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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
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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
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Euromines - European Association of Mining Industries,
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European Aluminium
European council of young farmers - CEJA
European Entrepreneurs CEA-PME
European Venture Fund Investors Network
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France Industrie
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International Lithium Association
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nucleareurope - Forum Atomique Européen
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Platform for Electromobility
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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 1C⋯CS
evaluating k
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"each",
"other",
"by",
"panels",
".",
"Participants",
"started",
"by",
"tasting",
"the",
"three",
"\n",
"versions",
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"Danone",
"Activia",
"Strawberry",
"yogurt",
",",
"alternating",
"each",
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"\n",
"with",
"a",
"sip",
"of",
"water",
"and",
"a",
"bite",
"of",
"toasted",
"bread",
".",
"They",
"then",
"used",
"a",
"tablet",
"to",
"\n",
"report",
"their",
"satisfaction",
"with",
"each",
"version",
"on",
"a",
"scale",
"of",
"0",
"(",
"‘",
"Very",
"unsat",
"-",
"\n",
"isfied",
"with",
"the",
"product",
"’",
")",
"to",
"10",
"(",
"‘",
"Very",
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"with",
"the",
"product",
"’",
")",
"(",
"sensory",
"\n",
"preference",
"score",
")",
"and",
"ranked",
"them",
"on",
"a",
"scale",
"from",
"1",
"(",
"‘",
"Best",
"’",
")",
"to",
"3",
"(",
"‘",
"Worst",
"’",
")",
"\n",
"Fig",
".",
"2.Stylized",
"example",
"of",
"cards",
"for",
"the",
"absence",
"(",
"left",
")",
"and",
"presence",
"(",
"right",
")",
"of",
"the",
"‘",
"made",
"for",
"’",
"claim",
"conditions",
"\u0000170",
"gr",
"bag",
"of",
"crisps",
".",
"\n",
"Table",
"2",
"\n",
"Attributes",
"and",
"levels",
"used",
"for",
"the",
"construction",
"of",
"the",
"choice",
"set",
".",
"\n",
"Attributes",
"Levels",
"\n",
"Price",
"Six",
"price",
"levels",
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"(",
"based",
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"prices",
"\n",
"for",
"the",
"studied",
"products",
"found",
"in",
"the",
"markets",
"\n",
"studied",
")",
"–",
"See",
"details",
"in",
"Appendix",
"C",
"\n",
"Nutritional",
"information",
"and",
"\n",
"list",
"of",
"ingredientsThree",
"levels",
"(",
"based",
"on",
"the",
"actual",
"information",
"of",
"the",
"\n",
"products",
"marketed",
"in",
"the",
"respective",
"countries",
")",
"\n",
"Brand",
"Brand",
"logo",
"/",
"Generic",
"brand",
"(",
"product",
"specific",
"brand",
")",
"\n",
"Source",
":",
"Authors",
"’",
"elaboration",
"\n",
"Fig",
".",
"3.Protocol",
"for",
"the",
"sensorial",
"evaluation",
"laboratory",
"experiment",
".",
"D.M.",
"Federica",
"et",
"al",
".",
" ",
"Food",
"Policy",
" ",
"131",
"(",
"2025",
")",
" ",
"102803",
" \n",
"5",
"(",
"order",
"score",
")",
".",
"Next",
",",
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"(",
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")",
"the",
"list",
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"\n",
"ingredients",
",",
"the",
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",",
"and",
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"each",
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"\n",
"versions",
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"Based",
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",",
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"This",
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"\n",
"Round",
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"Frame",
"1",
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"identical",
"as",
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"one",
",",
"except",
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"the",
"in-",
"\n",
"clusion",
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"the",
"‘",
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"’",
"claim",
".",
"Similarly",
",",
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"two",
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"Frame",
"2",
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"\n",
"identical",
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"round",
"one",
",",
"except",
"for",
"the",
"inclusion",
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"the",
"brand",
"name",
".",
"A",
"pair",
"\n",
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"coloured",
"stickers",
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"each",
"product",
"version",
"to",
"track",
"\n",
"changes",
"in",
"individual",
"choices",
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"rounds",
".",
"\n",
"3.2",
".",
"Econometric",
"approach",
"\n",
"The",
"key",
"objective",
"of",
"experiment",
"1",
"is",
"to",
"assess",
"which",
"composition",
"of",
"\n",
"the",
"same",
"product",
"consumers",
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".",
"The",
"key",
"objective",
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"1",
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"\n",
"to",
"assess",
"which",
"version",
"of",
"the",
"same",
"product",
"consumers",
"prefer",
".",
"We",
"model",
"\n",
"consumer",
"choice",
"in",
"the",
"presence",
"of",
"DFQ",
"as",
"a",
"discrete",
"-",
"choice",
"variable",
"\n",
"and",
"adopt",
"a",
"random",
"utility",
"approach",
".",
"We",
"conceptualize",
"our",
"model",
"at",
"the",
"\n",
"version",
"level",
"and",
"assume",
"that",
"consumer",
"r",
"from",
"country",
"s",
"
",
"s",
"1C⋯CS",
"\n",
"evaluating",
"k",
"
",
"k1C⋯CKproduct"
] | [] |
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",
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"of",
"\n",
"the",
"chamber",
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"\n",
"rt=¯tL/¯tH",
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"\n",
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"L(H",
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".",
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"Ref",
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"ever",
",",
"as",
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".",
"11",
",",
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"\n",
"the",
"nuclear",
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"kinetic",
"\n",
"energy",
".",
"\n",
"Hence",
",",
"we",
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"the",
"following",
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"func-",
"\n",
"tion",
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"TKE",
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"120",
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":",
"\n",
"ˆZL",
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"angbracketrightsym",
",",
"k",
"\n",
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"/",
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"\n",
"where",
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"and",
"/angbracketleftrt",
"/",
"angbracketrightsymare",
"the",
"nuclear",
"charge",
"and",
"mean",
"\n",
"drift",
"-",
"time",
"ratio",
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"the",
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",",
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",",
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"thenucleus",
"undergoes",
"symmetric",
"fission",
",",
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",",
"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",
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"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",
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"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",
",",
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"complementary",
"\n",
"light",
"fragments",
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"Pd",
"(",
"ZL=46",
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"and",
"Ru",
"(",
"ZL=44",
")",
",",
"respec-",
"\n",
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".",
"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 | [
"to",
"printing",
"X",
" \n",
"23.7Cutting",
",",
"shaping",
"and",
"finishing",
"of",
"\n",
"stone",
"X",
"21",
"Pharmaceuticals",
",",
"medicinal",
"chemicals",
",",
"etc",
".",
"X",
"NACE",
"Innovation",
"–",
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"\n",
"24.1Manufacture",
"of",
"basic",
"iron",
"and",
"\n",
"steel",
"and",
"of",
"ferro",
"-",
"alloys",
"X",
"25",
"Manufacture",
"of",
"fabricated",
"metal",
"products",
"X",
"12",
"Manufacture",
"of",
"tobacco",
"products",
"\n",
"25.9Manufacture",
"of",
"other",
"fabricated",
"\n",
"metal",
"products",
"X",
" ",
"15",
"Manufacture",
"of",
"leather",
"and",
"related",
"products",
"\n",
"35.1Electric",
"power",
"generation",
",",
"\n",
"transmission",
"and",
"distribution",
"X",
"SITC",
"Goods",
"exports",
"C",
"E",
"23",
"Manufacture",
"of",
"other",
"non",
"-",
"metallic",
"mineral",
"products",
"\n",
"41.2Construction",
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"residential",
"and",
"\n",
"non",
"-",
"residential",
"buildingsX",
" ",
"0",
"Live",
"animals",
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"than",
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"03",
"X",
"23.1",
"Manufacture",
"of",
"glass",
"and",
"glass",
"products",
"\n",
"42.2",
"Construction",
"of",
"utility",
"projects",
" ",
"X",
"7Coffee",
",",
"tea",
",",
"cocoa",
",",
"spices",
",",
"and",
"manufactures",
"\n",
"thereofX",
"23.4",
"Man",
".",
"of",
"other",
"porcelain",
"and",
"ceramic",
"products",
"\n",
"42.9Construction",
"of",
"other",
"civil",
"\n",
"engineering",
"projects",
"X",
"9",
"Miscellaneous",
"edible",
"products",
"and",
"preparations",
"X",
"24",
"Manufacture",
"of",
"basic",
"metals",
"\n",
"43.1",
"Demolition",
"and",
"site",
"preparation",
" ",
"X",
"11",
"Beverages",
"X",
"X",
"25.5",
"Forging",
",",
"pressing",
",",
"stamping",
"and",
"roll",
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"33Petroleum",
",",
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"specialised",
"\n",
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"X",
"41",
"Animal",
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"and",
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"X",
"29.1",
"Manufacture",
"of",
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"47.5Retail",
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"of",
"other",
"household",
"\n",
"equipment",
"in",
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"storesX",
"X",
"52",
"Inorganic",
"chemicals",
"X",
"31",
"Manufacture",
"of",
"furniture",
"\n",
"47.6Retail",
"sale",
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"cultural",
"and",
"\n",
"recreation",
"goods",
"in",
"spec",
".",
"storesX",
" ",
"54",
"Medicinal",
"and",
"pharmaceutical",
"products",
"X",
"X",
" \n",
"49.3",
"Other",
"passenger",
"land",
"transport",
" ",
"X",
"55Essential",
"oils",
"and",
"resinoids",
"and",
"perfume",
"\n",
"materials",
";",
"toilet",
",",
"etc",
".",
"X",
"NACE",
"Innovation",
"–",
"VC",
"&",
"start",
"-",
"ups",
"\n",
"49.4Freight",
"transport",
"by",
"road",
"and",
"\n",
"removal",
"services",
"X",
"59",
"Chemical",
"materials",
"and",
"products",
"X",
"62",
",",
"63",
"Software",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation129",
"130",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"53.2",
"Other",
"postal",
"and",
"courier",
"activities",
"X",
" ",
"62",
"Rubber",
"manufactures",
"X",
"X",
"62",
",",
"64",
"Financial",
"services",
"\n",
"55.1",
"Hotels",
"and",
"similar",
"accommodation",
"X",
"X",
"64Paper",
",",
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"of",
"paper",
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",",
"of",
"\n",
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"64",
"Lending",
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"investments",
"\n",
"56.1Restaurants",
"and",
"mobile",
"food",
"\n",
"service",
"activitiesX",
" ",
"66",
"Non",
"-",
"metallic",
"mineral",
"manufactures",
"X",
"53",
",",
"55",
",",
"79",
"Travel",
"and",
"tourism",
"\n",
"56.3",
"Beverage",
"serving",
"activities",
" ",
"X",
"67",
"Iron",
"and",
"steel",
"X",
"61",
",",
"63",
"Internet",
"services",
"\n",
"58.1Publishing",
"of",
"books",
",",
"periodicals",
"\n",
"and"
] | [] |
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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"\n"
] | [] |
(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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",",
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"54",
