Public research outputs.
Public research outputs document the evolution of APS Logic from phase-state analysis and Silent Convergence Window™ toward a domain-agnostic analytical framework for producing a quantitative reading of dynamic system state and resilience change.
APS Logic Foundations
Foundational publication defining APS Logic as a phase-state analytical approach to reading how system conditions change before visible instability.
Silent Convergence Window
Publication focused on the phase in which a system may appear stable while losing recovery capacity and entering a vulnerability window.
From Events to Conditions
Cyber-domain publication presenting APS Logic as a systemic framework for reading pre-event and pre-attack changes in system condition.
Cyber and post-quantum research outputs.
Additional public Zenodo records extend the APS Logic evidence track toward cyber fragility, national and European early warning models, post-quantum transition risk and cryptographic inventory assessment.
Cyber Fragility Index (CFI): A Composite Indicator for Predictive Cybersecurity Governance
Public research output introducing a composite indicator for predictive cybersecurity governance and cyber fragility assessment.
National and European Cyber Fragility Index (NCFI / ECFI): From National Predictive Surveillance to a European Early Warning System
Public research output extending the cyber fragility concept from organizational assessment toward national and European early warning perspectives.
Measuring Organizational Fragility in the Post-Quantum Transition: The Post Quantum Fragility Index (PQFI)
Public research output focused on organizational fragility during the transition toward post-quantum cryptographic readiness.
Crypto Inventory Framework for Automated Post-Quantum Fragility Assessment
Public research output addressing cryptographic inventory as a foundation for automated post-quantum fragility assessment.
Historical field applications.
APS Logic is connected to a broader methodological history in which predictive prevention and operational prioritization progressively revealed the value of reading changing system conditions before visible outcomes.
XLAW historical application
XLAW represents a historical field application of predictive prevention and operational prioritization in urban environments.
Historical field reporting associated with XLAW documents changes in predatory-crime outcomes and in-flagrante arrest performance. Detailed quantitative claims are presented only when accompanied by source-qualified documentation.
Prevention and patrol prioritization
The XLAW experience supports the principle that reading changes in system state can help move operational action from generic reaction to targeted prevention.
The historical operational model was based on anticipatory reading, territorial prioritization and earlier deployment of available resources.
From field evidence to APS Logic
XLAW is presented as a methodological root of the APS Logic approach: reading changing territorial conditions, prioritizing resources and acting before visible instability consolidates.
Operational applications.
Operational applications show how earlier system-state reading can support loss reduction, resilience, decision priority and measurable organizational value.
Loss and security management
Retail and GDO applications applied anticipatory system-state interpretation to store exposure, operational pressure, security allocation and inventory-related risk.
Historical corporate applications reported material changes in inventory differences, security allocation and security-management costs. Detailed quantitative results remain subject to source-qualified documentation.
Operational efficiency
The operational value is not limited to detection. It lies in earlier system-state interpretation, better prioritization of security resources and more focused intervention timing.
Economic relevance
Earlier interpretation can help reduce avoidable reaction costs, improve resource allocation and support more defensible resilience, insurance and continuity discussions.
Controlled experimental evidence.
Controlled experiments examine whether APS Logic can produce informative quantitative readings of pre-transition dynamics across different complex systems through a common analytical framework, without assuming domain equivalence or universal transfer of performance.
Controlled cyber validation
Controlled APS-CYBER validation examines whether PRE_ATTACK and Silent Convergence Window conditions correspond to measurable changes in cyber-system state before subsequent instability within declared test windows.
Validation reports examine PRE_ATTACK lifecycle, predictive lead time, SCW strength and outcomes, candidate false positives, false-negative diagnostics, operator burden and preventive-bridge safety. Results are interpreted only within the declared scenario, data and observation window.
APS-BIO-LAB experimental result
Controlled APS-BIO-LAB analysis on public atrial-fibrillation data identified a restricted dynamic phenotype compatible with a strong Silent Convergence Window™ in a subset of pre-event windows, supporting investigation of changing stability and resilience before the visible event.
In the current experimental configuration, the identified pattern showed high specificity but limited sensitivity. The result supports methodological investigation of pre-transition dynamics; it does not establish clinical prediction and is not intended for diagnosis.
Blind degradation validation
In a controlled blind evaluation on public run-to-failure degradation data, APS Logic measured the evolution of system state while the final outcome remained withheld during the assessment phase.
The current evidence shows that APS isolated the systems following the most critical degradation trajectories before the terminal reference was revealed. The same selection was reproduced in repeat testing and remained stable under reduced sensor input. APS Logic was not calibrated or modified for this result. The validation remains experimental and scoped to the declared test perimeter.
Cross-domain comparison is used to test whether the same APS Logic analytical framework remains informative across different complex systems. It does not imply equivalence between domains or universal transfer of performance.
Evidence policy
Detailed numbers, public claims and third-party references are published only when supported by public documents, official sources, publications or controlled validation reports. Quantitative metrics validate results only within their declared scenario, dataset or technical perimeter, observation window and validation method; they are not universal performance guarantees.
APS Logic does not publish protected formulas, weights, thresholds, guard rules, transition internals, memory logic, rule traces or reserved scoring internals.