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.
The Role of AI-Enabled Business Intelligence in Financial Risk Assessment and Liquidity Monitoring
This quantitative study examined the role of AI-enabled business intelligence in financial risk assessment and liquidity monitoring across organizations using digital financial and analytical systems. An explanatory cross-sectional design was employed, and data were collected through a structured five-point questionnaire administered to financial, managerial, risk, treasury, auditing, business intelligence, data, and information-technology professionals. Of 450 distributed questionnaires, 434 were returned and 412 valid responses were retained, producing a valid response rate of 91.6%. AI-enabled business intelligence recorded a mean of 4.11, financial risk assessment recorded 4.09, and liquidity monitoring recorded 4.05. Cronbach’s alpha coefficients ranged from .941 to .952, confirming strong internal consistency. AI-enabled business intelligence was positively correlated with financial risk assessment (r=.694r=.694r=.694) and liquidity monitoring (r=.651r=.651r=.651), while financial risk assessment was positively correlated with liquidity monitoring (r=.669r=.669r=.669). Hierarchical regression showed that AI-enabled business intelligence explained an additional 29.7% of variance in financial risk assessment and 25.3% in liquidity monitoring beyond participant and organizational controls. The final liquidity model explained 57.4% of the observed variance. Predictive capability, decision-support effectiveness, real-time processing, financial data integration, anomaly detection, and analytical automation significantly predicted financial risk assessment. Mediation analysis demonstrated a significant indirect effect of .26, with financial risk assessment transmitting 45.0% of the total relationship between AI-enabled business intelligence and liquidity monitoring. Technological readiness produced the largest subgroup effect, while organizational size, system-exposure duration, data quality, and management support also influenced the principal relationships. The findings established AI-enabled business intelligence as a multidimensional organizational capability that connected integrated data, predictive analytics, automated processing, and decision support with more accurate risk assessment and more effective liquidity control. Organizations required reliable data, adequate technological readiness, skilled employees, human oversight, and structured governance to use intelligent analytical systems effectively across diverse contemporary financial settings and organizational operating environments.
Copyright (c) 2025 Review of Applied Science and Technology
Predictive Analytics Models for Network Traffic Optimization in Distributed Enterprise Environments
This study examined the effectiveness of predictive analytics models for network traffic optimization in distributed enterprise environments, with particular emphasis on forecasting accuracy, computational performance, dynamic resource allocation, congestion reduction, latency, throughput, and bandwidth utilization. A quantitative experimental design was employed using 120,000 valid network traffic observations retained from an initial dataset of 128,640 records, representing a 93.28% retention rate after preprocessing. The final dataset was chronologically divided into 70% training, 15% validation, and 15% testing subsets, with 18,000 observations reserved for independent model evaluation. Six predictive approaches—Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Random Forest, XGBoost, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)—were evaluated using MAE, MSE, RMSE, MAPE, R², training time, and inference time. LSTM demonstrated the strongest forecasting performance, achieving an MAE of 21.84 Mbps, RMSE of 28.50 Mbps, MAPE of 3.91%, and R² of 0.958, followed by GRU with an RMSE of 30.42 Mbps and R² of 0.952. Relative to ARIMA, LSTM reduced MAE by 43.21% and RMSE by 43.94%. Predictive traffic management also produced substantial operational improvements compared with non-predictive management. Bandwidth utilization increased from 67.84% to 76.92%, throughput increased by 16.09%, and resource-allocation efficiency improved by 18.09%. End-to-end latency decreased by 24.70%, packet loss declined by 36.36%, congestion frequency decreased by 37.05%, and congestion duration was reduced by 34.07%. Prediction error was significantly associated with congestion frequency (r = 0.61, p < 0.001) and negatively associated with resource-allocation efficiency (r = −0.63, p < 0.001). Significant effects were also identified for predictive model type, traffic-load intensity, and forecasting horizon. The findings demonstrated that predictive analytics, particularly recurrent deep learning models, provided measurable improvements in traffic forecasting and network optimization while highlighting the importance of balancing predictive accuracy, computational efficiency, workload intensity, and forecasting horizon in distributed enterprise network management.
