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.
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.
Copyright (c) 2025 Review of Applied Science and Technology
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.
Copyright (c) 2026 Review of Applied Science and Technology
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)
Copyright (c) 2024 Review of Applied Science and Technology
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.
Copyright (c) 2023 Review of Applied Science and Technology
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.
HIGH-PERFORMANCE COMPUTING FRAMEWORKS FOR CLIMATE AND ENERGY INFRASTRUCTURE RISK ASSESSMENT
This study addresses a persistent problem in climate and energy infrastructure risk assessment: despite growing adoption of cloud and enterprise high performance computing (HPC) environments, decision makers still lack clear quantitative evidence on which HPC framework capabilities most improve the quality and usefulness of risk assessment outputs. The purpose of the study was to test, in a quantitative cross sectional, case-based design, how key HPC capability dimensions predict Risk Assessment Effectiveness (RAE) in operational settings. Data were collected using a 5-point Likert scale survey from a case sample of cloud and enterprise HPC users involved in climate exposed energy infrastructure risk work (N = 168), including 41.7% operations and reliability, 27.4% risk and resilience planning or asset management, 18.5% data and analytics, and 12.5% IT or HPC administration. The key independent variables were Scalability or Throughput (SCAL), Computational Efficiency (EFF), Data Integration Readiness (DATA), Simulation or Model Execution Capability (SIM), and Reliability or Availability (REL), with RAE as the dependent variable. The analysis plan included internal consistency testing, descriptive statistics, Pearson correlations, and multiple regression with multicollinearity diagnostics. The measures demonstrated strong reliability (Cronbach’s alpha ranged from 0.81 to 0.90; overall alpha = 0.93). Descriptive results indicated above neutral capability levels (SCAL M = 3.74, SD = 0.71; EFF M = 3.61, SD = 0.76; DATA M = 3.92, SD = 0.69; SIM M = 3.79, SD = 0.73; REL M = 3.58, SD = 0.78) and moderately high effectiveness (RAE M = 3.83, SD = 0.68). RAE correlated positively with all capability dimensions, with the strongest associations observed for DATA (r = 0.69) and SIM (r = 0.61). The regression model was statistically significant and explained 58% of the variance in RAE (F(5,162) = 44.72, p < .001; R² = 0.58; Adj. R² = 0.56), with acceptable multicollinearity (VIF 1.42 to 2.18). Headline findings show that DATA (β = 0.31, p < .001), SIM (β = 0.24, p = 0.002), and REL (β = 0.19, p = 0.008) were significant predictors, while SCAL was marginal (β = 0.11, p = 0.087) and EFF was not significant (β = 0.07, p = 0.210). These results imply that organizations seeking stronger climate and energy risk assessments should prioritize data integration pipelines, reliable platform availability, and scenario execution capacity, and treat raw scaling and efficiency as secondary unless they directly enable those capabilities.
AI-POWERED BUSINESS ANALYTICS FOR SMART MANUFACTURING AND SUPPLY CHAIN RESILIENCE
This study quantitatively examined the relationships among AI-powered business analytics capability, smart manufacturing integration, and supply chain resilience within manufacturing organizations operating in data-intensive supply chain environments. Using a cross-sectional research design, data were collected from 312 manufacturing firms that had adopted analytics-supported operational systems. Descriptive analysis indicated moderate-to-high levels of analytics capability (mean = 3.71, SD = 0.62), smart manufacturing integration (mean = 3.58, SD = 0.66), and supply chain resilience (mean = 3.64, SD = 0.61), suggesting a sample characterized by partial to advanced digital maturity. Correlation analysis revealed strong positive associations between analytics capability and manufacturing integration (r = 0.62), analytics capability and supply chain resilience (r = 0.55), and manufacturing integration and resilience (r = 0.59), supporting the coherence of the proposed model and construct distinctiveness. Reliability and validity assessments confirmed strong measurement quality, with Cronbach’s alpha values ranging from 0.88 to 0.91 and average variance extracted values exceeding 0.60 for all constructs. Regression analysis demonstrated that analytics capability significantly predicted smart manufacturing integration (β = 0.62, p < 0.001) and supply chain resilience (β = 0.33, p < 0.001). When smart manufacturing integration was included in the resilience model, its effect was significant (β = 0.41, p < 0.001), while the analytics coefficient decreased in magnitude, indicating partial mediation. The mediated model explained 52% of the variance in supply chain resilience, compared to 39% in the direct-effects model. Collinearity diagnostics confirmed stable estimation, with variance inflation factors below 2.0 across all predictors. Moderation analysis showed no significant interaction effect for environmental turbulence, indicating that the estimated relationships were stable across varying contextual conditions. Overall, the findings provided robust quantitative evidence that AI-powered business analytics capability contributed to supply chain resilience both directly and indirectly through smart manufacturing integration, reinforcing the role of analytics-enabled integration as a central mechanism linking digital capability development to resilient operational performance.
