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: AUTOMATION AND RISK MITIGATION IN HEALTHCARE CLAIMS: POLICY AND COMPLIANCE IMPLICATIONS
items: FEDERATED LEARNING-DRIVEN PREDICTIVE QUALITY ANALYTICS AND SUPPLY CHAIN OPTIMIZATION IN DISTRIBUTED MANUFACTURING NETWORKS
items: QUANTITATIVE ASSESSMENT OF SMART CITY IOT INTEGRATION FOR REDUCING URBAN INFRASTRUCTURE VULNERABILITIES
items: HUMAN-CENTERED INTERFACES IN INDUSTRIAL CONTROL SYSTEMS: A REVIEW OF USABILITY AND VISUAL FEEDBACK MECHANISMS
items: INTEGRATION OF LEAN SIX SIGMA AND ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWIN TECHNOLOGIES FOR SMART MANUFACTURING SYSTEMS
items: INFORMATION SYSTEM-BASED DECISION SUPPORT TOOLS: A SYSTEMATIC REVIEW OF STRATEGIC APPLICATIONS IN SERVICE-ORIENTED ENTERPRISES
items: A META-ANALYSIS OF LEAN MERCHANDISING STRATEGIES IN FASHION RETAIL: GLOBAL INSIGHTS FROM THE POST-PANDEMIC ERA
items: AI-DRIVEN ANOMALY DETECTION FOR DATA LOSS PREVENTION AND SECURITY ASSURANCE IN ELECTRONIC HEALTH RECORDS
items: PREDICTIVE ANALYTICS IN SUPPLY CHAIN MANAGEMENT A REVIEW OF BUSINESS ANALYST-LED OPTIMIZATION TOOLS
items: MACHINE LEARNING-BASED PROCESS MINING FOR ANOMALY DETECTION AND QUALITY ASSURANCE IN HIGH-THROUGHPUT MANUFACTURING ENVIRONMENTS
items: SWARM INTELLIGENCE-BASED AUTONOMOUS LOGISTICS FRAMEWORK WITH EDGE AI FOR INDUSTRY 4.0 MANUFACTURING ECOSYSTEMS
items: THE ROLE OF CALIBRATION ENGINEERING IN STRENGTHENING RELIABILITY OF U.S. ADVANCED MANUFACTURING SYSTEMS THROUGH ARTIFICIAL INTELLIGENCE
items: PREDICTIVE NEURAL NETWORK MODELS FOR CYBERATTACK PATTERN RECOGNITION AND CRITICAL INFRASTRUCTURE VULNERABILITY ASSESSMENT
items: TRUSTWORTHY AI: EXPLAINABILITY & FAIRNESS IN LARGE-SCALE DECISION SYSTEMS
items: ALIGNING FEDRAMP AND NIST FRAMEWORKS IN CLOUD-BASED GOVERNANCE MODELS: CHALLENGES AND BEST PRACTICES
items: REAL-TIME CYBER-PHYSICAL DEPLOYMENT AND VALIDATION OF H-DEABSF: MODEL PREDICTIVE CONTROL, AND DIGITAL-TWIN–DRIVEN PROCESS CONTROL IN SMART FACTORIES
items: PRESENT AND FUTURE OF DIGITAL BANKING SERVICES IN BANGLADESH
items: REAL-TIME ADAPTIVE MACHINE LEARNING FOR OPERATIONAL OPTIMIZATION ACROSS GLOBAL TRANSPORTATION, ENERGY, AND INDUSTRIAL INFRASTRUCTURE
items: MODELING OF AN AI-INTEGRATED PREDICTIVE FRAMEWORK FOR COASTAL ECOSYSTEM CARBON SEQUESTRATION AND WATER QUALITY ASSESSMENT
items: DEVELOPMENT OF A PREDICTIVE SIMULATION MODEL FOR SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE ANALYSIS CONSIDERING ENVIRONMENTAL, TECHNICAL, AND ECONOMIC EFFICIENCY FACTORS
items: DATA-DRIVEN BUSINESS ANALYSIS: A COMPREHENSIVE ANALYSIS OF PREDICTIVE ANALYTICS IN PRICING STRATEGIES, MARKETING DECISIONS AND OPERATIONAL EFFICIENCY
items: ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWINS FOR ENERGY EFFICIENCY IN SMART GRIDS
items: HYBRID MACHINE LEARNING–DRIVEN FINANCIAL FORECASTING MODELS: INTEGRATING LSTM, PROPHET, AND XGBOOST FOR ENHANCED STOCK PRICE AND RISK PREDICTION
items: ARTIFICIAL INTELLIGENCE DRIVEN HEALTHCARE FINANCIAL MANAGEMENT TOWARD SUSTAINABLE DEVELOPMENT
items: ONLINE RETAIL: PURCHASING OCCASIONAL GIFTS THROUGH ONLINE CHANNELS IS A PREVALENT TREND IN THE UNITED KINGDOM
items: THE ROLE OF ARTIFICIAL INTELLIGENCE-POWERED CHATBOTS AS A MARKETING TOOL IN U.S. BANKING: ENHANCING CUSTOMER SATISFACTION AND SERVICE EFFICIENCY
items: EXAMINING THE EFFECT OF AI-POWERED PERSONALIZATION ON CUSTOMER LOYALTY: A META-ANALYSIS OF E-COMMERCE STUDIES
items: AI-DRIVEN PREDICTIVE MAINTENANCE FOR HIGH-VOLTAGE X-RAY CT TUBES: A MANUFACTURING PERSPECTIVE
items: COMPREHENSIVE INSIGHTS INTO CO₂ CAPTURE: TECHNOLOGICAL PROGRESS AND CHALLENGES
items: THE IMPACT OF HUMAN-MACHINE INTERACTION ON ENGLISH PRONUNCIATION AND FLUENCY: CASE STUDIES USING AI SPEECH ASSISTANTS

AUTOMATION AND RISK MITIGATION IN HEALTHCARE CLAIMS: POLICY AND COMPLIANCE IMPLICATIONS
type: item
