Review of Applied Science and Technology
Review of Applied Science and Technology (RAST) is an international, peer-reviewed, open-access journal dedicated to publishing high-impact research and review articles in all areas of applied science and engineering technology. RAST serves as a critical platform for researchers, innovators, and practitioners aiming to translate scientific knowledge into practical solutions for industrial, environmental, technological, and societal advancement. With a commitment to academic rigor, technological innovation, and interdisciplinary collaboration, the journal bridges the gap between theoretical research and its practical implementation in diverse sectors, including energy, materials, information technology, mechanical systems, chemical processes, biomedical engineering, and sustainable development.
Mitigating Solar Curtailment in High-Penetration Interconnections: An AI-Driven Approach to Dynamic Load Balancing
This study addresses the persistent problem of solar curtailment in high-penetration interconnections, where available photovoltaic output is reduced because grid systems cannot absorb, transfer, or balance solar generation efficiently during peak periods. The purpose of the research was to examine whether AI-driven dynamic load balancing can mitigate solar curtailment by improving forecasting accuracy, balancing efficiency, system flexibility, and operational responsiveness within renewable-rich grid environments. Using a quantitative, cross-sectional, case-based design, the study collected data from 188 usable respondents drawn from cloud-enabled and enterprise-scale electricity and grid-operation cases, including grid operators, utility engineers, renewable energy managers, dispatch analysts, planners, and technical staff. The key variables examined were AI Forecasting Accuracy, Dynamic Load Balancing Efficiency, Grid Flexibility, Interconnection Capacity, AI Operational Responsiveness, Operational Trust in AI, and Solar Curtailment Mitigation. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling. The findings showed strong positive respondent agreement across the core variables, with mean scores of 4.18 for AI Forecasting Accuracy, 4.24 for Dynamic Load Balancing Efficiency, 4.11 for Grid Flexibility, 4.06 for Interconnection Capacity, 4.21 for AI Operational Responsiveness, 3.89 for Operational Trust in AI, and 4.27 for Solar Curtailment Mitigation. Reliability was high across all constructs, with Cronbach’s alpha values ranging from 0.811 to 0.896. Correlation results revealed that Dynamic Load Balancing Efficiency had the strongest association with Solar Curtailment Mitigation (r = .708, p < .01), followed by AI Forecasting Accuracy (r = .651, p < .01) and AI Operational Responsiveness (r = .624, p < .01). The regression model explained 62.4% of the variance in solar curtailment mitigation (R² = .624), and the model was statistically significant (F = 31.480, p < .001). Dynamic Load Balancing Efficiency emerged as the strongest predictor (β = .310, p < .001), followed by AI Forecasting Accuracy (β = .270, p = .002), Grid Flexibility (β = .220, p = .006), AI Operational Responsiveness (β = .190, p = .011), Interconnection Capacity (β = .160, p = .018), and Operational Trust in AI (β = .140, p = .031). The study implies that reducing solar curtailment requires not only better AI tools but also flexible infrastructure, responsive interconnections, and institutional trust to support practical adoption in modern electricity systems.
Copyright (c) 2026 Review of Applied Science and Technology
Quantitative Assessment of Data-Driven Pricing Optimization Strategies for E-Commerce Platforms in Developing Economies
This study examines the effect of data-driven pricing optimization strategies on the performance of e-commerce platforms in developing economies, where firms face intense price competition, high consumer price sensitivity, and persistent market constraints. The problem addressed is that many e-commerce firms in these contexts still struggle to translate customer data, competitor information, demand signals, and real-time analytics into pricing decisions that improve performance consistently. The purpose of the study was therefore to assess whether data-driven pricing optimization functions as a strategic capability that strengthens sales performance, customer satisfaction, customer retention, and competitive advantage. The research adopted a quantitative, cross-sectional, case-based design and collected primary data through structured questionnaires from 210 respondents drawn from cloud-enabled and enterprise-oriented e-commerce platform cases, including marketplace, direct-to-consumer, and hybrid platforms. The key independent variables were customer behavior analytics, competitor price monitoring, demand forecasting, real-time data integration, and dynamic pricing capability, while consumer price sensitivity and developing-market constraints were treated as contextual variables and platform performance as the dependent construct. Data were analyzed using descriptive statistics, Cronbach’s alpha, correlation, and multiple regression in SPSS. The findings show moderate-to-high adoption of data-driven pricing practices, with an overall grand mean of 4.08 and overall instrument reliability of 0.88. Among the predictors, real-time data integration emerged as the strongest positive determinant of platform performance (β = 0.29, p = 0.002), followed by customer behavior analytics (β = 0.24, p = 0.006), competitor price monitoring (β = 0.21, p = 0.011), demand forecasting (β = 0.18, p = 0.019), and dynamic pricing capability (β = 0.15, p = 0.041). The overall model was significant (F = 31.47, p < .001) and explained 57.0% of the variance in platform performance (R² = 0.570). However, consumer price sensitivity (β = -0.17, p = 0.028) and developing-market constraints (β = -0.19, p = 0.015) weakened performance gains. The study implies that firms in developing economies should invest in real-time analytics, integrated pricing systems, and context-sensitive pricing strategies to improve digital competitiveness and sustainable platform performance.
Copyright (c) 2024 Review of Applied Science and Technology
Quantitative Assessment of Data Privacy and Access Control Effectiveness in SAP/ERP Analytics Systems
Enterprise organizations increasingly rely on SAP and Enterprise Resource Planning (ERP) analytics systems to manage, process, and analyze large volumes of sensitive financial and operational data. Ensuring effective data privacy protection and access control within these integrated analytics environments has therefore become a critical requirement for maintaining enterprise information security and regulatory compliance. This study conducted a quantitative assessment of data privacy and access control effectiveness within SAP and ERP analytics systems operating in enterprise environments. The analysis was based on enterprise system records obtained from twelve SAP/ERP analytics platforms, comprising 18,750 authentication and authorization events extracted from audit logs, access control reports, and security monitoring systems. The study evaluated key security performance indicators including authorization accuracy, authentication response time, unauthorized access attempts, privilege escalation incidents, and audit compliance outcomes. Descriptive statistical analysis indicated that the evaluated enterprise systems achieved a mean authorization accuracy rate of 94.8%, demonstrating strong enforcement of role-based access control policies. The average authentication response time was 0.82 seconds, indicating stable system performance during user verification processes across the analyzed enterprise environments. Unauthorized access attempts occurred at a relatively low mean frequency of 29.7 incidents per evaluation cycle, while privilege escalation incidents averaged 7.0 events, suggesting that enterprise monitoring systems effectively restricted unauthorized access behavior. The analysis also revealed high levels of regulatory and policy adherence, with a mean audit compliance rate of 94.2% across the evaluated systems. Inferential statistical analysis further demonstrated statistically significant differences in authentication response time, unauthorized access attempts, and audit compliance performance across enterprise systems operating under varying levels of governance maturity and monitoring intensity. Systems equipped with stronger monitoring infrastructures and structured governance frameworks consistently achieved higher authorization accuracy rates and lower frequencies of access violations. Subgroup analysis additionally indicated that financial management and procurement modules recorded slightly higher audit exception frequencies due to the sensitivity of transactional enterprise data processed within these modules.
