Abstract This study investigates the dynamic interactions between lifestyle behaviors, specifically exercise, sleep, and nutrition, and digital habits, including screen time and sedentary behavior, to determine their impact on cognitive engagement and learning effectiveness through a neuroplasticity based... This study investigates the dynamic interactions between lifestyle behaviors, specifically exercise, sleep, and nutrition, and digital habits, including screen time and sedentary behavior, to determine their impact on cognitive engagement and learning effectiveness through a neuroplasticity based framework. Analyzing a dataset of 212 participants, the research employed a comprehensive multivariate approach, including Exploratory Factor Analysis, Principal Component Analysis, ordinal logistic regression, K means clustering, and Structural Equation Modeling. The factor analyses revealed a robust six-dimensional neuroplasticity structure encompassing cognitive engagement, physical activity, sleep, nutrition, digital distraction, and learning effectiveness. Regression and correlation results demonstrated that exercise, sleep quality, and nutrition are significant positive predictors of learning outcomes, whereas excessive screen time exerts a detrimental effect. Furthermore, cluster analysis identified four distinct behavioral profiles, notably the Healthy Neuroplasticity and Digitally Distracted groups, highlighting significant population heterogeneity. Crucially, Structural Equation Modeling confirmed that cognitive engagement acts as a central mediating mechanism, translating positive lifestyle habits into enhanced educational outcomes while digital distractions disrupt this pathway. These findings validate a multidimensional neuroplasticity model, emphasizing that cognitive resilience and academic success are deeply rooted in holistic lifestyle practices. The study show the necessity of integrating neurobiological principles into modern educational technologies and cognitive rehabilitation programs to foster adaptive, personalized learning environments that mitigate digital fatigue and promote optimal cognitive development across diverse student populations. Read More Read Less

Abstract This study investigates the determinants of Return on Investment (ROI) in enterprise Robotic Process Automation (RPA) implementations through an integrated analytical framework combining Exploratory Factor Analysis (EFA) and Random Forest regression modeling. Using project-level data comprising five... This study investigates the determinants of Return on Investment (ROI) in enterprise Robotic Process Automation (RPA) implementations through an integrated analytical framework combining Exploratory Factor Analysis (EFA) and Random Forest regression modeling. Using project-level data comprising five key performance indicators Robots Deployed, Budget (USD), Annual Savings (USD), ROI (%), and Employee Hours Saved the research identifies underlying latent structures and evaluates the predictive power of financial and operational variables on automation returns. EFA revealed a two-factor solution explaining 72.71% of total variance, interpreted as Financial Performance and Operational Efficiency. The Random Forest model demonstrated excellent predictive accuracy (Mean R² = 0.962, MAE = 7.90) with stable crossvalidation performance (SD = 0.0055). Feature importance analysis identified Annual Savings (35.91%), Budget (32.13%), and Employee Hours Saved (27.29%) as the dominant ROI predictors, while Robots Deployed contributed only 4.68%. SHAP-based explainability confirmed that realized economic benefits and workforce productivity gains constitute the primary mechanisms driving ROI generation. Although the Kaiser-Meyer- Olkin measure (0.412) indicated limited common variance, Bartlett's Test of Sphericity was significant (² = 117,907.48, p < .001), supporting factor extraction. The findings suggest that successful RPA implementations should prioritize measurable financial outcomes and operational efficiency over automation scale expansion. This research contributes empirical evidence on RPA value creation and provides actionable guidance for organizations seeking to optimize automation investments. Conceptual evaluation of XGBoost indicated only marginal expected improvements, confirming the robustness of the ensemble learning approach. Read More Read Less

Abstract This study investigates the relationship between Directed Acyclic Graph (DAG) structural properties and scheduling efficiency under the Heterogeneous Earliest Finish Time (HEFT) algorithm in heterogeneous computing environments. Despite HEFT's widespread adoption as a benchmark scheduling heuristic, systematic characterization... This study investigates the relationship between Directed Acyclic Graph (DAG) structural properties and scheduling efficiency under the Heterogeneous Earliest Finish Time (HEFT) algorithm in heterogeneous computing environments. Despite HEFT's widespread adoption as a benchmark scheduling heuristic, systematic characterization of how graph topology influences its performance across diverse workload regimes remains limited. Using a comprehensive benchmark of 300 DAG workflows spanning three complexity classes (9, 23, and 29 tasks), we employ multivariate statistical techniques including Principal Component Analysis, K-means clustering, hierarchical clustering, multiple regression, and Random Forest modeling to identify latent structural dimensions governing execution behavior. Results reveal three dominant variance components that explain 97.6% of the total variability: graph scale and computational complexity (PC1: 47.7%), communication intensity (PC2: 22.9%), and execution heterogeneity (PC3: 18.1%). Clustering analysis validates three distinct workload regimes with strong statistical separation (Silhouette score = 0.612). Critically, regression and feature importance analyses demonstrate that critical path length overwhelmingly dominates execution time (importance = 0.974), while total workload and graph size contribute secondarily. HEFT efficiency varies substantially across clusters, ranging from 0.771 (small graphs) to 0.408 (large graphs), with communication heavy and deep dependency structures exhibiting significant performance degradation. These findings support the development of Cluster Aware HEFT (CA-HEFT), an adaptive scheduling architecture that pre classifies incoming workflows and applies tailored prioritization and processor selection strategies. This research contributes an empirical taxonomy of DAG workloads, identifies critical path as the principal performance bottleneck, and provides a foundation for structure aware, adaptive scheduling in heterogeneous distributed systems. Read More Read Less