Review of Applied Science and Technology
type: channel
description: 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.
language: en
publication name: Review of Applied Science and Technology
items: The Role of AI-Enabled Business Intelligence in Financial Risk Assessment and Liquidity Monitoring
items: Predictive Analytics Models for Network Traffic Optimization in Distributed Enterprise Environments
items: Comparative Analysis of Machine-Learning Approaches for Pharmaceutical Sales Forecasting and Market-Demand Prediction
items: A Performance-Based Framework for Arc-Flash Hazard Reduction and Electrical Reliability Improvement in Brownfield Industrial Distribution Systems
items: Toward Secure and Trustworthy Artificial Intelligence in Financial Data Systems: A Governance and Risk-Control Framework
items: Reducing Worker Exposure and Electrical Failure Risk in Brownfield Manufacturing Facilities After COVID-Era Maintenance Delays
items: Federated Learning Architectures for Distributed Engineering Project Knowledge Management A Simulation-Based Analysis
items: A Meta-Analysis of Forensic Accounting Techniques as Financial Risk Management Tools in Banking Services Across Emerging Economies
items: Scalable AI For Project Portfolio Management: A Mixed-Methods Study Combining Distributed Computing Benchmarks
items: Zero-Trust Architecture Adoption on Financial Data Privacy in Public-Sector ERP Environments
items: Predictive Modeling and Failure Forecasting For AI-Controlled Electrical Systems in Robotics and Autonomous Vehicles
items: ITIL-Based Change Management For OT/SCADA Network Modifications in Critical Energy Environments: Reducing Downtime Risk in Fiber-Connected Utility Control Systems
items: Evidence-Based Psychosocial Interventions for Reducing Distress Among Displaced Women and SGBV Survivors
items: Neural Network–Based Customer Retention Forecasting in Mobile Wallet Services Using 200k Historical User Profiles
items: Quantitative Structural Retrofit Assessment Models for Strengthening Existing Steel Buildings Under Increased Load Demands
items: Integration of Lean Six Sigma and IOT-Based Real-Time Monitoring for Workplace Hazard Reduction in Industrial Facilities
items: Mixed-Method Analysis of Reliability-Centered Design Practices in Medium and Low-Voltage Electrical Distribution Systems
items: Digital Twin Architecture for Predictive Control of Solid-State Additive Manufacturing Processes
items: A Mixed-Methods Study Integrating Model Performance with Analyst Decision Workflows in Trustworthy AI for Financial Fraud Detection
items: A Quantitative Assessment of Data Accuracy and Operational Efficiency in Digital Service Platforms
items: Strengthening International Trade Finance Operations: Strategies for Preventing Trade-Based Money Laundering (TBML)
items: Mitigating Solar Curtailment in High-Penetration Interconnections: An AI-Driven Approach to Dynamic Load Balancing
items: Quantitative Assessment of Data-Driven Pricing Optimization Strategies for E-Commerce Platforms in Developing Economies
items: Quantitative Assessment of Data Privacy and Access Control Effectiveness in SAP/ERP Analytics Systems
items: Quantitative Performance Assessment of Distributed Machine Learning Frameworks for Real-Time Financial Analytics in Enterprise Data Platforms
items: Advanced Computational and Biotechnological Approaches to Systemic Family Therapy: Predicting Marital Satisfaction and Emotional Wellbeing in Couples
items: Data Preprocessing and Feature Engineering Strategies for Large-Scale Predictive Modeling Applications
items: Development of a Hybrid Machine Learning Model for Predictive Performance Optimization in Lean Manufacturing and Industry 4.0
items: A Systematic Review of Secure Health Data Information Systems for Pandemic Preparedness and Economic Continuity in the United States
items: High-Performance Computing–Assisted Modeling and Real-Time Analysis of Electrical Power Networks and Industrial Control Systems

The Role of AI-Enabled Business Intelligence in Financial Risk Assessment and Liquidity Monitoring
type: item
description: 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 fin…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Financial Risk
  topic: keywords
subject: Liquidity Monitoring
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Md Anisur Rahman Mollah
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-03-26
publication date: 2025-03-26
starting page: 195
ending page: 246
doi: 10.63125/p4eezd93