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")",
".",
"By",
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"\n",
"the",
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"urbanization",
"only",
",",
"people",
"are",
"more",
"exposed",
"to",
"\n",
"multi",
"-",
"hazards",
"if",
"they",
"live",
"in",
"either",
"high",
"-",
"income",
"urban",
"areas",
"\n",
"(",
"compared",
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"low",
"-",
"income",
"urban",
"areas",
")",
"or",
"low",
"-",
"income",
"ru-",
"\n",
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"areas",
"(",
"compared",
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"high",
"-",
"income",
"rural",
"areas",
")",
"(",
"Fig",
".",
"6c",
")",
".",
"\n",
"Nat",
".",
"Hazards",
"Earth",
"Syst",
".",
"Sci",
".",
",",
"25",
",",
"287–304",
",",
"2025",
"https://doi.org/10.5194/nhess-25-287-2025T.-E.",
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".",
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"Spatial",
"identification",
"of",
"regions",
"exposed",
"to",
"multi",
"-",
"hazards",
"at",
"pan",
"-",
"European",
"level",
"295",
"\n",
"Figure",
"5",
".",
"Regions",
"(",
"LAUs",
")",
"with",
"population",
"exposed",
"to",
"multi",
"-",
"hazards",
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"significance",
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"(",
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";",
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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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] | [] |
Young VR, Ajami AM (September 2001). "Glutamine: the emperor or his clothes?". The Journal of Nutrition. 131 (9 Suppl): 2449S – 59S, discussion 2486S–7S. doi:10.1093/jn/131.9.2449S. PMID 11533293.
Hosler JP, Ferguson-Miller S, Mills DA (2006). "Energy transduction: proton transfer through the respiratory complexes". Annual Review of Biochemistry. 75: 165–87. doi:10.1146/annurev.biochem.75.062003.101730. PMC 2659341. PMID 16756489.
Schultz BE, Chan SI (2001). "Structures and proton-pumping strategies of mitochondrial respiratory enzymes" (PDF). Annual Review of 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.
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.
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.
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.
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.
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.
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.
Barea JM, Pozo MJ, Azcón R, Azcón-Aguilar C (July 2005). "Microbial co-operation in the rhizosphere". Journal of Experimental Botany. 56 (417): 1761–78. doi:10.1093/jxb/eri197. PMID 15911555.
van der Meer MT, Schouten S, Bateson MM, Nübel U, Wieland A, Kühl M, et al. (July 2005). "Diel variations in carbon metabolism by green nonsulfur-like bacteria in alkaline siliceous hot spring microbial mats from Yellowstone National Park". Applied and Environmental Microbiology. 71 (7): 3978–86. Bibcode:2005ApEnM..71.3978V. doi:10.1128/AEM.71.7.3978-3986.2005. PMC 1168979. PMID 16000812.
Tichi MA, Tabita FR (November 2001). "Interactive control of Rhodobacter capsulatus redox-balancing systems during phototrophic metabolism". Journal of Bacteriology. 183 (21): 6344–54. doi:10.1128/JB.183.21.6344-6354.2001. PMC 100130. PMID 11591679.
Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2002). "Energy Conversion: Mitochondria and Chloroplasts". Molecular Biology of the Cell (4th ed.). Archived from the original on 15 December 2020. | [
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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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] | [] |
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 | [
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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 | [
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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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"Part",
"3",
".",
"\n",
"The",
"following",
"tables",
"present",
"the",
"clusters",
"for",
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"\n",
"EaP",
"countries",
"and",
"show",
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"\n",
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"E&I",
"and",
"S&T",
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"\n",
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".",
"\n",
"73",
"Ketels",
",",
"C.",
",",
"Protsiv",
",",
"S.",
",",
"Methodology",
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"Report",
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"\n",
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",",
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"for",
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"\n",
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",",
"\n",
"October",
"2014",
".",
"\n",
"236",
"\n ",
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"the",
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",",
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",",
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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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"\n",
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".",
"Armenia",
"\n",
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"\n",
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"\n",
"Georgia",
"\n",
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"\n",
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"\n",
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"130",
"1",
"471",
"1",
"756",
"42",
"980",
"\n",
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"130",
"49",
"73",
"26",
"138",
"\n",
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"471",
"49",
"1",
"440",
"83",
"1",
"268",
"\n",
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"756",
"73",
"1",
"440",
"58",
"1",
"058",
"\n",
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"26",
"83",
"58",
"202",
"\n",
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"138",
"1",
"268",
"1",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
"Moldova",
"\n",
"Ukraine",
"\n",
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"10",
"21",
"26",
"19",
"21",
"\n",
"Azerbaijan",
"10",
"8",
"11",
"8",
"11",
"\n",
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"21",
"8",
"20",
"17",
"33",
"\n",
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"26",
"11",
"20",
"23",
"32",
"\n",
"Moldova",
"19",
"8",
"17",
"23",
"25",
"\n",
"Ukraine",
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"11",
"33",
"32",
"25",
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"-",
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".",
"\n",
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"Overview",
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",",
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"Figure",
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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, | [
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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 | [
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OPUBLIC ADMINISTRATION AND DEFENCE; COMPULSORY SOCIAL
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84.2 Provision of services to the community as a whole
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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
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86.2 Medical and dental practice activities X X X X X
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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-
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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
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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
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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
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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
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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-
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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
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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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"recognize",
"the",
"aim",
"of",
"fake",
"content",
"for",
"its",
"subsequent",
"detection",
".",
"\n",
"Various",
"generation",
"strategies",
"require",
"different",
"approaches",
"to",
"find",
"it",
".",
"For",
"example",
",",
"\n",
"algorithms",
"for",
"detecting",
"word",
"salad",
"are",
"clearly",
"possible",
"and",
"are",
"not",
"particularly",
"dif",
"ficult",
"\n",
"to",
"implement",
".",
"A",
"statistical",
"approach",
"based",
"on",
"Zipf",
"’s",
"law",
"of",
"word",
"frequency",
"has",
"\n",
"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",
"\n",
"that",
"would",
"be",
"effective",
"against",
"completely",
"random",
"phrasing",
"but",
"might",
"be",
"fooled",
"by",
"\n",
"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",
".",
"\n",
"One",
"also",
"needs",
"to",
"estimate",
"the",
"data",
"capacity",
".",
"Text",
"corpuses",
"are",
"taken",
"depending",
"on",
"\n",
"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",
"\n",
"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",
"\n",
"summarizes",
"results",
"of",
"described",
"methods",
".",
"The",
"numerical",
"results",
"are",
"provided",
"by",
"theauthors",
"of",
"the",
"articles",
",",
"except",
"the",
"last",
"one",
".",
"\n",
"4",
"Conclusion",
"\n",
"This",
"work",
"presents",
"the",
"results",
"of",
"a",
"systematic",
"review",
"of",
"arti",
"ficial",
"content",
"detection",
"\n",
"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"
] | [] |
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.,
2023 Annex 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 | [
"products",
",",
"\n",
"observed",
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"both",
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] | [
{
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"label": "CITATION-REFEERENCE",
"start": 630
},
{
"end": 1837,
"label": "CITATION-REFEERENCE",
"start": 1819
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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 | [
"16.8",
"56.7",
"57.1",
"59.4",
"84.3",
"45.6",
"51.0",
"59.0",
"\n",
"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",
"\n",
"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",
"\n",
"Services",
"of",
"motor",
"vehicles",
"\n",
"(",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"Hotel",
"and",
"restaurants",
"\n",
"(",
"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",
"\n",
"Information",
"and",
"\n",
"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",
"\n",
"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",
"\n",
"Smart",
"Specialisation",
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"Partnership",
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"-",
"Potential",
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"knowledge",
"-",
"based",
"economic",
"cooperation89",
"90",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"Share",
"of",
"product",
"innovators",
"Share",
"of",
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"innovators",
"Share",
"of",
"product",
"and/or",
"process",
"innovators",
"\n",
"Name",
"of",
"industry",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"\n",
"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",
"\n",
"Tobacco",
"(",
"12",
")",
"\n",
"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",
"\n",
"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",
"\n",
"Leather",
"(",
"15",
")",
"1.253",
"1.399",
"0.348",
"0.000",
"1.864",
"1.136",
"1.062",
"1.186",
"0.752",
"\n",
"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",
"\n",
"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",
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"(",
"18",
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"1.070",
"0.734",
"1.185",
"1.633",
"0.388",
"0.990",
"1.343"
] | [] |
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-
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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-
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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-
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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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Fabio Petroni, Tim Rockt ¨aschel, Sebastian Riedel,
Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and
Alexander Miller. 2019. Language models as knowl-
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W. V . O. Quine. 1960. Word and Object . MIT Press.