AN EMPIRICAL STUDY OF BIG DATA–ENABLED PREDICTIVE ANALYTICS AND THEIR IMPACT ON FINANCIAL FORECASTING AND MARKET DECISION-MAKING
This study addresses the problem that enterprises in data intensive capital market operations may invest in big data and predictive analytics, yet decision quality remains uneven because forecasting signals are not always reliable, interpretable, or embedded in governance routines. The purpose was to test a capability to outcome model in which Big Data Capability (BDC) and Predictive Analytics Capability (PAC) improve Financial Forecasting Accuracy (FA), and FA improves Decision-Making Quality (DMQ). Using a quantitative cross-sectional, case-based survey design, data were collected once from professionals in an enterprise case context, yielding 226 responses and 210 usable cases after screening (max item missingness 1.9%; two outliers removed). Key variables were BDC and PAC (predictors), FA (mediator), and DMQ (outcome), measured on five-point Likert scales with internal consistency (α = .86 to .91). Analysis followed data screening, reliability testing, descriptive statistics, Pearson correlations, multiple regression (Model 1: FA; Model 2: DMQ), and mediation via 5,000 bootstrap resamples. Descriptively, respondents reported moderate to high capability and outcomes (BDC M = 3.78, SD = 0.61; PAC M = 3.69, SD = 0.66; FA M = 3.62, SD = 0.58; DMQ M = 3.74, SD = 0.55). All bivariate associations were positive and significant (BDC, FA r = .52; PAC, FA r = .57; FA, DMQ r = .63; p < .001). In regression, BDC (β = 0.28, p < .001) and PAC (β = 0.39, p < .001) jointly explained 44% of the variance in FA (R² = 0.44), and FA was the strongest predictor of DMQ (β = 0.49, p < .001) in a model explaining 52% of DMQ (R² = 0.52), with smaller direct effects from BDC (β = 0.14, p = .013) and PAC (β = 0.17, p = .004). Mediation results showed partial mediation of the PAC to DMQ link through FA (indirect effect = 0.19, 95% CI [0.12, 0.28]; direct effect = 0.11, 95% CI [0.04, 0.18]). These findings imply that improving decision quality requires stronger data governance and analytics discipline, plus integration of forecasts into decision protocols across analytics enabled workflows.
BLOCKCHAIN ADOPTION AND ORGANIZATIONAL LONG-TERM GROWTH IN SMALL AND MEDIUM ENTERPRISES (SMES)
This study addresses the problem that small and medium enterprises (SMEs) often lack quantitative evidence on whether deeper blockchain use, beyond awareness or pilot trials, is associated with sustained organizational growth and which readiness conditions enable adoption. The purpose was to test a Technology-Organization-Environment (TOE) model in which technological readiness, organizational readiness, and environmental pressure predict blockchain adoption intensity, and adoption intensity predicts a multidimensional long-term growth index. Using a quantitative, cross-sectional, case-based survey design, data were obtained from 220 SME enterprise cases spanning manufacturing (29.1%), retail/wholesale (25.0%), services (34.5%), and logistics/other sectors (11.4%). Most firms employed 10-49 staff (52.7%) in the case setting, and 34.1% reported active blockchain use in at least one business process. Key variables were measured on five-point Likert scales and showed strong internal consistency (Cronbach’s alpha: technological readiness = .86, organizational readiness = .88, environmental pressure = .82, adoption intensity = .90, and long-term growth = .89). The analysis plan applied descriptive statistics, Pearson correlations, and two-stage multiple regression, controlling for firm size, firm age, and sector. Descriptively, perceptions were moderate to high (technological readiness M = 3.74, SD = 0.63; adoption intensity M = 3.42, SD = 0.72; long-term growth M = 3.68, SD = 0.60), and operational efficiency was the highest growth dimension (M = 3.82, SD = 0.64). Adoption intensity correlated with long-term growth (r = 0.56, p < .01) and with TOE determinants (r = 0.49 with technological readiness, r = 0.52 with organizational readiness, and r = 0.41 with environmental pressure, all p < .01). In regression Model A, TOE determinants explained 42.1% of adoption-intensity variance (R² = 0.421; F(3,216) = 52.34, p < .001), with organizational readiness the strongest predictor (β = 0.34, p < .001), followed by technological readiness (β = 0.27, p < .001) and environmental pressure (β = 0.19, p = .001). In Model B, adoption intensity predicted long-term growth net of controls (R² = 0.420; F(5,214) = 31.08, p < .001), with a strong positive effect for adoption intensity (β = 0.49, p < .001) and a small effect for firm size (β = 0.12, p = .036), while firm age and sector were not significant. Supplementary models showed that adoption intensity was especially linked with operational efficiency (β = 0.51, p < .001) and innovation capability (β = 0.44, p < .001). Implications are that SMEs should treat blockchain as a governed capability-building program and prioritize managerial commitment, IT capability, and process readiness, because deeper routinized integration is associated with stronger growth outcomes through operational and innovation pathways.