description: This study addresses a persistent and consequential gap in the healthcare operations literature: although organizations have rapidly adopted digital and automated claims-processing technologies, empirical evidence directly linking specific automation configurations to measurable compliance and risk-mitigation outcomes remains limited. The purpose of this investigation was to quantify how automation maturity within contemporary cloud and enterprise claims environments relates to denials performance, audit exposure, and payment-integrity stability. Using a quantitative, cross-sectional, multi-case design, the study analysed 214 organizations spanning payer, provider, and third-party administrator settings, each representing heterogeneous cloud-native and enterprise claims platforms. Core variables included Automation Maturity, Compliance Posture, Policy Governance Quality, Data Protection Controls, and Risk Mitigation Outcomes, operationalized through standardized composites and objective metrics such as adjusted denial rate, first-pass resolution, audit exceptions, and payment-integrity flags. The analytic strategy incorporated reliability and dimensional validation, multivariable regression, and pre-specified mediation and moderation models, supported by robustness procedures including fractional logit and negative binomial estimators for bounded and count outcomes. Headline findings demonstrate that higher automation maturity was strongly associated with improved risk-mitigation performance (β = .31, p < .001), corresponding to a 2.4-percentage-point reduction in adjusted denial rate and a 2.6-percentage-point improvement in first-pass resolution between the lowest and highest maturity terciles. Compliance Posture operated as a significant partial mediator (a = .45; b = .22; indirect effect = .10), indicating that more mature automation environments tend to reinforce procedural and regulatory discipline. Policy Governance Quality further amplified the effectiveness…
subject: Articles
  topic: section
subject: Data Privacy
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Accounting Information Systems
  topic: keywords
subject: Financial Transparency
  topic: keywords
subject: Digital Governance
  topic: keywords
creator: Mohammad Mushfequr Rahaman
creator: Ashraful Islam
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-24
publication date: 2023-12-24
starting page: 124
ending page: 157
doi: 10.63125/v73gyg14

FEDERATED LEARNING-DRIVEN PREDICTIVE QUALITY ANALYTICS AND SUPPLY CHAIN OPTIMIZATION IN DISTRIBUTED MANUFACTURING NETWORKS
type: item
description: This study addresses a practical problem in distributed manufacturing networks: how to improve predictive quality and supply performance when raw data cannot be centralized due to privacy, sovereignty, and competitive constraints. The purpose is to evaluate whether federated learning–driven predictive quality analytics yields measurable gains in plant quality and supply outcomes. This study foregrounds federated learning–driven predictive quality analytics as a mechanism to fuse tacit, sensitive, or proprietary signals from geographically dispersed partners while reducing breach surfaces and preserving competitive boundaries. We employ a quantitative, cross-sectional, case-based design using a multi-firm survey and embedded vignettes. The sample comprises 204 usable responses from cloud or enterprise implementations across multi-site manufacturers with a 60.9 percent response rate. Key variables include FL adoption maturity, heterogeneity management, communication efficiency, data governance strength, network integration, PQA performance, and aligned outcome indices for quality and supply. The analysis plan specifies descriptives, bivariate correlations, and hierarchical regression with heteroskedasticity-robust errors, followed by moderation tests for governance and integration and mediation via bootstrapped indirect effects. Headline findings show that FL maturity, heterogeneity handling, and communication efficiency are positively associated with PQA (β=0.34, 0.21, 0.17; ΔR²=0.31). PQA in turn predicts better quality outcomes (β=0.49; ΔR²=0.24) and supply outcomes (β=0.44; ΔR²=0.22). Governance strengthens capability to PQA translation (interaction ΔR²≈0.03), and integration strengthens PQA to outcomes translation (interaction ΔR²≈0.04). Mediation tests confirm significant indirect effects from capabilities to outcomes through PQA. Case vignettes triangulate these effects, for example first-pass yield improved by 3.2 points, on-time delivery by 2.5 points, and ex…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Predictive Quality Analytics
