Westcliff University

Abstract Selecting an appropriate supportive response for a mental health question post is a key step toward scalable mental health question answering, yet it remains difficult due to semantic mismatch, noisy informal language, and the need to align supportive intent beyond surface lexical overlap. We propose MF-GAT, a novel multi view graph attention matching framework that constructs a Concept Interaction Graph (CIG) to explicitly encode post response concept alignments and their interaction structure. MF-GAT learns three complementary evidence streams, including local interaction features, multi view fusion features, and global context features, and applies view specific graph attention to propagate and reweight informative relational signals over the CIG. A gated fusion module then adaptively integrates the view representations into a unified matching vector for prediction. We evaluate MF-GAT both as a pair classification model and as a retrieval ranking model for selecting the best support from a candidate pool, reporting Accuracy and F1 together with standard ranking metrics including MRR and NDCG. On the MHQA benchmark, MF-GAT achieves 0.95 Accuracy and 0.85 F1, outperforming BERT (0.89, 0.67), CIG-GCN (0.92, 0.79), ARC-II (0.85, 0.60), and MatchPyramid (0.82, 0.59). These results show that novel multi view interaction graph modeling with attention based propagation improves both supportive response classification and practical retrieval quality for mental health support selection.

Background: In an increasingly volatile global business environment, supply chain resilience has become a strategic imperative, particularly for firms operating in developing economies. Guided by Dynamic Capabilities Theory (DCT), this study examines how AI-powered tools foster an innovation culture comprising communication, creativity, and learning, and how these dimensions enhance supply chain resilience measured through flexibility, efficiency, and velocity. Methods: A quantitative research design was employed using survey data collected from 270 supply chain and operations managers in Jordanian manufacturing firms. Twelve direct hypotheses were tested using Partial Least Squares Structural Equation Modeling. Results: The findings indicate that AI-powered tools significantly influence communication, creativity, and learning. Communication and creativity positively affect all three dimensions of supply chain resilience. Learning significantly improves efficiency but shows no significant effect on flexibility or velocity, indicating that learning is mainly utilized for process improvement rather than rapid adaptation. Conclusions: The study demonstrates that AI adoption alone is insufficient to build resilient supply chains unless supported by innovation-oriented cultural capabilities. The findings extend DCT by clarifying the differentiated role of learning in resilience building and provide actionable guidance for managers seeking to align AI investments with cultural development in resource-constrained manufacturing contexts and long-term competitive advantage.

U.S. financial institutions deploying AI-based fraud detection face a fragmented compliance landscape spanning four regulatory frameworks – OCC Bulletin 2011-12, SR 11-7, the CFPB AI circular, and FinCEN BSA/SAR requirements – with no integrated governance life cycle connecting these requirements to model development, validation, and monitoring practice. This paper presents the Regulatory Governance Framework for AI-Driven Financial Fraud Detection (RGF-AFFD), a three-tier governance architecture empirically anchored in a multi-study empirical program. Using the IEEE-CIS dataset (590,540 transactions) and ULB benchmark (284,807 transactions), we benchmark six architectures including an LSTM+XGBoost ensemble, and conduct ablation, temporal drift, SHAP interpretability, and BISG fairness analyses. The LSTM+XGBoost ensemble achieves ROC-AUC of 0.9289 (F1: 0.6360) with a benefit-cost ratio of 6:1. XGBoost demonstrates the strongest temporal stability (delta-AUC = -0.0017 versus -0.0626 for LSTM). The RDT-FG Regulatory Digital Twin meta-model translates metrics into four regulator-specific health scores and a composite Regulatory Fitness Index for continuous compliance monitoring. The RGF-AFFD is the first integrated deployment blueprint to simultaneously satisfy OCC, SR 11-7, CFPB, and FinCEN requirements, supported by a community bank implementation vignette and four evidence-based policy recommendations.

The popularity of drones, unmanned aerial vehicles (UAVs), and industrial-level cyber-physical systems has deepened external threats caused by network-based cyber-attacks. Such environments have a problem of dynamic traffic behavior, temporal dependencies, class imbalance and the existence of various type of attacks, such as denial of service, injection, replay attacks, scanning, and man in the middle attacks. This paper presents an effective and justifiable multi-class attack detection model in a heterogeneous environment that can be used as an intrusion detector. Three benchmark datasets, Drone IDS, UAVIDS-2025, and ICSCASD_MPLC were evaluated comprehensively with ensemble-based machine learning models (Random Forest, Extra trees, AdaBoost, XGBoost, and CatBoost) and those with deep learning architecture (ANN, CNN, RNN, LSTM, and ResNet). Within the framework of many preprocessing steps, the accuracy, macro-averaged precision, recall, F1-score, Matthews Correlation Coefficient, Cohen’s Kappa, log loss, and ROC-AUC were used to evaluate the models. According to experimental findings, Random Forest is more effective than other ensemble models, with macro F1-scores of 0.99964, 0.99844, and 0.99994 on Drone IDS, UAVIDS-2025, and ICSCASD_MPLC datasets, respectively, with nearly perfect ROC-AUC indicators. Compared to other deep learning methods, LSTM is best at learning patterns of attack over time, ANN is well-performing with minimal computing costs, and RNN is well-performing in generalizing on industrial traffic. The validity of statistical significance of results is tested with Friedman and Wilcoxon signed-rank tests with Holm correction, bootstrap confidence intervals, and McNemar test. Also, explainable tools of AI, including SHAP and LIME, provide both local and global explanations, which are both intuitive, as well as ablation testing demonstrates that a small set of flow-based and temporal features are capable of sustaining close-optimal performance. In general, the framework proposed provides real-time and safety–critical deployments with intrusion detection algorithms that are accurate, interpretable, and validated statistically.

Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity's sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.