1b, H1d, H2c and H2d. 3.1. Experimental design 3.1.1. Experiment I: Discrete choice experiment The DCE is applied to six products in six EU member states.4The six products were selected from the list of products whose ingredients and composition were systematically compared in the study of the European Commission (2019) and refined through a consumption frequency sur- vey and insights from country-specific focus group discussions5(Di Marcantonio et al., 2020 ). The six selected MS (Germany, Hungary, Lithuania, Romania, Spain and Sweden) ensure a wide geographical distribution and a comprehensive representation of socio-economic conditions across the EU. They also represent both Eastern and West- ern European countries mentioned in the DFQ political debate. To align with the sensorial evaluation laboratory experiment (see 3.1.2) and avoiding overburdening participations, consumers in each country evaluated only three out of the six different products, and each product was presented to the consumer in three different versions. The distri - bution of products-countries pairs is shown in Table 1. Respondents were informed that the purpose of the experiment was to evaluate their willingness to pay for three different versions of three products. The experiment was not incentivized, which raises the issue of hypothetical bias (Murphy et al., 2005 ).6 In total 6,000 participants (1,000 per country) were recruited by a market research company to participate in the online experiment. Par- ticipants were selected using stratified random sampling by age group (Mage18-24 ˆ13 %, Mage25-54 ˆ67 %, Mage55-75 ˆ20 %), gender (52 % female), and level of education (Mprimary ˆ3 %, Msecondary ˆ31 %, Mtertiary ˆ25 %, Muni-higher ˆ42 %). A short screening before the experiment was conducted to include only those who (a) had consumed the selected product categories over the past three months and (b) were the main shoppers for their household. Across the six countries, we randomly assigned the 6,000 re- spondents to one of the two experimental conditions in a between- subject experimental design (Ncontrol ˆ3,000, Nclaim ˆ3,000). For all consumers, the information presented in the choice sets included the main list of ingredients, nutritional facts, the price, and the brand. For ingredients and nutritional facts, the information provided corre - sponded to what was reported on the real products sold in local super -
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G10F; A45C; A63C; A63J; B62B; F23Q; G10G 32.5Manufacture of medical and dental instruments and supplies [32.5]A61B; A61H; B04B; G01T; A61C; A61D; A61F; A61J; A61L; A61M; C12M; A61K 8/*; G21G; A61G; A62B; B01LA61K 32.9 Manufacturing n.e.c. [32.9] B65D; G03D; G03F; G09B; G09F 42.2 Construction of utility projects [42.2] E03B; E03C 42.9Construction of other civil engineering projects [42.9]E02B 43 Specialised construction activities [43]E03F; E04G; E04B; E04H; E04C; E04D; E04F 62Computer programming, consultancy and related activities [62]G06Q Source: Van Looy, V. et al., Patent statistics: Concordance IPC v8 - NACE Rev.2, Eurostat, 2015. 332 Annexes Annex 8. NACE v2 to S&T domains correspondence tables via IPC, for each EaP countryThe following tables report the NACE to S&T do- main mappings obtained via patent IPC classes for all EaP countries. The mappings are obtained by determining the intersection of each S&T do- main with the IPC classes of the patent records associated with the domain and by leveraging IPC to NACE concordances. Notably, the NACE-S&T do- main mappings could vary from country to country because of the different possible overlaps between S&T domains and the relative patent classes. ARMENIA Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 10Manufacture of food productsAgrifood A23L; A23C 11Manufacture of beveragesAgrifood A23L 20Manufacture of chemicals and chemical productsAgrifood A61K 20Manufacture of chemicals and chemical productsHealth and wellbeing A61K 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsAgrifood C07H; A61K; C12P 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsHealth and wellbeing A61K 26Manufacture of computer, electronic and optical productsElectric and electronic technologies H05H; H01L; G06F; H01J 26Manufacture of computer, electronic and optical productsFundamental physics and mathematicsG06F; H04L 26Manufacture of computer, electronic and optical productsICT and computer science G06F 26Manufacture of computer, electronic and optical productsNanotechnology and materials H01L 32 Other manufacturing Agrifood A61K 32 Other manufacturing Health and wellbeing A61K 32 Other manufacturing Nanotechnology and materials G09F Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation333 AZERBAIJAN Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 20Manufacture of chemicals and chemical productsBiotechnology A61K 20Manufacture of chemicals and chemical productsChemistry and chemical engineering C07C; B01J 20Manufacture of chemicals and chemical productsEnergy C07C 20Manufacture of chemicals and chemical productsHealth and wellbeing A61K 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsBiotechnology A61K; A61P 21Manufacture of basic pharmaceutical products and pharmaceutical preparationsHealth
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"\n", "main", "mappings", "could", "vary", "from", "country", "to", "country", "\n", "because", "of", "the", "different", "possible", "overlaps", "between", "\n", "S&T", "domains", "and", "the", "relative", "patent", "classes", ".", "\n", "ARMENIA", "\n", "Concordances", "between", "NACE", "sectors", "and", "the", "intersection", "of", "IPC", "classes", "&", "S&T", "domains", "\n", "NACE", "sector", "S&T", "domain", "Mapping", "\n", "10Manufacture", "of", "food", "\n", "productsAgrifood", "A23L", ";", "A23C", "\n", "11Manufacture", "of", "\n", "beveragesAgrifood", "A23L", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsAgrifood", "A61", "K", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsHealth", "and", "wellbeing", "A61", "K", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsAgrifood", "C07H", ";", "A61", "K", ";", "C12P", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsHealth", "and", "wellbeing", "A61", "K", "\n", "26Manufacture", "of", "computer", ",", "\n", "electronic", "and", "optical", "\n", "productsElectric", "and", "electronic", "technologies", "H05H", ";", "H01L", ";", "G06F", ";", "H01J", "\n", "26Manufacture", "of", "computer", ",", "\n", "electronic", "and", "optical", "\n", "productsFundamental", "physics", "and", "\n", "mathematicsG06F", ";", "H04L", "\n", "26Manufacture", "of", "computer", ",", "\n", "electronic", "and", "optical", "\n", "productsICT", "and", "computer", "science", "G06F", "\n", "26Manufacture", "of", "computer", ",", "\n", "electronic", "and", "optical", "\n", "productsNanotechnology", "and", "materials", "H01L", "\n", "32", "Other", "manufacturing", "Agrifood", "A61", "K", "\n", "32", "Other", "manufacturing", "Health", "and", "wellbeing", "A61", "K", "\n", "32", "Other", "manufacturing", "Nanotechnology", "and", "materials", "G09F", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation333", "\n", "AZERBAIJAN", "\n", "Concordances", "between", "NACE", "sectors", "and", "the", "intersection", "of", "IPC", "classes", "&", "S&T", "domains", "\n", "NACE", "sector", "S&T", "domain", "Mapping", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsBiotechnology", "A61", "K", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsChemistry", "and", "chemical", "engineering", "C07C", ";", "B01J", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsEnergy", "C07C", "\n", "20Manufacture", "of", "chemicals", "\n", "and", "chemical", "productsHealth", "and", "wellbeing", "A61", "K", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsBiotechnology", "A61", "K", ";", "A61P", "\n", "21Manufacture", "of", "basic", "\n", "pharmaceutical", "products", "\n", "and", "pharmaceutical", "\n", "preparationsHealth" ]
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engineering and heavy machinery (897 | 43.06%)Mechanical engineering and heavy machinery (443 | 50.98%)Mechanical engineering and heavy machinery (600 | 40.38%)Mechanical engineering and heavy machinery (16 351 | 30.08%)Electric and electronic technologies (102 | 28.41%) Electric and electronic technologies (6 515 | 11.98%) Electric and electronic technologies (175 | 11.78%)Electric and electronic technologies (7009 | 11.79%) Electric and electronic technologies (173 | 8.31%)Electric and electronic technologies (67 | 7.71%)Health and wellbeing (11 726 | 19.73%)Health and wellbeing (152 | 34.86%) Health and wellbeing (339 | 16.27%) Health and wellbeing (40 | 4.6%)Health and wellbeing (284 | 19.11%)Health and wellbeing (10 940 | 20.12%)Nanotechnology and materials (500 | 24.0%) Nanotechnology and materials (5 886 | 10.83%)Nanotechnology and materials (6 641 | 11.17%) Nanotechnology and materials (58 | 16.16%) Nanotechnology and materials (32 | 7.34%)Nanotechnology and materials (79 | 9.09%) Nanotechnology and materials (127 | 8.55%)Agrifood (177 | 20.37%) Agrifood (206 | 13.86%) Agrifood (5 353 | 9.85%)Agrifood (62 | 17.27%) Agrifood (151 | 7.25%)Agrifood (5 907 | 9.94%)Energy (65 | 14.91%) Energy (5 828 | 9.8%)Energy (5 647 | 10.39%)Biotechnology (112 | 25.69%) Biotechnology (5 837 | 9.82%)Chemistry and chemical engineering (110 | 12.66%) Chemistry and chemical engineering (312 | 14.98%) Chemistry and chemical engineering (43 | 9.86%) Chemistry and chemical engineering (35 | 9.75%)Fundamental physics and mathematics (58 | 16.16%)Environmental sciences and industries (103 | 11.85%)Biotechnology (190 | 12.79%) Biotechnology (5 517 | 10.15%)ICT and computer science (222 | 10.66%) ICT and computer science (77 | 5.18%)ICT and computer science (37 | 8.49%) ICT and computer science (29 | 8.08%) Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation171 172 Part 3 Analysis of scientific and technological potential 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 of EC projects analysed in the country) EaP ARMENIA AZERBAIJAN GEORGIA MOLDOVA UKRAINE BELARUS Governance, culture, education and the economy (197 | 60.99%)Governance, culture, education and the economy (33 | 76.74%)Governance, culture, education and the economy (14 | 73.68%)Governance, culture, education and the economy (34 | 50.75%)Governance, culture, education and the economy (51 | 87.93%)Governance, culture, education and the economy (62 | 81.58%)Governance, culture, education and the economy (119 | 58.33%) Nanotechnology and materials (65 | 20.12%) Nanotechnology and materials (5 | 11.63%)Nanotechnology and materials (29 | 43.28%) Nanotechnology and materials (44
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"sciences", "\n", "and", "industries", "\n", "(", "103", "|", "11.85%)Biotechnology", "\n", "(", "190", "|", "12.79", "%", ")", "\n", "Biotechnology", "\n", "(", "5", "517", "|", "10.15%)ICT", "and", "computer", "\n", "science", "\n", "(", "222", "|", "10.66", "%", ")", "\n", "ICT", "and", "computer", "\n", "science", "\n", "(", "77", "|", "5.18%)ICT", "and", "computer", "\n", "science", "\n", "(", "37", "|", "8.49", "%", ")", "\n", "ICT", "and", "computer", "\n", "science", "\n", "(", "29", "|", "8.08", "%", ")", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation171", "172", "\n ", "Part", "3", "Analysis", "of", "scientific", "and", "technological", "potential", "\n", "Figure", "3.22", ".", "Top", "identified", "domains", "in", "each", "EaP", "country", "in", "EC", "projects", "(", "number", "of", "identified", "EC", "projects", "in", "the", "domain", "\n", "|", "percentage", "of", "the", "total", "number", "of", "EC", "projects", "analysed", "in", "the", "country", ")", "\n", "EaP", "ARMENIA", "AZERBAIJAN", "GEORGIA", "MOLDOVA", "UKRAINE", "BELARUS", "\n", "Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "197", "|", "60.99%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "33", "|", "76.74%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "14", "|", "73.68%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "34", "|", "50.75%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "51", "|", "87.93%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "62", "|", "81.58%)Governance", ",", "culture", ",", "\n", "education", "and", "the", "economy", " \n", "(", "119", "|", "58.33", "%", ")", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "(", "65", "|", "20.12", "%", ")", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "(", "5", "|", "11.63%)Nanotechnology", "and", "\n", "materials", "\n", "(", "29", "|", "43.28", "%", ")", "\n", "Nanotechnology", "and", "\n", "materials", "\n", "(", "44" ]
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Computer-Generated Text Detection Using Machine Learning: A Systematic Review Daria Beresneva(&) Moscow Institute of Physics and Technology, Russian Academy of National Economy and Public Administration, Anti-Plagiat Research, Moscow, Russia [email protected] Abstract. Computer-generated text or arti ficial text nowadays is in abundance on the web, ranging from basic random word salads to web scraping. In thispaper, we present a short version of systematic review of some existing auto- mated methods aimed at distinguishing natural texts from arti ficially generated ones. The methods were chosen by certain criteria. We further provide a sum-mary of the methods considered. Comparisons, whenever possible, use common evaluation measures, and control for differences in experimental set-up. Keywords: Artificial content /C1Generated text /C1Fake content detection 1 Introduction The biggest part of arti ficial content is generated for nourishing fake web sites designed to offset search engine indexes: at the scale of a search engine, usage of automaticallygenerated texts render such sites harder to detect than using copies of existing pages.Artificial content can contain text (word salad) as well as data plots, flow charts, and citations. The examples of automatically generated content include text translated by anautomated tool without human review or curation before publishing; text generatedthrough automated processes, such as Markov chains; text generated using automatedsynonymizing or obfuscation techniques. The aim of this paper is to review existing methods of arti ficial text detection. We survey these efforts, their results and their limitations. In spite of recent advances inevaluation methodology, many uncertainties remain as to the effectiveness oftext-generating filtering techniques and as to the validity of arti ficial text discovering methods. This is a short version of review according to poster publication rules. The rest of this paper is organized following systematic review guidelines. Section 2 presents the methods selected and their short description. A comparison of the methodsis available in Sect. 3. Section 4recaps our main findings and discusses various possible extensions of this work. ©Springer International Publishing Switzerland 2016 E. Métais et al. (Eds.): NLDB 2016, LNCS 9612, pp. 421 –426, 2016. DOI: 10.1007/978-3-319-41754-7_432 The Methods of Arti ficial Text Detection As said before, the way of arti ficial content detection depends on the method by which it was generated. Let us consider various existing features, that authors use for clas-sification. The information about results and datasets used for each method is given in the summary table. 2.1 Frequency Counting Method To discover whether a text