Copyright (c) 2024 Review of Applied Science and Technology
Comparative Analysis of Machine-Learning Approaches for Pharmaceutical Sales Forecasting and Market-Demand Prediction
Pharmaceutical sales forecasting and market-demand prediction are essential analytical functions that support production planning, inventory optimization, supply-chain coordination, resource allocation, and strategic decision-making within pharmaceutical industries. The increasing complexity of pharmaceutical markets, together with the availability of large commercial and healthcare datasets, has accelerated the adoption of machine-learning techniques capable of improving forecasting accuracy beyond conventional statistical approaches. This study conducted a comparative quantitative evaluation of 19 machine-learning algorithms for pharmaceutical sales forecasting and market-demand prediction using a standardized analytical framework. A retrospective quantitative research design was employed using an initial dataset of 9,250 pharmaceutical observations collected from integrated commercial and healthcare information systems. Following duplicate removal, missing-data treatment, outlier screening, feature engineering, feature selection, and quality validation, 8,640 valid observations were retained for model development and evaluation. Fourteen quantitative predictor variables, including historical sales volume, prescription frequency, drug pricing dynamics, promotional expenditure, physician prescribing behavior, pharmacy inventory levels, seasonal disease patterns, patient demographics, healthcare utilization indicators, insurance reimbursement policies, competitor activities, macroeconomic indicators, regulatory variables, and product life-cycle characteristics, were analyzed. Comparative model performance was evaluated using Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, Symmetric Mean Absolute Percentage Error, coefficient of determination, precision, recall, F1-score, receiver operating characteristic analysis, area under the curve, cross-validation accuracy, robustness assessment, and computational efficiency. The findings demonstrated statistically significant differences among the forecasting algorithms (F = 162.84, p < .001, η² = 0.894). The Hybrid Machine-Learning Framework achieved the strongest overall predictive performance with MAE = 119.48, RMSE = 183.57, MAPE = 2.41%, R² = 0.988, Precision = 0.989, Recall = 0.986, F1-score = 0.988, AUC = 0.997, and cross-validation accuracy of 99.41%. Feature importance analysis identified historical sales volume (21.40%), prescription frequency (17.60%), pharmacy inventory levels (14.10%), physician prescribing behavior (11.80%), and promotional expenditure (9.70%) as the strongest predictors of pharmaceutical demand. Overall, the findings established that ensemble learning, deep-learning architectures, and hybrid machine-learning frameworks consistently outperformed conventional regression-based approaches while providing superior forecasting accuracy, robustness, validation stability, and generalization capability. The study contributes an evidence-based comparative framework for pharmaceutical sales forecasting and market-demand prediction through standardized preprocessing, feature optimization, validation procedures, and comprehensive quantitative model evaluation.
Copyright (c) 2023 Review of Applied Science and Technology
A Performance-Based Framework for Arc-Flash Hazard Reduction and Electrical Reliability Improvement in Brownfield Industrial Distribution Systems
This study proposes a performance-based framework that integrates arc-flash hazard reduction and electrical reliability improvement into a single measurable objective for brownfield industrial plants. The framework evaluates combined safety and reliability effectiveness through six capabilities: baseline assessment, target setting and benchmarking, protection and mitigation engineering, reliability-centered intervention, verification and performance measurement, and performance-based mitigation maturity. Survey data from 156 valid respondents were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, multiple regression, a maturity index, and a capability priority matrix. All capabilities received high ratings, with strong internal consistency and significant positive correlations. The regression model explained 75.8% of the variance in combined effectiveness, with performance-based mitigation maturity emerging as the strongest predictor. The findings demonstrate that electrical safety and reliability improve most effectively when plants establish baselines, define measurable targets, implement coordinated interventions, and verify outcomes through a continuous performance management process.
Copyright (c) 2022 Review of Applied Science and Technology
Toward Secure and Trustworthy Artificial Intelligence in Financial Data Systems: A Governance and Risk-Control Framework
This study examined the problem of fragmented, inconsistent, and after-the-fact controls in the use of artificial intelligence within financial data systems, where AI models supporting credit, fraud, trading, and compliance decisions are exposed to adversarial manipulation, data leakage, bias, and opacity, and where governance, when present, is often disconnected from technical enforcement. The purpose of the study was to assess how a governance and risk-control framework, integrating data security and privacy controls, model robustness and resilience, transparency and explainability, governance and accountability, and human oversight, influences trustworthy AI assurance in financial data systems. A quantitative, cross-sectional, case-based design was adopted, and data were collected through a structured five-point Likert-scale questionnaire from 146 valid respondents out of 165 distributed questionnaires, representing an 88.5% valid response rate. The sample included risk and compliance officers, data scientists and machine-learning engineers, AI and model-governance leads, security and privacy engineers, and IT auditors and regulators, with 67.8% directly involved in AI governance, security, or risk-control activities. The key variables were data security and privacy controls, model robustness and resilience, transparency and explainability, governance and accountability, human oversight and institutional trust, framework design quality, and trustworthy AI assurance. The analysis plan included descriptive statistics, reliability testing using Cronbach's alpha, Pearson correlation, regression modeling, a framework maturity index, and an AI risk-control priority matrix. The headline findings showed that all major constructs were rated high, with trustworthy AI assurance recording the highest mean score of 4.23, followed by governance and accountability at 4.16 and framework design quality at 4.12. Reliability was acceptable to excellent, with Cronbach's alpha values ranging from 0.82 to 0.93. Correlation results showed significant positive relationships, including r = 0.79 between framework design quality and trustworthy AI assurance. Regression results confirmed that the model explained 69.3% of the variance in trustworthy AI assurance, R² = 0.693, adjusted R² = 0.680, F(6,139) = 52.28, p < 0.001. Framework design quality was the strongest predictor, β = 0.33, followed by governance and accountability, β = 0.25, and data security and privacy controls, β = 0.22. The findings imply that financial institutions should strengthen adversarial robustness, privacy protection, model interpretability, continuous monitoring, and human oversight to improve the security, transparency, and trustworthiness of AI in financial data systems.