Predictive Analytics Models for Network Traffic Optimization in Distributed Enterprise Environments
type: item
description: 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 pre…
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Network Traffic
  topic: keywords
subject: LSTM
  topic: keywords
subject: Network Optimization
  topic: keywords
subject: Enterprise Networks
  topic: keywords
creator: Md Shahid Nazir
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-03-28
publication date: 2024-03-28
starting page: 367
ending page: 419
doi: 10.63125/qm6cyf84

Comparative Analysis of Machine-Learning Approaches for Pharmaceutical Sales Forecasting and Market-Demand Prediction
type: item
description: 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 demonstra…
subject: Articles
  topic: section
subject: Pharmaceutical forecasting
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Demand prediction
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Ensemble learning
  topic: keywords
creator: Muhammad Zahidul Islam
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-06-28
publication date: 2023-06-28
starting page: 38
ending page: 95
doi: 10.63125/etcbqf13

A Performance-Based Framework for Arc-Flash Hazard Reduction and Electrical Reliability Improvement in Brownfield Industrial Distribution Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Performance-based framework
  topic: keywords
subject: Arc-flash hazard reduction
  topic: keywords
subject: Electrical reliability
  topic: keywords
subject: Brownfield systems
  topic: keywords
subject: Industrial distribution
  topic: keywords
creator: Mohammad Shoriful Hossan Shohel
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-10
publication date: 2022-12-10
starting page: 447
ending page: 463
doi: 10.63125/a70vj720

Toward Secure and Trustworthy Artificial Intelligence in Financial Data Systems: A Governance and Risk-Control Framework
type: item
description: 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…
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Digital twin
  topic: keywords
subject: Predictive maintenance
  topic: keywords
subject: Drinking water infrastructure
  topic: keywords
subject: Lifecycle asset management
  topic: keywords
creator: Sadia Zaman
creator: Md. Fardous
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-03-27
publication date: 2024-03-27
starting page: 343
ending page: 366
doi: 10.63125/z0165923

Reducing Worker Exposure and Electrical Failure Risk in Brownfield Manufacturing Facilities After COVID-Era Maintenance Delays
type: item
description: 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…
subject: Articles
  topic: section
subject: Maintenance backlog
  topic: keywords
subject: Worker exposure
  topic: keywords
subject: Electrical reliability
  topic: keywords
subject: Brownfield facilities
  topic: keywords
subject: Post-pandemic recovery
  topic: keywords
creator: Mohammad Shoriful Hossan Shohel
rights: Copyright (c) 2021 Review of Applied Science and Technology
date: 2021-12-13
publication date: 2021-12-13
starting page: 263
ending page: 279
doi: 10.63125/cm1c2c20

Federated Learning Architectures for Distributed Engineering Project Knowledge Management A Simulation-Based Analysis
type: item
description: 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 …
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Knowledge Management
  topic: keywords
subject: Engineering Projects
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Simulation Analysis
  topic: keywords
creator: Amir Razaq
creator: Chapal Barua
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-10
publication date: 2022-12-10
starting page: 411
ending page: 446
doi: 10.63125/4k4pjr64

A Meta-Analysis of Forensic Accounting Techniques as Financial Risk Management Tools in Banking Services Across Emerging Economies
type: item
description: 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. Heterogen…
subject: Articles
  topic: section
subject: Forensic Accounting
  topic: keywords
subject: Financial Risk
  topic: keywords
subject: Banking Governance
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Emerging Economies
  topic: keywords
creator: Samia Hossain Swarnali
creator: Binayan Dey
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-27
publication date: 2023-12-27
starting page: 352
ending page: 392
doi: 10.63125/n5jgwr22

Scalable AI For Project Portfolio Management: A Mixed-Methods Study Combining Distributed Computing Benchmarks
type: item
description: 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 th…
subject: Articles
  topic: section
subject: Scalable AI
  topic: keywords
subject: Project Portfolio
  topic: keywords
subject: Distributed Computing
  topic: keywords
subject: Decision Support
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Kazi Rakib Hasan Saurav
creator: H M Mahir Uddin
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-08
publication date: 2022-12-08
starting page: 375
ending page: 410
doi: 10.63125/0kk4wf20