Copyright (c) 2023 Review of Applied Science and Technology
Quantitative Performance Assessment of Distributed Machine Learning Frameworks for Real-Time Financial Analytics in Enterprise Data Platforms
The increasing volume and velocity of financial data generated in modern enterprise environments have created significant demand for scalable analytical infrastructures capable of supporting real-time financial analytics. Distributed machine learning frameworks have emerged as essential technologies for processing large-scale financial datasets across cluster-based computing environments. This study conducted a quantitative performance assessment of distributed machine learning frameworks used in enterprise data platforms for real-time financial analytics. An experimental benchmarking design was implemented to evaluate the computational performance of three distributed machine learning frameworks operating on enterprise-scale financial datasets. The experimental dataset consisted of 120 performance observations obtained from repeated analytical workloads executed across a distributed cluster infrastructure. Key performance indicators examined in the study included computational throughput, model training duration, analytical response latency, and system resource utilization. The results demonstrated measurable differences in performance across the evaluated frameworks. Framework A achieved the highest average computational throughput of 14.6 GB per minute, while Framework B and Framework C processed financial datasets at average throughputs of 12.3 GB and 11.5 GB per minute respectively. Model training efficiency also varied across frameworks, with Framework A completing distributed training tasks in an average of 34.2 minutes compared with 39.8 minutes for Framework B and 41.7 minutes for Framework C. Real-time analytical response latency averaged 198 milliseconds for Framework A, while Framework B and Framework C recorded response delays of 221 milliseconds and 232 milliseconds respectively. Scalability testing further indicated that throughput performance improved as cluster capacity increased from 4 nodes to 16 nodes, with Framework A achieving a maximum throughput of 18.4 GB per minute under expanded cluster configurations. Statistical analysis using analysis of variance confirmed that the observed performance differences were statistically significant at the p < 0.05 level. Effect size analysis also indicated moderate to large differences across the evaluated performance metrics. The findings demonstrate that distributed machine learning frameworks differ substantially in their ability to support enterprise financial analytics workloads. These results provide empirical evidence supporting the importance of framework architecture, distributed resource coordination, and scalability capabilities in determining the efficiency of large-scale financial analytics systems operating within enterprise data platforms.
Advanced Computational and Biotechnological Approaches to Systemic Family Therapy: Predicting Marital Satisfaction and Emotional Wellbeing in Couples
This quantitative, cross-sectional, case-study–based research addressed the problem that systemic couple distress is often assessed using subjective impressions without an integrated, data-driven model that jointly explains marital satisfaction and emotional wellbeing using measurable systemic interaction processes and biotech-informed regulation indicators. The purpose was to develop and test a predictive framework, grounded in systemic family therapy, that estimates marital satisfaction and emotional wellbeing from modifiable relationship-process variables plus stress-recovery indicators. Using a purposive, case-based sample of 180 couple cases within a bounded context, participants completed Likert 5-point composite measures for Communication Quality (CQ), Conflict Regulation (CR), Emotional Responsiveness (ER), Repair Capacity (RC), Stress Regulation Indicator (SRI), Sleep Quality Indicator (SQI), and outcomes Marital Satisfaction (MS) and Emotional Wellbeing (EWB). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, and hierarchical multiple regression for MS and EWB, followed by System Dynamics Index profiling (SDI = mean of CQ, CR, ER, RC) and prediction risk-banding. Descriptively, CQ (M = 3.62, SD = 0.71), ER (M = 3.69, SD = 0.68), MS (M = 3.58, SD = 0.74), and EWB (M = 3.46, SD = 0.73) were moderately high, with strong reliabilities (α = 0.82–0.90). Correlations showed robust systemic links to outcomes, especially ER with MS (r = .64) and EWB (r = .57), and CQ with MS (r = .61). In regression, systemic predictors explained substantial variance in MS (R² = .52), improved to R² = .56 when biotech indicators were added (ΔR² = .04, p = .012); ER remained the strongest MS predictor (β = .31). For EWB, systemic predictors explained R² = .44, rising to R² = .55 after adding SRI and SQI (ΔR² = .11, p < .001), with SRI (β = .27) and SQI (β = .19) significant. SDI profiling showed clear gradients: High SDI cases reported higher MS (M = 4.01) and EWB (M = 3.89) than Low SDI cases (MS M = 2.97; EWB M = 2.91). Implications indicate that therapy assessment can prioritize responsiveness, repair, and conflict regulation while adding brief stress and sleep screening to better identify wellbeing risk and tailor intervention intensity.
Data Preprocessing and Feature Engineering Strategies for Large-Scale Predictive Modeling Applications
Data preprocessing and feature engineering play a critical role in the effectiveness of predictive modeling, particularly in large-scale data environments where datasets often contain inconsistencies, missing values, and heterogeneous variable structures. This study examined the impact of structured preprocessing and feature engineering strategies on predictive modeling performance using a quantitative experimental design. A large-scale structured dataset consisting of 12,500 observations and 48 predictor variables was analyzed to evaluate how different preprocessing pipelines influenced machine learning outcomes. The study implemented multiple preprocessing techniques, including data cleaning, missing value imputation, normalization, statistical transformation, categorical encoding, feature construction, and feature selection. These preprocessing strategies were integrated with several supervised learning algorithms, including logistic regression, decision tree, random forest, support vector machine, and gradient boosting models. A baseline model trained on minimally processed data was first developed to establish a reference performance level, after which multiple preprocessing pipelines were evaluated through repeated 10-fold cross-validation. The results demonstrated that structured preprocessing significantly improved predictive model performance across all algorithms tested. The baseline model achieved an average classification accuracy of 71.4%, whereas models trained using comprehensive preprocessing pipelines achieved an average accuracy of 84.7%, representing an improvement of 13.3 percentage points. Feature engineering and feature selection techniques produced the strongest improvements, increasing the F1-score from 0.69 in the baseline model to 0.86 in the optimized models. Similarly, the area under the receiver operating characteristic curve increased from 0.74 to 0.91, indicating substantial improvement in predictive discrimination ability. Statistical testing confirmed that the improvements observed across preprocessing strategies were significant at the 0.05 significance level, and effect size analysis indicated moderate to large effects for feature engineering and feature selection interventions. The findings demonstrated that structured data preprocessing and feature engineering substantially enhanced predictive accuracy, model robustness, and analytical reliability in large-scale predictive modeling systems. The study highlighted the methodological importance of comprehensive data preparation pipelines and provided empirical evidence supporting the integration of preprocessing strategies as a fundamental component of predictive analytics workflows.
Development of a Hybrid Machine Learning Model for Predictive Performance Optimization in Lean Manufacturing and Industry 4.0
The convergence of Machine Learning (ML) and Lean Manufacturing within Industry 4.0 has opened new frontiers for predictive performance optimization in industrial engineering. However, a comprehensive synthesis of existing methodologies and hybrid modeling approaches remains limited in the literature. This study presents a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-based systematic review to identify, evaluate, and synthesize existing research on the integration of machine learning techniques with Lean Manufacturing principles in Industry 4.0 environments. A structured database search was conducted across Scopus, Web of Science, and Google Scholar, yielding an initial pool of 320 articles, of which 47 studies were selected following strict inclusion and exclusion criteria. The review systematically examines supervised, unsupervised, and hybrid ML algorithms applied to key Lean metrics including waste reduction, cycle time, defect prediction, and process efficiency. Findings reveal that hybrid ML models combining algorithms such as Random Forest, XGBoost, and Neural Networks demonstrate superior predictive accuracy compared to single-algorithm approaches. Furthermore, the study identifies critical research gaps in real-time data integration, model interpretability, and scalability across manufacturing sectors. Based on the synthesized evidence, a conceptual hybrid ML framework is proposed to guide future model development for predictive Lean performance optimization. This review contributes a structured foundation for researchers and practitioners seeking to advance data-driven decision-making in smart manufacturing systems.