Reducing Worker Exposure and Electrical Failure Risk in Brownfield Manufacturing Facilities After COVID-Era Maintenance Delays
Every skipped inspection leaves a mark that does not show up until later. When the pandemic forced manufacturers to thin their crews, restrict who could enter a plant, and postpone anything that was not an emergency, the work that got deferred first was often the electrical maintenance that keeps workers safe and equipment running: arc-flash studies left unrenewed, breakers and relays untested, thermographic scans skipped, preventive maintenance on aging switchgear pushed to a later date that kept receding. The result, in brownfield facilities already running on old infrastructure, was an accumulating backlog, a debt of deferred maintenance that quietly raised both the danger to workers and the likelihood of electrical failure. This study treats that debt as the central problem and asks how facilities pay it down: how they surface the backlog, size the risk hidden inside it, and work it off in the order that most reduces harm. It models a combined worker-safety and reliability recovery outcome as a function of six capabilities: maintenance-backlog assessment, deferred-risk quantification, worker-exposure control, electrical-reliability restoration, recovery scheduling and resourcing, and an integrating construct of post-pandemic maintenance recovery maturity. Evidence came from a structured five-point Likert survey completed by 154 valid respondents out of 172 distributed, an 89.5% valid response rate, drawn from maintenance and reliability engineers, electrical and power engineers, EHS and safety managers, plant and facilities managers, and maintenance planners, of whom 70.1% had directly managed electrical maintenance or safety through the pandemic and its aftermath. The data were analyzed with descriptive statistics, Cronbach's alpha, Pearson correlation, multiple regression, a maturity index, and a capability priority matrix. Every capability was rated in the high band, led by the combined safety-reliability recovery outcome at a mean of 4.29 and worker-exposure control at 4.20; reliability was strong, with alpha from 0.83 to 0.94. All correlations were positive and significant, the strongest being r = 0.80 between post-pandemic maintenance recovery maturity and the recovery outcome. The regression model explained 74.9% of the variance in the outcome, R² = 0.749, adjusted R² = 0.739, F(6,147) = 73.15, p < 0.001, with post-pandemic maintenance recovery maturity the leading predictor, β = 0.32, ahead of worker-exposure control, β = 0.25, and electrical-reliability restoration, β = 0.20. The central finding is that safety and reliability recover together, and stay recovered, only when a facility treats its pandemic maintenance backlog as a measured risk debt to be worked down deliberately, recovering the record, assessing the danger, prioritizing by risk, and restoring and retesting, rather than catching up haphazardly as time and budget allow.
Copyright (c) 2021 Review of Applied Science and Technology
Federated Learning Architectures for Distributed Engineering Project Knowledge Management A Simulation-Based Analysis
The increasing complexity of distributed engineering projects has created significant challenges in knowledge management, particularly regarding knowledge sharing, data privacy, communication efficiency, and collaborative decision-making across geographically dispersed stakeholders. Federated learning has emerged as a promising decentralized machine learning approach that enables collaborative intelligence generation without requiring direct sharing of sensitive organizational data. This study investigated the effectiveness of federated learning architectures for distributed engineering project knowledge management through a quantitative simulation-based analysis. Specifically, the study evaluated and compared centralized, decentralized, hierarchical, and hybrid federated learning architectures across key performance dimensions, including learning accuracy, convergence efficiency, communication overhead, scalability performance, computational efficiency, and knowledge-sharing effectiveness. A simulation environment consisting of 100 distributed engineering stakeholder nodes was developed to represent project owners, contractors, consultants, design teams, maintenance units, and operational management entities. The experimental design utilized synthetic engineering project datasets containing design records, construction data, operational logs, maintenance information, and project knowledge repositories distributed across heterogeneous nodes. Statistical analyses included descriptive statistics, one-way analysis of variance, correlation analysis, and multiple regression analysis at a significance level of p < 0.05. The findings revealed statistically significant differences among the evaluated architectures. The hybrid federated learning architecture achieved the highest learning accuracy (95.1%), knowledge-sharing effectiveness (93.6%), communication efficiency (92.6%), and computational efficiency (94.5%). Hierarchical federated learning demonstrated the strongest scalability performance (94.2%) and the fastest convergence rate, requiring only 31 communication rounds to achieve stable learning performance. Centralized federated learning produced reliable learning outcomes but generated the highest communication overhead (18.6 GB), while decentralized federated learning reduced dependence on centralized coordination but exhibited lower overall learning performance (87.9%). Regression analysis indicated that knowledge-sharing effectiveness (β = 0.53), communication efficiency (β = 0.48), and convergence speed (β = 0.42) were significant predictors of overall learning performance. The study concluded that hybrid and hierarchical federated learning architectures provided the most effective balance between learning quality, scalability, communication management, and computational efficiency. These findings contribute to the growing body of knowledge on privacy-preserving artificial intelligence and demonstrate the potential of federated learning as an effective framework for distributed engineering project knowledge management.
