Stabilarity Research Platform Is Now Open — Free API Access for All Researchers

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DOI: 10.5281/zenodo.18928330  52stabilfr·wdophcgmx
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[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI35%○≥80% have a Digital Object Identifier
[b]CrossRef20%○≥80% indexed in CrossRef
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[f]Free Access70%○≥80% are freely accessible
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure e[REDACTED]sing validated machine l[REDACTED]g models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016), providing composable, versioned endpoints for: (1) medical ima...

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DOI: 10.5281/zenodo.18928330 52stabilfr·wdophcgmx
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[s]Reviewed Sources30%○≥80% from editorially reviewed sources
[t]Trusted85%✓≥80% from verified, high-quality sources
[a]DOI35%○≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18928330
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥60% of references from 2025–2026. Current: 9%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Open Source AI Trust Gap: When Community Projects Do Not Meet Enterprise Standards

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20483567  76stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,826✗Minimum 2,000 words for a full research article. Current: 1,826
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20483567
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (93 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprises increasingly rely on artificial intelligence (AI) to gain competitive advantage, yet many hesitate to adopt open source AI solutions despite their technical promise and cost efficiency. This hesitation stems from a growing trust gap—a mismatch between the expectations of corporate stakeholders and the capabilities, governance, and reliability of community‑driven AI projects. Bridgin...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20483567 76stabilfr·wdophcgmx
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[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,826✗Minimum 2,000 words for a full research article. Current: 1,826
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20483567
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (93 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cross-Industry AI Transparency Stacks: Open Source Reference Architectures for XAI

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20422658  38stabilfr·wdophcgmx
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[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,508✗Minimum 2,000 words for a full research article. Current: 1,508
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20422658
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This article presents a comprehensive framework for building cross-industry explainable AI (XAI) transparency stacks, which are modular architectures designed to provide interpretable insights across diverse domains. As regulatory pressures mount for increased AI transparency, organizations require standardized yet adaptable frameworks to deploy XAI solutions that maintain operational efficienc...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20422658 38stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[l]Academic50%○≥80% from journals/conferences/preprints
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[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,508✗Minimum 2,000 words for a full research article. Current: 1,508
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20422658
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20417244  61stabilfr·wdophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,879✗Minimum 2,000 words for a full research article. Current: 1,879
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20417244
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]79%✓≥60% of references from 2025–2026. Current: 79%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Privacy-preserving machine l[REDACTED]g has matured into a diverse ecosystem of algorithms, protocols, and tooling designed to enable collaborative model training without e[REDACTED]sing raw data. Concurrently, explainable artificial intelligence (XAI) has emerged as a critical complement, granting stakeholders insight into model decisions while maintaining data confidentiality. This article su...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20417244 61stabilfr·wdophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,879✗Minimum 2,000 words for a full research article. Current: 1,879
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20417244
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]79%✓≥60% of references from 2025–2026. Current: 79%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Bus Factor of XAI: Community Risk in Critical Open Source Explainability Tools

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20410657  61stabilfr·wdophcgmx
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[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,105✗Minimum 2,000 words for a full research article. Current: 1,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20410657
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Explainability in artificial intelligence (AI) systems has become a pivotal concern for researchers, regulators, and practitioners seeking to deploy trustworthy AI solutions. While numerous frameworks and toolkits promise transparent model behavior, the sustainability of these open source initiatives often hinges on the concentration of maintainer resources—a modern manifestation of the classic...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20410657 61stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,105✗Minimum 2,000 words for a full research article. Current: 1,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20410657
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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License Implications for XAI Attribution: Legal Analysis of Open Source Explanation Dependencies

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20404245  59stabilfr·wdophcgmx
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[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]1,882✗Minimum 2,000 words for a full research article. Current: 1,882
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20404245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]70%✓≥60% of references from 2025–2026. Current: 70%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Abstract The rapid expansion of explainable artificial intelligence (XAI) systems raises legal questions about the use of open source components in explanatory modules. This article investigates how open source licenses affect attribution requirements, copyleft obligations, and commercial deployment strategies. We formulate three research questions: (1) Which licenses impose attribution duties ...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20404245 59stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]1,882✗Minimum 2,000 words for a full research article. Current: 1,882
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20404245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]70%✓≥60% of references from 2025–2026. Current: 70%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Transparency as Competitive Moat: Why Explainability Creates Sustainable Advantage

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20401398  78stabilfr·wdophcgmx
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[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]954✗Minimum 2,000 words for a full research article. Current: 954
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20401398
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (95 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

AI transparency has emerged as a critical strategic asset for enterprises seeking sustainable competitive advantage in the rapidly evolving artificial intelligence market. This article presents a strategic analysis of how explainability and transparency in AI systems translate into tangible economic benefits, including premium pricing, enhanced trust, compliance savings, and innovation accelera...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20401398 78stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]954✗Minimum 2,000 words for a full research article. Current: 954
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20401398
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (95 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Human-AI Collaboration Futures: When Explanations Enable Better Human-AI Teams

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20384760  75stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources59%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI94%✓≥80% have a Digital Object Identifier
[b]CrossRef59%○≥80% indexed in CrossRef
[i]Indexed59%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,627✗Minimum 2,000 words for a full research article. Current: 1,627
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20384760
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Abstract The rapid integration of artificial intelligence into knowledge work demands new frameworks for human-AI collaboration that go beyond opaque black-box decision-making. Recent advances in explainable AI (XAI) offer tools to make model behavior transparent, thereby fostering trust, accountability, and shared understanding. This article investigates how explainability mechanisms can be ...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20384760 75stabilfr·wdophcgmx
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[s]Reviewed Sources59%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI94%✓≥80% have a Digital Object Identifier
[b]CrossRef59%○≥80% indexed in CrossRef
[i]Indexed59%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,627✗Minimum 2,000 words for a full research article. Current: 1,627
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20384760
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Open Source AI in Government: Curated Trusted Stack for Public Sector AI

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20374059  71stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
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Government agencies are increasingly looking to artificial intelligence (AI) to modernize procurement workflows, strengthen fraud detection pipelines, and improve the delivery of public services while operating under tight budgetary constraints. Recent surveys reveal that more than 65 % of public‑sector technology officers consider open source AI components essential for achieving cost‑efficien...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20374059 71stabilfr·wdophcgmx
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EU AI Act Compliance for Ukrainian Tech: How Explanation Requirements Affect AI Exports

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20372892  62stabilfr·wdophcgmx
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This paper investigates ... placeholder text ...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20372892 62stabilfr·wdophcgmx
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