Technology-Organization-Environment (TOE)
QUANTUM-ENHANCED CHARGE TRANSPORT MODELING IN PEROVSKITE SOLAR CELLS USING NON-EQUILIBRIUM GREEN’S FUNCTION (NEGF) FRAMEWORK
Perovskite solar cells (PSCs) can reach high efficiencies, yet semiclassical modeling can under-resolve transport losses at interfaces and contacts, limiting mechanism-level diagnosis. This study tested whether NEGF-parameterized transport factors explain cross-case variation in PSC performance and whether experts perceive the framework as credible and usable. A quantitative, cross-sectional, case-study–based design analyzed 12 modeled PSC cases and an expert survey (n = 42). Predictors were trap density (X1), interface barrier height (X2), scattering index (X3), and contact coupling (X4); outcomes were Jsc, Voc, FF, and PCE. Analyses comprised descriptives, Pearson correlations, multiple regression predicting PCE, and survey reliability plus one-sample tests against the neutral midpoint. Case outputs ranged from Jsc 19.8–25.4 mA·cm⁻² and PCE 11.7–21.8%. Trap density was negatively associated with PCE (r = −0.76, p = 0.004), barrier height was negatively associated with Jsc (r = −0.79, p = 0.002), and contact coupling was positively associated with PCE (r = +0.69, p = 0.014). The multivariate PCE model was significant (R² = 0.861, F(4,7) = 10.86, p = 0.004), with trap density (β = −0.46, p = 0.018), barrier height (β = −0.39, p = 0.031), scattering (β = −0.28, p = 0.044), and coupling (β = +0.33, p = 0.027). Experts rated the approach positively (Accuracy M = 4.12, α = 0.87; Usefulness M = 4.28, α = 0.90; p < 0.001). These findings imply NEGF can function as a decision-support layer for interface and contact engineering, and should be deployed in auditable compute pipelines for enterprise or cloud/HPC execution.
Copyright (c) 2021 Review of Applied Science and Technology
PREDICTIVE ANALYTICS FOR RISK AND COMPLIANCE IN IT-ENABLED PROJECT MANAGEMENT SYSTEMS
A REVIEW ON THE INFLUENCE OF AI-ENABLED FIRE DETECTION AND SUPPRESSION SYSTEMS IN ENHANCING BUILDING SAFETY
This study addressed the problem that conventional fire detection and rule-based panels in buildings can generate nuisance alarms and delayed verification, weakening coordinated response and safety assurance. The purpose was to quantify how AI-enabled fire detection and suppression capabilities predict perceived Building Safety Enhancement (BSE) using a quantitative, cross-sectional, case-study based survey design. A purposive sample of 210 stakeholders from building cases (62.4% commercial or mixed-use and 37.6% institutional or industrial) reported AI detection dashboards in 71.0% of contexts and suppression decision support in 54.8% across routine monitoring, drills, and incident response. Key variables were AI-Enabled Detection Capability (ADC), AI-Enabled Suppression and Control Effectiveness (ASCE), Integration and Real-Time Monitoring (IRM), and Predictive Maintenance and Fault Diagnosis (PMFD), with BSE as the outcome. Analyses used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Results reported as a worked example showed high construct means (ADC M = 4.02, ASCE M = 3.88, IRM M = 3.95, PMFD M = 3.76, BSE M = 3.97) and strong reliability (alpha = 0.84 to 0.90). All predictors correlated positively with BSE (r = 0.48 to 0.62, p < .01). The regression model was significant (F(4,205) = 65.20, p < .001) and explained 56% of BSE variance (R squared = 0.56), with ADC (beta = 0.31) and IRM (beta = 0.27) strongest, followed by ASCE (beta = 0.19) and PMFD (beta = 0.14). Implications suggest prioritizing detection credibility and real-time integration, then strengthening suppression coordination and predictive maintenance to sustain readiness and reduce alarm fatigue.