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gen Blix, Yining Nie, Anna Alsop, Shikha Bordia,
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Jeretic, and Samuel R. Bowman. 2019. Investi-
gating BERT’s knowledge of language: Five anal-
ysis methods with NPIs. In Proceedings of the
2019 Conference on Empirical Methods in Natu-
ral Language Processing and the 9th International
Joint Conference on Natural Language Processing
(EMNLP-IJCNLP) , pages 2877–2887, Hong Kong,
China. Association for Computational Linguistics. | [
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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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"58",
"\n ",
"Part",
"2",
"Analysis",
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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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"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",
"n=7",
"1c",
"e",
"l",
"l",
"s",
")",
".",
"\n",
"Data",
"are",
"shown",
"as",
"mean",
"+",
"or",
"±SEM",
".",
"Details",
"of",
"statistical",
"analyses",
"are",
"provided",
"in",
"Table",
"S1",
".",
"\n",
"6Cell",
"Reports",
"39",
",",
"110893",
",",
"May",
"31",
",",
"2022Articlell",
"\n",
"OPEN",
"ACCESSFigure",
"4",
".",
"aIC",
"VIP+",
"IN",
"activity",
"is",
"required",
"for",
"aversive",
"learning",
"and",
"social",
"preference",
"\n",
"(",
"A",
")",
"Schematic",
"of",
"the",
"approach",
"used",
"for",
"optogenetic",
"loss",
"-",
"of",
"-",
"function",
"experiments",
".",
"\n",
"(",
"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",
".",
"\n",
"(",
"C",
")",
"Example",
"of",
"ArchT",
"selective",
"expression",
"in",
"aIC",
"VIP+",
"INs",
".",
"Scale",
"bar",
",",
"20",
"mm",
".",
"\n",
"(",
"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",
"\n",
"testing",
",",
"during",
"the",
"second",
"5",
"min",
"of",
"the",
"test",
".",
"\n",
"(",
"legend",
"continued",
"on",
"next",
"page",
")",
"\n",
"Cell",
"Reports",
"39",
",",
"110893",
",",
"May",
"31",
",",
"2022",
"7Articlell",
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",",
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"\n",
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"the",
"CS",
",",
"whereas",
"the",
"unexpected",
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"of",
"the",
"\n",
"US",
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"to",
"a",
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"increase",
"in",
"activity",
"(",
"Figure",
"5",
"F",
")",
".",
"\n",
"aIC",
"VIP+",
"INs",
"are",
"functionally",
"heterogeneous",
"\n",
"Our",
"analyses",
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"deep",
"-",
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"Ca2+imaging",
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"that",
"foot-",
"\n",
"shocks",
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"fear",
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"close",
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"with"
] | [] |
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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"an",
"epifluorescence",
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"(",
"Axio",
"Imager",
",",
"Carl",
"Zeiss",
",",
"Oberkochen",
",",
"Ger-",
"\n",
"many",
")",
"and",
"the",
"Openlab",
"software",
"(",
"Version",
"5.5.0",
")",
"or",
"an",
"Airy",
"Scan",
"LSM980",
"laser",
"scanning",
"microscope",
"(",
"Carl",
"Zeiss",
",",
"Oberkochen",
",",
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"-",
"many",
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"with",
"a",
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".",
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"confocal",
"images",
"were",
"channel",
"dye",
"separated",
"and",
"deconvolved",
"using",
"Huygens",
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"(",
"Sci-",
"\n",
"entific",
"Volume",
"Imaging",
",",
"Hilversum",
",",
"The",
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".",
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"processing",
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"IMARIS",
"9.7.0",
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"(",
"Oxford",
"\n",
"Instruments",
",",
"Bitplane",
",",
"Zurich",
",",
"Switzerland",
")",
".",
"\n",
"To",
"confirm",
"the",
"sensitivity",
"of",
"the",
"VIP",
"-",
"IHC",
"analysis",
",",
"we",
"carried",
"out",
"double",
"fluorescence",
"experiments",
"using",
"sections",
"(",
"2",
"/",
"animal",
";",
"N",
"=",
"3",
"\n",
"mice",
")",
"obtained",
"from",
"VIP",
"-",
"ires",
"-",
"cre",
":",
"Ai9",
"mice",
".",
"In",
"these",
"slices",
",",
"we",
"visualized",
"the",
"endogenous",
"fluorescence",
"of",
"the",
"reporter",
"molecule",
"tdTo-",
"\n",
"mato",
"expressed",
"under",
"the",
"endogenous",
"VIP",
"promoter",
"and",
"VIP",
"using",
"an",
"immune",
"-",
"complex",
"composed",
"of",
"a",
"rabbit",
"primary",
"antibody",
"\n",
"(",
"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",
"\n",
"Cell",
"Reports",
"39",
",",
"110893",
",",
"May",
"31",
",",
"2022",
"e4Articlell",
"\n",
"OPEN",
"ACCESS4.2",
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"and",
"a",
"20x",
"objective",
",",
"and",
"the",
"quantification",
"of",
"the",
"neurons",
"colocalizing",
"the",
"two",
"fluorescent",
"signals",
"was",
"manually",
"per-",
"\n",
"formed",
"offline",
"using",
"CaseViewer",
"(",
"3DHistech",
")",
".",
"\n",
"Image",
"acquisition",
"and",
"data",
"analysis",
"\n",
"For",
"mono",
"-",
"trans",
"-",
"synaptic",
"tracing",
",",
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"2",
"%",
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"in",
"PBS",
"and",
"cut",
"into",
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"sections",
".",
"Every",
"third",
"sectionfrom",
"each",
"brain",
"was",
"used",
"for",
"quantification",
"of",
"first",
"order",
"presynaptic",
"neurons",
"to",
"aIC",
"VIP",
"+",
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"Four",
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"and",
"after",
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"\n",
"injection",
"site",
"were",
"not",
"used",
"for",
"quantification",
"as",
"they",
"contained",
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"starter"
] | [
{
"end": 36,
"label": "CITATION-REFEERENCE",
"start": 22
},
{
"end": 285,
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"start": 270
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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 | [
" ",
"impact",
"on",
"output",
".",
"However",
",",
"if",
"the",
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"and",
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"outlined",
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",",
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".",
"\n",
"THE",
"ROOT",
"CAUSES",
"OF",
"LOW",
"INVESTMENT",
"FINANCING",
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"EUROPE",
"\n",
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"capital",
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"and",
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"capital",
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"are",
"lower",
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"While",
"the",
"Commission",
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"\n",
"to",
"build",
"a",
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"Markets",
"Union",
"(",
"CMU",
")",
",",
"three",
"main",
"fault",
"lines",
"remain",
".",
"First",
",",
"the",
"EU",
"lacks",
"a",
"single",
"securities",
"market",
"\n",
"regulator",
"and",
"a",
"single",
"rulebook",
"for",
"all",
"aspects",
"of",
"trading",
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"still",
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"of",
"regulations",
".",
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",",
"the",
"post",
"-",
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"clearing",
"and",
"settlement",
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"is",
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"\n",
"01",
".",
" ",
"Productive",
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"gross",
"fixed",
"capital",
"formation",
"minus",
"residential",
"investment",
".",
"\n",
"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",
"\n",
"Member",
"States",
"remain",
"substantially",
"unaligned",
".",
"EU",
"capital",
"markets",
"are",
"also",
"undersupplied",
"with",
"long",
"-",
"term",
"capital",
"\n",
"relative",
"to",
"other",
"major",
"economies",
",",
"owing",
"largely",
"to",
"the",
"underdevelopment",
"of",
"pension",
"funds",
".",
"In",
"2022",
",",
"the",
"level",
"of",
"\n",
"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",
"\n",
"to",
"100",
"%",
".",
"This",
"difference",
"reflects",
"the",
"fact",
"that",
"most",
"European",
"households",
"’",
"pension",
"wealth",
"takes",
"the",
"form",
"of",
"claims",
"on",
"\n",
"public",
"pay",
"-",
"as",
"-",
"you",
"-",
"go",
"social",
"security",
"systems",
".",
"EU",
"pension",
"assets",
"are",
"highly",
"concentrated",
"in",
"a",
"handful",
"of",
"Member",
"\n",
"States",
"with",
"more",
"developed",
"private",
"pension",
"systems",
".",
"The",
"combined",
"share",
"of",
"the",
"Netherlands",
",",
"Denmark",
"and",
"Sweden",
"\n",
"in",
"EU",
"pension",
"assets",
"amounts",
"to",
"62",
"%",
"of",
"the",
"EU",
"total",
".",
"\n",
"The",
"mirror",
"image",
"is",
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"the",
"EU",
"relies",
"excessively",
"on",
"bank",
"financing",
",",
"which",
"is",
"less",
"well",
"-",
"suited"
] | [] |
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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"B",
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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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"47THE",
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" ",
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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 - | [
".......................................................................................................................................................",
"233",
"\n",
"Table",
"4.2",
".",
"Combined",
"EIST",
"specialisation",
"domains",
"in",
"Armenia",
".............................................",
"236",
"\n",
"Table",
"4.3",
".",
"Combined",
"EIST",
"specialisation",
"domains",
"in",
"Azerbaijan",
"........................................",
"238",
"\n",
"Table",
"4.4",
".",
"Combined",
"EIST",
"specialisation",
"domains",
"in",
"Georgia",
"..............................................",
"240",
"\n",
"Table",
"4.5",
".",