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[]
Stouffer, S., DeVinney, L., and Suchmen, E.: The American soldier: Adjustment during army life, vol. 1, Princeton University Press Princeton, USA, ISBN 978-0691056286, 1949. Tarvainen, T., Jarva, J., and Greiving, S.: Spatial pattern of hazards and hazard interactions in Europe, in: Natural and Technological Hazards and Risks Affecting the Spatial Development of Euro- pean Regions, edited by: Schmidt-Thomé, P., Geological Survey of Finland, Espoo, Finland, 42, 83–91, https://scholar.google. com/scholar_lookup?hl=en&volume=42&publication_year= 2006&journal=Special+Paper+of+the+Geological+Survey+of+ Finland&issue=83&author=T.+Tarvainen&author=J.+Jarva& author=S.+Greiving&title=Spatial+pattern+of+hazards+and+ hazard+interactions+in+Europe (last access: 16 January 2025), 2006. Tiberiu-79: Tiberiu-79/Spatial-identification-of-regions- exposed-to-multi-hazards-at-pan-European-level- EMHERG-: v2.0.0 (v2.0.0), Zenodo [code]m https://doi.org/10.5281/zenodo.14190957, 2024. Tiberiu-79: Spatial-identification-of-regions-exposed-to-multi- hazards-at-pan-European-level-EMHERG-, GitHub [data set], https://github.com/Tiberiu-79/Spatial-identification-of-regions- exposed-to-multi-hazards-at (last access: 16 January 2025), 2025. Tilloy, A., Malamud, B. D., Winter, H., and Joly-Laugel, A.: A review of quantification methodologies for multi- hazard interrelationships, Earth-Sci. Rev., 196, 102881, https://doi.org/10.1016/j.earscirev.2019.102881, 2019. UNISDR – UN International Strategy for Disaster Reduction: Liv- ing with Risk: A Global Review of Disaster Reduction Initiatives, United Nations, Geneva, https://www.undrr.org/publication/ https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025304 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level living-risk-global-review-disaster-reduction-initiatives? (last access: 17 January 2017), 2004. van Westen, C., Kappes, M. S., Luna, B. Q., Frigerio, S., Glade, T., and Malet, J. P.: Medium-Scale Multi-hazard Risk Assessment of Gravitational Processes, in: Mountain Risks: From Predic- tion to Management and Governance, Advances in Natural and Technological Hazards Research, vol. 34, edited by: Van Asch, T., Corominas, J., Greiving, S., Malet, J. P., and Sterlacchini S., Springer, Dordrecht, eBook ISBN 978-94-007-6769-0, 2014. van Westen, C. J., Montoya, L., Boerboom, L., and Badilla Coto, E.: Multi-hazard risk assessment using GIS in urban areas: a case study for the city of Turrialba, Costa Rica, 120–136, https://api. semanticscholar.org/CorpusID:129619800 (last access: 16 Jan- uary 2025), 2002. Ward, P. J., Daniell, J., Duncan, M., Dunne, A., Hananel, C., Hochrainer-Stigler, S., Tijssen, A., Torresan, S., Ciurean, R., Gill, J. C., Sillmann, J., Couasnon, A., Koks, E., Padrón- Fumero, N., Tatman, S., Tronstad Lund, M., Adesiyun, A., Aerts, J. C. J. H., Alabaster, A., Bulder, B., Campillo Torres, C., Critto, A., Hernández-Martín, R., Machado, M., Mysiak, J., Orth, R., Palomino Antolín, I., Petrescu, E.-C., Reichstein, M., Tiggeloven, T., Van Loon, A. F., Vuong Pham, H., and de Ruiter, M. C.: Invited perspectives: A research agenda to- wards disaster risk management pathways in multi-(hazard- )risk assessment, Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, 2022.Whitlock, M. C.: Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach, J. Evo- lution. Biol., 18, 1368–1373, 2005.
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[]
is not limited. But given lots of form, could O perhaps learn to keep producing seemingly meaningful responses to A’s utterances without learning meaning? The prob- lem is that people constantly generate new commu- nicative intents to talk about their constantly evolv- ing inner and outer worlds, and thus O would need to memorize infinitely many stimulus-response pairs. Such an approach may be an avenue towards high scores in evaluations where perfection is not expected anyway; but it is probably not an avenue towards human-analogous NLU. “But aren’t neural representations meaning too?” The internal representations of a neural network have been found to capture certain aspects of meaning, such as semantic similarity (Mikolov et al., 2013; Clark, 2015). As we argued in x4, se- mantic similarity is only a weak reflection of actual meaning. Neural representations neither qualify as standing meanings ( s), lacking interpretations, nor as communicative intents ( i), being insufficient to e.g. correctly build a coconut catapult. An interesting recent development is the emer- gence of models for unsupervised machine transla- tion trained only with a language modeling objec- tive on monolingual corpora for the two languages (Lample et al., 2018). If such models were to reach the accuracy of supervised translation models, this would seem contradict our conclusion that meaning cannot be learned from form. A perhaps surprising consequence of our argument would then be thataccurate machine translation does not actually re- quire a system to understand the meaning of the source or target language sentence. “But BERT improves performance on meaning- related tasks, so it must have learned something about meaning.” It has probably learned some- thing about meaning, in the same sense that syntax captures something about meaning and semantic similarity captures something about meaning: a potentially useful, but incomplete, reflection of the actual meaning. McCoy et al. (2019) and Niven and Kao (2019) provide cautionary tales about over- estimating what that “something” is purely based on evaluation results on existing tasks. What ex- actly BERT and its relatives learn about meaning is a very interesting question, and we look forward to further findings from the field of BERTology. 10 Conclusion In this paper, we have argued that in contrast to some current hype, meaning cannot be learned from form alone. This means that even large lan- guage models such as BERT do not learn “mean- ing”; they learn some reflection of meaning into
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communicative in- tent. Thus, standing meanings evolve over time as speakers can different experiences (e.g. McConnell- Ginet, 1984), and a reflection of such change can be observed in their changing textual distribution (e.g. Herbelot et al., 2012; Hamilton et al., 2016). 8 On climbing the right hills What about systems which are trained on a task that is not language modeling — say, semantic pars- ing, or reading comprehension tests — and that use word embeddings from BERT or some other large LM as one component? Numerous papers over the past couple of years have shown that using such pretrained embeddings can boost the accuracy of the downstream system drastically, even for tasks that are clearly related to meaning. Our arguments do not apply to such scenarios: reading comprehension datasets include informa- tion which goes beyond just form, in that they spec- ify semantic relations between pieces of text, and thus a sufficiently sophisticated neural model might learn some aspects of meaning when trained on such datasets. It also is conceivable that whatever information a pretrained LM captures might help the downstream task in learning meaning, without being meaning itself. Recent research suggests that it is wise to in- terpret such findings with caution. As noted in x2, both McCoy et al. (2019) and Niven and Kao (2019) found that BERT picked up idiosyncratic patterns in the data for their tasks, and not “mean- ing”. Beyond such diagnostic research on why large pretrained LMs boost such tasks so much, wethink there is a more fundamental question to be asked here: Are we climbing the right hill? 8.1 Top-down and bottom-up theory-building There are two different perspectives from which one can look at the progress of a field. Under a bottom-up perspective, the efforts of a scientific community are driven by identifying specific re- search challenges. A scientific result counts as a success if it solves such a specific challenge, at least partially. As long as such successes are frequent and satisfying, there is a general atmosphere of sustained progress. By contrast, under a top-down perspective, the focus is on the remote end goal of offering a complete, unified theory for the entire field. This view invites anxiety about the fact that we have not yet fully explained all phenomena and raises the question of whether all of our bottom-up progress leads us in the right direction. There is no doubt
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economy S. Khechinashvili University Clinic 6 Health and wellbeing; Electric and electronic technologies Children's New Clinic 4 Health and wellbeing Aversi Clinic 4 Health and wellbeing Evex Medical Corporation 3 Health and wellbeing; Electric and electronic technologiesTable 3.24. Top private actors in Georgia by number of records, across all domains Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation203 Moldova The National Academy of Sciences dominates sci- entific production in the country, followed at a dis- tance by the Technical University of Moldova and Moldova State University. The Nicolae Testemițanu State University of Medicine and Pharmacy is the most active institution in Health and wellbeing.Health and medical centres and institutes are again the main public actors, with the ‘Chiril Dra- ganiuc’ Institute of Phtisiopneumology standing out. Private actors account for very few records.Nanotechnology and materials Health and wellbeing Chemistry and chemical engineering Fundamental physics and mathematics Mechanical engineering and heavy machinery Governance, culture, education and the economy Biotechnology Electric and electronic technologies Environmental sciences and industries Agrifood Optics and photonics Energy ICT and computer science Academy of Sciences of Moldova 868 105 390 284 110 118 234 209 178 141 151 43 89 Technical University of Moldova 255 21 6 62 143 49 3 71 11 14 11 72 17 Moldova State University 206 15 92 85 45 65 79 50 32 23 26 10 5 Nicolae Testemitanu State University of Medicine and Pharmacy9 339 19 7 30 20 21 4 3 6 2 6 5 Transnistrian State University 54 12 6 12 3 59 7 4 31 3 13 2 0 Practical Scientific Institute of Horticulture and Food Technology2 9 1 0 20 2 17 0 1 44 0 0 2 Tiraspol State University 16 2 22 13 0 14 1 8 3 1 3 0 1 Institute of Agricultural Technique ‘Mecagro’10 0 0 2 41 0 0 8 0 5 2 4 3 Institute of Phtisiopneumology ‘Chiril Draganiuc’1 50 1 0 2 4 2 0 1 0 0 0 1 Academy of Economic Studies of Moldova4 3 1 1 1 34 0 0 1 0 0 1 0Figure 3.43. Top actors in Moldova by number of records (all types), across all domains 204 Part 3 Analysis of scientific and technological potential MOLDOVA Top 10 actors classified as ‘Public sector (excluding higher education and research institutions)’ NameNo of recordsMain S&T domains Institute of Phtisiopneumology ‘Chiril
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[]
was selected as a neutral method for assessing the general central tendency, specifically the median, among pairs of variables at the NUTS3 level. The Spearman shows the de- gree to which two variables tend to change in the same direc-tion. Therefore, variables with high correlation increase and decrease simultaneously, while variables with low absolute correlation rarely increase and decrease together. The results presented in Fig. 10 refer to the Spearman correlation coefficients between population exposed and the number of fatalities (a) and count of events (b) from the em- pirical data. We find a rather inconclusive relationship be- tween the multi-hazard risk data and the empirical data if we consider all regions for all significance levels. The scat- terplots suggest a positive correlation between the variables, but their increasing monotonic relationship is weak ( rD0:37 with fatalities and rD0:25 with the event count). However, if we consider only the regions with higher significance ( p<0:01,p<0:05,p<0:10), we notice a stronger correlation (Table 2 and Fig. 11). This means that going towards more significant clustering (hotspots/cold 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 299 Table 2. Spearman correlation coefficient between the empirical data (fatalities and count of past events) and the population exposed to multi-hazards for regions (NUTS3) with different significance levels. Variables pvalue<0:01pvalue<0:05pvalue<0:10 All regions Fatalities absolute 0.59 0.51 0.46 0.37 Count of events 0.30 0.40 0.35 0.25 Figure 11. Regions (NUTS3) exposed to multi-hazards identified with high significance, p<0:01(a, c) andp<0:10(b, d) , as hotspots/cold spots and their correlation coefficient (Spearman r) with independent variables: (a, b) empirical data – fatalities – and (c, d) empirical data – count of events. spots), the independent variables used for the validation tend to better follow the changes in value of the population ex- posed to multi-hazards. Therefore, the more significant the multi-hazard cluster- ing, the stronger the relationship with the independent vari- ables. The monotonic relationship is strong ( rD0:59) with fatalities as the independent variable for the regions with the highest significance ( p<0:01), while the for the event count, the strongest correlation ( rD0:40) is reached for the regions with the significance p<0:05. This makes the recorded data on fatalities a better explanatory variable for the clustered population exposed to multi-hazards.5 Discussions The identification of exposure or risk on the DRMKC RDH platform is generally
