Overfitting remains a critical challenge in data-driven financial modeling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S P 500 companies spanning 2010-2024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98
Large Language Models appear competent when answering general questions but often fail when pushed into domain-specific details. No existing methodology provides an out-of-the-box solution for measuring how deeply LLMs can sustain accurate responses under adaptive follow-up questioning across arbitrary domains. We present DepthCharge, a domain-agnostic framework that measures knowledge depth through three innovations: adaptive probing that generates follow-up questions based on concepts the model actually mentions, on-demand fact verification from authoritative sources, and survival statistics with constant sample sizes at every depth level. The framework can be deployed on any knowledge domain with publicly verifiable facts, without requiring pre-constructed test sets or domain-specific expertise. DepthCharge results are relative to the evaluator model used for answer checking, making the framework a tool for comparative evaluation rather than absolute accuracy certification. Empirical validation across four diverse domains (Medicine, Constitutional Law, Ancient Rome, and Quantum Computing) with five frontier models demonstrates that DepthCharge reveals depth-dependent performance variation hidden by standard benchmarks. Expected Valid Depth (EVD) ranges from 3.45 to 7.55 across model-domain combinations, and model rankings vary substantially by domain, with no single model dominating all areas. Cost-performance analysis further reveals that expensive models do not always achieve deeper knowledge, suggesting that domain-specific evaluation is more informative than aggregate benchmarks for model selection in professional applications.
This study examines the pivotal, yet frequently overlooked, concept of risk velocity, the speed at which risk repercussions unfold after an initial trigger. It highlights how existing risk management, focused mainly on probability and impact, is inadequate for handling rapidly escalating events, as seen in crises like the COVID-19 pandemic, the CrowdStrike–Microsoft outage, the Change Healthcare ransomware attack, and the Aflac data breach. These incidents reveal how swift-moving threats can outpace traditional response mechanisms, causing severe operational and reputational harm. In today's fast-evolving threat landscape, technologies such as AI, AGI, ML, deep learning, and autonomous response act as risk multipliers and critical mitigation tools. AI-driven threats accelerate incident velocity but, conversely, enable faster detection and automated response. Building on models like Time-to-Impact Analysis and Agility and Resilience Theory, this chapter advocates for incorporating risk velocity alongside probability and impact, with velocity-aware matrices and AI-enhanced simulations.
This study presents the first dedicated bibliometric analysis of artificial intelligence (AI) and deep learning applications in pediatric radiology and medical imaging, mapping the intellectual structure of a rapidly evolving field. A total of 2688 articles and conference proceedings published between 2005 and 2025 were retrieved from the Web of Science Core Collection and analyzed using Bibliometrix R and VOSviewer. The findings reveal exponential growth in publications, from 7 papers in 2005 to 559 in 2025, with journal articles dominating the corpus (85.9%). The most-cited contributions, led by Kermany et al. (2018) with 2886 citations, are predominantly technical feasibility studies rather than clinical outcome trials, indicating a field that has advanced methodologically but remains in early stages of clinical translation. Thematic mapping identifies convolutional neural networks, pneumonia, and transfer learning as Motor Themes representing methodological maturity in chest imaging, while neuroimaging and image segmentation clusters occupy Niche Themes, reflecting insular development with limited cross-field connectivity. Geographic analysis reveals concentrated co-authorship along US–China and US–Europe corridors, with African, Latin American, and Southeast Asian institutions largely absent from knowledge production networks. Eight of the ten most productive affiliations are North American, highlighting structural inequities that risk producing AI tools optimized for high-resource settings rather than the global pediatric population. This analysis provides an empirical foundation for reorienting the field toward clinical validation, geographic inclusion, and methodological integration across isolated research communities.
This qualitative study examines healthcare cybercrime through the lens of criminal psychology and cybersecurity risk management, emphasizing the behavioral dynamics that enable repeated system compromise. Using semi-structured Zoom interviews with ten professionals holding graduate education in cyberpsychology, criminal psychology, criminal justice, cybersecurity, and human factors, the study explored offender strategies, organizational decision-making, and guardianship effectiveness. Findings indicate that cybercriminals systematically exploit cognitive overload, authority bias, and normalized risk behaviors within healthcare environments, while organizational fragmentation and short-term risk perceptions weaken defensive capacity. Participants emphasized that cybersecurity failures are rarely technical in isolation but emerge from predictable human and cultural vulnerabilities.