Copyright (c) 2025 Review of Applied Science and Technology
A Systematic Review of Secure Health Data Information Systems for Pandemic Preparedness and Economic Continuity in the United States
This study conducted a qualitative systematic review of secure health data information systems and their role in pandemic preparedness and economic continuity within the United States. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review applied transparent search, screening, and eligibility procedures to identify relevant peer-reviewed qualitative studies examining cybersecurity maturity, interoperability performance, data integrity, preparedness outcomes, and economic stabilization mechanisms. Following duplicate removal and multi-stage screening, a total of 43 qualitative studies met the inclusion criteria and were synthesized using thematic analysis. The findings indicated that secure health data systems functioned as foundational infrastructures supporting outbreak detection speed, cross-agency coordination, resource allocation precision, continuity planning maturity, and recovery capability. A substantial proportion of the reviewed literature linked cybersecurity governance, auditability, and access controls to improved operational resilience, particularly during ransomware incidents and digital disruptions. Interoperability challenges—especially inconsistent standards adoption and incomplete exchange participation—were recurrent themes affecting detection timeliness and policy coherence. Data integrity dimensions, including accuracy, completeness, consistency, and timeliness, were consistently identified as prerequisites for credible pandemic intelligence and decision confidence. The review further revealed that reliable health data reporting contributed to economic continuity by reducing uncertainty, strengthening policy legitimacy, and enabling targeted workforce and sectoral interventions. However, methodological variability, construct inconsistencies, limited cross-sector perspectives, and underrepresentation of low-resource settings constrained interpretive generalization. Overall, the synthesis demonstrated that secure, interoperable, and integrity-preserving health data systems operate as interconnected socio-technical infrastructures that shape both public health preparedness performance and economic stability during pandemic events in the United States.
High-Performance Computing–Assisted Modeling and Real-Time Analysis of Electrical Power Networks and Industrial Control Systems
This study examined the performance, robustness, and real-time feasibility of high-performance computing–assisted modeling and integrated analytical pipelines for electrical power networks coupled with industrial control systems. A quantitative experimental design was implemented using 360 structured run instances distributed across small, medium, and large benchmark grid tiers, multiple telemetry regimes, and standardized contingency libraries. Dependent outcomes included update latency, sustained throughput, state-estimation error, contingency ranking stability, and anomaly detection delay, while independent variables included compute architecture type, parallelism level, grid size tier, telemetry regime, contingency volume, and disturbance severity. Mixed-effects regression models with clustered random intercepts were used to quantify performance drivers. Results showed that mean update latency across the full sample was 148.2 ms (P95 = 268.0 ms), while sustained throughput averaged 6.8 updates per second. Hybrid CPU–GPU configurations reduced latency by 24.63 ms relative to CPU-only baselines (p < .001) and increased throughput by 1.18 updates per second. Large-grid tiers increased latency by 72.84 ms and reduced throughput by 4.27 updates per second compared with small-grid tiers (p < .001), confirming scale as the dominant workload driver. Convergence rates averaged 96.9%, indicating stable numerical robustness across tiers. Analytical quality improved under richer telemetry, with PMU-only configurations reducing estimation error by 0.008 per unit relative to SCADA-only (p < .001). Contingency ranking stability averaged 0.88, while missed critical contingencies averaged 0.37 per run under standardized runtime budgets. Anomaly detection delay averaged 312.5 ms and increased significantly under higher disturbance severity and larger grid tiers. Overall, the findings demonstrated that real-time cyber-physical feasibility in power–ICS environments was primarily governed by grid scale, telemetry design, and workload composition, while compute acceleration and parallelism provided statistically significant but workload-dependent performance improvements.
Integration Of Renewable Energy into Grid Utility: Challenges and Future Prospects
This study addressed the persistent problem that enterprise grid utilities struggle to integrate rising renewable penetration without increasing curtailment pressure and perceived reliability risk, largely because constraint intensity and organizational capability evolve unevenly across departments. Using the Technology–Organization–Environment (TOE) lens, the purpose was to quantify the severity of integration barriers, test how readiness and constraint domains predict Grid Integration Performance (GIP), and translate results into actionable diagnostics through the Grid Integration Stress Index (GISI), Renewable Readiness Gap Analysis (RRGA), and a Curtailment and Reliability Perception Map (CRPM). A quantitative, cross-sectional, case-based design was applied in an enterprise utility case, using purposive sampling of 180 professionals across system operations, planning, protection and control, asset management, regulatory/compliance, and SCADA/automation functions. Key independent variables were challenge domains (infrastructure constraints, operational challenges, policy/regulatory barriers, technical challenges, economic/financial barriers) and TOE readiness (technology, organizational, environmental), while dependent variables included GIP and, in CRPM, perceived curtailment risk and perceived reliability risk. Measurement quality was strong (Cronbach’s α = 0.81–0.90 across constructs). Descriptively, the most severe barriers were Infrastructure Constraints (M = 4.21, SD = 0.62) and Operational Challenges (M = 4.10, SD = 0.66), followed by Policy/Regulatory Barriers (M = 3.98, SD = 0.71). Multiple regression explained substantial variance in GIP (R² = 0.62, Adjusted R² = 0.60, F(8,171) = 34.9, p < 0.001), with Organizational Readiness (β = 0.34, p < 0.001), Technology Readiness (β = 0.22, p = 0.002), and Environmental Readiness (β = 0.15, p = 0.017) improving performance, while Operational Challenges (β = −0.27, p < 0.001), Infrastructure Constraints (β = −0.21, p = 0.003), and Policy/Regulatory Barriers (β = −0.14, p = 0.035) reduced performance. GISI showed highest stress in System Operations (M = 4.18, SD = 0.51) and Planning (M = 4.05, SD = 0.55), reinforcing that operational interfaces carry peak integration pressure. RRGA indicated the largest capability gaps for Battery Energy Storage (I = 4.62 vs R = 3.20, Gap = 1.42), Advanced Forecasting (Gap = 1.17), and DERMS/visibility (Gap = 1.08), while CRPM evidence suggested infrastructure constraints more strongly drive curtailment risk (β ≈ 0.31, p < .001) and operational challenges more strongly drive reliability risk (β ≈ 0.33, p < .001). Implications are that utilities should prioritize cross-department integration governance and workforce capability (organizational readiness) alongside targeted deliverability and flexibility interventions, using GISI and RRGA as ongoing internal monitoring tools to track stress reduction and readiness closure.
A Qualitative Study of Safety Professionals’ Experiences in Managing Chemical Exposure Risks and Hazardous Materials Controls in Industrial Facilities
This study addresses the problem that enterprises often manage chemical exposure risks with limited visibility into non-routine tasks and inconsistent verification of control performance, leaving residual risk even when hazards are recognized. The purpose was to quantify and compare how hazardous-materials controls are documented across enterprise cases using the Hierarchy of Controls and structured evidence coding. A quantitative, cross-sectional, case-based design analyzed 30 eligible enterprise case studies from 2006 to 2020 (n=30): manufacturing 40%, chemical processing 23%, oil and gas 17%, pharmaceuticals 10%, and mixed/other 10%. Key variables were sector, hazard profile and exposure route, plus Likert-coded indices (1–5) for engineering robustness, administrative strength, PPE strength, and monitoring and verification maturity. The analysis used descriptive statistics, cross-case comparison matrices, and ranked frequency-severity pattern analysis. Solvent/VOC scenarios were most common (37%, 11/30), followed by dust/particulates (27%, 8/30), corrosives (20%, 6/30), and mixed profiles (16%, 5/30), with inhalation emphasized in 53% (16/30) of cases. Across cases, monitoring and verification was lowest (mean 2.9/5), while engineering robustness averaged 3.0/5 (SD 0.8), administrative strength 3.4/5, and PPE strength 3.7/5. The most prevalent breakdown was monitoring not representative of non-routine tasks (63%, 19/30; severity 4.2/5), followed by unverified ventilation or enclosure performance (57%, 17/30; severity 4.0/5). Pharmaceuticals showed higher engineering robustness (4.1/5) and verification maturity (3.8/5) than mixed/other cases (2.9/5 and 2.8/5). Implications are to prioritize representative monitoring for non-routine work, routine verification of engineering performance, and point-of-work hazard communication.