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Network Integration
  topic: keywords
subject: Supply Chain Optimization
  topic: keywords
creator: Shaikh Shofiullah
creator: Aditya Dhanekula
rights: Copyright (c) 2021 Review of Applied Science and Technology
date: 2021-09-21
publication date: 2021-09-21
starting page: 74
ending page: 107
doi: 10.63125/k18cbz55

QUANTITATIVE ASSESSMENT OF SMART CITY IOT INTEGRATION FOR REDUCING URBAN INFRASTRUCTURE VULNERABILITIES
type: item
description: This study quantitatively examines the relationship between Internet of Things (IoT) integration and reductions in urban infrastructure vulnerabilities across global smart cities. Drawing on a multi-sector, multi-city panel dataset (2018–2024), the research evaluates how technological and governance dimensions of IoT maturity influence service reliability, outage duration, response lag, and failure rate reduction. IoT integration is conceptualized through five measurable dimensions—sensor coverage, data latency, interoperability, automation level, and data governance maturity—while vulnerability indicators capture operational stability and recovery capacity across energy, transportation, water, and emergency systems. Employing multilevel regression, difference-in-differences estimation, and structural equation modeling, the study finds that higher IoT integration significantly enhances infrastructure resilience, with interoperability and data governance maturity emerging as the strongest predictors of performance improvement. Automation and sensor coverage demonstrate complementary effects by reducing detection lag and restoration time, whereas high data latency negatively impacts operational efficiency. Mediation and moderation analyses reveal that response efficiency mediates the link between automation and reliability, while policy capacity and urban density moderate the effects of IoT maturity on vulnerability reduction. Developed cities display greater IoT integration and lower vulnerability levels, though developing cities achieve larger marginal gains per unit of technological advancement. Sectoral analysis confirms that energy and transportation infrastructures benefit most from IoT integration, while water and emergency sectors exhibit lower yet positive effects. The findings substantiate that IoT integration—supported by effective governance, data standardization, and automation—constitutes a statistically verifiable mechanism for enhancing urban resilienc…
subject: Articles
  topic: section
subject: Smart Cities
  topic: keywords
subject: IoT Integration
  topic: keywords
subject: Infrastructure Vulnerability
  topic: keywords
subject: Urban Resilience
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
creator: Md. Mominul Haque
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-12-21
publication date: 2024-12-21
starting page: 48
ending page: 93
doi: 10.63125/f2cj4507

HUMAN-CENTERED INTERFACES IN INDUSTRIAL CONTROL SYSTEMS: A REVIEW OF USABILITY AND VISUAL FEEDBACK MECHANISMS
type: item
description: This study investigates how human-centered interface attributes specifically usability and visual feedback quality affect operator cognition and performance within industrial control systems (ICS) such as supervisory control and data acquisition (SCADA), distributed control systems (DCS), and programmable logic controller (PLC) environments. Drawing on theoretical foundations from human–computer interaction (HCI), situation awareness (SA), and mental workload research, the study operationalizes usability as the degree of learnability, efficiency, error tolerance, and consistency, while visual feedback quality encompasses the clarity, salience, coding, trend fidelity, and timeliness of perceptual cues presented through human–machine interfaces (HMIs). A quantitative, cross-sectional, multi-site case-based design was employed across four industrial sectors, power, petrochemical, water/wastewater, and manufacturing, with 188 operators completing validated Likert-scale instruments, including the System Usability Scale (SUS), NASA-TLX workload indices, and SA/performance batteries. Reliability and validity analyses demonstrated strong internal