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on innovation activities at more highly aggregated levels; ■the DOCDB database of the European Pat- ent Office20 for aggregate patent data, which covers patents issued by most of the patent offices worldwide; ■the WIPO Global Brand Database21 for reg- istered trademark data for each Eastern Part- nership country. Trademarks are classified into 45 classes using the NICE Classification; ■the WIPO Global Design Database22 for in- dustrial design data for each Eastern Partner- ship country. Industrial designs are classified into 32 classes using the Locarno Classifica- tion; ■Crunchbase23, one of the world’s leading data sources for start-ups and venture cap- ital-backed companies. Crunchbase compiles data on companies’ industrial sectors, reve- 17 UNIDO, INDSTAT 4 Industrial Statistics Database at the 3- and 4-digit level of ISIC Revision 3 and ISIC Revision 4, Vienna, 2020. Available from http://stat.unido.org. 18 https://comtrade.un.org/. 19 Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank. 20 https://www.epo.org/searching-for-patents/data/bulk- data-sets/docdb.html. 21 https://www3.wipo.int/branddb/en/ 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. 22 https://www.wipo.int/reference/en/designdb/ 23 https://www.crunchbase.com/ 30 Part 1 Introduction and methodology nue, acquisition, funding and more via crowd- sourcing; ■the European Cluster Collaboration Plat- form24 (ECCP) and the assessment provided in the report Review of the state of devel- opment of clusters in EaP countries for an overview of sectoral industrial clusters in the various EaP countries. 2.5 Scientific and technological (S&T) potential and relative data sources In science, the notion of potential at international level goes hand in hand with that of excellence. The question here is in fact not only to under- stand whether there exists, in the EaP sectors and STI system, relative specialisation and/or critical mass in each preliminary priority domain, but also whether the agents possess the necessary capaci- ties to compete and collaborate in the internation- al arena. Excellence is, however, an even more elusive concept than potential, one which in the current context can only be indirectly related to bibliometric impact, for publications, and to the capacity of EaP actors to obtain Horizon 2020 funding – a high-quality threshold for any organisation, proving relevant STI capacities. Therefore, to assess the scientific and techno- logical potential, this study analyses a series of indicators that measure the critical mass and spe- cialisation of each EaP country
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the population ex- posed (between 50 %–60 %) is concentrated in city centres of middle-income metropolitan areas which are also the most populated. For the eastern European lower-income countries, the population exposed to multi-hazards is greater in the city centres compared with the functional urban areas: Latvia, Romania, and Poland ( >70 %) and Bulgaria, Slovenia, Slo- vakia, Hungary, and the Czech Republic (between 60 %– 70 %) (Fig. S24). However, we recognize that the intended comparison could be better explained through complex urban processes such as changing patterns of residential-choice be- haviour due to socioeconomic growth that we do not address in this work. 4 Validation The present validation is based on Spearman correlation analysis of the population exposed to multi-hazards with two empirical datasets as independent variables: the recorded DRMKC RDH data on fatalities from past events and the count of events with fatalities (for the period 1980–2019), for coastal floods, earthquakes, river floods, landslides, sub- sidence, and wildfires. The input data, both the popula- tion exposed to multi-hazards and the empirical data, are brought to a common geographical scale (NUTS3) and met- rics (Zscores andpvalues of clusters). We use the method- ological approach described in Sect. 2 to generate single- hazard hotspots (clusters). The single-hazard hotspots of em- pirical data (fatalities and event count) and population ex- https://doi.org/10.5194/nhess-25-287-2025 Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025298 T.-E. Antofie et al.: Spatial identification of regions exposed to multi-hazards at pan-European level Figure 9. Identified hotspot/cold-spot regions (NUTS3) with (a)population exposed to multi-hazards, (b)fatalities from multi-hazards, and (c)number of events with fatalities, used in Spearman correlation analysis for the purpose of validation. Figure 10. Spearman correlation between the multi-hazard clusters’ size ( Zscores) of population exposed with the empirical fatalities from past events (a)and the event count (b). posed to multi-hazards are combined through meta-analysis in order to obtain multi-hazard hotspots of fatalities, event counts, and hotspots of population exposed at the NUTS3 level (Fig. 9). Finally, hotspot/cold-spot regions of the two independent variables (fatalities and event count) are com- pared to the population exposed to multi-hazards. Using a correlation coefficient analysis, we aimed to cap- ture the strength of the relationship between the two paired datasets numerically. We employed a non-parametric test, the Spearman rank correlation test, due to its absence of distri- butional assumptions and its ability to capture monotonic re- lationships through rank-based computation. This approach
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a human language can do things like answer questions posed in the language about things in the world (or in this case, in pictures). In other words, what’s interesting here is not that the tasks are impossible, but rather what makes them impossible: what’s missing from the training data. The form of Java programs, to a system that has not observed the inputs and outputs of these programs, does not include information on how to execute them. Similarly, the form of English sentences, to a system that has not had a chance to acquire the meaning relation Cof English, and in the absence of any signal of communicative in- tent, does not include any information about what language-external entities the speaker might be re- ferring to. Accordingly, a system trained only on the form of Java or English has no way learn their respective meaning relations. 6 Human language acquisition One common reason for believing LMs might be learning meaning is the claim that human children can acquire language just by listening to it. This is not supported by scholarly work on language acquisition: rather, we find that human language learning is not only grounded in the physical world around us, but also in interaction with other people in that world. Kids won’t pick up a language from passive exposure such as TV or radio: Snow et al. (1976) note in passing that Dutch-speaking kids who watch German TV shows by choice nonethe- less don’t learn German. Kuhl (2007) shows exper- imentally that English-learning infants can learn Mandarin phonemic distinctions from brief interac-tions with a Mandarin-speaking experimenter but not from exposure to Mandarin TV or radio. Baldwin (1995) and others argue that what is critical for language learning is not just interaction but actually joint attention, i.e. situations where the child and a caregiver are both attending to the same thing and both aware of this fact. This theoreti- cal perspective is substantiated with experimental results showing that toddlers (observed at 15 and 21 months) whose caregivers “follow into” their attention and provide labels for the object of joint attention more have larger vocabularies (Tomasello and Farrar, 1986); that toddlers (18–20 months old) don’t pick up labels uttered by someone behind a screen, but do pick up labels uttered by some- one performing joint attention with them (Baldwin, 1995); and that at around 10–11 months of age ba- bies pay
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Activities of trade unions X X X 94.9 Activities of other membership organisations X X X 95 Repair of computers and personal and household goods 95.1 Repair of computers and communication equipment X X 95.2 Repair of personal and household goods 96 Other personal service activities X X X X TACTIVITIES OF HOUSEHOLDS AS EMPLOYERS; UNDIFFERENTIATED GOODS- AND SERVICES-PRODUCING ACTIVITIES OF HOUSEHOLDS FOR OWN USE 97 Activities of households as employers of domestic personnel 98Undifferentiated goods- and services-producing activities of private households for own use Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation295 296 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 98.1Undifferentiated goods-producing activities of private households for own use 98.2Undifferentiated service-producing activities of private households for own use U ACTIVITIES OF EXTRATERRITORIAL ORGANISATIONS AND BODIES 99 Activities of extraterritorial organisations and bodies Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation297 298 Annexes Annex 2. Results of the partial economic mapping analysis for Manufacturing for five EaP countriesAn ‘X’ in a yellow-coloured cell shows whether an industry passed an individual criterion, either for the number of employees (or employment) and turnover. An ‘X’ in a green-coloured cell shows whether an industry passed the criteria for both the number of employees (or employment) and turnover. ARMENIA AZERBAIJAN GEORGIA MOLDOVA UKRAINEEmploy- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover Employ- ment Turnover Employ- ment & turnover NACE Industry name Current Emerging Current Emerging Current Emerging Current Emerging Current Emerging 5 5 3 10 8 5 6 5 2 6 11 4 5 8 4 11 5 4 7 10 5 1 7 1
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Super laws and paramilitary groups watch over the world’s superheroes, which is a mix of that schtick ending, Planet Of The Apes II bit, and the Batman/Venom bit of last appeared in The Seventh Seal when Chris O’Donnell infiltrated one of the teams at some point, also wearing Staff. Machine He is considered to be the most terrifying man on the planet and people stay away from him. A guy asks him to do something and he says, ”My girlfriend’s so important to me... I don’t need to fight her any more.” And then, boom, there’s some in a corner crying inappropriately. Men: It’s gone in five minutes. Why do I have to be so sad? It’s cute,” says female member, who asks to remain anonymous. ”It’s what grew up to drive me crazy when I was a kid, seeing these women become the nurturing, wealthy things they are in this professional world I truly love.” And it’s nothing to do with her success. These men still actively fear being around the idea of a woman who might win Oscars, make movies or be audacious drivers. Human Dropbox and Google Drive are very different services that appeal to different users. While Drive is connected to the entire Google Apps (now known as G Suite) ecosystem, Dropbox is a lightweight, simple alternative for file storage. While both are useful, users need to look beyond features, and make sure the service they choose can adequately protect their data. Here’s how Dropbox encryption and Google Drive encryption stack up. Dropbox and Google Drive Encryption To their credit, both Dropbox and Google Drive protect user files with encryption. Both also allow users to enable two-step verification, which requires an extra code texted to the user’s phone to access the account, making it harder for hackers to access a user’s data. Human EVE Isk Per Hour(Eveiph) is hands down the best tool I’ve ever used to make isk in New Eden. It is a market helper program that is able to do a great deal of the work that is typically done by a traders spreadsheet. I’ve used it to go from a 200m/month trading income to 3b/month on my main trading character. Above you can see the blueprint manufacturing page which is located on the first tab of Eveiph. Here you can see the components required to make an item, the settings for the
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growth in Europe will stall. If the EU were to maintain its average labour productivity growth rate since 2015 of 0.7%, it would only be enough to keep GDP constant until 2050. In an environment of historically high public debt-to-GDP ratios, potentially higher real interest rates than seen in the last decade and rising spending needs for the decarbonisation, digitalisation and defence, stagnant GDP growth could eventually lead to public debt levels becoming unsustainable and Europe being forced to give up one or more of these goals. FIGURE 1 EU versus US labour productivity 1890-2022 Index (US=100) Note: The EU is proxied by backdating national accounting data from Germany, France, Italy, Spain, the Netherlands, Belgium, Ireland, Austria, Portugal, Finland and Greece. To build the labour productivity data, five different series were used: GDP, capital stock, employment, average hours worked, and population. Capital stock is built using two series of investment – construction and equipment. Investment and GDP are taken in volume and in national currency of 2010, they are then turned into $2010 using a ppp conversion rate. Source: Bergeaud, A., Cette, G., & Lecat, R., Productivity Trends in Advanced Countries between 1890 and 2012, Review of Income and Wealth, Vol. 62, No. 3, 2016, pp. 420-444 01. Measured in 2010 constant PPP prices. 23THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2The key driver of the rising productivity gap between the EU and the US has been digital technology (“tech”) – and Europe currently looks set to fall further behind . The main reason EU productivity diverged from the US in the mid-1990s was Europe’s failure to capitalise on the first digital revolution led by the internet – both in terms of generating new tech companies and diffusing digital tech into the economy. In fact, if we exclude the tech sector, EU productivity growth over the past twenty years would be broadly at par with the US [see Figure 2 and Box 2] . Europe is lagging in the breakthrough digital technologies that will drive growth in the future. Around 70% of foundational AI models have been developed in the US since 2017 and just three US “hyperscalers” account for over 65% of the global as well as of the European cloud market. The largest European cloud operator accounts for just 2% of the EU market. Quantum computing is poised to be the next major innovation, but