Copyright (c) 2022 Review of Applied Science and Technology
Engineering Resilient Flood Mitigation Using Geosynthetic and Composite Barrier Materials Performance Modeling and Environmental Impact Assessment
Engineering resilient flood mitigation increasingly relies on geosynthetic and composite barrier materials due to their ability to reduce seepage, improve stability performance, and limit construction-related environmental burdens. This quantitative study evaluated the predictors of flood mitigation performance and environmental impact outcomes using a structured survey dataset (N = 210) and regression-based statistical analysis. Five constructs were measured: geosynthetic barrier performance, seepage control effectiveness, slope stability contribution, installation quality sensitivity, and environmental impact assessment outcomes. Descriptive results showed the highest mean scores for seepage control effectiveness (M = 4.34, SD = 0.58) and geosynthetic barrier performance (M = 4.21, SD = 0.62), confirming strong agreement regarding hydraulic performance benefits. Installation quality sensitivity demonstrated the greatest dispersion (M = 3.88, SD = 0.79), indicating variability in respondent assessment of construction-driven performance risk. Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.81 to 0.91. Multiple regression results indicated that seepage control effectiveness was most strongly predicted by composite configuration (β = 0.34, p < .001) and installation quality sensitivity (β = 0.29, p < .001), with the model explaining 56% of variance (R² = 0.56). Environmental impact assessment outcomes were predicted by barrier type (β = 0.20, p = .002), composite configuration (β = 0.18, p = .007), and durability assumptions (β = 0.17, p = .008), while installation quality sensitivity was negatively associated (β = −0.31, p < .001). This environmental model explained 41% of variance (R² = 0.41). Overall, the findings demonstrated that resilient flood mitigation outcomes were primarily governed by system-level composite design and installation execution, while environmental outcomes were additionally shaped by durability and construction sensitivity considerations.
Data-Driven Framework for Service Issue Escalation and Resolution in Large Scale Insurance Portfolios
Service issue escalation and resolution in cloud enabled enterprise case management are difficult to govern in large insurance portfolios, where tickets can trigger avoidable escalation, stalled ownership, and inconsistent closure. This study developed and tested a data driven framework linking escalation criteria standardization (ECS), workflow automation support (WAS), cross functional coordination (CFC), data quality (DQ), analytics effectiveness (AE), and governance and accountability (GOV) to escalation effectiveness (EE) and resolution performance (RP). Using a quantitative, cross sectional, case-based design, a five-point Likert survey captured perceptions from 228 employees in one enterprise portfolio using a cloud-based case management workflow (frontline 42.1%, specialists 28.5%, supervisors 19.3%, QA or support 10.1%). Analysis included data screening, Cronbach reliability, descriptive statistics, Pearson correlation, two multiple regression models, and a mediation test. Internal consistency was high (alpha 0.83 to 0.91). Mean scores indicated moderate capability but uneven execution (3.42 to 4.11), and pathway integrity was weakest on documentation completeness (M 3.41) and ownership continuity (M 3.38) compared with routing accuracy (M 3.79). Correlations supported key links, including ECS with EE (r 0.56), WAS with EE (r 0.49), CFC with EE (r 0.52), DQ with AE (r 0.58), AE with RP (r 0.61), EE with RP (r 0.57), and GOV with RP (r 0.46), all with p below 0.001. Regression results showed ECS (beta 0.31), WAS (beta 0.24), and CFC (beta 0.29) explained 54% of variance in EE (R2 0.54), while AE (beta 0.33), EE (beta 0.28), GOV (beta 0.19), and DQ (beta 0.17) explained 62% of RP (R2 0.62). EE partially mediated ECS to RP, with the direct effect decreasing from beta 0.34 to 0.21 and an indirect effect of 0.13 (p below 0.01). These findings highlight actionable levers for service leaders and BI governance. Implications are that teams can improve closure speed and durability by enforcing complete handoff packages, expanding automated routing and aging controls, and using analytics dashboards under clear ownership rules.
An Early-Warning Predictive Framework for Financial Distress in U.S. Small Businesses
This study developed and empirically evaluated an early-warning predictive framework for financial distress in U.S. small businesses using a quantitative, time-dynamic modeling approach. The analysis was conducted on a sample of 482 small businesses spanning manufacturing, construction, trade, professional services, and hospitality sectors, with firms distributed across all major U.S. regions. Financial distress was defined using multiple economically meaningful impairment indicators rather than bankruptcy alone. Descriptive results showed notable variability across constructs, with mean liquidity of 0.62 (SD = 0.21), mean leverage of 0.54 (SD = 0.19), and mean credit behavior score of 0.47 (SD = 0.22), indicating heterogeneous financial conditions across firms. Reliability analysis confirmed strong internal consistency, with Cronbach’s alpha values ranging from 0.77 for efficiency indicators to 0.88 for credit behavior measures. Regression analysis revealed statistically significant associations between financial distress and liquidity (β = −0.21, p = 0.002), profitability (β = −0.14, p = 0.008), leverage (β = 0.25, p < 0.001), cash flow dynamics (β = −0.23, p = 0.001), credit behavior (β = 0.34, p < 0.001), and relationship-based indicators (β = −0.18, p = 0.005). Efficiency indicators were not statistically significant at the 5% level. Model explanatory power increased incrementally from R² = 0.31 in the baseline financial model to R² = 0.46 in the full integrated model, while variance inflation factors remained below 1.9 across all specifications. Hypothesis testing resulted in the rejection of six out of seven null hypotheses. Overall, the findings provide quantitative evidence that an integrated early-warning predictive framework can effectively identify emerging financial distress among U.S. small businesses by combining financial, cash flow, behavioral, and relational indicators.
VaR and CVaR-Based Stress Testing Using Deep Learning for Liquidity Risk Forecasting and Banking Stability Assessment
Liquidity risk remains a core driver of banking fragility because funding shocks and market illiquidity can force value destructive asset sales and destabilize cash flow obligations. This study addresses the problem that conventional liquidity ratios and linear models can understate tail risk, weakening early warning and stress testing for stability oversight. The purpose was to validate an integrated pipeline combining deep learning liquidity risk forecasting with VaR and CVaR based stress testing to strengthen banking stability assessment. In a quantitative cross-sectional, case-based design, four enterprise case banks (B = 4) were analyzed using 192 aligned bank observations and a survey of 220 liquidity risk professionals, producing 212 usable responses (96.4%). Key variables included liquidity buffer ratio (liquid assets/total assets), wholesale funding reliance, loan-to-deposit ratio, a Likert 1-5 governance and stress testing maturity index, VaR95 and VaR99, CVaR95 and CVaR99, and a stability proxy (Z-score). The analysis plan covered descriptive profiling, forecasting comparison (LSTM versus linear regression), tiered scenario stress testing (baseline, mild, adverse, severe), and correlation and regression models linking stressed tail risk and governance to stability. Governance measurement was reliable (Cronbach’s alpha = 0.81-0.89) and scenario realism was rated high (M = 4.08, SD = 0.58). The LSTM improved prediction accuracy over the benchmark (RMSE = 0.042 vs 0.061; MAE = 0.031 vs 0.047) and directional accuracy (67.5% vs 56.2%). Under severe stress, VaR0.95 rose from 1.90 to 2.83 (+48.9%) while CVaR0.95 rose from 2.66 to 4.39 (+65.0%); VaR0.99 rose from 3.12 to 4.71 (+51.0%) while CVaR0.99 rose from 4.48 to 7.96 (+77.7%), showing tail thickening captured by CVaR. Stressed CVaR0.99 correlated negatively with stability (r = -0.58, p < .001) and remained significant in regression (β = -0.46, p < .001; R² = .47). Governance predicted lower stressed CVaR (β = -0.29, p = .002) and moderated the CVaR to stability link (interaction β = +0.18, p = .010). Implications are that CVaR stress testing supports funding structure and liquidity buffer decisions, while governance maturity reduces liquidity vulnerability.