consistency (α ≥ .86) and discriminant coherence (HTMT < .85). Regression and mediation models, with cluster-robust standard errors by site, revealed that both usability (β = .27, p < .001) and visual feedback quality (β = .21, p = .002) significantly predicted self-reported performance, accounting for 34% of its variance. Mediation analyses confirmed that improvements in usability and visual feedback enhanced situation awareness and reduced workload, which in turn elevated performance, with the SA pathway explaining the larger share of the indirect effects (≈36–39%) and workload accounting for smaller but meaningful contributions (≈11–13%). A significant interaction (β = .11, p = .011) indicated complementarity: each attribute produced greater performance benefits when the other was simultaneously strong, underscoring the synergis…
subject: Articles
  topic: section
subject: Industrial control systems
  topic: keywords
subject: Human-machine interface usability
  topic: keywords
subject: Visual feedback
  topic: keywords
subject: Situation awareness
  topic: keywords
subject: Mental workload
  topic: keywords
creator: Md Al Amin Khan
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-24
publication date: 2022-12-24
starting page: 66
ending page: 97
doi: 10.63125/gr54qy93

INTEGRATION OF LEAN SIX SIGMA AND ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWIN TECHNOLOGIES FOR SMART MANUFACTURING SYSTEMS
type: item
description: This quantitative study investigated the integration of Lean Six Sigma (LSS), artificial intelligence (AI), and digital twin (DT) technologies as a unified framework for achieving measurable performance improvement in smart manufacturing systems. The research aimed to evaluate the extent to which AI-enabled digital twins could enhance Lean Six Sigma’s analytical and process control capabilities and to determine the quantitative impact of this integration on operational efficiency, defect reduction, and production reliability. Data were collected from 150 participants across 20 manufacturing organizations that had implemented digital transformation initiatives involving LSS, AI, and DT frameworks. Using descriptive, correlational, and multiple regression analyses, the study examined how these independent variables jointly influenced key performance indicators, including mean time between failures (MTBF), overall equipment effectiveness (OEE), and defect rate. The results indicated that the integration model was statistically significant, with an adjusted R² of 0.719, confirming that approximately 72% of the variance in performance outcomes could be explained by the combined influence of LSS, AI, and DT. Correlation analysis revealed strong positive associations between AI integration and OEE (r = 0.816) and between DT utilization and MTBF (r = 0.802), while defect rate demonstrated significant negative correlations with all three predictors. Reliability testing produced Cronbach’s alpha values exceeding 0.85 for all constructs, confirming instrument consistency, while validity testing established clear construct alignment through factor analysis. Regression coefficients demonstrated that AI integration had the highest predictive strength (β = 0.447, p < 0.001), followed by digital twin synchronization (β = 0.389, p < 0.001) and Lean Six Sigma implementation (β = 0.312, p < 0.001). These findings provided empirical evidence that combining process improvement methodolo…
subject: Articles
  topic: section
subject: Lean Six Sigma
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
creator: Md Foysal Hossain
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-09-21
publication date: 2025-09-21
starting page: 01
ending page: 35
doi: 10.63125/1med8n85

INFORMATION SYSTEM-BASED DECISION SUPPORT TOOLS: A SYSTEMATIC REVIEW OF STRATEGIC APPLICATIONS IN SERVICE-ORIENTED ENTERPRISES
type: item
