The rapid militarization of orbital space has created an urgent need for automated monitoring of satellite behavior. This article presents a comprehensive AI-driven framework for space domain awareness, focusing on satellite activity monitoring and anti-satellite threat detection. We introduce a multi-modal deep l[REDACTED]g architecture that integrates orbital telemetry, optical imaging, and r...
The rapid proliferation of AI-generated scholarly content has created an urgent need for reliable automated quality metrics that can supplement or replace human expert evaluation. This article investigates the calibration between expert evaluators and a suite of algorithmic metrics across a diverse corpus of synthetic articles. We examine three research questions: (RQ1) What is the correlation ...
Academic Citation: Ivchenko, Oleh, Ivchenko, Iryna (2026). Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI. Research article: Human‑in‑the‑Loop Auditing: Structured Interaction Patterns for Trust Calibration in High‑Stakes AI. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.21753075 · ...
Quantized model formats have become central to cost-effective deployment of large AI models in enterprise environments. This article compares three prominent quantization formats—GGUF, ONNX Runtime with QDQ, and AWQ—focusing on accuracy–cost trade‑offs, integration complexity, and scalability in on‑premise settings. We formulate three research questions (RQ1, RQ2, RQ3) that guide the analysis: ...
Enterprises increasingly deploy large language models (LLMs) as core components of their digital stack, yet the cost structure of provider access remains fragmented and opaque. This article investigates how organizations can systematically exploit price competition among LLM providers to achieve measurable reductions in inference spend. We frame the problem as a procurement challenge, map curre...
Enterprise AI adoption remains fragmented, with organizations struggling to diagnose their readiness across technical, human, and governance dimensions[1]. This article introduces the Capability Adoption Stack (CAS), a layered diagnostic model that maps data infrastructure, talent, governance, and integration maturity into a coherent framework[2]. By operationalizing these dimensions into measu...
Pandemic preparedness requires early detection of biological threats through sophisticated AI systems analyzing global health data, genomic sequences, and research activity. This article surveys AI models deployed for pandemic signal detection and dual-use biological research monitoring, evaluating their prediction accuracy, false positive management, and operational integration challenges. We ...
This article introduces a framework for measuring document-level coherence in AI-generated research papers, focusing on argument flow, section interdependency, and logical consistency across multiple sections.
Predictive observability addresses the growing need for anticipatory monitoring of complex system behaviors. This article introduces synthetic counterfactual data generators that simulate future distribution shifts, enabling organizations to preemptively recalibrate monitoring pipelines. We propose a novel framework that leverages probabilistic forecasting and synthetic data synthesis to infer ...
Draft created by Planner. Refs and charts pending.