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[]
Nat. Hazards Earth Syst. Sci., 25, 287–304, 2025 https://doi.org/10.5194/nhess-25-287-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Spatial identification of regions exposed to multi-hazards at the pan-European level Tiberiu-Eugen Antofie1,a, Stefano Luoni2, Aloïs Tilloy2, Andrea Sibilia3, Sandro Salari3, Gustav Eklund2, Davide Rodomonti3, Christos Bountzouklis2, and Christina Corbane2 1independent researcher 2Joint Research Centre, European Commission, 21027 Ispra, Italy 3Uni Systems (external consultancy for the European Commission), Milan, Italy aformally at: Joint Research Centre, European Commission, 21027 Ispra, Italy Correspondence: Tiberiu-Eugen Antofie (tiberiuantofi[email protected]) Received: 29 December 2023 – Discussion started: 5 January 2024 Revised: 12 August 2024 – Accepted: 13 November 2024 – Published: 20 January 2025 Abstract. The European Commission Disaster Risk Man- agement Knowledge Centre (DRMKC) has developed and hosts a web platform, the Risk Data Hub (RDH), designed to facilitate access to and sharing of curated, EU-wide risk data, tools, and methodologies, ultimately supporting disas- ter risk management (DRM) initiatives. Based on the RDH data, we propose a methodology for the identification of re- gions with multi-hazard exposure at the pan-European level (EU27+UK). This methodology aims to support disaster risk management (DRM) decision-making processes at both na- tional and subnational levels in the EU. By employing a meta-analysis approach and aggregating the hotspots of ex- posure to single hazards, we provide an objective, statisti- cally robust assessment of the European multi-hazard land- scape at the finest spatial subdivision level, local adminis- trative units (LAUs). Our results suggest that 21.4 % of Eu- ropean LAUs are exposed to multiple natural hazards, af- fecting around 87 million people (18.8 % of the European population). Furthermore, nearly half this population is ex- posed to more than three hazards. We find that beyond pop- ulation density, the income level (i.e. high, medium, low) is the primary driver that influences risk status at the local level, within both rural and urban areas. On average, we find higher multi-hazard exposure for people living in high-income ur- ban areas or low-income rural areas. We further validate our results by comparing them with empirical data on fatalities and disaster events, revealing a relatively high correlation between statistically significant multi-hazard hotspots and fatalities (rD0:59). By providing a detailed assessment ofmulti-hazard exposure at the pan-European scale, this study contributes to a better integration of multi-hazard risks in Eu- ropean disaster risk management plans. 1 Introduction Since the beginning of the 21st century, several
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3105 Instrumentation 467 105.37%Table 3.7. The Scopus subject fields that appear more frequently within each domain in comparison with the average publications 160 Part 3 Analysis of scientific and technological potential Domain ASJC Description No recordsRelative freq. Energy2213 Safety, risk, reliability and quality 294 290.13% 2102 Energy engineering and power technology 1 140 263.71% 2104 Nuclear energy and engineering 516 189.43% 2200 General engineering 454 188.25% 2103 Fuel technology 84 166.34% Environmental sciences and industries1105 Ecology, evolution, behavior and systematics 1 863 586.38% 2300 General environmental science 434 573.21% 1909 Geotechnical engineering and engineering geology 1 015 531.02% 1110 Plant science 612 392.73% 1900 General earth and planetary sciences 370 383.42% Fundamental physics and mathematics2600 General mathematics 4 206 933.35% 3106 Nuclear and high energy physics 5 979 566.81% 2604 Applied mathematics 3 190 497.80% 3103 Astronomy and astrophysics 2 127 477.76% 2613 Statistics and probability 1 601 472.83% Governance, culture, education and the economy1000 Multidisciplinary 200 247.62% 1405 Management of technology and innovation 585 203.41% 2000 General economics, econometrics and finance 1 023 199.42% 2002 Economics and econometrics 2 160 198.53% 3316 Cultural studies 189 197.91% Health and wellbeing2700 General medicine 4 361 624.00% 1311 Genetics 743 348.17% 1314 Physiology 470 283.70% 1300 General biochemistry, genetics and molecular biology 630 283.36% 1312 Molecular biology 220 275.69% ICT and computer science1710 Information systems 1 147 449.80% 1706 Computer science applications 1 809 406.87% 1705 Computer networks and communications 2 101 402.88% 2207 Control and systems engineering 1 431 305.99% 1700 General computer science 1 647 268.94% Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation161 Domain ASJC Description No recordsRelative freq. Mechanical engineering and heavy machinery2200 General engineering 684 283.62% 2210 Mechanical engineering 1 458 217.31% 2611 Modelling and simulation 19 165.22% 2502 Biomaterials 21 155.56% 2209 Industrial and manufacturing engineering 586 149.57% Nanotechnology and materials3104 Condensed matter physics 9 900 708.48% 2508 Surfaces, coatings and films 2 000 668.26% 2500 General materials science 6 602 552.43% 2504 Electronic, optical and magnetic materials 5 640 527.96% 1600 General chemistry 2 800 450.46% Optics and photonics3107 Atomic and molecular physics, and optics 1 456 220.61% 2504 Electronic, optical and magnetic materials 2 169 203.04% 2208 Electrical and electronic engineering 2 562 185.55% 3102 Acoustics and ultrasonics 44 166.04% 3109 Statistical and nonlinear physics 73 139.31% Transportation2606 Control and optimization 138 267.10% 3313 Transportation 135 197.08% 2202 Aerospace
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[]
F02B; B62D 30Manufacture of other transport equipmentTransportation B64C; B63B; B64G 32 Other manufacturing Biotechnology A61K 32 Other manufacturing Chemistry and chemical engineering A61K 32 Other manufacturingGovernance, culture, education and the economyA61B; A63B; G09B 338 Annexes UKRAINE Concordances between NACE sectors and the intersection of IPC classes & S&T domains NACE sector S&T domain Mapping 32 Other manufacturing Health and wellbeing A61K; A61B 32 Other manufacturing ICT and computer science A61B 32 Other manufacturingMechanical engineering and heavy machineryA61B 32 Other manufacturing Optics and photonics A61B 62Computer programming, consultancy and related activitiesGovernance, culture, education and the economyG06Q 62Computer programming, consultancy and related activitiesICT and computer science G06Q Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation339 Annex 9. NABS 2007 to NACE v2 correspondence tableThe table below shows the correspondence be- tween NABS 2007 socio-economic objectives and three-digit NACE sectors75 that we used to derive the mapping between ASJC Scopus subject fields and NACE sectors. 75 Stancik, J., A methodology for estimating public ICT R&D expenditures in the EU, JRC, 2012. NABS NACE NABS01Exploration and exploitation of the earth6 Extraction of crude petroleum and natural gas; 6.1 Extraction of crude petroleum; 6.2 Extraction of natural gas; 7 Mining of metal ores; 7.1 Mining of iron ores; 7.2 Mining of non-ferrous metal ores; 8 Other mining and quarrying; 8.1 Quarrying of stone, sand and clay; 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 NABS02 Environment38 Waste collection, treatment and disposal activities; materials recovery; 38.1 Waste collection; 38.2 Waste treatment and disposal; 38.3 Materials recovery; 39 Remediation activities and other waste management services; 39.0 Remediation activities and other waste management services NABS04Transport, telecommunication and other infrastructures30.2 Manufacture of railway locomotives and rolling stock; 30.3 Manufacture of air and spacecraft and related machinery; 36 Water collection, treatment and supply; 36.0 Water collection, treatment and supply; 37 Sewerage; 37.0 Sewerage; 41 Construction of buildings; 41.1 Development of building projects; 41.2 Construction of residential and non- residential buildings; 42 Civil engineering; 42.1 Construction of roads and railways; 42.2 Construction of utility projects; 42.9 Construction of other civil engineering projects; 43 Specialised construction activities; 43.1 Demolition and site preparation; 43.2 Electrical, plumbing and other construction installation activities; 43.3 Building completion and finishing; 43.9 Other specialised construction activities; 49.1 Passenger rail transport, interurban; 49.2 Freight rail
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[]
heavy equipment manufacturer NKMZ, with the remaining top private actors being in the domains of either Nanotechnology and ma- terials or Mechanical engineering.Nanotechnology and materials Fundamental physics and mathematics Biotechnology Health and wellbeing ICT and computer science Environmental sciences and industries Chemistry and chemical engineering Optics and photonics Electric and electronic technologies Governance, culture, education and the economy Mechanical engineering and heavy machinery Energy Agrifood Transportation National Academy of Sciences of Ukraine11 329 7 876 4 955 3 092 1 870 3 577 2 536 2 674 1 790 1 482 1 930 1 585 637 322 Taras Schevchenko National University of Kyiv2 077 1 982 1 487 746 597 794 999 505 271 870 139 123 171 33 National Technical University of Ukraine ‘Igor Sikorsky Kyiv Polytechnic Institute’1 113 809 294 244 1 089 215 203 414 667 337 677 584 50 166 National University Lviv Polytechnic1 235 581 508 117 1 164 180 173 190 432 558 339 326 49 134 National University of Kharkiv979 1 071 421 281 343 207 461 556 503 274 74 107 42 21 National Science Center Kharkiv Institute of Physics and Technology900 2 219 56 90 132 57 45 32 414 32 68 248 21 11 Ivan Franko National University of Lviv1 195 749 311 172 125 172 212 74 89 254 111 64 46 7 National Academy of Medical Sciences of Ukraine54 26 256 2 088 32 34 44 19 19 52 46 18 9 3 National Aviation University165 158 74 45 713 65 19 80 172 290 231 99 21 328 National University of Food Technologies102 53 296 20 27 45 173 14 41 66 372 126 881 14Figure 3.44. Top actors in Ukraine by number of records (all types), across all domains 206 Part 3 Analysis of scientific and technological potential UKRAINE Top 10 actors classified as ‘Public sector (excluding higher education and research institutions) NameNo of recordsMain S&T domains National Cancer Institute 288Health and wellbeing; Biotechnology; Environmental sciences and industries State Enterprise Research Institute Kvant227Electric and electronic technologies; Optics and photonics; Mechanical engineering and heavy machinery State Enterprise Yuzhnoye Design Office named after Mikhail Yangel119ICT and computer science; Transportation; Mechanical engineering and heavy machinery State Scientific and Technical Center for Nuclear and Radiation Safety119Energy; Governance, culture, education and the economy; ICT and computer science Ministry of Education and Science of Ukraine87ICT and computer science; Nanotechnology and materials; Fundamental
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[]
market, assuming equivalency with the same product marketed abroad. Yet, the lack of information on DFQ in the domestic product version can distort their utility, impacting their choices and preventing informed transactional decisions. For this reason, some stakeholders advocated introducing claims that provide a distinct warning to consumers about dual quality in food products (HMoA, 2017) or for including a logo to indicate consistent content and quality of branded products across Member States (EP, 2018). In the context of DFQ, such a claim could reduce asymmetric information, enhance consumer protection, and improve welfare (Russo et al., 2020 ). Therefore, in a second set of hypotheses, we test the use of a neutral ‘made for’ claim that informs consumers without providing any positive or negative connotations.3We expect the ‘made for’ claim to shape consumer expectations about quality attributes tied to specific markets (Graham et al., 2012 ) and to serve as a potential policy instrument to remove information asymmetry. We test these hypotheses by presenting the following propositions: H2a: The presence of a ‘made for’ claim changes consumers ’ WTP for both the generic (H2 a,G) and branded (H2 a,B) domestic versions. H2b: The presence of a ‘made for’ claim changes Eastern European consumers ’ WTP for both the generic (H2 b,G) and branded (H2 b,B) Western-country versions, and vice versa. H2c: The presence of a ‘made for’ claim changes consumers ’ perceived taste rating for the domestic versions. H2d: The presence of a ‘made for’ claim changes Eastern European consumers ’ perceived taste towards Western-country versions, and vice versa. 1An illustrative representation of the adapted Total Food Quality Model is presented in Fig. 1in the Appendix A.2In the present study we use the term generic to indicate products that were presented without a brand name. Similarly, the term branded was used to refer to products presented with a brand name. 3It is important to note that the use of the specific claim ‘made for country X’ is merely experimental, and other forms of claims should be tested to determine the most effective way to convey the message to consumers.D.M. Federica et al. Food Policy 131 (2025) 102803 2 While these hypotheses do not specify direction, potential trends can be inferred. Indeed, the use of a ‘made for’ claim could activate ste- reotypes associated with specific countries (Poppe and Linssen, 1999 ), impacting consumer preferences. Products made in more developed countries