Conditional Value at Risk (CVaR/Expected Shortfall)
Developing an AI-Powered Automation Framework to Streamline IT Support Tasks in Public Sector Organizations, Boosting Service Delivery and Digital Accessibility
Public sector organizations are under growing pressure to improve IT service delivery efficiency and digital accessibility while operating within rigid governance and budgetary constraints. This study investigated the effectiveness of an AI-powered automation framework for streamlining IT support tasks in public sector organizations using a quantitative, cross-sectional research design. Data were collected from 214 respondents holding senior IT management, service desk leadership, and digital transformation roles across ministries, departments, and public agencies. Descriptive analysis showed moderate to high adoption of AI automation capabilities, with workflow automation (M = 4.02, SD = 0.59) and automated ticket handling (M = 3.95, SD = 0.62) exhibiting higher implementation levels than predictive analytics (M = 3.41, SD = 0.71). Multiple regression results indicated that AI automation capabilities were strongly associated with service delivery efficiency, with workflow automation (β = 0.41, p < .001) and automated ticket handling (β = 0.36, p < .001) emerging as significant predictors. The efficiency model explained 58% of the variance in service delivery performance (R² = 0.58). Cost efficiency outcomes showed weaker but statistically significant relationships with automation (β = 0.22, p = .003), and the cost model accounted for 46% of variance (R² = 0.46). Governance and risk management demonstrated a strong direct effect on performance (β = 0.31, p < .001) and significantly moderated the relationship between automation and cost efficiency (β = 0.19, p = .005). Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.84 to 0.91. Digital accessibility indicators recorded moderate levels (M = 3.56, SD = 0.65), indicating partial integration of inclusive practices. Overall, the findings demonstrate that AI-powered automation significantly enhances IT support efficiency and service quality in public sector organizations, particularly when supported by strong governance and ITSM maturity, while accessibility outcomes require more intentional design integration.
PLC–SCADA–Integrated Electrical Automation Frameworks for Process Optimization in Water and Wastewater Treatment Facilities
This quantitative study examined the operational impact of PLC–SCADA–integrated electrical automation frameworks on process optimization in water and wastewater treatment facilities using high-frequency operational data and quasi-experimental evaluation methods. A longitudinal interrupted time series design was applied to 720 daily observations, equally divided between pre-integration and post-integration periods, with statistical adjustment for influent flow, temperature, operating regime, and major equipment availability. The findings showed consistent and statistically significant improvements across efficiency, stability, workload, and event-based performance indicators. Energy intensity exhibited an immediate post-integration level reduction of 0.041 kWh/m³ (95% CI −0.056 to −0.026, p < .001) and an additional post-integration trend improvement of 0.00018 kWh/m³ per day (p = .002), indicating both immediate and sustained efficiency gains. Process stability improved through a reduction in composite stability deviation of 0.043 units (p < .001) and a 22% decrease in excursion event rates (IRR = 0.78, 95% CI 0.70–0.87). Supervisory workload outcomes improved markedly, with alarm rates declining by 28.4 alarms per day (p < .001), alarm flood events reduced to 73% of baseline frequency (IRR = 0.73, p < .001), and manual override time reduced by 1.6 percentage points (p < .001). Chemical intensity showed a statistically significant immediate reduction of 2.6 mg/L (p = .001) but no significant post-integration trend change. Mechanism-oriented analyses indicated a 9.2% reduction in VFD speed variability and a 0.36-unit reduction in loop oscillation proxy scores (p < .001), supporting a control-stability pathway for observed outcomes. Sensitivity analyses using storm-only periods, influent-matched windows, and outage-excluded datasets produced consistent effect directions and comparable magnitudes. Overall, the results demonstrated that PLC–SCADA–electrical integration was associated with measurable and robust improvements in energy efficiency, process stability, abnormal-event frequency, and operator workload under real treatment-plant operating variability.
An Integrated PMBOK ISO 9001 DMAIC Framework for Lean Six Sigma Driven Project Quality Management
This quantitative study examined an integrated PMBOK–ISO 9001–DMAIC framework for Lean Six Sigma–driven project quality management to explain variations in project quality performance. Data were collected from 214 completed and near-completion projects across construction, manufacturing, engineering services, and information systems sectors. Descriptive analysis showed that project quality governance capability recorded a mean score of 3.87 (SD = 0.61), while quality management system maturity demonstrated the highest mean of 3.92 (SD = 0.58), indicating well-established governance and system controls across the sampled projects. Data-driven quality improvement capability achieved a mean of 3.78 (SD = 0.65), and process efficiency orientation recorded a mean of 3.69 (SD = 0.63), reflecting moderate to strong application of DMAIC and Lean practices. Project quality performance outcomes indicated high acceptance compliance with a mean of 3.96 (SD = 0.57), while defect density and rework frequency exhibited lower means of 3.34 (SD = 0.72) and 3.29 (SD = 0.75), respectively, suggesting observable variability in execution quality. Reliability analysis confirmed strong internal consistency, with Cronbach’s alpha values ranging from 0.87 for process efficiency orientation to 0.93 for data-driven quality improvement capability. Multiple regression analysis revealed that project quality governance capability (β = 0.31, p < 0.001) and quality management system maturity (β = 0.34, p < 0.001) significantly influenced project quality performance, jointly explaining 47% of the variance. Mediation analysis demonstrated that data-driven quality improvement capability exerted a strong positive effect on project quality performance (β = 0.39, p < 0.001), while moderation analysis showed that process efficiency orientation significantly strengthened this relationship (interaction β = 0.17, p < 0.001), increasing explanatory power to 58%. These findings empirically confirmed that project quality performance, reflected through defects, rework, acceptance compliance, process stability, and cost of quality indicators, was most effectively explained through an integrated governance, system control, analytical improvement, and efficiency-oriented framework.
Robotics and Computer Vision for Automated Inspection of Substation and Treatment-Facility Electrical Infrastructure
This study addresses a persistent problem in safety critical electrical infrastructure inspection: robotics and computer vision pilots often show promising fault detection, but many organizations fail to operationalize these systems into compliant, auditable, and CMMS linked inspection workflows, limiting scalable adoption in substations and treatment facility electrical environments. The purpose of the study was to quantify, using real enterprise cases, how field capability and organizational enabling conditions influence perceived inspection effectiveness and adoption intention for robotics plus computer vision inspection supported by cloud and enterprise integration. A quantitative, cross sectional, case-based design was applied across two enterprise contexts, with N = 214 valid survey responses (substation cases n = 112, 52.3%; treatment facility cases n = 102, 47.7%) from maintenance engineers, inspection technicians, safety officers, and supervisors. Key variables included Robot Mobility and Coverage (MC), Vision Reliability and Evidence Quality (VR), Environmental Robustness (ER), Integration Readiness (IR), Safety and Procedure Compatibility (SP), Inspection Effectiveness (IE), Safety Improvement Perception (SIP), and Adoption Intention (AI); construct reliability was strong (Cronbach’s alpha = 0.82 to 0.90). The analysis plan used descriptive statistics, internal consistency testing, Pearson correlation, and multiple regression models, with IE predicted by MC, VR, and ER, and AI predicted by IR, SP, and IE. Headline findings showed SIP was highest (M = 4.22, SD = 0.58) and VR was also high (M = 4.10, SD = 0.63), while IR was lowest (M = 3.61, SD = 0.71), indicating that workflow and systems integration remain the main adoption bottleneck. Correlation results indicated IE was most strongly associated with VR (r = 0.61, p < .001), and AI was strongly related to IR (r = 0.56, p < .001) and IE (r = 0.58, p < .001). Regression results showed VR was the strongest predictor of IE (β = 0.39, p < .001), with MC (β = 0.21, p = .002) and ER (β = 0.18, p = .006) jointly explaining R² = 0.49. Adoption intention was driven primarily by IR (β = 0.31, p < .001), followed by IE (β = 0.28, p < .001) and SP (β = 0.19, p = .004), explaining R² = 0.54. Case level implications indicate that organizations should treat robotics plus computer vision inspection as an enterprise pipeline rather than a device purchase: prioritize high criticality assets where weighted IE was highest (4.31 in substations; 4.12 in treatment facilities), and accelerate adoption by improving CMMS integration readiness (M = 3.34) and formalizing procedural permissions for routine robot routes (M = 3.29) alongside evidence traceability and governance.