description: This quantitative systematic review examines the strategic applications of Information System-Based Decision Support Tools (IS-DSTs) in service-oriented enterprises, integrating empirical evidence from 102 studies across finance, healthcare, logistics, education, hospitality, and public administration. The research employed meta-analytical and regression-based techniques to evaluate the relationship between IS-DST adoption and key organizational performance indicators, including decision accuracy, operational efficiency, profitability, and customer satisfaction. Findings indicate that IS-DST implementation significantly enhances decision quality and organizational performance, with adjusted R² values ranging from .53 to .64 across models. Decision Intelligence emerged as a key mediating construct, translating technological capability into measurable performance outcomes through system usability, perceived usefulness, and user trust. Moderating factors such as IT maturity, governance quality, and leadership support were found to amplify these effects, underscoring the importance of infrastructural and institutional readiness. Cross-sectoral analyses revealed that industries characterized by high data intensity—particularly banking and healthcare—derive the strongest benefits, while regional comparisons showed that emerging economies experience greater relative performance gains from IS-DST adoption. Reliability, validity, and collinearity diagnostics confirmed the robustness of the measurement model, establishing the empirical integrity of the study. The results validate theoretical perspectives derived from the Resource-Based View, bounded rationality, and sociotechnical systems theory, demonstrating that decision support technologies function as both strategic assets and cognitive enablers. Overall, the study provides statistically grounded evidence that IS-DSTs are integral to achieving data-driven agility, managerial rationality, and sustainable competitive advan…
subject: Articles
  topic: section
subject: Decision support systems
  topic: keywords
subject: Information systems
  topic: keywords
subject: Service enterprises
  topic: keywords
subject: Strategic applications
  topic: keywords
subject: Organizational performance
  topic: keywords
creator: Rakibul Hasan
creator: Samia Akter
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-12-21
publication date: 2022-12-21
starting page: 26
ending page: 65
doi: 10.63125/w3cevz78

A META-ANALYSIS OF LEAN MERCHANDISING STRATEGIES IN FASHION RETAIL: GLOBAL INSIGHTS FROM THE POST-PANDEMIC ERA
type: item
description: This study addresses the problem of how fashion retailers can raise sell-through, inventory turnover, and value while reducing markdown exposure under short life cycles and post-pandemic volatility. The purpose is to quantify the association between lean merchandising adoption and performance, and to test whether operational agility transmits those effects under differing omnichannel maturity and supplier collaboration. Design: quantitative, cross-sectional, case-based. Sample: N = 272 manager responses from 18 international fashion retail enterprise cases. Key variables: Lean Merchandising Adoption, Operational Agility, Omnichannel Maturity, Supplier Collaboration, with KPIs sell-through, inventory turnover, GMROI, markdown percentage, stockout rate, and lead time, plus structural controls. Analysis plan: descriptive statistics and psychometrics, correlation mapping, hierarchical regressions with robust errors and cluster adjustments, mediation via bootstrapped indirect effects for agility, moderation via interaction terms with simple-slopes and Johnson Neyman intervals, and sensitivity checks. Headline findings: higher lean adoption aligns with higher sell-through, turnover, and GMROI, and with lower markdown percentage and lead time, with a small favourable reduction in stockout rate; part of the effect operates through agility; the payoff increases where unified inventory visibility and order orchestration are mature and where supplier collaboration enables flexible minimums and reliable in-season rebuys. Implications: retailers should treat lean as precise commitment and rapid read and react supported by truthful inventory visibility, cross-functional cadence, and vendor flexibility, rather than inventory minimalism; investments in order management, inventory accuracy, and collaboration amplify value and reduce clearance dependence. These results provide a diagnostics-checked, replicable blueprint for decision makers in global fashion portfolios today.