DEEP LEARNING AND GRAPH NEURAL NETWORKS FOR REAL-TIME CYBERSECURITY THREAT DETECTION
This study addresses the problem that many cloud and enterprise security operations still struggle to achieve reliable real-time threat detection because advanced analytics (deep learning and graph neural networks) often fail to translate into operational effectiveness when data pipelines, infrastructure, workflow integration, and analyst trust are weak. The purpose was to quantify how Deep Learning Capability (DLC), Graph Neural Network Capability (GNNC), Data Readiness (DR), Infrastructure Adequacy (IA), Integration Readiness (IR), and Analyst Trust and Actionability (ATA) predict Real-Time Threat Detection Effectiveness (RTTDE) in a quantitative cross-sectional, case-based design anchored in operational monitoring contexts. A structured 5-point Likert survey was used with a sample of N = 180 respondents across cloud and enterprise security cases (SOC analysts 38.9%, security engineers 27.8%, incident responders 18.9%, managers 14.4%). Constructs showed strong reliability (α = .82–.89), with RTTDE rated above neutral (M = 3.74, SD = 0.64), while IR was the lowest readiness area (M = 3.48, SD = 0.73). The analysis plan applied descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression with RTTDE as the dependent variable. Correlations were positive and significant for all predictors (e.g., DLC r = .62, GNNC r = .55, ATA r = .58; all p < .001). In regression, the model explained 57% of RTTDE variance (R² = .57; F(6,173) = 38.6, p < .001), with DLC (β = .29, p < .001), ATA (β = .21, p = .001), GNNC (β = .17, p = .006), DR (β = .12, p = .042), and IA (β = .14, p = .020) as significant predictors, while IR was positive but not significant at .05 (β = .09, p = .099). These findings imply that improving real-time detection requires combined investment in hybrid DL plus GNN capability and in operational enablers, especially telemetry readiness, low-latency infrastructure, and analyst-facing trust and actionability.
Real-Time Threat Detection Effectiveness
A QUANTITATIVE STUDY ON ERP-INTEGRATED DECISION SUPPORT SYSTEMS IN HEALTHCARE LOGISTICS
The study titled A Quantitative Study on ERP-Integrated Decision Support Systems in Healthcare Logistics examined the measurable effects of Enterprise Resource Planning (ERP) and Decision Support System (DSS) integration on operational efficiency, data quality, and analytical accuracy within healthcare logistics environments. Grounded in empirical inquiry, this research employed a cross-sectional quantitative design that combined survey-based responses and institutional logistics performance metrics collected from 30 hospitals and 500 respondents. The analytical framework incorporated hierarchical multiple regression and structural equation modelling (SEM) to evaluate both direct and indirect relationships among ERP-DSS integration, data quality, analytics accuracy, training effectiveness, interoperability, and key logistics performance indicators such as order fulfilment accuracy, procurement cycle time, cost control, and service continuity. A total of 126 peer-reviewed journal articles and empirical studies published between 2010 and 2025 were systematically reviewed to construct the theoretical foundation and operational model of this research. The analysis revealed that ERP-DSS integration was a statistically significant predictor of logistics efficiency, explaining 41% of the variance independently and up to 62% when mediating and moderating variables were introduced. Data quality and analytics accuracy served as strong mediators, confirming that high-fidelity data and predictive capability were critical channels through which integration influenced performance outcomes. Training effectiveness and system interoperability acted as positive moderators, indicating that well-trained users and technologically aligned systems amplified the operational benefits of ERP-DSS frameworks. The findings demonstrated that institutions with advanced integration achieved measurable gains in decision speed, cost reduction, and supply chain responsiveness. Furthermore, the study validated that ERP-DSS systems transformed healthcare logistics from reactive operational structures into proactive, data-driven ecosystems characterized by transparency, analytical intelligence, and real-time adaptability. The review of 126 earlier studies supported these empirical results, reinforcing the theoretical assertion that ERP-DSS convergence enhances not only logistical coordination but also institutional resilience and service continuity. Overall, the study provided quantitative evidence that ERP-DSS integration represents a critical enabler of strategic digital transformation in healthcare logistics, offering a statistically grounded model for performance optimization, operational agility, and sustainable healthcare supply management.