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scintillators, as discussed previously. In order to remove the background caused by prompt γ- rays and neutrons and to keep only γ-rays from isomers, a time cut was applied to the analyzed data. From the neutrons’time-of-flight from the chamber to the LaBr 3(Ce) detectors (see Table 1for the distances), it was calculated that the con- tribution from neutrons having a kinetic energy En/greaterorsimilar100 keV was gated out by selecting only γ-rays detected more than 35 ns after fission. Hence, in the following late emission γ- rays will refer to γ-rays emitted after this 35 ns threshold. It is noteworthy that for such times the fission fragments werealready stopped inside the chamber, hence no Doppler shift was expected for the late emission γ-rays. In total, 8 .2×10 6 FF-γ-γcoincidence events were detected between 35 ns and 100 µs after fission. The isomers found in the VESPA coincidence data were identified using the NuDat database [ 13,14], based on ENSDF Adopted Levels and Gamma files. This analysis wasbased on the energy and time information of the detected γ- rays. For instance, γ-γmatrices with different time cuts are shown in Fig. 4. Several spots in these matrices are visible, corresponding to abundant γ-γcoincidences from isomeric γcascades that could be easily identified. The identification could be confirmed by studying the post-neutron mass distribution of the corresponding fission 123 5 Page 4 of 12 Eur. Phys. J. A (2025) 61:5 Fig. 4 γ-γcoincidence matrix obtained with VESPA at different time cuts relative to fission, between 50 and 450 keV fragments obtained from the IC. Indeed, when gating on a γ−γcoincidence corresponding to one particular isomer, the resulting mass distribution consisted of two Gaussians,as shown in Fig. 5. The two peaks correspond to the emit- ting fragment and its complementary one, both smeared by the mass resolution of the IC. In contrast, no distinct peak came from coincidences that did not originate from a spe-cific fragment. This was, for instance, the case of the γ-γ coincidence from 81Br that came from the LaBr 3(Ce) scin- tillator. This nucleus could be excited up to an isomeric state(E ∗=536 keV and T1/2=34.6µs) by the inelastic scat- tering of prompt fission neutrons in the crystals. The mass distribution associated with such a spurious coincidence fol-lowed the Y(A post)distribution represented in Fig. 5.Fig. 5 Mass distributions corresponding to95Sr (red) and134Te (blue) isomers (and their complementary fragments, which are lighter shaded)from
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[]
early-stage VC for hydrogen and fuel cells, but this share declined to 10% from 2020 to 2022. The clean tech sector is suffering from the same barriers to innovation, commercialisation and scaling up in Europe that afflict the digital sector: a total of 43% and 55% of medium and large companies, respectively, cite consistent regulation within the Single Market as the main way to foster commercialisation, while 43% of small companies identify lack of finance as an obstacle to growthix. As in the digital sector, the lower capacity of EU clean tech companies to scale up leads to a gap between the EU and US in later-stage funding. Europe’s innovation potential is not translating into manufacturing superiority for clean tech, despite the size of its domestic market . The EU is the second largest market in terms of demand for solar PV, wind and EVs. In many of these sectors, the EU has enjoyed an industrial “first-mover” advantage and has established leadership, but it has not been able to maintain that lead consistently. In certain sectors, such as solar PV, the EU has already lost its manufacturing capacities, with production now dominated by China [see Figure 7] . In others, such as wind power generation equipment, Europe has a solid position but is facing increasing challenges. For example, although Europe retains primacy in wind turbine assembly – serving 85% of domestic demand and acting as a net exporter – it has lost significant market shares to China in last few years, declining from 58% in 2017 to 30% in 2022. In several sectors the EU retains its technological edge, such as electrolysers and carbon capture and storage. But many EU players still prefer to produce at scale in China owing to higher construction costs in Europe, permitting delays and more restricted access to critical raw materials. For example, electrolyser production requires at least 40 raw materials and the EU currently produces just 1-5% of these domestically. Overall, despite the EU’s ambition to maintain and develop clean tech manufacturing capacity, there are multiple signs of an evolution in the opposite direction, with EU companies announcing production cuts, shutdowns and partial or full relocation. FIGURE 7 Clean technology manufacturing capacity by region %, 2021 Source: European Commission, 2024. Based on IEA, Bruegel. 46THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 3The threat to Europe’s position in clean tech owes mainly
[ " ", "early", "-", "stage", "VC", "for", "hydrogen", "and", "fuel", "cells", ",", "but", "this", "share", "declined", "to", "10", "%", "from", "2020", "to", "2022", ".", "The", "clean", "tech", "\n", "sector", "is", "suffering", "from", "the", "same", "barriers", "to", "innovation", ",", "commercialisation", "and", "scaling", "up", "in", "Europe", "that", "afflict", "the", "\n", "digital", "sector", ":", "a", "total", "of", "43", "%", "and", "55", "%", "of", "medium", "and", "large", "companies", ",", "respectively", ",", "cite", "consistent", "regulation", "within", "\n", "the", "Single", "Market", "as", "the", "main", "way", "to", "foster", "commercialisation", ",", "while", "43", "%", "of", "small", "companies", "identify", "lack", "of", "finance", "\n", "as", "an", "obstacle", "to", "growthix", ".", "As", "in", "the", "digital", "sector", ",", "the", "lower", "capacity", "of", "EU", "clean", "tech", "companies", "to", "scale", "up", "leads", "\n", "to", "a", "gap", "between", "the", "EU", "and", "US", "in", "later", "-", "stage", "funding", ".", "\n", "Europe", "’s", "innovation", "potential", "is", "not", "translating", "into", "manufacturing", "superiority", "for", "clean", "tech", ",", "despite", "the", "\n", "size", "of", "its", "domestic", "market", ".", "The", "EU", "is", "the", "second", "largest", "market", "in", "terms", "of", "demand", "for", "solar", "PV", ",", "wind", "and", "EVs", ".", "\n", "In", "many", "of", "these", "sectors", ",", "the", "EU", "has", "enjoyed", "an", "industrial", "“", "first", "-", "mover", "”", "advantage", "and", "has", "established", "leadership", ",", "\n", "but", "it", "has", "not", "been", "able", "to", "maintain", "that", "lead", "consistently", ".", "In", "certain", "sectors", ",", "such", "as", "solar", "PV", ",", "the", "EU", "has", "already", "\n", "lost", "its", "manufacturing", "capacities", ",", "with", "production", "now", "dominated", "by", "China", "[", "see", "Figure", "7", "]", ".", "In", "others", ",", "such", "as", "wind", "\n", "power", "generation", "equipment", ",", "Europe", "has", "a", "solid", "position", "but", "is", "facing", "increasing", "challenges", ".", "For", "example", ",", "although", "\n", "Europe", "retains", "primacy", "in", "wind", "turbine", "assembly", "–", "serving", "85", "%", "of", "domestic", "demand", "and", "acting", "as", "a", "net", "exporter", "–", "\n", "it", "has", "lost", "significant", "market", "shares", "to", "China", "in", "last", "few", "years", ",", "declining", "from", "58", "%", "in", "2017", "to", "30", "%", "in", "2022", ".", "In", "several", "\n", "sectors", "the", "EU", "retains", "its", "technological", "edge", ",", "such", "as", "electrolysers", "and", "carbon", "capture", "and", "storage", ".", "But", "many", "EU", "\n", "players", "still", "prefer", "to", "produce", "at", "scale", "in", "China", "owing", "to", "higher", "construction", "costs", "in", "Europe", ",", "permitting", "delays", "and", "\n", "more", "restricted", "access", "to", "critical", "raw", "materials", ".", "For", "example", ",", "electrolyser", "production", "requires", "at", "least", "40", "raw", "materials", "\n", "and", "the", "EU", "currently", "produces", "just", "1", "-", "5", "%", "of", "these", "domestically", ".", "Overall", ",", "despite", "the", "EU", "’s", "ambition", "to", "maintain", "and", "\n", "develop", "clean", "tech", "manufacturing", "capacity", ",", "there", "are", "multiple", "signs", "of", "an", "evolution", "in", "the", "opposite", "direction", ",", "with", "\n", "EU", "companies", "announcing", "production", "cuts", ",", "shutdowns", "and", "partial", "or", "full", "relocation", ".", "\n", "FIGURE", "7", "\n", "Clean", "technology", "manufacturing", "capacity", "by", "region", " \n", "%", ",", "2021", "\n", "Source", ":", "European", "Commission", ",", "2024", ".", "Based", "on", "IEA", ",", "Bruegel", ".", "\n", "46THE", "FUTURE", "OF", "EUROPEAN", "COMPETITIVENESS", " ", "—", "PART", "A", "|", "CHAPTER", "3The", "threat", "to", "Europe", "’s", "position", "in", "clean", "tech", "owes", "mainly" ]
[]
their companies adopt technology, or into good jobs in new sectors. The EU will also have to ensure that its cohesion policy remains consistent with a push towards increasing innovation and completing the Single Market . Accelerating innovation and integrating the Single Market may have different effects on intra-EU convergence than in the past. Traditionally, increasing intra-EU trade in goods has acted as a “convergence engine”, spreading prosperity to poorer regions as supply chains relocate where produc - tion factors are cheaperxvii. However, much of the future growth in intra-EU trade will be in services, which tend to cluster in large and rich cities. Innovation and its benefits also tend to agglomerate in a few metropolitan areas. In the US, for example, a small set of superstar cities has been thriving in recent years and pulling away from the rest of the country. In 1980, average earnings in the top three US cities were 8% higher than average earnings in the rest of the top 10 cities. By 2016, average earnings in the same top three cities were 25% higherxviii. While the EU has a longstanding tradition of programmes that foster convergence across regions, these programmes should be updated to reflect the changing dynamics of trade and innovation. The EU must ensure that more cities and regions can participate in the sectors that will drive future growth, building on existing initiatives such as Innovation Valleys Net, Zero Acceleration Valleys and Hydrogen Valleys. This will require new types of investments in cohesion and reforms at the subnational level in many Member States. Specifically, cohesion policies will need to be re-focused on areas such as education, transport, housing, digital connectivity and planning which can increase the attractiveness of a range of different cities and regions. Europe should learn from the mistakes that were made in the phase of “hyper-globalisation” and prepare for a fast-changing future . Globalisation brought many benefits for the European economy as well as lifting hundreds of millions out of poverty around the world. But policymakers were arguably too insensitive to its perceived social consequences, especially its apparent effect on labour income. In G7 economies, total exports and imports of goods as a share of GDP increased by around 9 percentage points from the early 1980s to the great financial crisis, while the labour share of income dropped around 6 percentage points in that time – the steepest drop
[ "their", "companies", "adopt", "technology", ",", "or", "into", "good", "jobs", "in", "new", "sectors", ".", "\n", "The", "EU", "will", "also", "have", "to", "ensure", "that", "its", "cohesion", "policy", "remains", "consistent", "with", "a", "push", "towards", "increasing", "\n", "innovation", "and", "completing", "the", "Single", "Market", ".", "Accelerating", "innovation", "and", "integrating", "the", "Single", "Market", "may", "\n", "have", "different", "effects", "on", "intra", "-", "EU", "convergence", "than", "in", "the", "past", ".", "Traditionally", ",", "increasing", "intra", "-", "EU", "trade", "in", "goods", "has", "\n", "acted", "as", "a", "“", "convergence", "engine", "”", ",", "spreading", "prosperity", "to", "poorer", "regions", "as", "supply", "chains", "relocate", "where", "produc", "-", "\n", "tion", "factors", "are", "cheaperxvii", ".", "However", ",", "much", "of", "the", "future", "growth", "in", "intra", "-", "EU", "trade", "will", "be", "in", "services", ",", "which", "tend", "to", "\n", "cluster", "in", "large", "and", "rich", "cities", ".", "Innovation", "and", "its", "benefits", "also", "tend", "to", "agglomerate", "in", "a", "few", "metropolitan", "areas", ".", "In", "\n", "the", "US", ",", "for", "example", ",", "a", "small", "set", "of", "superstar", "cities", "has", "been", "thriving", "in", "recent", "years", "and", "pulling", "away", "from", "the", "rest", "\n", "of", "the", "country", ".", "In", "1980", ",", "average", "earnings", "in", "the", "top", "three", "US", "cities", "were", "8", "%", "higher", "than", "average", "earnings", "in", "the", "rest", "\n", "of", "the", "top", "10", "cities", ".", "By", "2016", ",", "average", "earnings", "in", "the", "same", "top", "three", "cities", "were", "25", "%", "higherxviii", ".", "While", "the", "EU", "has", "a", "\n", "longstanding", "tradition", "of", "programmes", "that", "foster", "convergence", "across", "regions", ",", "these", "programmes", "should", "be", "updated", "\n", "to", "reflect", "the", "changing", "dynamics", "of", "trade", "and", "innovation", ".", "The", "EU", "must", "ensure", "that", "more", "cities", "and", "regions", "can", "\n", "participate", "in", "the", "sectors", "that", "will", "drive", "future", "growth", ",", "building", "on", "existing", "initiatives", "such", "as", "Innovation", "Valleys", "Net", ",", "\n", "Zero", "Acceleration", "Valleys", "and", "Hydrogen", "Valleys", ".", "This", "will", "require", "new", "types", "of", "investments", "in", "cohesion", "and", "reforms", "\n", "at", "the", "subnational", "level", "in", "many", "Member", "States", ".", "Specifically", ",", "cohesion", "policies", "will", "need", "to", "be", "re", "-", "focused", "on", "areas", "\n", "such", "as", "education", ",", "transport", ",", "housing", ",", "digital", "connectivity", "and", "planning", "which", "can", "increase", "the", "attractiveness", "of", "a", "\n", "range", "of", "different", "cities", "and", "regions", ".", "\n", "Europe", "should", "learn", "from", "the", "mistakes", "that", "were", "made", "in", "the", "phase", "of", "“", "hyper", "-", "globalisation", "”", "and", "prepare", "for", "\n", "a", "fast", "-", "changing", "future", ".", "Globalisation", "brought", "many", "benefits", "for", "the", "European", "economy", "as", "well", "as", "lifting", "hundreds", "\n", "of", "millions", "out", "of", "poverty", "around", "the", "world", ".", "But", "policymakers", "were", "arguably", "too", "insensitive", "to", "its", "perceived", "social", "\n", "consequences", ",", "especially", "its", "apparent", "effect", "on", "labour", "income", ".", "In", "G7", "economies", ",", "total", "exports", "and", "imports", "of", "goods", "\n", "as", "a", "share", "of", "GDP", "increased", "by", "around", "9", "percentage", "points", "from", "the", "early", "1980s", "to", "the", "great", "financial", "crisis", ",", "while", "the", "\n", "labour", "share", "of", "income", "dropped", "around", "6", "percentage", "points", "in", "that", "time", "–", "the", "steepest", "drop" ]