"Combined",
"EIST",
"specialisation",
"domains",
"in",
"Moldova",
".............................................",
"241",
"\n",
"Table",
"4.6",
".",
"Combined",
"EIST",
"specialisation",
"domains",
"in",
"Ukraine",
"..............................................",
"244",
"\n",
"Table",
"4.7",
".",
"Pairs",
"of",
"economic",
"clusters",
"and",
"S&T",
"domains",
"that",
"can",
"be",
"identified",
"in",
"at",
"least",
"\n",
"two",
"countries",
"...............................................................................................................................................",
"246",
"\n",
"268",
"\n",
"Annexes",
"\n",
"Annexes",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation269",
"270",
"\n",
"Annexes",
"\n",
"Annex",
"1",
".",
"Results",
"of",
"the",
"\n",
"full",
"economic",
"mapping",
"\n",
"analysis",
"for",
"Georgia",
",",
"\n",
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"\n",
"ingEmerg-",
"\n",
"ingEmerg-",
"\n",
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"CurrentEmerg-",
"\n",
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"\n",
"ingEmerg-",
"\n",
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"countries",
"-"
] | [] |
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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] | [
{
"end": 2409,
"label": "CITATION-REFEERENCE",
"start": 2388
}
] |
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 13 %, Mage25-54 67 %, Mage55-75 20 %), gender (52 %
female), and level of education (Mprimary 3 %, Msecondary 31 %,
Mtertiary 25 %, Muni-higher 42 %). 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 3,000, Nclaim 3,000). 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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"\n",
"H1b",
",",
"H1d",
",",
"H2c",
"and",
"H2d",
".",
"\n",
"3.1",
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"\n",
"3.1.1",
".",
"Experiment",
"I",
":",
"Discrete",
"choice",
"experiment",
"\n",
"The",
"DCE",
"is",
"applied",
"to",
"six",
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"in",
"six",
"EU",
"member",
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"six",
"\n",
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"\n",
"composition",
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"(",
"2019",
")",
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"sur-",
"\n",
"vey",
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"-",
"specific",
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"group",
"discussions5(Di",
"\n",
"Marcantonio",
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"al",
".",
",",
"2020",
")",
".",
"The",
"six",
"selected",
"MS",
"(",
"Germany",
",",
"Hungary",
",",
"\n",
"Lithuania",
",",
"Romania",
",",
"Spain",
"and",
"Sweden",
")",
"ensure",
"a",
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"\n",
"distribution",
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"a",
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"representation",
"of",
"socio",
"-",
"economic",
"\n",
"conditions",
"across",
"the",
"EU",
".",
"They",
"also",
"represent",
"both",
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"and",
"West-",
"\n",
"ern",
"European",
"countries",
"mentioned",
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"the",
"DFQ",
"political",
"debate",
".",
"To",
"align",
"\n",
"with",
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"sensorial",
"evaluation",
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"(",
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")",
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"\n",
"avoiding",
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",",
"consumers",
"in",
"each",
"country",
"\n",
"evaluated",
"only",
"three",
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"six",
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",",
"and",
"each",
"product",
"\n",
"was",
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"versions",
".",
"The",
"distri",
"-",
"\n",
"bution",
"of",
"products",
"-",
"countries",
"pairs",
"is",
"shown",
"in",
"Table",
"1",
".",
"\n",
"Respondents",
"were",
"informed",
"that",
"the",
"purpose",
"of",
"the",
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"was",
"\n",
"to",
"evaluate",
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"three",
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"versions",
"of",
"three",
"\n",
"products",
".",
"The",
"experiment",
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"not",
"incentivized",
",",
"which",
"raises",
"the",
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"\n",
"hypothetical",
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"(",
"Murphy",
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".",
",",
"2005",
")",
".6",
"\n",
"In",
"total",
"6,000",
"participants",
"(",
"1,000",
"per",
"country",
")",
"were",
"recruited",
"by",
"a",
"\n",
"market",
"research",
"company",
"to",
"participate",
"in",
"the",
"online",
"experiment",
".",
"Par-",
"\n",
"ticipants",
"were",
"selected",
"using",
"stratified",
"random",
"sampling",
"by",
"age",
"group",
"\n",
"(",
"Mage18",
"-",
"24",
"13",
"%",
",",
"Mage25",
"-",
"54",
"67",
"%",
",",
"Mage55",
"-",
"75",
"20",
"%",
")",
",",
"gender",
"(",
"52",
"%",
"\n",
"female",
")",
",",
"and",
"level",
"of",
"education",
"(",
"Mprimary",
"3",
"%",
",",
"Msecondary",
"31",
"%",
",",
"\n",
"Mtertiary",
"25",
"%",
",",
"Muni",
"-",
"higher",
"42",
"%",
")",
".",
"A",
"short",
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"\n",
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"to",
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"(",
"a",
")",
"had",
"consumed",
"\n",
"the",
"selected",
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"three",
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"and",
"(",
"b",
")",
"were",
"\n",
"the",
"main",
"shoppers",
"for",
"their",
"household",
".",
"\n",
"Across",
"the",
"six",
"countries",
",",
"we",
"randomly",
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"the",
"6,000",
"re-",
"\n",
"spondents",
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"one",
"of",
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"conditions",
"in",
"a",
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"\n",
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"experimental",
"design",
"(",
"Ncontrol",
"3,000",
",",
"Nclaim",
"3,000",
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".",
"For",
"all",
"\n",
"consumers",
",",
"the",
"information",
"presented",
"in",
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"choice",
"sets",
"included",
"the",
"\n",
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"ingredients",
",",
"nutritional",
"facts",
",",
"the",
"price",
",",
"and",
"the",
"brand",
".",
"For",
"\n",
"ingredients",
"and",
"nutritional",
"facts",
",",
"the",
"information",
"provided",
"corre",
"-",
"\n",
"sponded",
"to",
"what",
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"on",
"the",
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"sold",
"in",
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"super",
"-",
"\n"
] | [
{
"end": 657,
"label": "CITATION-REFEERENCE",
"start": 629
},
{
"end": 1566,
"label": "CITATION-REFEERENCE",
"start": 1547
}
] |
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 | [
"G10F",
";",
"A45C",
";",
"A63C",
";",
"A63J",
";",
"B62B",
";",
"F23Q",
";",
"\n",
"G10",
"G",
"\n",
"32.5Manufacture",
"of",
"medical",
"and",
"dental",
"\n",
"instruments",
"and",
"supplies",
"[",
"32.5]A61B",
";",
"A61H",
";",
"B04B",
";",
"G01",
"T",
";",
"A61C",
";",
"A61D",
";",
"\n",
"A61F",
";",
"A61J",
";",
"A61L",
";",
"A61",
"M",
";",
"C12",
"M",
";",
"A61",
"K",
"8/",
"*",
";",
"\n",
"G21",
"G",
";",
"A61",
"G",
";",
"A62B",
";",
"B01LA61",
"K",
"\n",
"32.9",
"Manufacturing",
"n.e.c",
".",
"[",
"32.9",
"]",
"B65D",
";",
"G03D",
";",
"G03F",
";",
"G09B",
";",
"G09F",
"\n",
"42.2",
"Construction",
"of",
"utility",
"projects",
"[",
"42.2",
"]",
"E03B",
";",
"E03C",
"\n",
"42.9Construction",
"of",
"other",
"civil",
"engineering",
"\n",
"projects",
"[",
"42.9]E02B",
"\n",
"43",
"Specialised",
"construction",
"activities",
"[",
"43]E03F",
";",
"E04",
"G",
";",
"E04B",
";",
"E04H",
";",
"E04C",
";",
"E04D",
";",
"\n",
"E04F",
"\n",
"62Computer",
"programming",
",",
"consultancy",
"and",
"\n",
"related",
"activities",
"[",
"62]G06Q",
"\n",
"Source",
":",
"Van",
"Looy",
",",
"V.",
"et",
"al",
".",
",",
"Patent",
"statistics",
":",
"Concordance",
"IPC",
"v8",
"-",
"NACE",
"Rev.2",
",",
"Eurostat",
",",
"2015",
".",
"\n",
"332",
"\n",
"Annexes",
"\n",
"Annex",
"8",
".",
"NACE",
"v2",
"to",
"S&T",
"\n",
"domains",
"correspondence",
"\n",
"tables",
"via",
"IPC",
",",
"for",
"each",
"\n",
"EaP",
"countryThe",
"following",
"tables",
"report",
"the",
"NACE",
"to",
"S&T",
"do-",
"\n",
"main",
"mappings",
"obtained",
"via",
"patent",
"IPC",
"classes",
"\n",
"for",
"all",
"EaP",
"countries",
".",
"The",
"mappings",
"are",
"obtained",
"\n",
"by",
"determining",
"the",
"intersection",
"of",
"each",
"S&T",
"do-",
"\n",
"main",
"with",
"the",
"IPC",
"classes",
"of",
"the",
"patent",
"records",
"\n",
"associated",
"with",
"the",
"domain",
"and",
"by",
"leveraging",
"IPC",
"\n",
"to",
"NACE",
"concordances",
".",
"Notably",
",",
"the",
"NACE",
"-",
"S&T",
"do-",
"\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"
] | [] |
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 | [
"engineering",
"\n",
"and",
"heavy",
"machinery",
"\n",
"(",
"897",
"|",
"43.06%)Mechanical",
"engineering",
"\n",
"and",
"heavy",
"machinery",
"\n",
"(",
"443",
"|",
"50.98%)Mechanical",
"engineering",
"\n",
"and",
"heavy",
"machinery",
"\n",
"(",
"600",
"|",
"40.38%)Mechanical",
"engineering",
"\n",
"and",
"heavy",
"machinery",
"\n",
"(",
"16",
"351",
"|",
"30.08%)Electric",
"and",
"electronic",
"\n",
"technologies",
"\n",
"(",
"102",
"|",
"28.41",
"%",
")",
"\n",
"Electric",
"and",
"electronic",
"\n",
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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
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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-
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EMHERG-: v2.0.0 (v2.0.0), Zenodo [code]m
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Tilloy, A., Malamud, B. D., Winter, H., and Joly-Laugel,
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UNISDR – UN International Strategy for Disaster Reduction: Liv-
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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
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van Westen, C., Kappes, M. S., Luna, B. Q., Frigerio, S., Glade, T.,
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uary 2025), 2002.