Zero-Trust Architecture Adoption on Financial Data Privacy in Public-Sector ERP Environments
type: item
description: 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 frame…
subject: Articles
  topic: section
subject: Zero-Trust Architecture
  topic: keywords
subject: ERP Security
  topic: keywords
subject: Financial Data Privacy
  topic: keywords
subject: Cybersecurity Governance
  topic: keywords
subject: Public-Sector Systems
  topic: keywords
creator: S M Arif Al Sany
creator: Siful Islam
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-06
publication date: 2022-12-06
starting page: 323
ending page: 374
doi: 10.63125/j8cas279

Predictive Modeling and Failure Forecasting For AI-Controlled Electrical Systems in Robotics and Autonomous Vehicles
type: item
description: 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 …
subject: Articles
  topic: section
subject: Predictive Modeling
  topic: keywords
subject: Failure Forecasting
  topic: keywords
subject: Autonomous Systems
  topic: keywords
subject: Electrical Reliability
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Shamsul Arifeen
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-12-26
publication date: 2024-12-26
starting page: 367
ending page: 401
doi: 10.63125/ycc3n924

ITIL-Based Change Management For OT/SCADA Network Modifications in Critical Energy Environments: Reducing Downtime Risk in Fiber-Connected Utility Control Systems
type: item
description: 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 plan…
subject: Articles
  topic: section
subject: ITIL Change Management
  topic: keywords
subject: OT/SCADA Systems
  topic: keywords
subject: Downtime Risk Reduction
  topic: keywords
subject: Critical Energy Infrastructure
  topic: keywords
subject: Operational Resilience
  topic: keywords
creator: Abu Naser Md Golam Mosharraf
creator: Amir Razaq
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-04
publication date: 2022-12-04
starting page: 283
ending page: 322
doi: 10.63125/e2gqtp57

Evidence-Based Psychosocial Interventions for Reducing Distress Among Displaced Women and SGBV Survivors
type: item
description: 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 sign…
subject: Articles
  topic: section
subject: Psychosocial Interventions
  topic: keywords
subject: Distress Reduction
  topic: keywords
subject: Displaced Women
  topic: keywords
subject: SGBV Survivors
  topic: keywords
subject: Trauma-Informed Care
  topic: keywords
creator: Mahmuda Mahmuda
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-27
publication date: 2023-12-27
starting page: 308
ending page: 351
doi: 10.63125/4gwwbv38

Neural Network–Based Customer Retention Forecasting in Mobile Wallet Services Using 200k Historical User Profiles
type: item
description: 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.
subject: Articles
  topic: section
subject: Neural Networks
  topic: keywords
subject: Customer Retention
  topic: keywords
subject: Mobile Wallets
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Behavioral Analytics
  topic: keywords
creator: Iftekhar Ahmed
creator: Binayan Dey
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-09-11
publication date: 2023-09-11
starting page: 67
ending page: 114
doi: 10.63125/ee5eas98

Quantitative Structural Retrofit Assessment Models for Strengthening Existing Steel Buildings Under Increased Load Demands
type: item
description: 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 posi…
subject: Articles
  topic: section
subject: Structural Retrofit Assessment
  topic: keywords
subject: Existing Steel Buildings
  topic: keywords
subject: Increased Load Demands
  topic: keywords
subject: Strengthening Effectiveness
  topic: keywords
subject: Reliability-Based Structural Safety
  topic: keywords
creator: Md. Mainuddin Faisal
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-12-29
publication date: 2024-12-29
starting page: 325
ending page: 366
doi: 10.63125/yyqnte84

Integration of Lean Six Sigma and IOT-Based Real-Time Monitoring for Workplace Hazard Reduction in Industrial Facilities
type: item
description: 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 e…
subject: Articles
  topic: section
subject: Lean Six Sigma
  topic: keywords
subject: IoT-Based Real-Time Monitoring
  topic: keywords
subject: Workplace Hazard Reduction
  topic: keywords
subject: Industrial Safety
  topic: keywords
subject: Socio-Technical Systems
  topic: keywords
creator: Md Abubakar Siddique Akash
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-12-28
publication date: 2024-12-28
starting page: 285
ending page: 324
doi: 10.63125/xmhyhj07