[]
Analytics; Financial Services6 J63 – Information service activitiesInformation Technology; Internet Services; Software; Apps5 K64 – Financial service activities, except insurance and pension fundingFinancial Services; Lending and Investment; Payments,3 M71 – Architectural and engineering activities; technical testing and analysisScience and Engineering 1 M72 – Scientific research and development Science and Engineering 1 M73 – Advertising and market researchSales and Marketing; Data Analytics2 N79 – Travel agency, tour operator and other reservation service and related activitiesTravel and Tourism 2Table 2.55. Concordance between NACE industries and recommended industry groups for start-ups and venture capital-backed companies The table shows a preliminary concordance between NACE sectors and Crunchbase Industry Groups. The right column represents the number of different countries with one or more selected industry groups from the middle column. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation117 Armenia Azerbaijan Belarus Georgia Moldova Ukraine Total ECCP’s Cluster Mapping Tool 1 0 3 4 2 23 33 Additional clusters in the EaP PLUS report3 1 1 1 1 1 8Table 2.56. Number of cluster organisations by EaP country The following sections present all of the identified organisation clusters by country, first those fea- tured in the Cluster Mapping Tool and then those featured in the EaP PLUS report, as well as some additional considerations. A final section includes a table featuring the num- ber of clusters per sector for each country. Armenia In the Cluster Mapping Tool, Armenia presents one single cluster: Green Energy Cluster, with 19 member organisations, and Barva Innovation as its technological centre. The cluster is related to the sectors of Environmental services and Educa- tion and knowledge creation. The EaP PLUS report also highlights two clusters in the field of IT: the Gyumri Technology Center and the Vanadzor Technology Center. Addition- ally, the Armenian Society of Food Science and Technologies is also considered a cluster in the fields of Food and IT. As of 2017, the Government of the Republic of Armenia had not yet formed specific cluster-re- lated policies – but SME development, research and innovation structure development – and pub- lic-private partnership facilitation in several key fields has been a strong government policy focus in past years. Azerbaijan Azerbaijan currently has no clusters featured on the ECCP mapping platform. The EaP PLUS report highlights a single cluster or- ganisation in the field of Energy: the Petrochem- ical cluster of Azerbaijan. It is led by SOCAR
[ "Analytics", ";", "Financial", "\n", "Services6", "\n", "J63", "–", "Information", "service", "activitiesInformation", "Technology", ";", "\n", "Internet", "Services", ";", "Software", ";", "\n", "Apps5", "\n", "K64", "–", "Financial", "service", "activities", ",", "except", "insurance", "and", "\n", "pension", "fundingFinancial", "Services", ";", "Lending", "\n", "and", "Investment", ";", "Payments,3", "\n", "M71", "–", "Architectural", "and", "engineering", "activities", ";", "technical", "\n", "testing", "and", "analysisScience", "and", "Engineering", "1", "\n", "M72", "–", "Scientific", "research", "and", "development", "Science", "and", "Engineering", "1", "\n", "M73", "–", "Advertising", "and", "market", "researchSales", "and", "Marketing", ";", "Data", "\n", "Analytics2", "\n", "N79", "–", "Travel", "agency", ",", "tour", "operator", "and", "other", "reservation", "\n", "service", "and", "related", "activitiesTravel", "and", "Tourism", "2Table", "2.55", ".", "Concordance", "between", "NACE", "industries", "and", "recommended", "industry", "groups", "for", "start", "-", "ups", "and", "venture", "\n", "capital", "-", "backed", "companies", "\n", "The", "table", "shows", "a", "preliminary", "concordance", "between", "NACE", "sectors", "and", "Crunchbase", "Industry", "Groups", ".", "The", "right", "column", "\n", "represents", "the", "number", "of", "different", "countries", "with", "one", "or", "more", "selected", "industry", "groups", "from", "the", "middle", "column", ".", "\n", "Smart", "Specialisation", "in", "the", "Eastern", "Partnership", "countries", "-", "Potential", "for", "knowledge", "-", "based", "economic", "cooperation117", "\n", "Armenia", "Azerbaijan", "Belarus", "Georgia", "Moldova", "Ukraine", "Total", "\n", "ECCP", "’s", "Cluster", "Mapping", "Tool", "1", "0", "3", "4", "2", "23", "33", "\n", "Additional", "clusters", "in", "the", "EaP", "PLUS", "\n", "report3", "1", "1", "1", "1", "1", "8Table", "2.56", ".", "Number", "of", "cluster", "organisations", "by", "EaP", "country", "\n", "The", "following", "sections", "present", "all", "of", "the", "identified", "\n", "organisation", "clusters", "by", "country", ",", "first", "those", "fea-", "\n", "tured", "in", "the", "Cluster", "Mapping", "Tool", "and", "then", "those", "\n", "featured", "in", "the", "EaP", "PLUS", "report", ",", "as", "well", "as", "some", "\n", "additional", "considerations", ".", "\n", "A", "final", "section", "includes", "a", "table", "featuring", "the", "num-", "\n", "ber", "of", "clusters", "per", "sector", "for", "each", "country", ".", "\n", "Armenia", "\n", "In", "the", "Cluster", "Mapping", "Tool", ",", "Armenia", "presents", "one", "\n", "single", "cluster", ":", "Green", "Energy", "Cluster", ",", "with", "19", "\n", "member", "organisations", ",", "and", "Barva", "Innovation", "as", "\n", "its", "technological", "centre", ".", "The", "cluster", "is", "related", "to", "\n", "the", "sectors", "of", "Environmental", "services", "and", "Educa-", "\n", "tion", "and", "knowledge", "creation", ".", "\n", "The", "EaP", "PLUS", "report", "also", "highlights", "two", "clusters", "\n", "in", "the", "field", "of", "IT", ":", "the", "Gyumri", "Technology", "Center", "\n", "and", "the", "Vanadzor", "Technology", "Center", ".", "Addition-", "\n", "ally", ",", "the", "Armenian", "Society", "of", "Food", "Science", "\n", "and", "Technologies", "is", "also", "considered", "a", "cluster", "in", "\n", "the", "fields", "of", "Food", "and", "IT", ".", "\n", "As", "of", "2017", ",", "the", "Government", "of", "the", "Republic", "of", "\n", "Armenia", "had", "not", "yet", "formed", "specific", "cluster", "-", "re-", "\n", "lated", "policies", "–", "but", "SME", "development", ",", "research", "\n", "and", "innovation", "structure", "development", "–", "and", "pub-", "\n", "lic", "-", "private", "partnership", "facilitation", "in", "several", "key", "\n", "fields", "has", "been", "a", "strong", "government", "policy", "focus", "\n", "in", "past", "years", ".", "\n", "Azerbaijan", "\n", "Azerbaijan", "currently", "has", "no", "clusters", "featured", "on", "\n", "the", "ECCP", "mapping", "platform", ".", "\n", "The", "EaP", "PLUS", "report", "highlights", "a", "single", "cluster", "or-", "\n", "ganisation", "in", "the", "field", "of", "Energy", ":", "the", "Petrochem-", "\n", "ical", "cluster", "of", "Azerbaijan", ".", "It", "is", "led", "by", "SOCAR" ]
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for which the degree of specialisa- tion and relative size for both export values are above the thresholds for at least 6 out of 8 years shown in the first two columns in Table 2.21. Emerging service categories with in- creasing degrees of specialisation are identified as those service categories for which the chang- es in the degree of specialisation for export values are above the thresholds shown in the last column in Table 2.21. To identify current strengths for each country, the same minimum relative size of 0.1% has been used, and for the degree of spe- cialisation a minimum location quotient of 1.25. For changes over time, changes in the degree of specialisation have to be positive for at least 2 out of 3 time periods for all countries. Current strengths Emerging strengths* Degree of specialisationRelative size Change in degree of specialisation Armenia > 1.25 > 0.1% > 0 Azerbaijan > 1.25 > 0.1% > 0 Georgia > 1.25 > 0.1% > 0 Moldova > 1.25 > 0.1% > 0 Ukraine > 1.25 > 0.1% > 0Table 2.21. Thresholds used to identify services export specialisations * For the degree of specialisation to increase, services exports in a particular country have to grow faster than the average for all EaP countries. So, if average growth is e.g. 5%, export growth below 5% does not lead to an increasing degree of specialisation and the services exports are not seen as an emerging strength. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation81 Mapping of services export specialisa- tions – results for Armenia Results of the export mapping for Armenia are shown in Table 2.22. The 5 services categories with current strength represent 65% of the total exports for 2011-2018. Specialised exports in Per-sonal travel account for almost 51% of the to- tal exports, and those in Construction services for 12%. The 10 services categories with emerging strength represent, at 22%, a much smaller share of total exports. All specialised product categories are relatively small, except for Other transport. Current strength% share of exportsEmerging strength% share of exports 5 65.4% 10 22.4% 1.3 Other transport X 9.4% 2.2 Personal travel X 50.8% 3 Communications services X 2.3% 3.2 Telecommunications services X 2.3% 4 Construction services X 12.0% 5 Insurance services X 1.3% X 1.3% 6 Financial services X 0.5% 7.2 Information services X 0.6% 9 Other business
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[]
countries, can apply these strategies. The EU’s deci - sion-making rules are based on a valid internal logic – to achieve consensus or at least reach a broad majority – but they appear slow and cumbersome in comparison with developments taking place externally. Crucially, Europe’s decision-making rules have not substantially evolved as the EU has enlarged and as the global environment facing Europe has become more hostile and complex. Decisions are typically made issue-by-issue in different sub-com - mittees, with little coordination across policy areas. Multiple veto players can delay or dilute action. The upshot is a legislative process with an average time of 19 months to agree new laws01 – from the Commission’s proposal to the signing of the adopted act – and which even then does not deliver results at the level and pace EU citizens expect. Strengthening the EU requires Treaty changes, but it is not a precondition for Europe to move forward: much can be done with targeted adjustments. Until the consensus for Treaty changes is in place, a renewed European partnership should be built on three overarching goals: refocusing the work of the EU, accelerating EU action and integration, and simplifying rules. REFOCUSING THE WORK OF THE EU The report recommends establishing a new “Competitiveness Coordination Framework” to foster EU-wide coordination in priority areas, replacing other overlapping coordination instruments . The EU has a variety of tools to coordinate policies, such as the European Semester for economic policies and National Energy and Climate Plans for energy policies. In most cases, however, the established processes have so far proven to be largely bureaucratic and ineffective at fostering genuine EU-wide policy coordination. The new framework would address only EU-level strategic priorities – “EU Competitiveness Priorities” – which would be formulated and adopted by the European Council. These priorities would be defined at the beginning of each European political cycle in a European Council debate and adopted in European Council conclusions02. Thereafter, the coordination of all economic policies relevant to the EU’s agreed strategic priorities would be merged into the new coordination framework, excluding fiscal policy surveillance which would continue to be governed by the European Semester exercise. Not only would this rationalisation help to organise and focus the EU’s activities, it would also represent a major simplifi - cation exercise for both EU and national administrations. The Competitiveness Coordination Framework would be divided into Competitiveness Action Plans