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Aerts, J. C. J. H., Alabaster, A., Bulder, B., Campillo Torres,
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the weighted Z-method is superior to Fisher’s approach, J. Evo-
lution. Biol., 18, 1368–1373, 2005. | [
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Guyton AC, Hall JE (2006). Textbook of Medical Physiology. Philadelphia: Elsevier. pp. 855–6. ISBN 978-0-7216-0240-0. | [
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Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with theauthor(s) or other rightsholder(s); author self-archiving of the acceptedmanuscript version of this article is solely governed by the terms of suchpublishing agreement and applicable law.
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34 52 28 61 64 40 31 29 15 50 47 21 55 40 35 83 57 34
14.3 Manufacture of knitted and crocheted apparel X X
15 Manufacture of leather and related products
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15.2 Manufacture of footwear X X X X
16Manufacture of wood and of products of wood and cork, except
furniture; manufacture of articles of straw and plaiting materials
16.1 Sawmilling and planing of wood X X X X
16.2 Manufacture of products of wood, cork, straw and plaiting materials X X X X X X X
17 Manufacture of paper and paper products
17.1 Manufacture of pulp, paper and paperboard X
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18 Printing and reproduction of recorded media
18.1 Printing and service activities related to printing X X X
18.2 Reproduction of recorded media
19 Manufacture of coke and refined petroleum products
19.1 Manufacture of coke oven products X X X
19.2 Manufacture of refined petroleum products X X X X
20 Manufacture of chemicals and chemical products
20.1Manufacture of basic chemicals, fertilisers and nitrogen compounds,
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20.2 Manufacture of pesticides and other agrochemical products
20.3Manufacture of paints, varnishes and similar coatings, printing ink
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20.4Manufacture of soap and detergents, cleaning and polishing
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20.5 Manufacture of other chemical products X X
20.6 Manufacture of man-made fibres
21Manufacture of basic pharmaceutical products and pharmaceutical
preparations
21.1 Manufacture of basic pharmaceutical products X
21.2 Manufacture of pharmaceutical preparations X X
22 Manufacture of rubber and plastic products
Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation275 276
Annexes
GEORGIA MOLDOVA UKRAINEEmploy-
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Turnover
Employ-
ment &
turnover
Employ-
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Turnover
Employ-
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turnover
Employ-
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Turnover
Employ-
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Employ-
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Turnover
Employ-
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Employ-
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Turnover
Employ-
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turnover
Employ-
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Turnover
Employ-
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turnover
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ingEmerg-
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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
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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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"of",
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"\n",
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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
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95 Repair of computers and personal and household goods
95.1 Repair of computers and communication equipment X X
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TACTIVITIES OF HOUSEHOLDS AS EMPLOYERS; UNDIFFERENTIATED
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97 Activities of households as employers of domestic personnel
98Undifferentiated goods- and services-producing activities of private
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Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation295 296
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GEORGIA MOLDOVA UKRAINEEmploy-
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Employ-
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Employ-
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Employ-
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Employ-
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Employ-
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Employ-
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U ACTIVITIES OF EXTRATERRITORIAL ORGANISATIONS AND BODIES
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Smart Specialisation in the Eastern Partnership countries - Potential for knowledge-based economic cooperation297 298
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Manufacturing for five
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Super laws and paramilitary groups watch over the
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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
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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
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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 | [
"3105",
"Instrumentation",
"467",
"105.37%Table",
"3.7",
".",
"The",
"Scopus",
"subject",
"fields",
"that",
"appear",
"more",
"frequently",
"within",
"each",
"domain",
"in",
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"with",
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"average",
"\n",
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"\n",
"160",
"\n ",
"Part",
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"and",
"technological",
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"\n",
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"ASJC",
"Description",
"No",
"recordsRelative",
"\n",
"freq",
".",
"\n",
"Energy2213",
"Safety",
",",
"risk",
",",
"reliability",
"and",
"quality",
"294",
"290.13",
"%",
"\n",
"2102",
"Energy",
"engineering",
"and",
"power",
"technology",
"1",
"140",
"263.71",
"%",
"\n",
"2104",
"Nuclear",
"energy",
"and",
"engineering",
"516",
"189.43",
"%",
"\n",
"2200",
"General",
"engineering",
"454",
"188.25",
"%",
"\n",
"2103",
"Fuel",
"technology",
"84",
"166.34",
"%",
"\n",
"Environmental",
"\n",
"sciences",
"and",
"\n",
"industries1105",
"Ecology",
",",
"evolution",
",",
"behavior",
"and",
"systematics",
"1",
"863",
"586.38",
"%",
"\n",
"2300",
"General",
"environmental",
"science",
"434",
"573.21",
"%",
"\n",
"1909",
"Geotechnical",
"engineering",
"and",
"engineering",
"geology",
"1",
"015",
"531.02",
"%",
"\n",
"1110",
"Plant",
"science",
"612",
"392.73",
"%",
"\n",
"1900",
"General",
"earth",
"and",
"planetary",
"sciences",
"370",
"383.42",
"%",
"\n",
"Fundamental",
"\n",
"physics",
"and",
"\n",
"mathematics2600",
"General",
"mathematics",
"4",
"206",
"933.35",
"%",
"\n",
"3106",
"Nuclear",
"and",
"high",
"energy",
"physics",
"5",
"979",
"566.81",
"%",
"\n",
"2604",
"Applied",
"mathematics",
"3",
"190",
"497.80",
"%",
"\n",
"3103",
"Astronomy",
"and",
"astrophysics",
"2",
"127",
"477.76",
"%",
"\n",
"2613",
"Statistics",
"and",
"probability",
"1",
"601",
"472.83",
"%",
"\n",
"Governance",
",",
"\n",
"culture",
",",
"education",
"\n",
"and",
"the",
"economy1000",
"Multidisciplinary",
"200",
"247.62",
"%",
"\n",
"1405",
"Management",
"of",
"technology",
"and",
"innovation",
"585",
"203.41",
"%",
"\n",
"2000",
"General",
"economics",
",",
"econometrics",
"and",
"finance",
"1",
"023",
"199.42",
"%",
"\n",
"2002",
"Economics",
"and",
"econometrics",
"2",
"160",
"198.53",
"%",
"\n",
"3316",
"Cultural",
"studies",
"189",
"197.91",
"%",
"\n",
"Health",
"and",
"\n",
"wellbeing2700",
"General",
"medicine",
"4",
"361",
"624.00",
"%",
"\n",
"1311",
"Genetics",
"743",
"348.17",
"%",
"\n",
"1314",
"Physiology",
"470",
"283.70",
"%",
"\n",
"1300",
"General",
"biochemistry",
",",
"genetics",
"and",
"molecular",
"biology",
"630",
"283.36",
"%",
"\n",
"1312",
"Molecular",
"biology",
"220",
"275.69",
"%",
"\n",
"ICT",
"and",
"computer",
"\n",
"science1710",
"Information",
"systems",
"1",
"147",
"449.80",
"%",
"\n",
"1706",
"Computer",
"science",
"applications",
"1",
"809",
"406.87",
"%",
"\n",
"1705",
"Computer",
"networks",
"and",
"communications",
"2",
"101",
"402.88",
"%",
"\n",
"2207",
"Control",
"and",
"systems",
"engineering",
"1",
"431",
"305.99",
"%",
"\n",
"1700",
"General",
"computer",
"science",
"1",
"647",
"268.94",
"%",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation161",
"\n",
"Domain",
"ASJC",
"Description",
"No",
"recordsRelative",
"\n",
"freq",
".",
"\n",
"Mechanical",
"\n",
"engineering",
"and",
"\n",
"heavy",
"machinery2200",
"General",
"engineering",
"684",
"283.62",
"%",
"\n",
"2210",
"Mechanical",
"engineering",
"1",
"458",
"217.31",
"%",
"\n",
"2611",
"Modelling",
"and",
"simulation",
"19",
"165.22",
"%",
"\n",
"2502",
"Biomaterials",
"21",
"155.56",
"%",
"\n",
"2209",
"Industrial",
"and",
"manufacturing",
"engineering",
"586",
"149.57",
"%",
"\n",
"Nanotechnology",
"\n",
"and",
"materials3104",
"Condensed",
"matter",
"physics",
"9",
"900",
"708.48",
"%",
"\n",
"2508",
"Surfaces",
",",
"coatings",
"and",
"films",
"2",
"000",
"668.26",
"%",
"\n",
"2500",
"General",
"materials",
"science",
"6",
"602",
"552.43",
"%",
"\n",
"2504",
"Electronic",
",",
"optical",
"and",
"magnetic",
"materials",
"5",
"640",
"527.96",
"%",
"\n",
"1600",
"General",
"chemistry",
"2",
"800",
"450.46",
"%",
"\n",
"Optics",
"and",
"\n",
"photonics3107",
"Atomic",
"and",
"molecular",
"physics",
",",
"and",
"optics",
"1",
"456",
"220.61",
"%",
"\n",
"2504",
"Electronic",
",",
"optical",
"and",
"magnetic",
"materials",
"2",
"169",
"203.04",
"%",
"\n",
"2208",
"Electrical",
"and",
"electronic",
"engineering",
"2",
"562",
"185.55",
"%",
"\n",
"3102",
"Acoustics",
"and",
"ultrasonics",
"44",
"166.04",
"%",
"\n",
"3109",
"Statistical",
"and",
"nonlinear",
"physics",
"73",
"139.31",
"%",
"\n",
"Transportation2606",
"Control",
"and",
"optimization",
"138",
"267.10",
"%",
"\n",
"3313",
"Transportation",
"135",
"197.08",
"%",
"\n",
"2202",
"Aerospace"
] | [] |
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 | [
"F02B",
";",
"B62D",
"\n",
"30Manufacture",
"of",
"other",
"\n",
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"32",
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"A61",
"K",
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"32",
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",",
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"\n",
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"\n",
"32",
