Manufacturing AI Observability: Monitoring Explanation Quality in Predictive Maintenance Systems

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486205  59stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources17%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,068✓Minimum 1,000 words of article body. Current: 1,068
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486205
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥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
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (44 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

As AI-driven predictive maintenance (PdM) systems become integral to smart manufacturing operations, ensuring the quality and reliability of their explanations is critical for safety, compliance, and operational trust [1]. This article extends the AI observability framework to manufacturing AI systems, focusing on explanation quality monitoring in predictive maintenance contexts. We define a sp...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.23486205 59stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources17%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,068✓Minimum 1,000 words of article body. Current: 1,068
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486205
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥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
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (44 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Embodied Intelligence as a UIB Dimension: Measurement Framework and Evaluation Protocol

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19759259  76stabilfr·wuedophcgmx
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[s]Reviewed Sources10%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,175✓Minimum 1,000 words of article body. Current: 1,175
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]13✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 13
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19759259
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[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 (69 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

The Universal Intelligence Benchmark (UIB) proposes an eight-dimensional, cost-normalized framework for measuring intelligence across diverse AI systems. This article operationalizes the second UIB dimension — Embodied Intelligence (Dembodied) — defining it as the capacity for intelligent behavior arising from physical interaction with an environment, encompassing spatial reasoning, physics und...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19759259 76stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources10%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic85%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,175✓Minimum 1,000 words of article body. Current: 1,175
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]13✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 13
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19759259
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[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 (69 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Machine Learning for Shadow Economy Detection — Classification of Suspicious Transaction Patterns

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19513733  59stabilfr·wuedcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted63%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,961✓Minimum 1,000 words of article body. Current: 1,961
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19513733
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (36 × 60%) + Required (4/4 × 30%) + Optional (3/4 × 10%)

Detecting shadow economy activities through financial transaction monitoring is a critical challenge for regulators and financial institutions. This article investigates the application of machine learning algorithms to classify suspicious transaction patterns, using synthetic transaction data that mimics real‑world features such as amount, frequency, and entropy. We pose three research questio...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19513733 59stabilfr·wuedcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted63%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,961✓Minimum 1,000 words of article body. Current: 1,961
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19513733
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (36 × 60%) + Required (4/4 × 30%) + Optional (3/4 × 10%)
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Fresh Repositories Watch: Cybersecurity — Threat Detection and Response Frameworks

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486183  68stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources29%○≥80% from editorially reviewed sources
[t]Trusted79%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,969✓Minimum 1,000 words of article body. Current: 1,969
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]4✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 4
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486183
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness57%○≥60% of references from 2025–2026. Current: 57%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (53 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

This article investigates the landscape of AI-powered cybersecurity frameworks for open source threat detection and response. We examine three research questions: (1) how AI-driven threat detection compares to manual approaches in terms of accuracy and speed, (2) what vulnerability patterns dominate in open source projects in 2025-2026, and (3) how security tool adoption correlates with trust s...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.23486183 68stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources29%○≥80% from editorially reviewed sources
[t]Trusted79%○≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,969✓Minimum 1,000 words of article body. Current: 1,969
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]4✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 4
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486183
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness57%○≥60% of references from 2025–2026. Current: 57%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (53 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)
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Real-Time Shadow Economy Indicators — Building a Dashboard from Open Data

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19582647  62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted70%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access90%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,401✓Minimum 1,000 words of article body. Current: 2,401
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19582647
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness60%✓≥60% of references from 2025–2026. Current: 60%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (42 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Monitoring shadow economy activity in near real-time remains a critical gap for policymakers, tax authorities, and international organizations. Traditional estimation methods—MIMIC models, household surveys, and currency demand approaches—produce estimates with lags of months to years, leaving decision-makers without timely signals. This article investigates whether open data sources can serve ...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19582647 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted70%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed10%○≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access90%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,401✓Minimum 1,000 words of article body. Current: 2,401
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19582647
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness60%✓≥60% of references from 2025–2026. Current: 60%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (42 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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The Second-Order Gap: When Adopted AI Creates New Capability Gaps

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19556491  53stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,498✓Minimum 1,000 words of article body. Current: 1,498
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19556491
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness87%✓≥60% of references from 2025–2026. Current: 87%
[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 (29 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

When organizations successfully adopt AI systems, they often discover that adoption creates as many problems as it solves. This phenomenon—the second-order gap—occurs when AI adoption reveals or generates new capability deficiencies that organizations had not anticipated. This article examines the mechanisms driving second-order gap formation, quantifies their prevalence across enterprise conte...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.19556491 53stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,498✓Minimum 1,000 words of article body. Current: 1,498
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19556491
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness87%✓≥60% of references from 2025–2026. Current: 87%
[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 (29 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)
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Neural Network Estimation of Shadow Economy Size — Improving on MIMIC Models

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19545656  59stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted73%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic53%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,031✓Minimum 1,000 words of article body. Current: 2,031
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19545656
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness30%○≥60% of references from 2025–2026. Current: 30%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[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 (38 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

The MIMIC (Multiple Indicator Multiple Cause) model has been the dominant framework for shadow economy estimation since the 1970s. However, its linear, latent-variable architecture imposes constraints that modern machine learning methods can overcome. This article evaluates neural network approaches to shadow economy estimation, comparing their predictive accuracy, non-linear pattern recognitio...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19545656 59stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted73%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic53%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,031✓Minimum 1,000 words of article body. Current: 2,031
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19545656
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness30%○≥60% of references from 2025–2026. Current: 30%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[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 (38 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)
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Agent-Based Modeling of Tax Compliance — Simulating Government-Citizen Interactions

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19534434  62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,017✓Minimum 1,000 words of article body. Current: 2,017
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (41 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Tax compliance is a central determinant of shadow economy size, yet the behavioral mechanisms linking government enforcement to citizen reporting decisions remain poorly understood. Agent-based modeling (ABM) offers a bottom-up computational approach to simulating how individual taxpayers respond to audit probability, penalty severity, and peer behavior. This article applies ABM to tax complian...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19534434 62stabilfr·wuedophcgmx
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[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
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[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,017✓Minimum 1,000 words of article body. Current: 2,017
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19534434
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness62%✓≥60% of references from 2025–2026. Current: 62%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (41 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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The AI Mirror: What AI Reveals About Being Human

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486197  56stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
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[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,351✓Minimum 1,000 words of article body. Current: 1,351
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Every technology is a mirror. The telescope revealed our cosmic insignificance; the microscope revealed the teeming life we cannot see. Artificial intelligence, particularly large language models, is the latest mirror—and perhaps the strangest. It reflects not the physical cosmos but the cognitive one: language, thought, reasoning, and the architecture of mind itself. [1]

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.23486197 56stabilfr·wuedophcgmx
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[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,351✓Minimum 1,000 words of article body. Current: 1,351
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486197
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥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 (4/4 × 30%) + Optional (2/7 × 10%)
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Ubiquitous AI Integration: When Every Human Action Has an AI Partner

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19503250  48stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]4,445✓Minimum 1,000 words of article body. Current: 4,445
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503250
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness33%○≥60% of references from 2025–2026. Current: 33%
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[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (1/7 × 10%)

We stand at an inflection point where artificial intelligence is transitioning from a specialized tool invoked for discrete tasks to an ambient partner woven into the fabric of every human decision. This article examines the trajectory toward ubiquitous AI integration---a state in which AI participates in virtually every action a person takes, much as automatic balance calculations underpin eve...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.19503250 48stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]4,445✓Minimum 1,000 words of article body. Current: 4,445
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503250
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness33%○≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (4/4 × 30%) + Optional (1/7 × 10%)
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