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same minimum relative size of 0.1% has been used, and for the degree of spe- cialisation a minimum location quotient of 1.5. For changes over time, changes in the degree of spe- cialisation have to be positive for at least 2 out of 3 time periods for all countries. Results for all countries combined are shown in Annex 3. Next, we present the results for each in- dividual country. 66 Part 2 Analysis of economic and innovation potential Mapping of goods export specialisations – results for Armenia Results of the export mapping for Armenia are shown in Table 2.16. The 19 goods categories with current strength represent 75.5% of the total ex- ports for 2012-2019. Specialised exports in Bev- erages and tobacco (SITC 1) account for almost 20% of the total exports; those in Crude materi- als, inedible, except fuels (SITC 2) for almost 24%; those in Manufactured goods classified chiefly by material (SITC 6) for almost 15%. In addition, Gold, non-monetary (SITC 971) is a specialised export category accounting for almost 7% of the total exports. The 12 goods categories with emerging strength represent, at almost 16%, a much smaller share of the total exports. All specialised product catego- ries are relatively small except for Gold, non-mon- etary (SITC 971). Armenia’s export specialisation suggests an over- all specialisation in low value-added activities like farming and mining. Specialised exports of high-value goods are largely missing. SITC Goods nameCurrent strength% share of exportsEmerging strength% share of exports 19 76.5% 12 15.6% 0 Food and live animals 001 Live animals other than animals of division 03 X 0.2% 024 Cheese and curd X 0.5% 034 Fish, fresh (live or dead), chilled or frozen X 0.9% 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 0.9% 056 Vegetables, roots and tubers, prepared or preserved, n.e.s. X 0.4% X 0.4% 058Fruit, preserved, and fruit preparations (excluding fruit juices)X 0.6% 071 Coffee and coffee substitutes X 0.3% 073Chocolate and other food preparations containing cocoa, n.e.s. X 0.3% 1 Beverages and tobacco 112 Alcoholic beverages X 10.5% 122Tobacco, manufactured (whether or not containing tobacco substitutes)X 9.0% 2 Crude materials, inedible, except fuels 278 Other crude minerals X 0.2% 283Copper ores and concentrates; copper mattes; cement copperX 20.7% 287 Ores and concentrates of base metals, n.e.s. X 1.3% Table 2.16. Goods export specialisation
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imaging and rabies tracing experiments. Cell Reports 39, 110893, May 31, 2022 e6Articlell OPEN ACCESS
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on the use of AI in the lifecycle of medicines, in particular analysis of ‘raw’ clinical data transmitted to the European Medicines Agency and data collected for pharmacovigilance purposes. In parallel, regulators should aim to boost the attractiveness of the EU for conducting clinical trials and to expedite access to markets for novel medicines. These goals can be supported, among other things, by reviewing rules for studies combining medicines with medical devices and the application of AI and streamlining guidance across different agencies to industry on unmet medical needs, the design of clinical trials and the use of real-world evidence. Finally, to compensate for the financing gap in pharma, EU funding should be refocused on the development of a limited number of world-class innovation hubs in life sciences for advanced therapy medicinal products. The pharma sector would also benefit from the proposals for financing innovation. 35THE FUTURE OF EUROPEAN COMPETITIVENESS — PART A | CHAPTER 2Closing skills gaps Europe is suffering from skills gaps across the economy, reinforced by a declining labour force [see the chapter on skills] . The European economy displays from persistent skills shortages in several sectors and occupa - tions, for both low- and high-skilled workers [see Figure 10] . Around one-quarter of European companies have faced difficulties in finding employees with the right skills, while another half report some difficulties. 77% of EU companies report that even newly recruited employees do not have the required skills. Skills are also lacking at the managerial level. The uneven adoption of basic management practices – especially those needed to manage human capital – is likely responsible for the sluggish adoption of ICT in the EU in the late 1990s and the 2000s, especially among micro and small companies08. While challenges related to skills shortages are widespread across advanced economies, the need to address them is particularly acute in the EU. Demographic headwinds imply a shrinking labour force in Europe, while the US population is projected to expand in the coming decades. In this setting, a European strategy to address skills gaps – focused on all stages of education – is essential. Many of the skills gaps can be traced back to the underuse of existing talent, as witnessed by deep gender gaps in some occupations. FIGURE 10 Skills shortages in the EU Job vacancy rate (% of total posts) Source: Eurostat Skills shortages are acting as a barrier
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deviation of the distribution of the number of records in that domain. This facilitates the identification of niches where there is clear alignment with NACE codes between S&T domains (for relative frequencies close to and above 100%, such as Agrifood with NACE code 10 ‘Manufacture of food products’), in relation to mappings where S&T production is more evenly spread. As shown in Annex 1, the IPC to NACE v2 concordance table contains mappings 72 An IPC symbol is the specific full taxonomic classification associated with a patent record.at different granularity levels, namely two-digit and three-digit NACE codes. Thus, no proper con- cordance table-based mapping between the final two-digit summary results of Part 2, on one side, and the IPC patent classification, on the other, can be fully produced. The results of the S&T domains to NACE mapping carried out via patents is presented in Annex 8 for each EaP country. On expert assessment, the results of this mapping exercise as expressed by pairs of S&T domains and two-digit NACE codes seems satisfactory. The presence of multiple assignations, that is, the appearance of a given NACE code in several S&T specialisation domains, as well as some low rel- ative frequency figures, are an indication of the natural transversality of and/or overlap between some S&T domains, which have the potential to impact knowledge-based development in several sectors. Steps 3-5 – Quantified mapping of S&T domains to E&I domains, via scientific publications In the work presented in Part 3, EaP scientific publications were semantically analysed, togeth- er with patents and Horizon 2020 projects, and classified into a list of 14 S&T domains, which emerged directly from the textual content of the analysed records via topic modelling. Here, the taxonomic classification of publications (by far the largest data set analysed in this work) via ASJC subject fields (ASJC – All Science Journal Classification, the taxonomy adopted by Elsevi- er’s Scopus) is exploited to find a link between the S&T domains uncovered in Part 2 and the NACE sectors which underpin the E&I domains identified in Part 2. To do so, ASJC subject fields were first mapped to NABS and then official NABS to NACE concordance tables were exploited to achieve the ASJC to NACE proposed mapping. The NABS to NACE concordance table is presented in Annex 2. As only first-level NABS are mapped to NACE sectors – while Scopus ASJC fields are mapped to
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underly- ing spatial processes that are driven by non-random mech- anisms (Getis and Ord, 1992). We argue that these spatial patterns (hotspots), once combined across multiple hazards, will describe the statistically significant multi-hazard expo- sure across regions. Various methods for combining single-hazard datasets have been explored in the literature, including classifications and index developments. For more information on this topic, the reader can refer to Kappes et al. (2012c). For this study, the G i.d/statistic is used for local spa- tial autocorrelation analysis using the Python-based Ex- ploratory Spatial Data Analysis (PySAL-esda) package (Rey and Anselin, 2007). The method describes the spatial au- tocorrelation as the Zscore (standard deviations), pvalue (probability), and confidence level (significance) for each feature (each LAU region). Very high (positive) or very low (negative)Zscores, associated with very small pvalues (e.g. values ofp<0:1), describe spatial clusters as hotspots and cold spots, respectively, with a high significance level. We consider that for a pvalue<0:10, the observed spatial clus- ter is highly unlikely to be the result of a random statistical process, thereby suggesting statistically significant cluster- ing. In the field of disaster risk reduction and management, identifying both cold spots and hotspots is crucial for allocat- ing resources efficiently. Here, hotspots refer to areas or re-gions with higher susceptibility to multi-hazard risks, while the cold spots can be considered less prone to multi-hazard risks. Conceptualization of spatial relationship A known characteristic is that the statistics we are interested in (highZscores, lowpvalues) are placed in the tails of the distribution and therefore are susceptible to noise and spatial outliers. Moreover, the skewness of a distribution can bias the statistics (Cousineau and Chartier, 2010). These aspects are important to consider because the resulting distribution areas of the single-hazard clusters need to be homogeneous in or- der to be correctly combined in a multi-hazard spatial cluster through meta-analysis (Hak et al., 2016). Therefore, to en- sure reliable results, we address noise and outliers through a spatial weight matrix. This matrix defines neighbouring re- gions defined with the k-nearest neighbours (KNN) (Fix and Hodges, 1951; Cover and Hart, 1967) algorithm. The KNN algorithm is based on the proximity ( k) information in order to represent the spatial relationship between regions (LAUs). We opted for the KNN weight method over contiguity-based weights as it avoids the “island” problem, where isolated polygons lack shared boundaries with other polygons, and ensures that every region has
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$36 m Professional Services 12 Mobile 3 200 Manufacturing $31 m Other 11 Apps 3 105 Consumer Goods $30 mTable 2.41. Largest industry groups – Georgia For Georgia, the table shows the raw number of companies, number of employees and estimated revenue featured in the Crunchbase database by Industry Group. 106 Part 2 Analysis of economic and innovation potential Moldova # firms CM Firms # employees CM Employees # est. revenue CM Revenue Software 35 Software 980 Software $105 m Information Technology 20 Information Technology 780 Information Technology $100 m Internet Services 15 Food and Beverage 755Lending and Investments$77 m Professional Services 13 Financial Services 530 Sustainability $77 m Commerce and Shopping12Lending and Investments460 Financial Services $77 m Hardware 12 Sustainability 390 Energy $76 m Mobile 10 Energy 380 Other $13 m Media and Entertainment9Commerce and Shopping315 Hardware $12 m Financial Services 9 Hardware 205 Professional Services $8 m Apps 8 Professional Services 205 Sales and Marketing $6 mTable 2.42. Largest industry groups – Moldova For Moldova, the table shows the raw number of companies, number of employees and estimated revenue featured in the Crunchbase database by Industry Group. Ukraine # firms CM Firms # employees CM Employees # est. revenue CM Revenue Software 1 404 Information Technology 102 855Media and Entertainment$12 366 m Sales and Marketing 1 002 Software 97 245 Software $10 667 m Commerce and Shopping844 Hardware 81 745 Video $10 391 m Information Technology 813Commerce and Shopping77 760 Information Technology $8 660 m Internet Services 711 Manufacturing 73 485 Sales and Marketing $7 543 m Professional Services 672 Professional Services 66 715 Design $7 354 m Media and Entertainment629Science and Engineering58 975Commerce and Shopping$6 510 m Hardware 594 Financial Services 55 910 Manufacturing $5 943 m Design 585 Sales and Marketing 44 425 Food and Beverage $5 832 m Advertising 574 Transportation 41 260 Data and Analytics $5 825 mTable 2.43. Largest industry groups – Ukraine For Ukraine, the table shows the raw number of companies, number of employees and estimated revenue featured in the Crunchbase database by Industry Group. Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation107 Specialisation with respect to the East- ern Partnership The relative sectoral specialisation sij of the Country i in the Industrial Group j is the ratio be- tween the share of variable n (number of compa- nies, number of employees, estimated revenue) in a
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[]
made up their mind0.000.050.100.150.200.250.300.350.400.45Fraction of all annotationsPoint of Convergence for Annotations of Human-Written Text 16 32 64 128 192 Length at which rater made up their mind0.000.050.100.150.200.250.30Fraction of all annotationsPoint of Convergence for Annotations of Machine-Generated Text Figure 5: On average, it takes much less text for raters to decide an excerpt is human-written than to decide an excerpt is machine-generated.Dataset  random sampling 72.47 1.02 top-k= 40 88.06 0.59 top-p= 0:96 74.4 0.76 Table 7: Average ( ) and standard deviation ( ) of ac- curacy on out-of-domain datasets across five runs of au- tomatic discriminator finetuning. Accuracy Count 61.3% 83 57.8% 51 66.7% 51 69.8% 51 79.5% 48 84.6% 40 82.4% 39 65.6% 36 78.1% 34 84.0% 26 58.8% 18 92.3% 14 90.0% 11 100.0% 9 50.0% 8 60.0% 5 100.0% 5 100.0% 2 0.0% 2 0.0% 1 100.0% 1 0.0% 1 Table 8: Our expert rater pool consisted of 22 raters. The average accuracy of each rater on the longest ex- cerpt length (192 tokens) is shown here along with the total number of excerpts they annotated.Human I recently got the chance to try the new Oil Essentials line. With six potent blends to choose from–at $13 each–these cute little bottles offer a great, affordable way to partake in the skin and hair care oil craze. I tested each product in the line, massaging them onto my face every night before bed and running any leftover oil through my hair to tame frizziness. You could also add a few drops to your bath, favorite moisturizer, or even your shampoo and conditioner. Here’s a quick rundown of each oil. Revitalize: Omega 3, 6, 9 & Evening Primrose This was the first one I tried (I went in ROYGBIV order to keep things straight) and my first impression was that it smells lovely but a little strong. The fragrance smells genuinely like flowers. Machine Red Lanterns, the lead exposure to a movie starring the Batman solo movie alum Margot Robbie taken under Wonder Woman’s wing have reignited that rivalry with their whispery premiere. They played it as much as they possibly could, even though people who didn’t ever watch Justice League or might have missed it waiting in line for the theater were still talking about as I spilled coffee. The gist? An overextended (OK, a sore) Adam West films set up a Legion of Super-Heroes situation. How aggro?