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"A61",
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"\n",
"32",
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"A61B",
"\n",
"32",
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"32",
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",",
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"Annex",
"9",
".",
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"\n",
"NACE",
"v2",
"correspondence",
"\n",
"tableThe",
"table",
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",",
"JRC",
",",
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"7.1",
"Mining",
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"Mining",
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"8",
"Other",
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"8.1",
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",",
"sand",
"and",
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";",
"8.9",
"Mining",
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"\n",
"n.e.c",
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";",
"9",
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";",
"9.1",
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"\n",
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";",
"9.9",
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"\n",
"quarrying",
"\n",
"NABS02",
"Environment38",
"Waste",
"collection",
",",
"treatment",
"and",
"disposal",
"activities",
";",
"materials",
"recovery",
";",
"\n",
"38.1",
"Waste",
"collection",
";",
"38.2",
"Waste",
"treatment",
"and",
"disposal",
";",
"38.3",
"Materials",
"\n",
"recovery",
";",
"39",
"Remediation",
"activities",
"and",
"other",
"waste",
"management",
"services",
";",
"\n",
"39.0",
"Remediation",
"activities",
"and",
"other",
"waste",
"management",
"services",
"\n",
"NABS04Transport",
",",
"telecommunication",
"\n",
"and",
"other",
"infrastructures30.2",
"Manufacture",
"of",
"railway",
"locomotives",
"and",
"rolling",
"stock",
";",
"30.3",
"\n",
"Manufacture",
"of",
"air",
"and",
"spacecraft",
"and",
"related",
"machinery",
";",
"36",
"Water",
"\n",
"collection",
",",
"treatment",
"and",
"supply",
";",
"36.0",
"Water",
"collection",
",",
"treatment",
"and",
"\n",
"supply",
";",
"37",
"Sewerage",
";",
"37.0",
"Sewerage",
";",
"41",
"Construction",
"of",
"buildings",
";",
"41.1",
"\n",
"Development",
"of",
"building",
"projects",
";",
"41.2",
"Construction",
"of",
"residential",
"and",
"non-",
"\n",
"residential",
"buildings",
";",
"42",
"Civil",
"engineering",
";",
"42.1",
"Construction",
"of",
"roads",
"and",
"\n",
"railways",
";",
"42.2",
"Construction",
"of",
"utility",
"projects",
";",
"42.9",
"Construction",
"of",
"other",
"civil",
"\n",
"engineering",
"projects",
";",
"43",
"Specialised",
"construction",
"activities",
";",
"43.1",
"Demolition",
"\n",
"and",
"site",
"preparation",
";",
"43.2",
"Electrical",
",",
"plumbing",
"and",
"other",
"construction",
"\n",
"installation",
"activities",
";",
"43.3",
"Building",
"completion",
"and",
"finishing",
";",
"43.9",
"Other",
"\n",
"specialised",
"construction",
"activities",
";",
"49.1",
"Passenger",
"rail",
"transport",
",",
"interurban",
";",
"\n",
"49.2",
"Freight",
"rail"
] | [] |
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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Information from the Slovak delegation ”. (Document No. 10287/24 AGRI 435
DENLEG 35 FOOD 70). General Secretariat of the Council. https://data.consilium.
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Duivenvoorde, B., 2019. The Upcoming Changes in the Unfair Commercial Practices
Directive: A Better Deal for Consumers? Journal of European Consumer and Market
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by the Consumer Programme of the DG Justice of the European Commission.
https://www.fightdualfood.eu/files/uploads/2021/11/D3.1-ECO-Manual_
compressed.pdf.
Erdem, T., Swait, J., Valenzuela, A., 2006. Brands as Signals: A Cross-Country Validation
Study. Journal of Marketing 70 (01/01), 34–49.
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https://www.focos-food.com/dual-quality-east-west-usa-uk/.
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Customers Really Prefer Products Tailored to Their Preferences? Journal of
Marketing 73 (5), 103–121.
Franke, N., Schreier, M., Kaiser, U., 2010. The ‘‘I designed ItMyself ’’ Effect in Mass
Customization. Management Science 65, 125–140. | [
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",",
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"\n",
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"?",
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",",
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",",
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",",
"Steger",
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"Schreier",
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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 | [
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"\n",
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" \n",
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",",
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",",
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".",
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"\n",
"46THE",
"FUTURE",
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" ",
"—",
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"A",
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"CHAPTER",
"3The",
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"to",
"Europe",
"’s",
"position",
"in",
"clean",
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"owes",
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] | [] |
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 | [
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"\n",
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"time",
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"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",
"–",
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"\n",
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"2.55",
".",
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"\n",
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"\n",
"Armenia",
"Azerbaijan",
"Belarus",
"Georgia",
"Moldova",
"Ukraine",
"Total",
"\n",
"ECCP",
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"PLUS",
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"2.56",
".",
"Number",
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"EaP",
"country",
"\n",
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"following",
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"\n",
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"SOCAR"
] | [] |
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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"time",
"periods",
"for",
"all",
"countries",
".",
"\n",
"Current",
"strengths",
"Emerging",
"strengths",
"*",
"\n",
"Degree",
"of",
"\n",
"specialisationRelative",
"size",
"Change",
"in",
"degree",
"of",
"specialisation",
"\n",
"Armenia",
">",
"1.25",
">",
"0.1",
"%",
">",
"0",
"\n",
"Azerbaijan",
">",
"1.25",
">",
"0.1",
"%",
">",
"0",
"\n",
"Georgia",
">",
"1.25",
">",
"0.1",
"%",
">",
"0",
"\n",
"Moldova",
">",
"1.25",
">",
"0.1",
"%",
">",
"0",
"\n",
"Ukraine",
">",
"1.25",
">",
"0.1",
"%",
">",
"0Table",
"2.21",
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"\n",
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"For",
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"\n",
"Mapping",
"of",
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"specialisa-",
"\n",
"tions",
"–",
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"Armenia",
"\n",
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"Per",
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"22.4",
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"2.3",
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"1.3",
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"0.5",
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"0.6",
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"\n",
"9",
"Other",
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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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"0.3",
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"1",
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" \n",
"112",
"Alcoholic",
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"X",
"10.5",
"%",
" \n",
"122Tobacco",
",",
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"export",
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] | [] |
imaging and rabies tracing experiments.
Cell Reports 39, 110893, May 31, 2022 e6Articlell
OPEN ACCESS | [
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",",
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",",
"May",
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"\n",
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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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"35THE",
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" ",
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"FIGURE",
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"Source",
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"Eurostat",
"\n",
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"shortages",
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"as",
"a",
"barrier"
] | [] |
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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"\n"
] | [] |
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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"\n",
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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-
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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 $13 each–these cute little bottles offer a great, affordable way to
partake in the skin and hair care oil craze.
I tested each product in the line, massaging them onto my face every night before bed and running any leftover oil through my hair to tame frizziness. You could also
add a few drops to your bath, favorite moisturizer, or even your shampoo and conditioner.
Here’s a quick rundown of each oil.
Revitalize: Omega 3, 6, 9 & Evening Primrose
This was the first one I tried (I went in ROYGBIV order to keep things straight) and my first impression was that it smells lovely but a little strong. The fragrance
smells genuinely like flowers.
Machine Red Lanterns, the lead exposure to a movie starring the Batman solo movie alum Margot Robbie taken under Wonder Woman’s wing have reignited that rivalry with
their whispery premiere. They played it as much as they possibly could, even though people who didn’t ever watch Justice League or might have missed it waiting in
line for the theater were still talking about as I spilled coffee.
