Proprioception and Internal State Estimation: Joint Encoders, Torque Sensing, and Body Schema for Humanoid Robots

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Engineering Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19057213  76stabilfr·wuedophcgmx
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A humanoid robot that cannot sense its own body is a humanoid robot that falls down. Proprioception — the internal sensing of joint positions, velocities, torques, and overall body configuration — is the foundation upon which every higher-level capability depends. Without accurate proprioceptive state estimation, locomotion controllers cannot maintain balance, manipulation pipelines cannot clos...

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Engineering Research by Oleh Ivchenko DOI: 10.5281/zenodo.19057213 76stabilfr·wuedophcgmx
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[l]Academic78%○≥80% from journals/conferences/preprints
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[w]Words [REQ]3,034✓Minimum 1,000 words of article body. Current: 3,034
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19057213
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Deterministic Guardrails for Enterprise Agents — Compliance Without Killing Autonomy

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19053079  57stabilfr·wuedophcgmx
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[i]Indexed45%○≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
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[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,846✓Minimum 1,000 words of article body. Current: 2,846
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The enterprise AI agent landscape in 2026 faces a paradox: organizations deploy autonomous agents to reduce costs and increase throughput, yet every autonomous action introduces compliance risk. The EU AI Act reaches full enforcement on August 2, 2026, NIST has launched its AI Agent Standards Initiative, and enterprises face penalties of up to 7% of global turnover for non-compliance. This arti...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19053079 57stabilfr·wuedophcgmx
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[l]Academic27%○≥80% from journals/conferences/preprints
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AI Boom vs. Geopolitics: How Political Instability Reprices Artificial Intelligence

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19047758  63stabilfr·wuedophcgmx
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[a]DOI30%○≥80% have a Digital Object Identifier
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[l]Academic30%○≥80% from journals/conferences/preprints
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The artificial intelligence investment boom of 2024–2026 has collided with an era of escalating geopolitical fragmentation. While global AI spending surpassed $300 billion in cumulative commitments by early 2026, the simultaneous intensification of chip export controls, sovereign AI mandates, and regional conflicts has introduced a new class of repricing risk into AI capital allocation. This ar...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19047758 63stabilfr·wuedophcgmx
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[l]Academic30%○≥80% from journals/conferences/preprints
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[w]Words [REQ]2,035✓Minimum 1,000 words of article body. Current: 2,035
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Container Orchestration for AI — Kubernetes Cost Optimization

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19043029  48stabilfr·wuedophcgmx
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[t]Trusted21%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,481✓Minimum 1,000 words of article body. Current: 2,481
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Container orchestration for AI workloads presents a unique economic challenge: the intersection of expensive hardware (GPUs), bursty demand patterns (training vs. inference), and the operational complexity of multi-tenant scheduling. This article provides a systematic analysis of Kubernetes cost optimization strategies for AI — from GPU partitioning and spot instance economics to autoscaling po...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19043029 48stabilfr·wuedophcgmx
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[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access75%○≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,481✓Minimum 1,000 words of article body. Current: 2,481
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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The Computer & Math 33%: Why the Most AI-Capable Occupation Group Still Automates Only a Third of Its Tasks

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19040207  48stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,718✓Minimum 1,000 words of article body. Current: 2,718
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19040207
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The Anthropic Economic Index (Massenkoff & McCrory, 2026) identifies computer and mathematical occupations as theoretically the most AI-exposed occupation group in the U.S. economy, with 94% of tasks rated as feasible for LLM acceleration. Yet observed automation covers only 33% of those tasks — producing a 61-percentage-point capability-adoption gap that is the largest absolute gap of any occu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19040207 48stabilfr·wuedophcgmx
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[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
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[w]Words [REQ]2,718✓Minimum 1,000 words of article body. Current: 2,718
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19040207
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Frontier AI Consolidation Economics: Why the Big Get Bigger