Machine Learning Applications in Digital Marketing Performance Measurement and Customer Engagement Analytics
Machine learning applications have become increasingly integral to digital marketing performance measurement and customer engagement analytics due to the volume, velocity, and behavioral richness of digital interaction data. This study quantitatively examined the relationships between machine learning–derived engagement indicators, marketing exposure variables, and digital marketing performance outcomes using an observational dataset of 1,250 user-level records. Descriptive analysis revealed substantial behavioral variability, with interaction frequency averaging 14.6 interactions per user (SD = 6.3), engagement recency averaging 4.1 days (SD = 2.7), and session depth averaging 6.2 actions per session (SD = 2.1). Reliability assessment confirmed strong internal consistency across all multi-item constructs, with Cronbach’s alpha values ranging from 0.82 for conversion outcomes to 0.93 for the customer value index. Multivariate regression results indicated that engagement intensity was the strongest predictor across all performance outcomes, with standardized coefficients of 0.38 for conversion, 0.42 for retention, and 0.41 for customer value, all statistically significant at p < .001. Engagement frequency also demonstrated positive and significant effects, with coefficients of 0.31 for conversion and 0.29 for customer value. Engagement recency showed a negative association across models, with coefficients ranging from −0.17 to −0.23, indicating declining performance as interaction gaps increased. Exposure frequency exhibited smaller yet significant effects, with coefficients between 0.18 and 0.24. The regression models demonstrated satisfactory explanatory power, reporting adjusted R² values of 0.39 for conversion, 0.44 for retention, and 0.47 for customer value. Hypothesis testing results showed that 11 of the 12 proposed hypotheses were supported. Overall, the findings demonstrated that machine learning–enabled engagement analytics substantially enhanced digital marketing performance measurement by capturing behavioral mechanisms underlying conversion, retention, and value creation more effectively than exposure-based metrics alone.
Simulation-Based Forecasting and Inventory Control Models For Consumer Goods Networks: A Quantitative Study Using Monte Carlo Simulation and Time-Series Methods
This quantitative study examined the integrated effects of time-series demand forecasting and inventory control policies within consumer goods networks using Monte Carlo simulation. The study was designed to evaluate how demand uncertainty, forecast accuracy, forecast error dispersion, lead-time variability, and inventory policy structure jointly influenced service performance, stockout behavior, cost variability, and overall inventory stability. Historical demand data were modeled at the SKU level using time-series methods, and empirically estimated forecast error distributions were embedded into a simulation framework to generate repeated stochastic demand and lead-time scenarios. The analytical sample consisted of 312 SKUs observed over a median horizon of 104 weekly periods, yielding 32,448 SKU–period observations. Demand segmentation indicated that 51.9% of SKUs exhibited stable high-volume patterns, 30.8% displayed moderate variability, and 17.3% were characterized by intermittent demand with frequent zero observations. Descriptive results showed pronounced heterogeneity in demand uncertainty, with coefficients of variation increasing from 0.42 for stable SKUs to 1.76 for intermittent SKUs. Forecast accuracy deteriorated with horizon length and demand irregularity, with mean absolute error increasing from 9.6 units for stable SKUs at short horizons to 31.6 units for intermittent SKUs at longer horizons. Simulation-based inventory evaluation demonstrated that continuous review policies achieved higher median fill rates (0.963) and lower stockout frequency (0.42 stockouts per cycle) than periodic review policies, which exhibited lower median fill rates (0.941) and higher stockout frequency (0.88). Total cost variability, measured by the coefficient of variation, was lower under continuous review policies (0.31) than under periodic review policies (0.47). Regression analyses confirmed that forecast error magnitude, forecast error dispersion, demand uncertainty, and lead-time variability were statistically significant predictors of total cost, service performance, and inventory dispersion. Interaction effects indicated that forecast error impacts were amplified under higher lead-time variability and attenuated under continuous review policies. Overall, the findings demonstrated that distribution-sensitive forecasting evaluation and integrated simulation-based inventory analysis provided stronger evidence on service stability, cost dispersion, and policy robustness than approaches relying on point forecasts or mean performance metrics alone.
Quantitative Benchmarking of Machine Learning Models for Risk Prediction: A Comparative Study Using AUC/F1 Metrics and Robustness Testing
This study addresses a problem in enterprise risk decision systems: model selection is often justified by baseline accuracy, although cloud and enterprise pipelines face measurement noise, missing values, and distribution shift that can weaken predictions. The purpose was to quantitatively benchmark machine learning classifiers for binary risk prediction and determine which approach delivers the best combination of effectiveness and stability. Using a quantitative, cross-sectional, case-based design, the study analyzed N = 4,200 enterprise case records with adverse-event prevalence of 8.6% (n = 361) and benchmarked Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting (XGB), and a Multilayer Perceptron. Key variables were model family and stress condition (baseline versus perturbation), with outcome variables AUC, F1-score, precision, recall, and robustness degradation (ΔAUC, ΔF1). The analysis plan applied standardized preprocessing and stratified validation, then computed baseline AUC and F1, followed by robustness testing under noise injection, 15% added missingness, and segment-shift distribution perturbation; descriptive statistics summarized performance, while correlation and regression tested relationships between robustness and performance, and a 5-point Likert survey (n = 120) captured adoption perceptions. Baseline results showed XGB as the top performer (AUC = 0.872, F1 = 0.532), followed by RF (0.846, 0.498), MLP (0.831, 0.474), SVM (0.804, 0.431), and LR (0.781, 0.402). Under robustness testing, XGB retained the smallest degradation (ΔAUC = -0.027; ΔF1 = -0.032) while MLP was most unstable (ΔAUC = -0.071; ΔF1 = -0.079), reshaping the composite ranking to XGB, RF, LR, SVM, then MLP. Robustness correlated with baseline performance (AUC: r = 0.62, p = 0.004; F1: r = 0.58, p = 0.008) and significantly predicted benchmark score (robustness index β = 0.44, p < 0.001), with noise, missingness, and distribution perturbation showing negative effects (β = -0.21, -0.18, -0.27; all p ≤ 0.009). Survey reliability was strong (Cronbach’s alpha = 0.86) and respondents rated perceived reliability (M = 4.18, SD = 0.54), supporting deployment implications: enterprise risk teams should adopt multi-metric, stress-tested benchmarking before production rollouts in practice.
Corpus-Based Evaluation Models for Quality Assurance Of AI-Generated ESL Learning Materials
This study addresses the problem that AI-generated ESL learning materials can appear fluent yet vary in accuracy, level appropriateness, and coherence, weakening quality assurance for large-scale cloud and enterprise deployment. The purpose was to develop and validate a corpus-based evaluation model that links corpus indicators to stakeholder quality judgments. Using a quantitative cross-sectional, case-based design, N = 120 evaluators assessed M = 80 AI-generated texts across four categories (reading passages, dialogues, grammar explanations, and practice prompts) using a five-point Likert instrument. Key dependent variables were overall QA and subscales for accuracy, clarity, coherence, level appropriateness, and pedagogical usefulness; key independent variables were readability control index, lexical appropriacy score, cohesion score, lexical diversity (HD-D), and grammar error rate (errors per 100 words). Analyses used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression with text-type stability checks. Overall perceived quality was acceptable (overall QA M = 3.84, SD = 0.53), with clarity highest (M = 3.96) and accuracy lowest (M = 3.72). Reliability was strong (overall α = .91). Corpus to human alignment was substantial: readability control correlated with level appropriateness (r = .61), cohesion with coherence (r = .58), lexical appropriacy with clarity (r = .52) and usefulness (r = .49), and grammar error rate with accuracy (r = −.67), all p < .001. A five-predictor regression model predicted overall QA (F (5,74) = 21.64, p < .001; R² = .59; Adj. R² = .56), with grammar error rate the strongest predictor (β = −.41), followed by readability (β = .29), cohesion (β = .24), and lexical appropriacy (β = .21); performance remained stable across text types (R² = .52–.61). Implications are that organizations can operationalize QA as automated gates for error density, readability bands, cohesion thresholds, and vocabulary profile alignment, then reserve human review for borderline cases to improve safety, consistency, and turnaround time in enterprise content workflows. Average indicators were overall readability 0.64, lexical appropriacy 0.71, cohesion 0.59, lexical diversity 0.82, and grammar error rate 2.40 per 100 words.