The gist? An overextended (OK, a sore) Adam West films set up a Legion of Super-Heroes situation. How aggro? | [
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"along",
"with",
"the",
"\n",
"total",
"number",
"of",
"excerpts",
"they",
"annotated",
".",
"Human",
"I",
"recently",
"got",
"the",
"chance",
"to",
"try",
"the",
"new",
"Oil",
"Essentials",
"line",
".",
"With",
"six",
"potent",
"blends",
"to",
"choose",
"from",
"–",
"at",
"$",
"13",
"each",
"–",
"these",
"cute",
"little",
"bottles",
"offer",
"a",
"great",
",",
"affordable",
"way",
"to",
"\n",
"partake",
"in",
"the",
"skin",
"and",
"hair",
"care",
"oil",
"craze",
".",
"\n",
"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",
"\n",
"add",
"a",
"few",
"drops",
"to",
"your",
"bath",
",",
"favorite",
"moisturizer",
",",
"or",
"even",
"your",
"shampoo",
"and",
"conditioner",
".",
"\n",
"Here",
"’s",
"a",
"quick",
"rundown",
"of",
"each",
"oil",
".",
"\n",
"Revitalize",
":",
"Omega",
"3",
",",
"6",
",",
"9",
"&",
"Evening",
"Primrose",
"\n",
"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",
"\n",
"smells",
"genuinely",
"like",
"flowers",
".",
"\n",
"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",
"\n",
"their",
"whispery",
"premiere",
".",
"They",
"played",
"it",
"as",
"much",
"as",
"they",
"possibly",
"could",
",",
"even",
"though",
"people",
"who",
"did",
"n’t",
"ever",
"watch",
"Justice",
"League",
"or",
"might",
"have",
"missed",
"it",
"waiting",
"in",
"\n",
"line",
"for",
"the",
"theater",
"were",
"still",
"talking",
"about",
"as",
"I",
"spilled",
"coffee",
".",
"\n",
"The",
"gist",
"?",
"An",
"overextended",
"(",
"OK",
",",
"a",
"sore",
")",
"Adam",
"West",
"films",
"set",
"up",
"a",
"Legion",
"of",
"Super",
"-",
"Heroes",
"situation",
".",
"How",
"aggro",
"?"
] | [] |
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 | [
"55.1",
"Hotels",
"and",
"similar",
"accommodation",
"X",
"X",
"\n",
"56.1",
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"and",
"mobile",
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"service",
"activities",
"X",
" \n",
"56.3",
"Beverage",
"serving",
"activities",
" ",
"X",
"\n",
"J",
"INFORMATION",
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" \n",
"58.1",
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"books",
",",
"periodicals",
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"activities",
" ",
"X",
"\n",
"60.2",
"Television",
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"activities",
" ",
"X",
"\n",
"61.1",
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" ",
"X",
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"61.2",
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" ",
"X",
"\n",
"K",
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"ACTIVITIES",
" \n",
"64.9",
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"service",
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",",
"except",
"insurance",
"and",
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"X",
" \n",
"66.1",
"Activities",
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"to",
"financial",
"services",
",",
"except",
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"pension",
"funding",
" ",
"X",
"\n",
"L",
"REAL",
"ESTATE",
"ACTIVITIES",
" \n",
"68.2",
"Rental",
"and",
"operating",
"of",
"own",
"or",
"leased",
"real",
"estate",
"X",
" \n",
"68.3",
"Real",
"estate",
"activities",
"on",
"a",
"fee",
"or",
"contract",
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" ",
"X",
"\n",
"M",
"PROFESSIONAL",
",",
"SCIENTIFIC",
"AND",
"TECHNICAL",
"ACTIVITIES",
" \n",
"69.1",
"Legal",
"activities",
"X",
" \n",
"69.2",
"Accounting",
",",
"bookkeeping",
"and",
"auditing",
"activities",
";",
"tax",
"consultancy",
"X",
"X",
"\n",
"71.1",
"Architectural",
"and",
"engineering",
"activities",
"and",
"related",
"technical",
"consultancy",
" ",
"X",
"\n",
"72.2",
"Research",
"and",
"experimental",
"development",
"on",
"social",
"sciences",
"and",
"humanities",
"X",
" \n",
"73.1",
"Advertising",
"X",
"X",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation43",
"\n",
"NACE",
"Industry",
"nameCurrent",
"\n",
"strengthEmerging",
"\n",
"strength",
"\n",
"N",
"ADMINISTRATIVE",
"AND",
"SUPPORT",
"SERVICE",
"ACTIVITIES",
" \n",
"77.1",
"Rental",
"and",
"leasing",
"of",
"motor",
"vehicles",
"X",
" \n",
"77.3",
"Rental",
"and",
"leasing",
"of",
"other",
"machinery",
",",
"equipment",
"and",
"tangible",
"goods",
"X",
" \n",
"79.1",
"Travel",
"agency",
"and",
"tour",
"operator",
"activities",
"X",
" \n",
"80.1",
"Private",
"security",
"activities",
" ",
"X",
"\n",
"82.9",
"Business",
"support",
"service",
"activities",
"n.e.c",
".",
"*",
" ",
"X",
"\n",
"O",
"PUBLIC",
"ADMINISTRATION",
"AND",
"DEFENCE",
";",
"COMPULSORY",
"SOCIAL",
"SECURITY",
" \n",
"P",
"EDUCATION",
" \n",
"85.1",
"Pre",
"-",
"primary",
"education",
"X",
" \n",
"85.3",
"Secondary",
"education",
"X",
" \n",
"85.5",
"Other",
"education",
"X",
" \n",
"Q",
"HUMAN",
"HEALTH",
"AND",
"SOCIAL",
"WORK",
"ACTIVITIES",
" \n",
"86.1",
"Hospital",
"activities",
" ",
"X",
"\n",
"88.1",
"Social",
"work",
"activities",
"without",
"accommodation",
"for",
"the",
"elderly",
"and",
"disabled",
"X",
" \n",
"88.9",
"Other",
"social",
"work",
"activities",
"without",
"accommodation",
"X",
" \n",
"R",
"ARTS",
",",
"ENTERTAINMENT",
"AND",
"RECREATION",
" \n",
"90",
"Creative",
",",
"arts",
"and",
"entertainment",
"activities",
"X",
" \n",
"91",
"Libraries",
",",
"archives",
",",
"museums",
"and",
"other",
"cultural",
"activities",
"X",
" \n",
"92",
"Gambling",
"and",
"betting",
"activities",
"X",
"X",
"\n",
"93.1",
"Sports",
"activities",
"X",
" \n",
"93.2",
"Amusement",
"and",
"recreation",
"activities",
"X",
" \n",
"S",
"OTHER",
"SERVICE",
"ACTIVITIES",
" \n",
"94.1",
"Activities",
"of",
"business",
",",
"employers",
"and",
"professional",
"membership",
"organisations",
"X",
" \n",
"94.2",
"Activities",
"of",
"trade",
"unions",
"X",
" \n",
"94.9",
"Activities",
"of",
"other",
"membership",
"organisations",
"X",
" \n",
"96",
"Other",
"personal",
"service",
"activities",
" ",
"X",
"\n",
"n.e.c",
".",
"=",
"not",
"elsewhere",
"classified",
"\n",
"*",
"n.e.c",
".",
",",
"’",
"frequently",
"used",
"throughout",
"the",
"report",
",",
"stands",
"for",
"‘",
"not",
"elsewhere",
"classified",
"’",
".",
"\n",
"44",
"\n ",
"Part",
"2",
"Analysis",
"of",
"economic",
"and",
"innovation",
"potential",
"\n",
"Mapping",
"the",
"economic",
"potential",
"–",
"results",
"\n",
"for",
"Moldova",
"\n",
"Results",
"of",
"the",
"economic",
"mapping",
"for",
"Moldova",
"are",
"\n",
"shown",
"in",
"Table",
"2.4",
".",
"In",
"total",
",",
"15",
"industries",
"have",
"\n",
"been",
"identified",
"as",
"having",
"a",
"current",
"strength",
"and",
"\n",
"21",
"industries",
"have"
] | [] |
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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"1.016",
"0.469",
"0.974",
"0.761",
"1.060",
"0.945",
"1.112",
"1.533",
"0.364",
"0.986",
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"(",
"19",
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"1.650",
"1.030",
"0.871",
"1.031",
"0.303",
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"1.420",
"0.618",
"0.985",
"\n",
"Basic",
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"(",
"24",
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"2.541",
"0.000",
"0.000",
"2.195",
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"0.000",
"0.000",
"2.081",
"0.000",
"1.764",
"2.484",
"0.000",
"0.000",
"2.280",
"0.000",
"1.236",
"\n",
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"(",
"25",
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"1.421",
"1.517",
"0.672",
"0.694",
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"3.118",
"0.000",
"0.242",
"0.996",
"0.810",
"1.524",
"1.388",
"0.615",
"0.663",
"\n",
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"(",
"26",
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"1.306",
"1.340",
"0.355",
"3.000",
"0.000",
"0.000",
"1.306",
"1.340",
"0.355",
"\n",
"Electronics",
"(",
"27",
")",
"0.586",
"1.269",
"1.144",
"0.413",
"1.096",
"1.491",
"0.553",
"1.347",
"1.101",
"\n",
"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",
"\n",
"Transport",
"machines",
"(",
"29",
"+",
"30",
")",
"\n",
"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",
"\n",
"Recycling",
"(",
"33",
")",
"1.462",
"0.410",
"1.128",
"2.086",
"0.668",
"0.246",
"1.265",
"0.760",
"0.976",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"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",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern",
"Partnership",
"countries",
"-",
"Potential",
"for",
"knowledge",
"-",
"based",
"economic",
"cooperation91",
"\n",
"3.2",
".",
"Patents",
"\n",
"Patent",
"data",
"is",
"collected",
"from",
"the",
"DOCDB",
"da-",
"\n",
"tabase41",
"of",
"the",
"European",
"Patent",
"Office",
",",
"which",
"\n",
"covers",
"patents",
"issued",
"by",
"most",
"of",
"the"
] | [] |
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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",",
"solid",
"fuel",
"\n",
"Class",
"24",
"–",
"Medical",
"and",
"laboratory",
