Ukraine's shadow economy remains one of the largest in Europe, with wartime conditions creating both new evasion channels and shifting the composition of existing ones. This article develops a comprehensive typology of tax evasion mechanisms operating in Ukraine, classifying shadow economy channels along three dimensions: mechanism type, sectoral concentration, and detection difficulty. Drawing...
The financial technology open-source ecosystem experienced rapid growth in early 2026, driven by the convergence of AI-powered trading agents, prediction market infrastructure, and quantitative research frameworks. This article surveys 89 newly created repositories (January-March 2026) across trading automation, risk management, portfolio optimization, and payment infrastructure. We evaluate re...
The developer infrastructure landscape is undergoing a fundamental transformation driven by two converging forces: the rapid adoption of AI-augmented development pipelines and the escalating frequency of software supply chain attacks targeting CI/CD systems. This article surveys open-source repositories created within the past 60 days (January-March 2026) that address build tooling, pipeline au...
The music industry faces a persistent and costly challenge: determining whether a track will achieve viral reach before it is released to the public. Conventional approaches to music popularity prediction rely on post-publication engagement signals — streams, likes, shares, and historical interaction data — making them structurally incapable of informing pre-release decisions. GROMUS addresses ...
Social media platforms — foremost TikTok and Instagram — generate billions of interaction events daily, creating stochastic, high-velocity Big Data streams whose trend trajectories prove notoriously difficult to forecast with classical statistical models. This paper presents FLAI, an intelligent information-analytical system for predicting the behaviour of social-network objects, with emphasis ...
Medical artificial intelligence has long suffered from a critical epistemic gap: models produce predictions without producing justifications. Clinicians, regulators, and patients cannot evaluate the validity of a decision if they can only see its output. ScanLab addresses this gap through a deliberate architectural choice — making explainability a mandatory, non-negotiable layer of the inferenc...
This methodological note describes the novel aspects of heuristic rules introduced in the FLAI (Framework for Leveraging AI in Social Media) prediction system. Specifically, we demonstrate how the three core heuristic mechanisms — base weight initialization (bW), daily repost forecast error (DRFE), and generative weight dynamic recalibration (GW) — differ fundamentally from standard weight appr...
As large language models approach ceiling performance on standard benchmarks, the question shifts from "how smart is this model?" to "how smart is this model per unit of resource consumed?" This article proposes the UIB-Efficiency dimension — a resource-normalized intelligence score that integrates accuracy with computational cost, energy consumption, memory footprint, and inference latency. We...
The gap between what AI can do and what organizations actually deploy continues to widen in 2026. While previous articles in this series quantified the magnitude of this gap across sectors, the underlying friction mechanisms remain poorly categorized. This article introduces a four-quadrant Adoption Friction Taxonomy (AFT) that classifies eight empirically identified barrier categories along tw...
The healthcare AI open-source ecosystem is experiencing unprecedented growth in early 2026, driven by federated learning platforms, foundation models for medical imaging, and synthetic data generators that enable privacy-preserving research collaboration. This article applies the Trusted Open Source Index methodology established in our previous work to evaluate nine prominent healthcare AI repo...