subject: Articles
  topic: section
subject: Lean Merchandising
  topic: keywords
subject: Fashion Retail
  topic: keywords
subject: Operational Agility
  topic: keywords
subject: Omnichannel Maturity
  topic: keywords
subject: Supplier Collaboration
  topic: keywords
subject: GMROI
  topic: keywords
creator: Md Mesbaul Hasan
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-16
publication date: 2023-12-16
starting page: 94
ending page: 123
doi: 10.63125/y8x4k683

AI-DRIVEN ANOMALY DETECTION FOR DATA LOSS PREVENTION AND SECURITY ASSURANCE IN ELECTRONIC HEALTH RECORDS
type: item
description: The study titled AI-Driven Anomaly Detection for Data Loss Prevention and Security Assurance in Electronic Health Records explored how artificial intelligence enhances the protection, monitoring, and assurance mechanisms within modern healthcare information systems. The research followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure transparency, replicability, and methodological rigor. A total of 96 peer-reviewed studies published between 2014 and 2024 were systematically analysed, encompassing empirical, experimental, and theoretical investigations drawn from major academic databases such as Scopus, IEEE Xplore, PubMed, and ScienceDirect. The analysis revealed that machine-learning and deep-learning algorithms—particularly autoencoders, recurrent neural networks, and ensemble hybrid models—significantly improved the precision and recall of anomaly detection in electronic health records (EHRs) when compared to traditional rule-based and signature-based systems. More than half of the reviewed studies reported detection accuracies exceeding 90%, confirming AI’s ability to identify subtle irregularities in user access, data transmission, and system behaviour. Furthermore, findings demonstrated that AI integration led to measurable improvements in data loss prevention (DLP), with reported reductions in unauthorized data transfers ranging between 40% and 65%. Quantitative linkages were also established between AI-driven detection accuracy and assurance outcomes, including higher compliance audit success rates, shortened incident dwell times, and increased containment efficiency. Contextual and behavioural analytics were identified as critical contributors to model performance, enabling systems to distinguish legitimate clinical variability from potential security threats. However, the review also identified methodological limitations, such as the absence of standardized benchmark datasets, limited real-world validati…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Data Loss Prevention
  topic: keywords
subject: Security Assurance
  topic: keywords
subject: Electronic Health Records
  topic: keywords
creator: Md. Tarek Hasan
creator: Ishtiaque Ahmed
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-09-22
publication date: 2025-09-22
starting page: 35
ending page: 67
doi: 10.63125/dzyr0648

PREDICTIVE ANALYTICS IN SUPPLY CHAIN MANAGEMENT A REVIEW OF BUSINESS ANALYST-LED OPTIMIZATION TOOLS
type: item
description: This quantitative study investigates the transformative role of predictive analytics in optimizing supply chain management (SCM) processes through business analyst-led decision frameworks. Predictive analytics, which integrates statistical modeling, machine learning, and data mining techniques, enables organizations to forecast demand, anticipate disruptions, and enhance operational efficiency across global logistics networks. The research examined 150 operational sites across manufacturing, retail, and logistics sectors using a quasi-experimental design to quantify the impact of predictive analytics adoption and business analyst mediation on performance indicators such as forecast accuracy, inventory turnover, fill rate, lead-time variability, and cost efficiency. Descriptive and inferential analyses revealed that predictive-adopting organizations achieved a 40% improvement in forecasting precision, a 47% reduction in lead-time variability, and over a 30% decrease in cost-to-serve compared with non-adopting firms. Regression and mediation analyses confirmed that predictive maturity significantly enhanced key performance indicators, while the Analyst Mediation Index (AMI) partially mediated the relationship between predictive analytics sophistication and operational outcomes, validating the critical interpretive role of analysts in translating algorithmic insights into strategic actions. The results demonstrated that predictive analytics maturity, when supported by digital connectivity and cloud-based integration, yields substantial gains in decision intelligence, service reliability, and financial performance. The study concludes that predictive analytics functions not only as a technological instrument but also as a managerial paradigm in which human analytical competence and data-driven foresight converge to produce measurable competitive advantage. Recommendations emphasize the institutionalization of analytical governance, development of business analyst compet…
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Supply Chain Optimization
  topic: keywords
subject: Business Analyst Mediation