A Meta-Analysis of Forensic Accounting Techniques as Financial Risk Management Tools in Banking Services Across Emerging Economies
This study examined forensic accounting techniques as financial risk management tools within banking services across emerging economies through a quantitative meta-analysis of empirical studies published between 2005 and 2025. The study synthesized findings from 68 quantitative empirical studies involving a combined sample size of 31,648 participants obtained from banking sectors across Africa, Asia, and Latin America, including Nigeria, India, South Africa, Malaysia, Brazil, Kenya, Bangladesh, and Mexico. The objective of the study was to evaluate the effectiveness of forensic accounting techniques in improving fraud detection, strengthening internal control systems, enhancing compliance monitoring, and increasing governance accountability within banking institutions operating in emerging financial environments. The study adopted a random-effects meta-analytical design because substantial heterogeneity existed among the included studies. Data analysis was conducted using SPSS version 29, Comprehensive Meta-Analysis software version 4, and R statistical software. The findings revealed a strong positive relationship between forensic accounting implementation and financial risk management outcomes, with an overall pooled effect size coefficient of 0.67 at a statistically significant level of p = 0.001. Digital fraud prevention generated the highest effect size of 0.79, while fraud detection efficiency and internal control enhancement produced effect sizes of 0.74 and 0.69 respectively. Compliance monitoring and governance accountability recorded pooled effect sizes of 0.61 and 0.64, indicating moderate to strong positive effects across banking institutions. The subgroup analysis showed that Asian banking sectors achieved the strongest pooled regional effect size of 0.73 due to advanced technological infrastructure and digital banking integration, while African and Latin American banking institutions produced pooled effect sizes of 0.64 and 0.59 respectively. Heterogeneity analysis revealed substantial variability among studies with Cochran’s Q statistic of 184.52 and an I² heterogeneity index of 71.6%, indicating that governance quality, technological readiness, and regulatory enforcement significantly influenced forensic accounting effectiveness. Publication bias analysis demonstrated limited reporting bias with Egger’s regression value of 1.21 and fail-safe N of 2,184, confirming strong statistical robustness. The study concluded that forensic accounting significantly improved banking stability, fraud prevention, operational transparency, compliance effectiveness, and governance accountability across emerging economy banking systems.
Scalable AI For Project Portfolio Management: A Mixed-Methods Study Combining Distributed Computing Benchmarks
This study examined the effectiveness of scalable artificial intelligence in enhancing project portfolio management through a quantitative, quasiexperimental design integrating distributed computing benchmarks and decision support evaluation. The research analyzed 312 project instances across six portfolio environments alongside 128 professional respondents involved in portfolio decision-making. The findings revealed that scalable AI systems significantly improved portfolio performance, with regression analysis indicating that key predictors such as predictive accuracy (β = 0.46), system throughput (β = 0.39), and resource utilization efficiency (β = 0.29) had strong positive effects on decision quality, while system latency showed a negative relationship (β = -0.34). The model explained approximately 69% of the variance in portfolio decision outcomes, demonstrating substantial explanatory power. Benchmarking results showed that high scalability configurations achieved throughput levels of 289.4 tasks per second and decision accuracy rates of 88.6%, compared to baseline systems with 182.3 tasks per second and 72.5% accuracy. Correlation analysis further confirmed strong associations between AI performance and portfolio success indicators, with predictive accuracy showing the highest correlation with decision accuracy (r = 0.68) and portfolio responsiveness (r = 0.66). Sub-group analysis revealed that large-scale portfolios experienced more pronounced efficiency gains, while experienced users reported higher trust and satisfaction levels, with mean trust scores increasing from 3.21 to 4.26 across experience groups. Effect size analysis indicated moderate to strong impacts, confirming the practical significance of the findings. Overall, the results demonstrated that scalable AI systems substantially enhanced decision-making accuracy, reduced processing delays, and optimized resource allocation in complex portfolio environments. The study provided empirical evidence that integrating AI with distributed computing infrastructure leads to measurable improvements in both computational performance and portfolio-level outcomes, offering a robust quantitative foundation for advancing intelligent project portfolio management systems.
Zero-Trust Architecture Adoption on Financial Data Privacy in Public-Sector ERP Environments
The increasing dependence on Enterprise Resource Planning systems within public-sector institutions has intensified concerns regarding cybersecurity governance, financial data privacy, and protection of sensitive governmental information against evolving cyber threats. This study examined the influence of Zero-Trust Architecture adoption on financial data privacy performance within public-sector ERP environments. The study adopted a quantitative cross-sectional research design grounded in Zero-Trust Security Theory and Information Security Governance Theory. Data were collected from 312 cybersecurity professionals, ERP administrators, IT managers, compliance officers, and digital governance personnel working across ministries, treasury departments, taxation agencies, procurement authorities, municipal administrations, and pension management institutions utilizing ERP systems for financial management operations. Structured questionnaires measured identity governance implementation, multi-factor authentication usage, network segmentation practices, continuous monitoring technologies, access governance effectiveness, and financial data privacy performance. Data analysis was conducted using descriptive statistics, Pearson correlation analysis, and multiple regression analysis through the Statistical Package for the Social Sciences software. The findings demonstrated high implementation levels of multi-factor authentication systems (M = 4.35, SD = 0.58), identity governance frameworks (M = 4.28, SD = 0.63), and continuous monitoring technologies (M = 4.18, SD = 0.71) across participating institutions. Correlation analysis revealed strong positive relationships between identity governance frameworks and financial information confidentiality (r = 0.812, p < 0.01), while multi-factor authentication showed significant association with unauthorized access prevention capability (r = 0.825, p < 0.01). Multiple regression analysis further indicated that identity governance frameworks (β = 0.384, p = 0.000) and multi-factor authentication systems (β = 0.331, p = 0.000) significantly predicted financial data privacy performance within governmental ERP systems. The regression model explained 71.8% of the variance associated with financial data privacy outcomes (R² = 0.718). The findings established that Zero-Trust Architecture significantly strengthened ERP cybersecurity governance, improved institutional resilience, enhanced confidentiality protection, and reduced operational exposure to unauthorized access incidents affecting public-sector financial information systems.