Mixed-Method Analysis of Reliability-Centered Design Practices in Medium and Low-Voltage Electrical Distribution Systems
type: item
description: 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 outpe…
subject: Articles
  topic: section
subject: Reliability-Centered Design
  topic: keywords
subject: Electrical Distribution Systems
  topic: keywords
subject: Reliability Performance
  topic: keywords
subject: Fault Isolation Capability
  topic: keywords
subject: System Dependability
  topic: keywords
creator: Md Siam Taluckder
creator: Md. Shahinur Islam
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-12-28
publication date: 2024-12-28
starting page: 244
ending page: 284
doi: 10.63125/kw8cab16

Digital Twin Architecture for Predictive Control of Solid-State Additive Manufacturing Processes
type: item
description: 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 manufacturin…
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Predictive Control
  topic: keywords
subject: Additive Manufacturing
  topic: keywords
subject: Process Optimization
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
creator: Md. Ashfaq Siddiquee
creator: Manam Ahmed
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-26
publication date: 2023-12-26
starting page: 266
ending page: 307
doi: 10.63125/tt00s684

A Mixed-Methods Study Integrating Model Performance with Analyst Decision Workflows in Trustworthy AI for Financial Fraud Detection
type: item
description: 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% …
subject: Articles
  topic: section
subject: Trustworthy AI
  topic: keywords
subject: Financial Fraud Detection
  topic: keywords
subject: Analyst Decision Workflows
  topic: keywords
subject: Model Performance
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
creator: Istiaq Ahmed
creator: Md. Hasan Or Rashid
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-06-06
publication date: 2024-06-06
starting page: 41
ending page: 91
doi: 10.63125/xdmkbj34

A Quantitative Assessment of Data Accuracy and Operational Efficiency in Digital Service Platforms
type: item
description: 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…
subject: Articles
  topic: section
subject: Data Accuracy
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: Digital Platforms
  topic: keywords
subject: Data Quality
  topic: keywords
subject: System Performance
  topic: keywords
creator: Tanjina Binte Sohrab
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-03-26
publication date: 2024-03-26
starting page: 303
ending page: 342
doi: 10.63125/mcdf7a94

Strengthening International Trade Finance Operations: Strategies for Preventing Trade-Based Money Laundering (TBML)
type: item
description: 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 charact…
subject: Articles
  topic: section
subject: Trade Finance
  topic: keywords
subject: TBML
  topic: keywords
subject: MIS invoicing
  topic: keywords
subject: Governance
  topic: keywords
subject: Risk Analysis
  topic: keywords
creator: Mostafa Kamal
rights: Copyright (c) 2026 Review of Applied Science and Technology
date: 2026-04-10
publication date: 2026-04-10
starting page: 158
ending page: 201
doi: 10.63125/m7r1s960

Mitigating Solar Curtailment in High-Penetration Interconnections: An AI-Driven Approach to Dynamic Load Balancing
type: item
description: 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 curt…
subject: Articles
  topic: section
subject: Solar Curtailment Mitigation
  topic: keywords
subject: AI-Driven Dynamic Load Balancing
  topic: keywords
subject: High-Penetration Interconnections
  topic: keywords
subject: Grid Flexibility
  topic: keywords
subject: AI Forecasting Accuracy
  topic: keywords
creator: Riadul Islam
rights: Copyright (c) 2026 Review of Applied Science and Technology
date: 2026-02-04
publication date: 2026-02-04
starting page: 119
ending page: 157
doi: 10.63125/ar5cbf55

Quantitative Assessment of Data-Driven Pricing Optimization Strategies for E-Commerce Platforms in Developing Economies
type: item
description: 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 si…
subject: Articles
  topic: section
subject: Data-driven pricing optimization
  topic: keywords
subject: E-commerce platform performance
  topic: keywords
subject: Real-time data integration
  topic: keywords
subject: Consumer price sensitivity
  topic: keywords
subject: Developing economies
  topic: keywords
creator: Rajib Sarkar
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-06-04
publication date: 2024-06-04
starting page: 01
ending page: 40
doi: 10.63125/g5va6e03

