AI-Driven Sanction Evasion Detection: Real-Time Monitoring of Illicit Financial Flows

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22053107  65stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]1,927✗Minimum 2,000 words for a full research article. Current: 1,927
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22053107
[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 (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Financial sanctions administered by state actors now target algorithmic laundering channels that dynamically reshape network topology. This paper investigates automated detection of sanction evasion through machine l[REDACTED]g, focusing on three core questions:

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22053107 65stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]1,927✗Minimum 2,000 words for a full research article. Current: 1,927
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22053107
[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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Longitudinal Citation Impact of AI-Generated Technical Articles: Measuring Scholarly Influence Over Time

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Quality Research
Quality Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22048646  64stabilfr·wdophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,501✗Minimum 2,000 words for a full research article. Current: 1,501
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22048646
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid integration of large language models into technical documentation has sparked debate about the authenticity and longevity of AI‑generated scholarly content [1]. While early indicators suggest higher initial citation velocities for AI‑authored papers, the durability of this effect remains under‑explored [2]. This article investigates how citation frequency evolves across the first 24 m...

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Quality Research by Oleh Ivchenko DOI: 10.5281/zenodo.22048646 64stabilfr·wdophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,501✗Minimum 2,000 words for a full research article. Current: 1,501
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22048646
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Multimodal AI in Scientific Discovery: 2025 Benchmarks in Drug Discovery and Materials Science (Draft)

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22035668  68stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI94%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,118✗Minimum 2,000 words for a full research article. Current: 1,118
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22035668
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]60%✓≥60% of references from 2025–2026. Current: 60%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (79 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Scientific discovery increasingly relies on the ability to integrate heterogeneous data sources, a challenge exemplified by drug discovery and materials design where molecular, imaging, and experimental data must be synthesized to generate testable hypotheses. This article addresses the growing need for robust multimodal artificial intelligence frameworks that can accurately predict scientific ...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22035668 68stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI94%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,118✗Minimum 2,000 words for a full research article. Current: 1,118
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22035668
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]60%✓≥60% of references from 2025–2026. Current: 60%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (79 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Multimodal AI in Scientific Discovery: 2025 Benchmarks in Drug Discovery and Materials Science

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22029873  54stabilfr·wdophcgmx
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[s]Reviewed Sources17%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]841✗Minimum 2,000 words for a full research article. Current: 841
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22029873
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[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 (66 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Scientific discovery increasingly relies on the integration of heterogeneous data modalities, including textual abstracts, experimental protocols, spectroscopic signatures, and structural diagrams. Multimodal artificial intelligence (AI) systems promise substantial gains in predictive accuracy, accelerated hypothesis generation, and reduced resource consumption across domains such as drug disco...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.22029873 54stabilfr·wdophcgmx
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[s]Reviewed Sources17%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]841✗Minimum 2,000 words for a full research article. Current: 841
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22029873
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]27%✗≥60% of references from 2025–2026. Current: 27%
[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 (66 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI-Assisted Wealth Register Verification: Cross-Border Asset Disclosure and Hidden Wealth Detection

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.22033833  65stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI94%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,870✗Minimum 2,000 words for a full research article. Current: 1,870
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22033833
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]76%✓≥60% of references from 2025–2026. Current: 76%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence (AI) is rapidly transforming financial compliance, particularly in the verification of wealth register declarations and cross-border asset disclosure. Tax authorities worldwide are deploying machine l[REDACTED]g models to parse public corporate registries, property transaction logs, and offshore entity disclosures in order to detect hidden wealth and inconsistencies. Thi...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.22033833 65stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI94%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,870✗Minimum 2,000 words for a full research article. Current: 1,870
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22033833
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]76%✓≥60% of references from 2025–2026. Current: 76%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Specification-First Fine-Tuning: Generating Training Data from Behavioral Specs

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22022096  60stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]866✗Minimum 2,000 words for a full research article. Current: 866
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22022096
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Behavioral specifications — formal, machine‑readable descriptions of desired system conduct — have emerged as a transformative mechanism for reducing reliance on costly human annotation in large‑scale AI training pipelines. Recent empirical analyses demonstrate that specification‑first fine‑tuning can achieve alignment fidelity comparable to manually curated datasets while lowering labeling exp...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.22022096 60stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]866✗Minimum 2,000 words for a full research article. Current: 866
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22022096
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Energy-Aware Observability: Real-Time Carbon Footprint Monitoring of Distributed Models

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22017319  66stabilfr·wdophcgmx
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[s]Reviewed Sources3%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed3%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,341✗Minimum 2,000 words for a full research article. Current: 1,341
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22017319
[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]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Measuring and mitigating the environmental impact of large-scale AI deployments has become a critical concern for both industry and academia. While prior work has focused on static carbon accounting for training pipelines, far less attention has been paid to the real-time carbon intensity of inference workloads in production. This article investigates how low-latency carbon emission estimators ...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.22017319 66stabilfr·wdophcgmx
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[s]Reviewed Sources3%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI97%✓≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed3%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,341✗Minimum 2,000 words for a full research article. Current: 1,341
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22017319
[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]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Vulnerability Disclosure in AI: Coordinated Disclosure Norms and Responsible Reporting Practices

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21998501  49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access78%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,265✓Minimum 2,000 words for a full research article. Current: 2,265
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21998501
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]47%✗≥60% of references from 2025–2026. Current: 47%
[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 (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Vulnerability disclosure frameworks for artificial intelligence systems are rapidly evolving as researchers confront novel attack surfaces including model extraction, prompt injection, and data poisoning. While traditional coordinated disclosure models originated in the software security community, their adaptation to AI-specific threat classes presents unique challenges around intellectual pro...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.21998501 49stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic61%○≥80% from journals/conferences/preprints
[f]Free Access78%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,265✓Minimum 2,000 words for a full research article. Current: 2,265
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21998501
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]47%✗≥60% of references from 2025–2026. Current: 47%
[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 (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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World Models for AI Planning: Current State and Gaps Between Research and Deployment

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21995856  52stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]930✗Minimum 2,000 words for a full research article. Current: 930
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[g]Code—○Source code available on GitHub
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Score = Ref Trust (63 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

World models—formal representations of environmental dynamics—are critical for reliable AI planning in real-world domains. This article surveys the most influential research contributions from the past three years, analyzes benchmark-to-practice gaps, and identifies the technical and operational barriers that prevent research-grade world models from deployment at scale. We formulate three resea...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.21995856 52stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]930✗Minimum 2,000 words for a full research article. Current: 930
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21995856
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Informal Economy Measurement with Satellite Data: Night-Light and Activity Indicators

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21985901  57stabilfr·wdophcgmx
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[a]DOI90%✓≥80% have a Digital Object Identifier
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[l]Academic94%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]1,058✗Minimum 2,000 words for a full research article. Current: 1,058
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21985901
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
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Satellite‑derived indicators have become a pivotal method for estimating informal economic activity in emerging markets [1] [2]. This article investigates the comparative effectiveness of three remote‑sensing proxies—night‑light intensity, parking‑lot occupancy, and shipping‑container movement—in measuring informal economic output across a set of pilot regions [3]. We formulate three research q...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21985901 57stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI90%✓≥80% have a Digital Object Identifier
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[l]Academic94%✓≥80% from journals/conferences/preprints
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[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,058✗Minimum 2,000 words for a full research article. Current: 1,058
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21985901
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[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 (70 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Shadow Economy Dyn…Read More
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