Predictive Modeling and Failure Forecasting For AI-Controlled Electrical Systems in Robotics and Autonomous Vehicles
This study explores the application of predictive modeling and failure forecasting techniques for enhancing the reliability and operational safety of AI-controlled electrical systems in robotics and autonomous vehicles. As these systems rely heavily on interconnected electrical components such as sensors, actuators, battery systems, and embedded controllers, even minor faults can lead to significant performance degradation or safety risks. The research employs a combination of machine learning models, including Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, to analyze both historical and real-time sensor data. Experimental findings demonstrate that the proposed predictive framework achieves a fault detection accuracy of 94.6%, with LSTM-based models outperforming traditional approaches by improving prediction precision by approximately 12.3% in time-series forecasting tasks. Additionally, the model successfully predicts the remaining useful life (RUL) of critical components with a mean absolute error (MAE) of less than 8.7%, enabling more effective maintenance planning. The integration of digital twin simulations further enhances system monitoring by reducing diagnostic latency by 21% and improving anomaly detection rates by 18% compared to conventional threshold-based methods. Results also indicate that implementing edge computing for on-device analytics reduces response time by nearly 35%, which is crucial for real-time decision-making in autonomous environments. Despite these advancements, challenges related to data quality, model interpretability, and cybersecurity vulnerabilities persist, requiring further research and robust system design. Overall, the study highlights that predictive modeling and failure forecasting significantly reduce unexpected system failures by up to 40% and maintenance costs by approximately 25%, demonstrating their critical role in advancing resilient, efficient, and safe AI-driven electrical systems in robotics and autonomous vehicles.
ITIL-Based Change Management For OT/SCADA Network Modifications in Critical Energy Environments: Reducing Downtime Risk in Fiber-Connected Utility Control Systems
This study investigates the critical problem of downtime risk during OT/SCADA network modifications in fiber-connected utility control systems, where poorly governed changes can disrupt real-time monitoring, control visibility, and operational continuity in energy infrastructure. The purpose of the research is to quantitatively assess how ITIL-based change management practices contribute to reducing downtime risk and improving system resilience in critical energy environments. A quantitative, cross-sectional, case-based research design was adopted, using a structured Likert-scale survey administered to a purposive sample of 214 professionals drawn from cloud-integrated enterprise and utility control system environments, including OT engineers, SCADA operators, network engineers, and ITIL practitioners. The study examined key independent variables such as change planning, change risk assessment, approval and authorization, rollback and recovery planning, and post-change review and documentation, with downtime risk reduction as the dependent variable. Data were analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression modeling. The findings reveal strong statistical relationships, with the regression model explaining 67.5% of the variance in downtime risk reduction (R² = 0.675, p < 0.001). Change risk assessment emerged as the strongest predictor (β = 0.31), followed by rollback and recovery planning (β = 0.27) and change planning (β = 0.22). Correlation results further confirm significant positive associations, particularly between change risk assessment and downtime reduction (r = 0.72, p < 0.001). The overall downtime risk reduction score was high (M = 4.20), while system resilience was also strong (M = 4.12), although moderate risk exposure remained (M = 3.96). These findings indicate that structured ITIL-based governance significantly enhances operational continuity. The study implies that integrating risk-aware planning, recovery readiness, and formal authorization into change processes can reduce failures and improve resilience in critical infrastructure systems.
Evidence-Based Psychosocial Interventions for Reducing Distress Among Displaced Women and SGBV Survivors
This study examined the effectiveness of evidence-based psychosocial interventions in reducing distress among displaced women and survivors of sexual and gender-based violence, addressing the persistent problem of trauma-related fear, anxiety, hopelessness, social withdrawal, and reduced coping capacity in humanitarian settings. The purpose was to determine whether intervention accessibility, perceived quality, participation frequency, safe-service trust, and barriers to support utilization significantly influenced distress reduction within a quantitative, cross-sectional, case-study-based design. The study drew on a sample of 220 displaced women and SGBV survivors from a defined displacement-affected case setting, where respondents had been exposed to counseling, group psychosocial support, peer/community support, or case management services. Data were collected through a structured five-point Likert-scale questionnaire and analyzed using descriptive statistics, correlation analysis, and multiple regression in SPSS. The dependent variable was distress reduction, while the key independent variables were accessibility of interventions, intervention quality, participation frequency, safe-service trust and survivor-centered care, and barriers to psychosocial support utilization. Findings showed strong overall perceived distress reduction, with the mean distress-reduction score reaching 4.06 out of 5, while the overall distress profile declined from 4.19 before intervention to 2.58 after intervention, reflecting a mean improvement of 1.61 points. Safe-service trust had the highest mean among service variables at 4.18 and emerged as the strongest predictor of distress reduction in regression analysis (β = .36, p < .001), followed by intervention quality (β = .29, p < .001), accessibility (β = .21, p = .003), and participation frequency (β = .17, p = .011), whereas barriers showed a significant negative effect (β = -.19, p = .006). The overall model was statistically significant, F(5,214) = 46.83, p < .001, explaining 52.2% of the variance in distress reduction (R² = .522). The study implies that psychosocial interventions are most effective when they are accessible, high-quality, repeated, and delivered through trauma-informed, safe, and survivor-centered care, with practical efforts to reduce stigma, fear of disclosure, and access barriers.