"equipment",
"\n",
"Class",
"25",
"–",
"Building",
"units",
"and",
"construction",
"ele-",
"\n",
"ments",
"\n",
"Class",
"26",
"–",
"Lighting",
"apparatus",
"\n",
"Class",
"27",
"–",
"Tobacco",
"and",
"smokers",
"’",
"supplies",
"\n",
"Class",
"28",
"–",
"Pharmaceutical",
"and",
"cosmetic",
"products",
",",
"\n",
"toilet",
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"and",
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"\n",
"Class",
"29",
"–",
"Devices",
"and",
"equipment",
"against",
"fire",
"\n",
"hazards",
",",
"for",
"accident",
"prevention",
"and",
"for",
"rescue",
"\n",
"Class",
"30",
"–",
"Articles",
"for",
"the",
"care",
"and",
"handling",
"of",
"\n",
"animals",
"\n",
"Class",
"31",
"–",
"Machines",
"and",
"appliances",
"for",
"preparing",
"\n",
"food",
"or",
"drink",
",",
"not",
"elsewhere",
"specified",
"\n",
"Class",
"32",
"–",
"Graphic",
"symbols",
"and",
"logos",
",",
"surface",
"\n",
"patterns",
",",
"ornamentation",
"\n",
"328",
"\n",
"Annexes",
"\n",
"Annex",
"7",
".",
"IPC",
"to",
"NACE",
"v2",
"\n",
"correspondence",
"tableThe",
"table",
"below",
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"be-",
"\n",
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"IPC",
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"-",
"digit",
"\n",
"NACE",
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".",
"\n",
"74",
"Van",
"Looy",
",",
"V.",
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"al",
".",
",",
"Patent",
"Statistics",
":",
"Concordance",
"IPC",
"\n",
"v8",
"-",
"NACE",
"Rev.2",
",",
"Eurostat",
",",
"2015",
".",
"\n",
"NACE",
"sector",
"IPC",
"class",
"NOT",
"matching",
"\n",
"10",
"Manufacture",
"of",
"food",
"products",
"[",
"10]A23J",
";",
"A01H",
";",
"A21D",
";",
"A23D",
";",
"A23F",
";",
"A23",
"G",
";",
"\n",
"A23L",
";",
"A23L",
";",
"A23P",
";",
"C13F",
";",
"C13J",
";",
"C13",
"K",
";",
"\n",
"A23B",
";",
"C12J",
";",
"A23C",
";",
"A23",
"K",
";",
"C13B",
"\n",
"10.5",
"Manufacture",
"of",
"dairy",
"products",
"[",
"10.5",
"]",
"A01J",
"\n",
"11",
"Manufacture",
"of",
"beverages",
"[",
"11",
"]",
"A23L",
";",
"C12C",
";",
"C12F",
";",
"C12",
"G",
";",
"C12H",
"\n",
"12",
"Manufacture",
"of",
"tobacco",
"products",
"[",
"12",
"]",
"A24B",
";",
"A24D",
";",
"A24F",
"\n",
"13",
"Manufacture",
"of",
"textiles",
"[",
"13]D04D",
";",
"D04",
"G",
";",
"D04H",
";",
"D06",
"M",
";",
"D06N",
";",
"D06P",
";",
"\n",
"D06C",
";",
"D06Q",
";",
"D06J",
"\n",
"14",
"Manufacture",
"of",
"wearing",
"apparel",
"[",
"14",
"]",
"A41B",
";",
"A41C",
";",
"A41D",
";",
"A41F",
"\n",
"15Manufacture",
"of",
"leather",
"and",
"related",
"\n",
"products",
"[",
"15]A43B",
";",
"A43C",
";",
"B68B",
";",
"B68C",
"\n",
"16Manufacture",
"of",
"wood",
"and",
"of",
"products",
"\n",
"of",
"wood",
"and",
"cork",
",",
"except",
"furniture",
";",
"\n",
"manufacture",
"of",
"articles",
"of",
"straw",
"and",
"\n",
"plaiting",
"materials",
"[",
"16]B27D",
";",
"B27H",
";",
"B27",
"M",
";",
"B27N",
"\n",
"17Manufacture",
"of",
"paper",
"and",
"paper",
"products",
"\n",
"[",
"17]B42F",
";",
"D21C",
";",
"D21H",
";",
"D21J",
"\n",
"18.1Printing",
"and",
"service",
"activities",
"related",
"to",
"\n",
"printing",
"[",
"18.1]B41",
"M",
";",
"B42D",
";",
"B44F",
"\n",
"19Manufacture",
"of",
"coke",
"and",
"refined",
"\n",
"petroleum",
"products",
"[",
"19]C10",
"G",
";",
"C10L",
"\n",
"20.1Manufacture",
"of",
"basic",
"chemicals",
",",
"\n",
"fertilisers",
"and",
"nitrogen",
"compounds",
",",
"\n",
"plastics",
"and",
"synthetic",
"rubber",
"in",
"primary",
"\n",
"forms",
"[",
"20.1]B01J",
";",
"B09B",
";",
"B09C",
";",
"C01D",
";",
"C01F",
";",
"C01",
"G",
";",
"\n",
"C05C",
";",
"C05D",
";",
"C05F",
";",
"C07B",
";",
"A61",
"K",
"8/",
"*",
";",
"C07C",
";",
"\n",
"C07F",
";",
"C07",
"G",
";",
"C08B",
";",
"C08F",
";",
"C08",
"G",
";",
"C08L",
";",
"\n",
"C09B",
";",
"C09C",
";",
"C10C",
";",
"C10H",
";",
"C10J",
";",
"C12S",
";",
"\n",
"C25B",
";",
"F17C",
";",
"F17D",
";",
"C01B",
";",
"C02F",
";",
"C05",
"G",
";",
"\n",
"C08J",
";",
"C09",
"K",
";",
"C10",
"K",
";",
"F25J",
";",
"C01C",
";",
"C05B",
";",
"\n",
"C08",
"K",
";",
"C10B",
";",
"G21FA61",
"K",
"\n",
"20.2Manufacture",
"of",
"pesticides",
"and",
"other",
"\n",
"agrochemical",
"products",
"[",
"20.2]A01N",
";",
"A01P",
"\n",
"20.3Manufacture",
"of",
"paints",
",",
"varnishes",
"and",
"\n",
"similar",
"coatings",
",",
"printing",
"ink",
"and",
"mastics",
"\n",
"[",
"20.3]B27",
"K",
";",
"C09D",
"\n",
"20.4Manufacture",
"of",
"soap",
"and",
"detergents",
",",
"\n",
"cleaning",
"and",
"polishing",
"preparations",
",",
"\n",
"perfumes",
"and",
"toilet",
"preparations",
"[",
"20.4]A61",
"K",
"8/",
"*",
";",
"A61Q",
";",
"C09F",
";",
"C11D",
";",
"D06L",
"\n",
"20.5Manufacture",
"of",
"other",
"chemical",
"products",
"\n",
"[",
"20.5]A62D",
";",
"C06B",
";",
"C06C",
";",
"C06D",
";",
"C08H",
";",
"C09",
"G",
";",
"\n",
"C09H",
";",
"C09J",
";",
"C11B",
";",
"C11C",
";",
"C14C",
";",
"C40B",
";",
"\n",
"A61",
"K",
"8/",
"*",
";",
"F42D",
";",
"C10",
"M",
";",
"C23F",
";",
"D01C",
";",
"\n",
"C10N",
";",
"C23",
"G",
";",
"F42BA61",
"K",
"\n",
"Smart",
"Specialisation",
"in",
"the",
"Eastern"
] | [] |
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 | [
"mean",
"+",
"or",
"±SEM",
".",
"Details",
"of",
"statistical",
"analyses",
"are",
"provided",
"in",
"Table",
"S1",
".",
"\n",
"Cell",
"Reports",
"39",
",",
"110893",
",",
"May",
"31",
",",
"2022",
"11Articlell",
"\n",
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"\n",
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"These",
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"are",
"characterized",
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"\n",
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"\n",
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"Ma",
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",",
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"Naskar",
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",",
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"We",
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",",
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",",
"\n",
"exclude",
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"that",
"methodological",
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",",
"e.g.",
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"used",
",",
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"Further",
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"potential",
"issue",
"and",
"could",
"include",
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"other",
"\n",
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"\n",
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"of",
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"aIC",
".",
"\n",
"The",
"IC",
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"complex",
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"that",
"can",
"be",
"divided",
"into",
"an",
"ante-",
"\n",
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"and",
"a",
"posterior",
"part",
".",
"They",
"markedly",
"differ",
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"connectivity",
"\n",
"with",
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"brain",
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"subserve",
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"(",
"Gehr-",
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";",
"Gogolla",
",",
"2017",
";",
"Livneh",
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"Andermann",
",",
"2021",
")",
".",
"\n",
"At",
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"interface",
"between",
"these",
"two",
"parts",
",",
"a",
"middle",
"insular",
"zone",
"\n",
"exhibits",
"mixed",
"anterior",
"and",
"posterior",
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"features",
"(",
"Ud",
"-",
"din",
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".",
",",
"2017",
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"Gehrlach",
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"al",
".",
",",
"2020",
")",
".",
"Our",
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"\n",
"the",
"aIC",
"given",
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"involvement",
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"multisensory",
"and",
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"-",
"modal",
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"(",
"Beer",
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"al",
".",
",",
"2013",
";",
"Uddin",
",",
"2015",
";",
"Uddin",
"et",
"al",
".",
",",
"\n",
"2017",
")",
"in",
"the",
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"(",
"Miura",
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"al",
".",
",",
"2020",
")",
"and",
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"valence",
"do-",
"\n",
"mains",
"(",
"Wu",
"et",
"al",
".",
",",
"2020",
")",
".",
"In",
"line",
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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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] | [] |
Subsets and Splits