Abstract The rapid proliferation of Large Language Models (LLMs) and multimodal AI systems has created an urgent need for systematic, analyzable resources to track architectural innovation, organizational strategy, and computational scaling trends. This paper presents a structured analysis... The rapid proliferation of Large Language Models (LLMs) and multimodal AI systems has created an urgent need for systematic, analyzable resources to track architectural innovation, organizational strategy, and computational scaling trends. This paper presents a structured analysis of the "LLMs & Frontier AI Models Dataset" (Kaggle, 2026), a curated tabular resource documenting metadata for 47 attributes across notable AI models released through mid-2026. We examine organizational productivity patterns, frontier model compute allocations, strategic divergences between closed and open weight development paradigms, and the shifting balance between industrial and academic contributions. Our findings reveal a highly concentrated ecosystem dominated by a small cohort of well-resourced laboratories, with distinct strategic specializations emerging across geographic and organizational boundaries. We discuss implications for research reproducibility, policy development, and future dataset curation practices, while acknowledging limitations inherent to static, crowd-sourced metadata collections. Read More Read Less

Abstract Cybersecurity incidents have emerged as a major source of financial disruption for organizations across industries. The increasing frequency and sophistication of cyberattacks necessitate quantitative approaches to evaluate operational exposure, financial volatility, and systemic cyber risk. This study... Cybersecurity incidents have emerged as a major source of financial disruption for organizations across industries. The increasing frequency and sophistication of cyberattacks necessitate quantitative approaches to evaluate operational exposure, financial volatility, and systemic cyber risk. This study presents a comprehensive analytical framework for cyber-financial risk assessment using a structured cybersecurity incident dataset containing 1,902 observations. The research integrates correlation analysis, predictive regression modeling, and Monte Carlo simulation to evaluate relationships among operational variables, predict financial damage, and estimate tail-risk exposure under uncertain cyberattack conditions. Pearson, Spearman, and Kendall Tau correlations were applied to identify linear and non-linear associations among incident response metrics, recovery costs, downtime duration, and reputational impact indicators. Multiple regression techniques, including Linear Regression, Ridge Regression, Random Forest Regressor, and Gradient Boosting Regressor, were employed to predict financial damage and identify the most influential predictors of cyber-financial loss. Furthermore, a stochastic compound risk framework and Monte Carlo simulation were implemented to estimate expected annual loss, Value at Risk (VaR), and Conditional Value at Risk (CVaR). The findings reveal substantial heavy-tailed financial exposure, significant predictive importance of reputational impact severity, and catastrophic loss scenarios that conventional averagebased risk metrics fail to capture. The study contributes a practical and scalable quantitative framework for cyber risk management, cyber insurance planning, and strategic financial resilience. Read More Read Less

Abstract Stroke remains a leading cause of global mortality and long term disability, necessitating reliable predictive frameworks for early risk identification. While machine learning applications in stroke forecasting have expanded, many studies bypass rigorous exploratory data analysis, compromising... Stroke remains a leading cause of global mortality and long term disability, necessitating reliable predictive frameworks for early risk identification. While machine learning applications in stroke forecasting have expanded, many studies bypass rigorous exploratory data analysis, compromising model interpretability and clinical generalizability. This study presents a comprehensive exploratory investigation of demographic, clinical, and lifestyle determinants of stroke using a structured healthcare dataset of 5,110 patient records. Through descriptive statistics, univariate and bivariate analyses, correlation mapping, and stratified prevalence assessments, we systematically evaluated feature distributions, missingness patterns, and multivariate relationships. The cohort exhibited severe class imbalance, with stroke-positive cases comprising only 4.9% of observations. Age emerged as the strongest univariate predictor (r = 0.245), followed by preexisting heart disease, elevated average glucose levels, and hypertension. Conversely, body mass index demonstrated weak independent discriminative power, suggesting its influence operates indirectly through metabolic and cardiovascular pathways. Pairwise visualizations and correlation heatmaps confirmed minimal multicollinearity and highlighted clinically meaningful interaction effects between aging and dysglycemia. These findings establish a statistically rigorous foundation for subsequent machine learning development, emphasizing the necessity of imbalance aware evaluation metrics and targeted feature engineering. By bridging raw clinical data and algorithmic deployment, this work provides a transparent, reproducible roadmap to advance clinically actionable stroke risk stratification tools. Read More Read Less