Neural Network–Based Customer Retention Forecasting in Mobile Wallet Services Using 200k Historical User Profiles
This study developed and evaluated a neural network–based retention forecasting model using 200,000 historical mobile wallet user profiles. Retention was operationalized as continued transactional activity within a defined post-observation horizon, with 68% of users classified as retained and 32% classified as churned. Descriptive results indicated substantial behavioral heterogeneity, with mean transaction frequency of 12.8 transactions (SD = 21.3), median of 6.0, and mean recency gap of 18.6 days (SD = 27.4). Retained users demonstrated higher average merchant diversity (M = 6.8) compared with churned users (M = 2.9), and shorter inter-transaction gaps (M = 11.2 days vs. 34.7 days). Logistic regression results showed significant effects for recency (β = -0.042, p < .001), transaction frequency (β = 0.087, p < .001), and engagement trend (β = 0.054, p < .001). The logistic baseline achieved an AUC of 0.78 and PR-AUC of 0.64, while regularized regression improved performance to an AUC of 0.80 and PR-AUC of 0.67. The neural network model achieved superior discrimination with an AUC of 0.86 and PR-AUC of 0.74, and produced a lift of 3.5 in the top 10% risk segment. Segment-level results remained stable across new users (AUC = 0.84) and mature users (AUC = 0.88). Findings demonstrated that combining intensity, diversity, and stability constructs within a neural architecture significantly enhanced retention forecasting accuracy in large-scale mobile wallet environments.
Quantitative Structural Retrofit Assessment Models for Strengthening Existing Steel Buildings Under Increased Load Demands
This study examined quantitative structural retrofit assessment models for strengthening existing steel buildings exposed to increased load demands. The problem addressed was the growing mismatch between the original design capacity of aging steel buildings and the higher structural demands created by occupancy intensification, heavy equipment installation, rooftop service additions, vertical expansion, functional conversion, and updated code requirements. The purpose of the study was to determine how load demand escalation, structural deficiencies, retrofit technique suitability, and retrofit decision drivers influence strengthening effectiveness and structural safety improvement. A quantitative, cross-sectional, case-based research design was adopted, using structured Likert-scale data from 210 technical respondents, including structural engineers, civil engineers, retrofit consultants, construction/project managers, and building assessors, all involved in steel building assessment or strengthening projects. The key variables were increased load demand, structural deficiency, retrofit technique suitability, retrofit decision drivers, and strengthening effectiveness. Data analysis was planned and conducted through descriptive statistics, Pearson correlation analysis, and multiple regression modeling. The findings showed strong agreement that retrofit assessment is necessary under increased load conditions, with mean scores of 4.18 for increased load demand, 4.09 for structural deficiency, 4.23 for retrofit suitability, 4.27 for retrofit decision drivers, and 4.31 for strengthening effectiveness. Connection weakness ranked as the leading structural deficiency (M = 4.17), while heavy equipment installation was the highest-ranked load escalation factor (M = 4.26). Connection strengthening was rated the most suitable retrofit technique (M = 4.35), and structural safety requirement was the highest decision priority (M = 4.41). Correlation results showed significant positive relationships between assessment variables and strengthening effectiveness, with coefficients ranging from r = 0.593 to r = 0.711, all at p < .001. Regression results further confirmed that the model explained 61.0% of the variance in strengthening effectiveness (R² = 0.610, F (4,205) = 80.13, p < .001), with retrofit suitability emerging as the strongest predictor (β = 0.331). The study implies that steel-building retrofit decisions should be guided by structured, data-based assessment models that align load demand, system deficiencies, intervention suitability, and safety-centered decision priorities to improve reliability and continued serviceability.
Integration of Lean Six Sigma and IOT-Based Real-Time Monitoring for Workplace Hazard Reduction in Industrial Facilities
This study examined the integration of Lean Six Sigma and IoT-based real-time monitoring for workplace hazard reduction in industrial and enterprise facility cases, addressing the persistent problem that conventional safety systems often depend on periodic inspections, manual reporting, and reactive corrective action, which may delay hazard detection and control. The purpose of the study was to determine whether structured process-improvement practices and real-time digital monitoring jointly improve hazard reduction outcomes in industrial facilities. A quantitative, cross-sectional, case-based research design was applied using primary questionnaire data collected from managers, supervisors, engineers, safety officers, technicians, and operational staff across selected enterprise/industrial cases. Out of 320 distributed questionnaires, 286 valid responses were analyzed, producing an effective response rate of 89.4%. The key variables were Lean Six Sigma implementation, IoT-based real-time monitoring, and workplace hazard reduction. The analysis plan included descriptive statistics, reliability testing, Pearson correlation, multiple regression, and interaction-effect testing. Findings showed that Lean Six Sigma recorded a positive grand mean of 3.91, IoT-based monitoring recorded a stronger mean of 4.08, and workplace hazard reduction recorded a mean of 4.02. Reliability was strong, with Cronbach’s Alpha values of 0.87 for Lean Six Sigma, 0.89 for IoT monitoring, 0.85 for hazard reduction, and 0.87 for the full instrument. Correlation results showed significant positive relationships between Lean Six Sigma and hazard reduction, r = 0.61, p < .01, and between IoT monitoring and hazard reduction, r = 0.68, p < .01. Regression findings confirmed that Lean Six Sigma, β = 0.29, p < .001, IoT monitoring, β = 0.37, p < .001, and their interaction, β = 0.18, p = .004, significantly predicted hazard reduction, explaining 44.2% of variance, R² = 0.442. The study implies that enterprise safety performance improves most when process discipline, sensor-based visibility, real-time alerts, and corrective-action systems operate as one integrated socio-technical framework.