PREDICTING SUICIDE RISK THROUGH MACHINE LEARNING–BASED ANALYSIS OF PATIENT NARRATIVES AND DIGITAL BEHAVIORAL MARKERS IN CLINICAL PSYCHOLOGY SETTINGS
This study addresses the persistent problem that conventional suicide risk screening and clinician judgment often struggle to achieve operationally useful precision, especially when risk signals are multidimensional and base rates are imbalanced; therefore, the purpose was to develop and benchmark an explainable, data-driven risk stratification approach that integrates psychometric and clinical indicators to classify individuals into high vs low or moderate suicide-risk tiers within a bounded case context using a quantitative, cross-sectional, case-based design. The sample comprised 320 clinical cases drawn from an enterprise service setting that included both outpatient (61.3%) and emergency or acute contacts (38.7%), with a mean age of 29.8 (SD 8.7) and a high-risk prevalence of 27.8% (n = 89). Key variables included five-point Likert composite predictors capturing distress severity, hopelessness, perceived burdensomeness, psychosocial strain, sleep disturbance, impulsivity, perceived social support, and coping capacity, alongside binary clinical indicators for prior suicide attempt history and substance-use concern. Descriptively, the cohort showed elevated distress and cognitive burden (for example distress M = 3.62, SD = 0.78; hopelessness M = 3.41, SD = 0.83) with comparatively lower protective resources (social support M = 2.64, SD = 0.92; coping M = 2.71, SD = 0.88). All Likert constructs demonstrated acceptable to high reliability (Cronbach’s α range 0.80 to 0.89). Risk classification correlated positively with distress (r = .49) and hopelessness (r = .44) and negatively with social support (r = −.40) and coping (r = −.35), supporting coherent construct behavior prior to modeling. In regression, distress (OR = 2.18, p < .001) and hopelessness (OR = 1.71, p < .001) increased odds of high-risk classification while social support reduced odds (OR = 0.63, p < .001), establishing a strong interpretable benchmark (Nagelkerke R² = .41; AUC = .82). Headline performance results showed that gradient boosting outperformed the baseline with AUC = .88, sensitivity = .81, specificity = .78, precision = .56, F1 = .66, and improved calibration (Brier score = .14); importantly, it captured 46.1% of all high-risk cases within the top 10% highest-risk score band, indicating high-yield triage potential. Explainability highlighted clinically interpretable drivers, led by distress severity and hopelessness, with protective deficits such as low social support also ranking highly. These findings imply that enterprise clinical workflows can benefit from a transparent, construct-grounded stratification pipeline that improves sensitivity and concentrates risk into actionable high-risk quantiles while preserving interpretability for decision support and resource-aware triage planning.
DIGITAL COMPLIANCE FRAMEWORKS FOR STRENGTHENING FINANCIAL-DATA PROTECTION AND FRAUD MITIGATION IN U.S. ORGANIZATIONS
This quantitative study examined the relationship between digital compliance framework maturity and two critical organizational outcomes—financial-data protection performance and fraud mitigation effectiveness—in U.S.-based organizations that manage financial and transactional data. Using a cross-sectional explanatory research design, organizational-level data were analyzed from a final analytic sample of 120 organizations operating across financial services, healthcare finance operations, retail and e-commerce payment environments, and transaction-intensive business services. Digital compliance maturity was operationalized through a composite index reflecting automated evidence collection, audit-trail completeness, continuous control monitoring frequency, policy digitization coverage, and control standardization. Financial-data protection performance and fraud mitigation effectiveness were measured using normalized, auditable indicators derived from security incidents, audit records, access governance systems, and fraud case management platforms. Descriptive findings indicated substantial variability in compliance maturity (mean = 67.4, SD = 12.6, range = 34–92), supporting its suitability as an explanatory construct. Correlation analysis showed that higher compliance maturity was moderately associated with lower breach incidence (r = −0.46), faster containment time (r = −0.51), fewer audit exceptions (r = −0.48), lower fraud loss rates (r = −0.44), and fewer confirmed fraud cases (r = −0.39). Multivariate regression results demonstrated that digital compliance maturity remained a statistically significant predictor of both outcome domains after controlling for industry risk tier, organizational size, and system complexity. In the financial-data protection model, compliance maturity exhibited a positive association with performance (β = 0.18, p < .01) in the fully specified model, which explained 49% of outcome variance. In the fraud mitigation model, higher compliance maturity was associated with lower fraud loss rates (β = −0.016, p < .05), with the final model explaining 46% of variance. Governance oversight strength, compliance staffing intensity, and regulatory examination pressure contributed additional explanatory power. Monitoring-quality indicators partially accounted for the maturity–outcome relationships, and moderation analysis showed that regulatory pressure and governance strength amplified maturity effects. Robustness checks using alternative outcome operationalizations produced consistent coefficient directions and magnitudes. Overall, the findings established digital compliance maturity as a distinct, measurable organizational capability associated with improved data protection and fraud mitigation outcomes under controlled statistical conditions.
DIGITAL COMPLIANCE FRAMEWORKS FOR PROTECTING CUSTOMER DATA ACROSS SERVICE AND HOSPITALITY OPERATIONS PLATFORMS
Digital compliance frameworks are essential for protecting customer data across service and hospitality operations platforms characterized by high transaction intensity, workforce turnover, and extensive system integration. This quantitative study examined the extent to which compliance framework maturity influenced customer data protection performance across service and hospitality properties, focusing on governance, process execution, and technical control layers. Property-level data were collected from 240 service and hospitality sites operating multiple customer-facing platforms. Hierarchical regression models were estimated to assess the relationships between compliance maturity and three outcome domains—compliance performance, security outcomes, and operational data handling outcomes—while controlling for property size, transaction volume, workforce turnover, geographic region, and cloud adoption level. Descriptive results indicated moderate-to-high average compliance maturity (M = 5.04, SD = 0.79 on a seven-point scale), with substantial variation across properties. Regression findings showed that compliance framework maturity explained an additional 19% of variance in audit pass performance, increasing overall explained variance to 41%. Higher compliance maturity was significantly associated with lower audit finding severity (β = −0.49, p < .001) and shorter remediation cycle times (β = −0.29, p < .001). Security-related outcomes were also affected, as compliance maturity was associated with fewer security incidents (β = −0.38, p < .001) and fewer unauthorized access events (β = −0.32, p < .001). Operational data handling outcomes demonstrated similar patterns, with maturity predicting lower policy violation rates (β = −0.44, p < .001). Moderation analysis indicated that platform ecosystem complexity and third-party dependency strengthened these relationships, accounting for an additional 4%–6% of explained variance in high-complexity environments. Comparative layer analysis revealed that governance quality had the strongest association with audit outcomes (β = 0.33), process execution showed the strongest association with operational handling outcomes (β = −0.34), and technical controls demonstrated the strongest association with security outcomes (β = −0.31). Overall, the models explained between 30% and 49% of variance across outcome categories, demonstrating that digital compliance frameworks functioned as measurable operational capabilities that significantly influenced customer data protection performance across complex service and hospitality platform ecosystems.
