How Our War Prediction Model Anticipated the Iran Conflict

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DOI: 10.5281/zenodo.18816597  27stabilfr·wdophcgmx
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On February 28, 2026, the United States and Israel launched coordinated military strikes on Iran, marking the most significant Middle Eastern conflict escalation since the Iraq War. Our Stabilarity War Prediction Model had been tracking Iran's conflict probability for weeks, showing a 49.7% conflict probability with an increasing trend — a warning that materialized into reality within hours of ...

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DOI: 10.5281/zenodo.18816597 27stabilfr·wdophcgmx
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Enterprise AI: A Comprehensive Guide to Navigating Complexity and Avoiding the 80% Failure Rate

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18772218  30stabilfr·wdophcgmx
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Executive Summary: Despite unprecedented investment and executive enthusiasm, 80-85% of enterprise AI projects fail to deliver meaningful business value. This comprehensive analysis examines the technical, organizational, and economic factors driving this failure rate, drawing from academic research and industry studies. We present evidence-based frameworks for total cost of ownership (TCO) ana...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18772218 30stabilfr·wdophcgmx
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Multi-Provider Strategies: Avoiding Vendor Lock-in While Maximizing Value

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18769559  24stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (5 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise adoption of large language models (LLMs) has introduced a new dimension of vendor lock-in that differs fundamentally from traditional software dependencies. Unlike switching ERP systems or databases—where migration paths are well-understood—LLM provider transitions involve prompt re-engineering, model behavior differences, and hidden integration costs that can reach six figures even ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18769559 24stabilfr·wdophcgmx
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Testing and Validation Costs in Enterprise AI: Economic Analysis of Quality Assurance Investment

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18755863  28stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (13 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Testing and validation represent 10-15% of total AI development costs, yet inadequate investment in this phase contributes significantly to the 80-95% failure rate of AI projects. This paper presents an economic framework for analyzing testing and validation costs across the AI lifecycle, from initial test data acquisition through continuous production monitoring. We examine cost structures of ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18755863 28stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
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Architecting Spec-Compliant AI Systems: Patterns and Anti-Patterns

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18745394  49stabilfr·wdophcgmx
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Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence into enterprise systems demands rigorous architectural approaches that ensure reliability, maintainability, and compliance with specifications. This article explores architectural patterns that support spec-driven development of AI systems, contrasting proven design patterns with common anti-patterns that lead to technical debt. We examine contract-bas...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18745394 49stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Specialized vs General Models — When to Use Domain-Specific AI

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18743173  45stabilfr·wdophcgmx
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[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 (40 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). Specialized vs General Models — When to Use Domain-Specific AI. Cost-Effective Enterprise AI Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18746111 Abstract The enterprise AI landscape is undergoing a fundamental shift from general-purpose large language models (LLMs) to domain-specific language models (DSLMs) optimized for particula...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18743173 45stabilfr·wdophcgmx
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[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
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[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access51%○≥80% are freely accessible
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[w]Words [REQ]4,232✓Minimum 2,000 words for a full research article. Current: 4,232
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18743173
[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%
[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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Integration Economics: Legacy System Adaptation for AI Deployment

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18740871  34stabilfr·wdophcgmx
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[a]DOI15%○≥80% have a Digital Object Identifier
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[l]Academic23%○≥80% from journals/conferences/preprints
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[w]Words [REQ]4,661✓Minimum 2,000 words for a full research article. Current: 4,661
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18740871
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
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Score = Ref Trust (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Integrating artificial intelligence into existing enterprise infrastructure represents one of the most significant economic challenges in AI deployment. While substantial research examines AI development costs, the economics of legacy system adaptation remain inadequately explored. This paper presents a comprehensive economic framework for understanding integration costs, analyzing cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18740871 34stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
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[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References40 refs✓Minimum 10 references required
[w]Words [REQ]4,661✓Minimum 2,000 words for a full research article. Current: 4,661
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18740871
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Agents Operate With Minimal Safety Disclosures: MIT Study Reveals Transparency Gap

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741627  34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,130✓Minimum 2,000 words for a full research article. Current: 3,130
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

MIT CSAIL's 2025 AI Agent Index analyzed 30 prominent AI agents and found a striking transparency deficit: while 70% provide documentation and nearly half publish code, only 19% disclose formal safety policies and fewer than 10% report external safety evaluations. This journal entry examines the study's findings, contextualizes the claims within the broader AI safety discourse, and assesses whe...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18741627 34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,130✓Minimum 2,000 words for a full research article. Current: 3,130
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741627
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18735965  51stabilfr·wdophcgmx
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[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted69%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
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Armed conflict prediction represents one of the most critical challenges in computational social science and international relations. This paper presents a multi-factor machine l[REDACTED]g approach to predicting armed conflict probability at the country level, combining ensemble l[REDACTED]g methods with diverse data sources including ACLED, UCDP, World Bank economic indicators, SIPRI military...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18735965 51stabilfr·wdophcgmx
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[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted69%○≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access38%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,846✓Minimum 2,000 words for a full research article. Current: 3,846
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18735965
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (51 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Development Paradigms Compared: Spec-Driven, Experiment-Driven, and Hybrid Approaches

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18741619  46stabilfr·wdophcgmx
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[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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The development of AI systems presents unique challenges that traditional software engineering paradigms struggle to address. This article provides a comprehensive comparative analysis of four major development approaches: spec-driven development, experiment-driven development, data-centric AI, and model-centric AI. We examine each paradigm's theoretical foundations, practical workflows, and su...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18741619 46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
[i]Indexed44%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access28%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]3,797✓Minimum 2,000 words for a full research article. Current: 3,797
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18741619
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
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[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 (43 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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