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

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DOI: 10.5281/zenodo.18928330  56stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning 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 imaging classi...

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DOI: 10.5281/zenodo.18928330 56stabilfr·wdophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 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.19596258  38stabilfr·wdophcgmx
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[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1,665✗Minimum 2,000 words for a full research article. Current: 1,665
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19596258
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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[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 (31 × 60%) + Required (2/5 × 30%) + Optional (3/4 × 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.19596258 38stabilfr·wdophcgmx
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[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (31 × 60%) + Required (2/5 × 30%) + Optional (3/4 × 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  51stabilfr·wdophcgmx
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 Access75%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3✗Minimum 2,000 words for a full research article. Current: 3
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19582647
[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 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 (3/5 × 30%) + Optional (3/4 × 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 51stabilfr·wdophcgmx
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 Access75%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3✗Minimum 2,000 words for a full research article. Current: 3
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19582647
[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 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 (3/5 × 30%) + Optional (3/4 × 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  42stabilfr·wdophcgmx
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[t]Trusted44%○≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
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[l]Academic28%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]705✗Minimum 2,000 words for a full research article. Current: 705
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19556491
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (28 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 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 42stabilfr·wdophcgmx
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[t]Trusted44%○≥80% from verified, high-quality sources
[a]DOI22%○≥80% have a Digital Object Identifier
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[l]Academic28%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]705✗Minimum 2,000 words for a full research article. Current: 705
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19556491
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (28 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 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  48stabilfr·wdophcgmx
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[t]Trusted73%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic53%○≥80% from journals/conferences/preprints
[f]Free Access60%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,051✓Minimum 2,000 words for a full research article. Current: 2,051
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19545656
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]30%✗≥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 (3/5 × 30%) + Optional (3/4 × 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 48stabilfr·wdophcgmx
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
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[l]Academic53%○≥80% from journals/conferences/preprints
[f]Free Access60%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,051✓Minimum 2,000 words for a full research article. Current: 2,051
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19545656
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]30%✗≥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 (3/5 × 30%) + Optional (3/4 × 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  50stabilfr·wdophcgmx
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[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 Access88%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,944✗Minimum 2,000 words for a full research article. Current: 1,944
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19534434
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]62%✓≥60% of references from 2025–2026. Current: 62%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[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 (41 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 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 50stabilfr·wdophcgmx
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 Access88%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,944✗Minimum 2,000 words for a full research article. Current: 1,944
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19534434
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]62%✓≥60% of references from 2025–2026. Current: 62%
[c]Data Charts3✓Original data charts from reproducible analysis (min 2). Current: 3
[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 (41 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 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  50stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI28%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access72%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,973✗Minimum 2,000 words for a full research article. Current: 1,973
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19513733
[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 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 (40 × 60%) + Required (3/5 × 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 50stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI28%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access72%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]1,973✗Minimum 2,000 words for a full research article. Current: 1,973
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19513733
[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 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 (40 × 60%) + Required (3/5 × 30%) + Optional (3/4 × 10%)
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The AI Mirror: What AI Reveals About Being Human

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19503440  32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted57%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,332✗Minimum 2,000 words for a full research article. Current: 1,332
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503440
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥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 (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 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.

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.19503440 32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted57%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,332✗Minimum 2,000 words for a full research article. Current: 1,332
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503440
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥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 (29 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI Memory Architecture: From Fixed Windows to Persistent State

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19503438  31stabilfr·wdophcgmx
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Score = Ref Trust (27 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The dominant paradigm for AI memory—fixed-size context windows processed through self-attention—faces fundamental scalability barriers as large language models are deployed in long-horizon agentic tasks requiring hundreds of interaction sessions. This article investigates the transition from fixed context windows to persistent memory architectures through three research questions addressing sca...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.19503438 31stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]969✗Minimum 2,000 words for a full research article. Current: 969
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503438
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥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
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[x]Cited by0○Referenced by 0 other hub article(s)
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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  34stabilfr·wdophcgmx
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[a]DOI17%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503250
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (27 × 60%) + Required (3/5 × 30%) + Optional (0/4 × 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 34stabilfr·wdophcgmx
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[a]DOI17%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[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)
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Conscious Products: When AI Is the Product Personality Itself

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19503244  59stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503244
[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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[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
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Beyond the Tool Paradigm: How Artificial Intelligence is Becoming the Core Identity of the Products We Create

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.19503244 59stabilfr·wdophcgmx
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[a]DOI50%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References2 refs○Minimum 10 references required
[w]Words [REQ]4,229✓Minimum 2,000 words for a full research article. Current: 4,229
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19503244
[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]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (4/5 × 30%) + Optional (0/4 × 10%)
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