ADVANCED CYBERSECURITY ARCHITECTURES FOR RESILIENCE IN U.S. CRITICAL INFRASTRUCTURE CONTROL NETWORKS
This study examined predictive analytics for risk and compliance in IT-enabled project management systems by modeling how operational performance indicators and governance-control indicators jointly explained adverse project outcomes. A retrospective quantitative design was applied to 312 projects drawn from an initial pool of 353, with 41 projects excluded for incomplete baselines or missing workflow trails, producing an inclusion rate of 88.4%. The sample comprised 39.7% infrastructure projects (n = 124), 34.6% software/IT delivery projects (n = 108), and 25.6% mixed/operational projects (n = 80). Descriptive analysis indicated that 29.5% of projects met the schedule distress criterion (n = 92) and 20.5% met the cost distress criterion (n = 64), while 13.1% met the combined distress definition (n = 41). Compliance deviations were recorded in 17.9% of projects (n = 56), with repeated exceptions in 7.4% (n = 23). Key variables showed heavy-tailed distributions, including change requests with a mean of 9.8, median 7.0, and maximum 88, and documentation completeness with a mean of 91.6%, median 94.0%, and missingness of 11.5%; access-control anomaly indicators had the highest missingness at 18.6%. Correlation results showed alignment between instability and governance signals, including schedule variance with cost variance (r = .46) and change intensity (r = .41), and documentation completeness with exception recurrence (ρ = −.52) and approval latency with exception recurrence (ρ = .44). Stratified results showed stronger schedule variance–change coupling in software/IT projects (r = .48) than infrastructure projects (r = .31), and stronger late-phase governance–exception relationships for approval latency (ρ = .57) than early phase (ρ = .28). Reliability testing supported construct consistency with α = .88 for governance adherence, α = .91 for documentation completeness, and α = .84 for workflow conformance. Collinearity reduction lowered maximum VIF from 7.4 to 3.4 and maximum condition index from 28.3 to 17.6. Regression findings showed risk distress was positively associated with schedule variance (β = 0.41, p < .001) and change intensity (β = 0.29, p = .002), while compliance exceptions were strongly associated with documentation completeness (β = −0.47, p < .001) and approval adherence (β = −0.39, p < .001). Model performance improved with governance predictors, increasing AUC from 0.68 to 0.83 for risk distress and from 0.66 to 0.86 for compliance exceptions, with top-decile capture reaching 61% for risk distress and 69% for compliance exceptions.
OPTIMIZATION ALGORITHMS FOR ENHANCING HIGH DIMENSIONAL BIOMEDICAL DATA PROCESSING EFFICIENCY
High-dimensional biomedical data processing places substantial computational demands on optimization algorithms due to extreme feature dimensionality, sparsity, missingness, and heterogeneous task structures. This study quantitatively evaluated how optimization algorithm families influenced processing efficiency under fixed task-quality constraints across representative biomedical workflows, including predictive modeling, feature selection, and reconstruction tasks. A controlled benchmarking design was applied to 12 high-dimensional datasets, producing 1,680 algorithm executions across 14 algorithm variants and 7 algorithm families, with 10 repeated runs per condition. Processing efficiency was operationalized using multiple indicators, including wall-clock time-to-target, peak memory usage, iterations or epochs to convergence, throughput, and numerical stability outcomes. Descriptive results showed that time-to-target runtime ranged from 2.8 s to 1,420.6 s, with a median of 96.4 s, while peak memory usage ranged from 0.9 GB to 21.6 GB, with a median of 6.3 GB. Constraint failure occurred in 6.8% of runs, and numerical error events were observed in 2.1% of executions. Mixed-effects regression analyses demonstrated statistically significant differences across optimization algorithm families for runtime, memory usage, and convergence behavior after controlling for feature dimensionality, sparsity ratio, missingness rate, and task type. Scaling analysis indicated that increasing feature dimensionality from 10,000 to 250,000 features increased median runtime from 42.3 s to 188.9 s and median peak memory from 3.1 GB to 9.7 GB under constant performance constraints. Reliability analysis supported the internal consistency of the composite efficiency framework, with an overall Cronbach’s alpha of 0.89. Overall, the findings demonstrated that optimization algorithm choice produced statistically measurable differences in efficiency, stability, and scalability in high-dimensional biomedical data processing, highlighting the importance of structured, constraint-based benchmarking for robust comparative evaluation.
EDGE ARTIFICIAL INTELLIGENCE BASED AUTOMATION FOR ULTRA-LOW-LATENCY CONTROL IN INDUSTRIAL ROBOTIC SYSTEMS
This quantitative, cross-sectional, case-based study addressed the problem that cloud-connected industrial robotic systems often struggle to sustain ultra-low-latency closed-loop control because perception and decision workloads, network transit, and controller integration overhead introduce delay and jitter that can reduce motion precision and operational safety. The purpose was to quantify Edge AI based automation maturity and test whether it predicts perceived ultra-low-latency control performance (ULLCP) in industrial robotic cells using edge-to-cloud and enterprise OT/IT architectures. Data were collected from N = 162 practitioners (33.3% robotics or automation engineers, 28.4% operators or technicians, 19.8% maintenance, 18.5% OT/IT) across cloud and enterprise integrated robotic workcells. Key variables were Edge AI Automation (EA overall and four dimensions: local inference, real-time edge processing, controller integration readiness, reliability or failover readiness) and ULLCP (responsiveness, timing consistency, robustness), with task complexity and exposure level as controls. The analysis plan applied descriptive statistics, internal consistency reliability, Pearson correlations, and multiple regression. Findings showed high perceived EA maturity (M = 3.84, SD = 0.62) and high ULLCP (M = 3.77, SD = 0.58), and the scales were reliable (EA alpha = 0.91; ULLCP alpha = 0.88). EA correlated strongly with ULLCP (r = 0.61, p < 0.001). In regression, EA significantly predicted ULLCP (beta = 0.58, t = 9.42, p < 0.001) controlling for task complexity and exposure, explaining 46% of the variance (R2 = 0.46; F(4,157) = 32.94, p < 0.001). A dimension model explained 48% of variance (R2 = 0.48) and identified local inference (beta = 0.27, p = 0.001), controller integration (beta = 0.19, p = 0.012), and reliability or failover (beta = 0.22, p = 0.004) as the strongest contributors; real-time processing was positive but marginal (beta = 0.13, p = 0.058). These results imply that organizations seeking deterministic, low-latency robotics should prioritize near-device inference, stable controller interfaces, and resilient failover mechanisms alongside edge compute capacity, treating integration and reliability as first-class performance levers in Industry 4.0 modernization. Local inference (M = 3.92) rated highest, while integration (M = 3.73) and failover (M = 3.69) lagged; task complexity slightly reduced ULLCP (beta = -0.14) in practice.
GEOTECHNICAL AND HYDRAULIC SIMULATION MODELS FOR SLOPE STABILITY AND DRAINAGE OPTIMIZATION IN RAIL INFRASTRUCTURE PROJECTS
Rail earthworks often experience rainfall-driven pore-pressure increases and drainage bottlenecks that produce recurring wet spots, deformation, and higher instability risk, yet quantitative evidence linking simulation effectiveness to improved drainage and slope outcomes remains limited. This study tested a coupled geo-hydraulic performance model to quantify predictive relationships among Geotechnical Simulation Effectiveness (GSE), Hydraulic–Hydrologic Simulation Effectiveness (HSE), Drainage Optimization Outcomes (DOO), and Slope Stability Performance (SSP) in an enterprise rail-corridor case context. Using a quantitative cross-sectional, case-based design, data were collected through a structured 5-point Likert questionnaire from N = 210 rail-infrastructure professionals representing geotechnical, hydraulic/drainage, construction, and maintenance functions with direct exposure to simulation-informed decisions and field results. Composite variables were computed as item-mean indices, and analysis included descriptive statistics, reliability testing, Pearson correlation, and staged regression aligned to hypotheses, including an optional mediation path (HSE → DOO → SSP). Measurement reliability was strong (α = 0.87–0.90). Mean scores were above the midpoint (GSE 3.84 ± 0.58; HSE 3.76 ± 0.62; DOO 3.69 ± 0.60; SSP 3.73 ± 0.57). Correlations supported the model (GSE–SSP r = 0.61; HSE–DOO r = 0.67; DOO–SSP r = 0.55; all p < .001). The combined regression predicting SSP was significant (F(3,206) = 62.84, p < .001; R² = 0.48), with unique effects for GSE (β = 0.31), DOO (β = 0.24), and HSE (β = 0.16, p = 0.012). Reduced HSE influence after adding DOO suggested partial mediation, indicating hydraulic modelling improves stability partly via drainage.