Mixed-Method Analysis of Reliability-Centered Design Practices in Medium and Low-Voltage Electrical Distribution Systems
This study addresses the persistent reliability challenges in medium- and low-voltage electrical distribution systems, where interruptions, delayed restoration, weak fault isolation, and uneven design quality continue to undermine service continuity and operational efficiency. Its purpose was to examine how reliability-centered design practices influence reliability performance, operational efficiency, and system dependability across selected case-based electrical distribution environments. Using a quantitative, cross-sectional, case-based design, the study collected primary data from 120 usable respondents out of 150 distributed questionnaires, yielding an 80.0% usable response rate. The sample comprised electrical engineers, maintenance engineers, distribution planners, operations officers, and technical supervisors/managers working across medium-voltage systems, low-voltage systems, and both system classes. The key independent variables were protective coordination, redundancy planning, fault isolation capability, switching and restoration readiness, and equipment standardization, while the dependent variables were reliability performance, operational efficiency, and system dependability. Data were analyzed using descriptive statistics, a Reliability-Centered Design Practice Index, correlation, t-test, and multiple regression in SPSS. The findings showed a high overall level of design-practice adoption with an RCDPI mean of 3.87, while reliability performance, operational efficiency, and system dependability recorded means of 3.91, 3.83, and 3.88 respectively. Protective coordination had the highest mean score at 4.02, followed by fault isolation capability at 3.94. Reliability-centered design practices were strongly and positively associated with reliability performance (r = 0.71, p < 0.001) and operational efficiency (r = 0.66, p < 0.001), while the strongest relationship was observed with system dependability (r = 0.74, p < 0.001). Medium-voltage systems outperformed low-voltage systems, with RCDPI scores of 3.95 and 3.78 respectively, and the difference was statistically significant (t = 2.41, p = 0.017). Regression results further showed that design variables explained 58.4% of the variance in reliability performance and 61.7% of the variance in system dependability, confirming that reliability-centered design is a critical engineering and planning mechanism for improving distribution-system outcomes.
Digital Twin Architecture for Predictive Control of Solid-State Additive Manufacturing Processes
This study examined the effectiveness of a digital twin architecture integrated with predictive control for optimizing solid-state additive manufacturing processes. A quantitative experimental design was employed using 120 validated manufacturing runs, where process parameters such as tool speed (mean = 1185 rpm), applied pressure (mean = 5.10 kN), and temperature (mean = 421°C) were systematically varied and monitored through real-time sensor systems. The digital twin model was synchronized with physical operations to generate predictive outputs, which were compared with observed manufacturing results. The findings demonstrated strong predictive alignment, with correlation coefficients ranging from 0.89 to 0.94 across key variables including temperature, deformation index, and bonding strength. The implementation of predictive control resulted in significant performance improvements, with defect rates reduced from 14.2% to 6.3%, dimensional deviation decreasing from 0.48 mm to 0.33 mm, and temperature variability reduced by 29.0%. Process stability improved from 71% under baseline conditions to 92% with predictive control integration. Statistical analysis confirmed that these improvements were significant at the 0.05 level, with large effect sizes observed for defect reduction (d = 0.92) and dimensional accuracy (d = 0.81). Sub-group analysis revealed that moderate parameter ranges produced optimal performance, achieving the highest predictive accuracy (93%) and lowest deformation variability (9%). Regression analysis further indicated strong explanatory power of the digital twin model, with coefficients of determination exceeding 0.87 across all evaluated outputs. The results also highlighted the importance of real-time data synchronization, system scalability, and computational efficiency in maintaining effective predictive control. Overall, the study provided empirical evidence that digital twin-based predictive control systems significantly enhance manufacturing efficiency, process stability, and product quality, supporting their application in advanced data-driven manufacturing environments.