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19028157  42stabilfr·wuedophcgmx
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[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access23%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]2,173✓Minimum 1,000 words of article body. Current: 2,173
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19028157
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[h]Freshness52%○≥60% of references from 2025–2026. Current: 52%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The frontier AI industry is consolidating at a pace that mirrors — and in some dimensions exceeds — the platform monopolization patterns of previous technology waves. As of early 2026, three providers control approximately 88% of enterprise AI API spending, with Anthropic commanding 40%, OpenAI 27%, and Google 21% of enterprise market share. Training costs for frontier models now exceed $100 mi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19028157 42stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic10%○≥80% from journals/conferences/preprints
[f]Free Access23%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]2,173✓Minimum 1,000 words of article body. Current: 2,173
[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.19028157
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[h]Freshness52%○≥60% of references from 2025–2026. Current: 52%
[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
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Score = Ref Trust (16 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Silicon War Economics: The Cost Structure of Chip Nationalism

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19021816  45stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[f]Free Access17%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]2,658✓Minimum 1,000 words of article body. Current: 2,658
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]31✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 31
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19021816
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness43%○≥60% of references from 2025–2026. Current: 43%
[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 (20 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The global semiconductor industry, projected to reach $1 trillion in revenue by late 2026, has become the primary arena for a new form of economic warfare: chip nationalism. Nations are pouring hundreds of billions of dollars into domestic fabrication capacity, driven not by comparative advantage but by strategic anxiety. This paper examines the economic cost structure of semiconductor reshorin...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19021816 45stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[f]Free Access17%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]2,658✓Minimum 1,000 words of article body. Current: 2,658
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]31✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 31
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19021816
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness43%○≥60% of references from 2025–2026. Current: 43%
[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 (20 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Enterprise AI Agents as the New Insider Threat: A Cost-Effectiveness Analysis of Autonomous Risk

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19019216  47stabilfr·wuedophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted30%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed24%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access30%○≥80% are freely accessible
[r]References33 refs✓Minimum 10 references required
[w]Words [REQ]3,128✓Minimum 1,000 words of article body. Current: 3,128
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Score = Ref Trust (22 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

The rapid deployment of autonomous AI agents across enterprise environments has introduced a novel category of insider threat that traditional cybersecurity frameworks are ill-equipped to address. According to the Thales 2026 Data Threat Report, 61% of organizations now cite AI as their top data security concern, while only 34% maintain visibility into where all their data resides. This article...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19019216 47stabilfr·wuedophcgmx
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Score = Ref Trust (22 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Policy Implications and a Decision Framework for Shadow Economy Reduction in Ukraine

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19016590  68stabilfr·wuedophcgmx
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[r]References25 refs✓Minimum 10 references required
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Score = Ref Trust (59 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Paper 3 of 3 in the series "Shadow Economy Dynamics." Builds on Paper 1: Problem Landscape and Paper 2: Scenario Analysis.

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19016590 68stabilfr·wuedophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
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[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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Score = Ref Trust (59 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Scenario Analysis: Modeling Three Futures for Ukraine’s Shadow Economy (2025–2030)

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19016584  62stabilfr·wuedophcgmx
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[a]DOI55%○≥80% have a Digital Object Identifier
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[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,485✓Minimum 1,000 words of article body. Current: 2,485
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Score = Ref Trust (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

In Paper 1 of this series (Ivchenko, Ivchenko & Grybeniuk, 2026a), we established that Ukraine's shadow economy has remained persistently high — between 30% and 45% of official GDP over the decade 2015–2025. We identified two competing feedback loops: a reinforcing cycle where high tax burdens push economic actors into informality, and a balancing mechanism where digitalization increases tr...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19016584 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted55%○≥80% from verified, high-quality sources
[a]DOI55%○≥80% have a Digital Object Identifier
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[f]Free Access82%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,485✓Minimum 1,000 words of article body. Current: 2,485
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[o]ORCID✓✓Author ORCID verified for academic identity
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Score = Ref Trust (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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