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55.1 Hotels and similar accommodation X X 56.1 Restaurants and mobile food service activities X 56.3 Beverage serving activities X J INFORMATION AND COMMUNICATION 58.1 Publishing of books, periodicals and other publishing activities X 60.2 Television programming and broadcasting activities X 61.1 Wired telecommunications activities X 61.2 Wireless telecommunications activities X K FINANCIAL AND INSURANCE ACTIVITIES 64.9 Other financial service activities, except insurance and pension funding X 66.1 Activities auxiliary to financial services, except insurance and pension funding X L REAL ESTATE ACTIVITIES 68.2 Rental and operating of own or leased real estate X 68.3 Real estate activities on a fee or contract basis X M PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES 69.1 Legal activities X 69.2 Accounting, bookkeeping and auditing activities; tax consultancy X X 71.1 Architectural and engineering activities and related technical consultancy X 72.2 Research and experimental development on social sciences and humanities X 73.1 Advertising X X Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation43 NACE Industry nameCurrent strengthEmerging strength N ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES 77.1 Rental and leasing of motor vehicles X 77.3 Rental and leasing of other machinery, equipment and tangible goods X 79.1 Travel agency and tour operator activities X 80.1 Private security activities X 82.9 Business support service activities n.e.c.* X O PUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIAL SECURITY P EDUCATION 85.1 Pre-primary education X 85.3 Secondary education X 85.5 Other education X Q HUMAN HEALTH AND SOCIAL WORK ACTIVITIES 86.1 Hospital activities X 88.1 Social work activities without accommodation for the elderly and disabled X 88.9 Other social work activities without accommodation X R ARTS, ENTERTAINMENT AND RECREATION 90 Creative, arts and entertainment activities X 91 Libraries, archives, museums and other cultural activities X 92 Gambling and betting activities X X 93.1 Sports activities X 93.2 Amusement and recreation activities X S OTHER SERVICE ACTIVITIES 94.1 Activities of business, employers and professional membership organisations X 94.2 Activities of trade unions X 94.9 Activities of other membership organisations X 96 Other personal service activities X n.e.c. = not elsewhere classified * n.e.c.,’ frequently used throughout the report, stands for ‘not elsewhere classified’. 44 Part 2 Analysis of economic and innovation potential Mapping the economic potential – results for Moldova Results of the economic mapping for Moldova are shown in Table 2.4. In total, 15 industries have been identified as having a current strength and 21 industries have
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[]
1.438 1.016 0.469 0.974 0.761 1.060 0.945 1.112 1.533 0.364 0.986 Refined petroleum product (19) Chemicals (20+21) 0.898 0.000 1.213 1.000 1.960 0.928 0.813 0.000 1.844 1.429 0.000 1.914 0.829 0.000 1.120 1.357 1.810 0.884 Plastics & rubber (22) 0.904 1.140 0.367 1.783 0.807 0.534 1.659 0.328 0.564 1.915 0.833 1.148 0.435 1.643 0.942 Non-metallic mineral products (23) 1.043 0.321 1.741 1.308 0.615 0.972 0.820 0.673 0.956 1.650 1.030 0.871 1.031 0.303 1.643 1.420 0.618 0.985 Basic metals (24) 2.541 0.000 0.000 2.195 0.000 1.264 2.155 0.000 0.000 2.081 0.000 1.764 2.484 0.000 0.000 2.280 0.000 1.236 Fabricated metal products (25) 0.695 1.421 1.517 0.672 0.694 0.644 3.118 0.000 0.242 0.996 0.810 1.524 1.388 0.615 0.663 Precision instruments (26) 1.306 1.340 0.355 3.000 0.000 0.000 1.306 1.340 0.355 Electronics (27) 0.586 1.269 1.144 0.413 1.096 1.491 0.553 1.347 1.101 Machinery and equipment (28) 0.841 0.803 1.520 0.835 0.577 0.445 1.992 0.985 0.837 0.799 1.513 0.851 Transport machines (29+30) Furniture (31) 0.992 1.001 1.040 1.420 0.654 0.894 0.496 0.000 0.850 2.473 0.877 1.304 0.960 0.968 1.006 1.428 0.773 0.865 Recycling (33) 1.462 0.410 1.128 2.086 0.668 0.246 1.265 0.760 0.976 Construction (section F) 1.533 0.122 1.020 1.010 1.205 1.109 2.122 0.000 2.305 0.713 0.478 0.382 1.759 0.103 1.211 0.849 1.125 0.954 Services of motor vehicles (45) 0.776 1.372 0.720 0.142 1.548 1.442 0.000 0.587 2.193 0.040 0.000 3.180 0.645 1.292 1.028 0.118 1.287 1.630 Wholesale (46) 0.821 1.326 1.019 1.251 0.999 0.583 0.000 1.505 1.506 1.427 0.918 0.644 0.715 1.374 1.070 1.113 1.056 0.672 Retail (47) 1.183 0.493 0.761 1.597 0.972 0.993 1.344 0.283 1.622 1.351 0.779 0.622 1.146 0.458 0.942 1.429 1.065 0.960 Transport (Section H) 1.104 1.590 0.643 1.285 0.977 0.400 2.359 0.000 1.122 0.851 1.307 0.361 2.107 0.590 0.928 0.980 0.999 0.397 Hotel and restaurants (Section I) 1.288 0.692 1.666 1.639 0.714 0.000 0.347 1.278 1.409 1.944 1.021 0.000 1.149 0.831 1.764 1.394 0.862 0.000 Information and communication (Section J) 0.522 0.868 0.874 1.259 1.482 0.995 0.831 0.000 3.631 0.362 0.000 1.176 0.484 0.804 1.249 1.166 1.374 0.922 Total 1.012 0.649 1.135 1.221 1.046 0.937 0.814 0.567 1.720 1.089 0.951 0.859 0.987 0.669 1.237 1.104 1.096 0.907Table 2.29. Specialisation in innovation Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation91 3.2. Patents Patent data is collected from the DOCDB da- tabase41 of the European Patent Office, which covers patents issued by most of the
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[]
ment, solid fuel Class 24 – Medical and laboratory equipment Class 25 – Building units and construction ele- ments Class 26 – Lighting apparatus Class 27 – Tobacco and smokers’ supplies Class 28 – Pharmaceutical and cosmetic products, toilet articles and apparatus Class 29 – Devices and equipment against fire hazards, for accident prevention and for rescue Class 30 – Articles for the care and handling of animals Class 31 – Machines and appliances for preparing food or drink, not elsewhere specified Class 32 – Graphic symbols and logos, surface patterns, ornamentation 328 Annexes Annex 7. IPC to NACE v2 correspondence tableThe table below shows the correspondence be- tween IPC classes for patents and three-digit NACE sectors74. 74 Van Looy, V. et al., Patent Statistics: Concordance IPC v8 - NACE Rev.2, Eurostat, 2015. NACE sector IPC class NOT matching 10 Manufacture of food products [10]A23J; A01H; A21D; A23D; A23F; A23G; A23L; A23L; A23P; C13F; C13J; C13K; A23B; C12J; A23C; A23K; C13B 10.5 Manufacture of dairy products [10.5] A01J 11 Manufacture of beverages [11] A23L; C12C; C12F; C12G; C12H 12 Manufacture of tobacco products [12] A24B; A24D; A24F 13 Manufacture of textiles [13]D04D; D04G; D04H; D06M; D06N; D06P; D06C; D06Q; D06J 14 Manufacture of wearing apparel [14] A41B; A41C; A41D; A41F 15Manufacture of leather and related products [15]A43B; A43C; B68B; B68C 16Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials [16]B27D; B27H; B27M; B27N 17Manufacture of paper and paper products [17]B42F; D21C; D21H; D21J 18.1Printing and service activities related to printing [18.1]B41M; B42D; B44F 19Manufacture of coke and refined petroleum products [19]C10G; C10L 20.1Manufacture of basic chemicals, fertilisers and nitrogen compounds, plastics and synthetic rubber in primary forms [20.1]B01J; B09B; B09C; C01D; C01F; C01G; C05C; C05D; C05F; C07B; A61K 8/*; C07C; C07F; C07G; C08B; C08F; C08G; C08L; C09B; C09C; C10C; C10H; C10J; C12S; C25B; F17C; F17D; C01B; C02F; C05G; C08J; C09K; C10K; F25J; C01C; C05B; C08K; C10B; G21FA61K 20.2Manufacture of pesticides and other agrochemical products [20.2]A01N; A01P 20.3Manufacture of paints, varnishes and similar coatings, printing ink and mastics [20.3]B27K; C09D 20.4Manufacture of soap and detergents, cleaning and polishing preparations, perfumes and toilet preparations [20.4]A61K 8/*; A61Q; C09F; C11D; D06L 20.5Manufacture of other chemical products [20.5]A62D; C06B; C06C; C06D; C08H; C09G; C09H; C09J; C11B; C11C; C14C; C40B; A61K 8/*; F42D; C10M; C23F; D01C; C10N; C23G; F42BA61K Smart Specialisation in the Eastern
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[]
mean + or ±SEM. Details of statistical analyses are provided in Table S1 . Cell Reports 39, 110893, May 31, 2022 11Articlell OPEN ACCESSnegligible number of presynaptic neurons providing first-order input to VIP+ INs in the ventral complex of thalamic nuclei and ol-factory areas compared with GABAergic INs taken as a whole.Moreover, the presynaptic neurons in the piriform cortex target- ing VIP+ INs were proportionally much higher than those re- ported for aIC GABAergic INs. These dissimilarities support theview that different cortical INs are characterized by a distinct pattern and relative weight of long-range presynaptic inputs (Ma et al., 2021 ;Naskar et al., 2021 ). We cannot, however, exclude at present that methodological differences between our study and the one by Gehrlach et al. (2020) , e.g., the retro- grade tracing efficacy or tropism of the rabies virus used, mighthave contributed to these differences. Further studies will haveto address this potential issue and could include inputs to other IN types to provide a better understanding of the connectivity patterns of inhibitory circuits of the aIC. The IC is a complex structure that can be divided into an ante- rior and a posterior part. They markedly differ in their connectivity with other brain regions and subserve different functions ( Gehr- lach et al., 2020 ;Gogolla, 2017 ;Livneh and Andermann, 2021 ). At the interface between these two parts, a middle insular zone exhibits mixed anterior and posterior connectivity features ( Ud-din et al., 2017 ;Gehrlach et al., 2020 ). Our study focused on the aIC given its primary involvement in multisensory and multi-modal responses ( Beer et al., 2013 ;Uddin, 2015 ;Uddin et al., 2017 ) in the social ( Miura et al., 2020 ) and negative valence do- mains ( Wu et al., 2020 ). In line with the known dense connectivity between the aIC and pIC, we found a large number of presynap-tic neurons in the pIC innervating aIC VIP+ INs. This suggests that information related to salient sensory stimuli is also trans- mitted from the pIC to aIC VIP+ INs. Our study shows that aICVIP+ INs were strongly activated by footshocks during the acqui- sition of fear conditioning, although their inhibition during the US presentation did not affect fear expression. On the other hand,upon fear memory retrieval we found a reduced freezingbehavior that we attribute to a decrease in stimulus salience and consequently in the strength of the
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NACE three-digit data), innovation survey data will not be used in this report. Results on innovation activities at more highly ag- gregated levels are available from the World Bank Enterprise Survey37. Most recent WB Enterprise Survey data are available for 2018 for Azerbai- jan, Georgia, Moldova and Ukraine and for 2020 for Armenia. Here, we will analyse the industry aggregates using the firm-level data available to researchers from the Enterprise Survey data por- tal38. These results should be interpreted with care as sample sizes are relatively small (Table 2.27 shows the total weighted number of enterprises included39) and a further breakdown into two-digit NACE industries will lead to only a very small num- ber of enterprises in particular industries. As a cut- off point, results are only used when the weighted 37 Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank. 38 https://login.enterprisesurveys.org/content/sites/fi- nanceandprivatesector/en/signin.html 39 In the Enterprise Survey there are weights for each firm, which take into account how well the firm/industry repre- sents the economy. For example, if textiles accounts for 10% of the economy, but in a sample of 1 000 firms only 50 are from textiles, each textile firm receives a higher weight to compensate for the fact that the industry is un- derrepresented in the survey sample.number of enterprises, using the median weight as available in the firm-level databases, is at least 10. The following two questions from the Enterprise Survey have been used: ■H.1 During the last three years, has this estab- lishment introduced new or improved products or services? ■H.5 During the last three years, has this es- tablishment introduced any new or improved process? These include: • methods of manufacturing products or of- fering services, • logistics, delivery, or distribution methods for inputs, products, or services, • or supporting activities for processes. A new indicator has been constructed by combin- ing both questions to identify enterprises having introduced a product and/or process innovation. Results are shown in Table 2.28 for industries40 covered by the Enterprise Survey and where the weighted number of enterprises is at least 10. The average share of product innovators in the EaP countries is 35.1%, the average share of pro- cess innovators is 15.5% and the average share of product and/or process innovators is 41.1%. The share of enterprises that innovate is lowest in Azerbaijan. The corresponding degrees of specialisation are shown in Table 2.29, with industries where the degree of
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