Quantitative Assessment of Data Privacy and Access Control Effectiveness in SAP/ERP Analytics Systems
type: item
description: 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 …
subject: Articles
  topic: section
subject: Data Privacy
  topic: keywords
subject: Access Control
  topic: keywords
subject: SAP ERP Security
  topic: keywords
subject: Enterprise Analytics
  topic: keywords
subject: Privacy Governance
  topic: keywords
creator: Rukaiya Khatun Moury
creator: Zakia Afroz
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-03-28
publication date: 2023-03-28
starting page: 259
ending page: 300
doi: 10.63125/vb03b363

Quantitative Performance Assessment of Distributed Machine Learning Frameworks for Real-Time Financial Analytics in Enterprise Data Platforms
type: item
description: 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 through…
subject: Articles
  topic: section
subject: Distributed Machine Learning
  topic: keywords
subject: Financial Analytics
  topic: keywords
subject: Enterprise Data Platforms
  topic: keywords
subject: Performance Benchmarking
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
creator: MD Fardous
creator: Md. Morshedul Islam
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-12-28
publication date: 2024-12-28
starting page: 198
ending page: 243
doi: 10.63125/ky027n68

Advanced Computational and Biotechnological Approaches to Systemic Family Therapy: Predicting Marital Satisfaction and Emotional Wellbeing in Couples
type: item
description: 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 high…
subject: Articles
  topic: section
subject: Systemic Family Therapy
  topic: keywords
subject: Marital Satisfaction
  topic: keywords
subject: Emotional Wellbeing
  topic: keywords
subject: Emotional Responsiveness
  topic: keywords
subject: Hierarchical Regression
  topic: keywords
creator: Amena Begum Sumi
creator: Mst Kaniz Fatema
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-26
publication date: 2023-12-26
starting page: 228
ending page: 265
doi: 10.63125/4sy9qa21

Data Preprocessing and Feature Engineering Strategies for Large-Scale Predictive Modeling Applications
type: item
description: 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…
subject: Articles
  topic: section
subject: Data preprocessing
  topic: keywords
subject: Feature engineering
  topic: keywords
subject: Predictive modeling
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Data analytics
  topic: keywords
creator: Mohammad Robel Miah
creator: Md. Morshedul Islam
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-03-30
publication date: 2024-03-30
starting page: 263
ending page: 302
doi: 10.63125/tqqqed47

Development of a Hybrid Machine Learning Model for Predictive Performance Optimization in Lean Manufacturing and Industry 4.0
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Cyber Risk Quantification
  topic: keywords
subject: Threat Scoring
  topic: keywords
subject: Financial Services
  topic: keywords
subject: Operational Risk
  topic: keywords
creator: Sayeda Sufia Sumi
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-09-24
publication date: 2025-09-24
starting page: 68
ending page: 108
doi: 10.63125/xc77bk30

A Systematic Review of Secure Health Data Information Systems for Pandemic Preparedness and Economic Continuity in the United States
type: item
description: 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 …
subject: Articles
  topic: section
subject: Secure health data systems
  topic: keywords
subject: Pandemic preparedness
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subject: Cybersecurity governance
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subject: Interoperability
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subject: Economic continuity
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creator: Md. Mehedi Hasan
creator: Khairum Nahar Pinky
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-03-27
publication date: 2023-03-27
starting page: 227
ending page: 258
doi: 10.63125/77h2m531

High-Performance Computing–Assisted Modeling and Real-Time Analysis of Electrical Power Networks and Industrial Control Systems
type: item
description: 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 go…
subject: Articles
  topic: section
subject: High-Performance Computing
  topic: keywords
subject: Power Networks
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subject: Industrial Control Systems
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subject: Real-Time Analytics
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subject: Cybersecurity
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creator: Md. Sultan Mahamud
creator: K M Tanvir Anjum Anick
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-03-26
publication date: 2023-03-26
starting page: 185
ending page: 226
doi: 10.63125/727j5j39