A Mixed-Methods Study Integrating Model Performance with Analyst Decision Workflows in Trustworthy AI for Financial Fraud Detection
This study examined the relationship between artificial intelligence model performance and analyst decision workflows in trustworthy financial fraud detection environments. The study was motivated by the need to move beyond purely model-centered evaluation and assess whether fraud detection systems that perform well statistically also support effective, consistent, and operationally credible analyst decision-making. A quantitative, cross-sectional explanatory design was adopted to investigate how model precision, recall, false positive rate, explanation quality, and perceived reliability were associated with key workflow outcomes, including alert acceptance, review efficiency, escalation quality, and decision consistency. Data were collected from 268 initial responses and matched workflow records drawn from fraud analysts, senior investigators, fraud operations supervisors, and AI-supported risk review personnel working across commercial banks, digital payment providers, insurance companies, and fintech lending platforms. After data screening and exclusion of incomplete or invalid cases, 240 valid cases were retained for final analysis, yielding a usable response rate of 89.6%. The findings showed that model performance and trustworthy AI characteristics were significantly associated with analyst workflow outcomes. Correlation analysis indicated that model precision had a strong positive relationship with alert acceptance rate (r = .68, p < .001), while model recall was strongly associated with escalation quality (r = .65, p < .001). False positive rate had a strong negative relationship with review efficiency (r = -.62, p < .001). Regression analysis further showed that model precision significantly predicted alert acceptance (beta = .36, p < .001), model recall significantly predicted escalation quality (beta = .43, p < .001), and perceived reliability significantly predicted decision consistency (beta = .35, p < .001). The regression models explained between 48% and 61% of the variance in the major workflow outcomes. Subgroup analysis also showed that highly explainable systems produced stronger workflow stability, with a large effect size (d = 1.28), while high false positive environments were associated with weaker workflow stability (d = 1.34). The study concluded that trustworthy AI in financial fraud detection should be evaluated through the combined lens of predictive accuracy, interpretability, reliability, and workflow usability because operational effectiveness depends on both model quality and human decision integration.
A Quantitative Assessment of Data Accuracy and Operational Efficiency in Digital Service Platforms
This study presented a quantitative assessment of the relationship between data accuracy and operational efficiency in digital service platforms, with a focus on measuring how data quality dimensions influenced system performance outcomes. A cross-sectional explanatory research design was adopted, utilizing a dataset of 210 observations derived from system-generated records and structured questionnaire responses. Key variables included data accuracy dimensions such as validity, consistency, and completeness, alongside operational efficiency indicators including processing time, response latency, throughput, and workflow completion rate. Descriptive analysis revealed that data validity (M = 3.94, SD = 0.62), consistency (M = 3.88, SD = 0.67), and completeness (M = 3.91, SD = 0.59) exhibited moderate variability, while operational efficiency measures such as throughput (M = 145.3 transactions per minute) and workflow completion rate (M = 91.6%) indicated relatively stable performance levels across platforms. Inferential analysis demonstrated statistically significant relationships between data accuracy and operational efficiency. Correlation coefficients ranged from 0.41 to 0.68, indicating moderate to strong associations, with data consistency showing the strongest relationship with workflow completion rate (r = 0.68). Multiple regression analysis revealed that data accuracy variables collectively explained 57% of the variance in operational efficiency (Adjusted R² = 0.57, p < 0.001). Among predictors, data consistency (β = 0.42) and completeness (β = 0.36) exhibited the highest influence, while validity (β = 0.29) also contributed significantly. Effect size analysis further confirmed the practical importance of these relationships, with an overall large effect size (f² = 0.28). Subgroup analysis indicated that high-volume platforms exhibited stronger relationships between data accuracy and efficiency, while systems with advanced validation mechanisms achieved higher performance levels (M = 4.35). The findings established that data accuracy is a critical determinant of operational efficiency, with both statistical and practical significance, providing a robust empirical foundation for understanding performance optimization in digital service platforms.
Strengthening International Trade Finance Operations: Strategies for Preventing Trade-Based Money Laundering (TBML)
This study examined the effectiveness of strategies for strengthening international trade finance operations in preventing trade-based money laundering (TBML) through a comprehensive quantitative approach. A longitudinal research design was employed using panel data from 48 countries over a ten-year period, resulting in 480 country-year observations. The analysis integrated trade finance indicators, institutional quality indices, and regulatory performance measures to assess the determinants of TBML risk. Descriptive findings indicated that the mean trade discrepancy was 12.85%, with high-risk sectors such as precious metals and electronics exhibiting discrepancy levels exceeding 18%, while low-risk sectors remained below 9%. The econometric results revealed that trade discrepancies had a strong positive effect on TBML risk (β = 0.412, p < 0.001), while institutional quality (β = -0.365, p < 0.001) and regulatory stringency (β = -0.284, p < 0.001) significantly reduced risk levels. The model explained approximately 61% of the variation in TBML indicators, demonstrating substantial explanatory power. Panel data analysis further confirmed that improvements in regulatory frameworks and financial monitoring systems were associated with consistent reductions in TBML risk over time, with the risk score declining from 0.49 in 2014 to 0.34 in 2023. Sectoral and geographic analyses revealed that developing economies exhibited higher TBML risk (mean = 0.52) compared to developed economies (mean = 0.34), with high-risk trade corridors reaching levels as high as 0.67. Advanced analytical techniques, including clustering and network analysis, identified concentrated transaction clusters associated with repeated counterparties and high-risk jurisdictions, indicating structured patterns of suspicious trade activity. The findings highlighted that TBML risk is influenced by a combination of trade complexity, institutional capacity, regulatory enforcement, and sector-specific characteristics. The study demonstrated that strengthened governance frameworks, enhanced compliance systems, and the integration of data-driven analytical tools significantly improve the detection and mitigation of TBML within global trade finance systems.
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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.
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.
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.
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.
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.