  topic: keywords
subject: Forecasting Accuracy
  topic: keywords
subject: Data-Driven Decision Intelligence
  topic: keywords
creator: Md Redwanul Islam
creator: Md Nahid Hossain
creator: Md. Zahid Hasan Tusar
rights: Copyright (c) 2021 Review of Applied Science and Technology
date: 2021-09-20
publication date: 2021-09-20
starting page: 34
ending page: 73
doi: 10.63125/5aypx555

MACHINE LEARNING-BASED PROCESS MINING FOR ANOMALY DETECTION AND QUALITY ASSURANCE IN HIGH-THROUGHPUT MANUFACTURING ENVIRONMENTS
type: item
description: This study investigated the effectiveness of a machine learning–based process-mining framework for anomaly detection and quality assurance in high-throughput manufacturing environments. The research aimed to determine whether integrating process-mining metrics with data-driven machine learning techniques could outperform the traditional Statistical Process Control (SPC) system in predicting product non-conformance and operational inefficiencies. A quantitative research design was employed using a dataset of 142,368 production instances collected from Manufacturing Execution System (MES), Supervisory Control and Data Acquisition (SCADA), and Quality Management System (QMS) logs across three automated production lines. Variables included cycle-time variation, waiting time, machine utilization, rework frequency, conformance fitness score, and sensor-derived telemetry aggregates. A binary logistic regression model was developed to estimate the probability of product non-conformance. The model achieved a statistically significant fit (χ² = 482.76, p < 0.001) and explained 46.2% of the variance in defect occurrence (Nagelkerke R² = 0.462). Key predictors included Conformance Fitness Score (β = –0.72, p < 0.001) and Rework Frequency (β = 0.58, p < 0.01), indicating that lower process conformance and higher rework activity substantially increased the likelihood of defects. Comparative performance analysis showed that the machine learning–based model achieved higher predictive accuracy (91.3%) than the SPC baseline (84.9%), along with improved AUROC (0.93 vs. 0.82) and AUPRC (0.48 vs. 0.41) scores. These findings demonstrated that the proposed model provided superior anomaly detection capability, reduced false-alarm rates, and enhanced predictive precision. Overall, the integration of machine learning and process-mining analytics significantly improved operational reliability and quality assurance performance. The study concluded that adopting such intelligent process-monito…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Process Mining
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Quality Assurance
  topic: keywords
subject: High-Throughput Manufacturing
  topic: keywords
subject: Statistical Process Control (SPC)
  topic: keywords
creator: Sudipto Roy
creator: Md Mesbaul Hasan
rights: Copyright (c) 2021 Review of Applied Science and Technology
date: 2021-09-20
publication date: 2021-09-20
starting page: 01
ending page: 33
doi: 10.63125/t5dcb097

SWARM INTELLIGENCE-BASED AUTONOMOUS LOGISTICS FRAMEWORK WITH EDGE AI FOR INDUSTRY 4.0 MANUFACTURING ECOSYSTEMS
type: item
description: This study presents a quantitative investigation into a Swarm Intelligence-Based Autonomous Logistics Framework integrated with Edge Artificial Intelligence (Edge AI) for optimizing performance in Industry 4.0 manufacturing ecosystems. The research aims to empirically evaluate how decentralized swarm coordination combined with edge-level inference enhances logistics efficiency compared to conventional centralized and cloud-based control architectures. Using a multi-site experimental design and statistical modeling, the study examined relationships among swarm coordination metrics (agent density, communication frequency) and edge-computing parameters (node density, inference delay) on key logistics indicators such as throughput, latency, cycle time, energy consumption, and fault tolerance. The data were analyzed using correlation, regression, and structural equation modeling (SEM), yielding significant results: swarm density (β = 0.41, p < .001) and communication frequency (β = 0.36, p < .01) were strong positive predictors of throughput, while edge-inference delay exhibited a negative effect (β = –0.32, p < .01). The overall model demonstrated robust explanatory power (R² = 0.78) and good structural fit (χ²/df = 2.23, CFI = 0.96, RMSEA = 0.045). Comparative analysis revealed that the hybrid swarm-edge system achieved a 45% latency reduction, 22% increase in throughput, and 19% improvement in energy efficiency relative to traditional architectures. These findings validate the hypothesis that distributed intelligence enhances operational responsiveness and sustainability in cyber-physical manufacturing environments. The study contributes a statistically verified model for real-time logistics optimization, aligning with previous works by Hamann (2018), Lu et al. (2023), and Iftikhar et al. (2022), and establishes a foundational quantitative framework for future research on autonomous, data-driven logistics systems under Industry 4.0.
subject: Articles
  topic: section
subject: Swarm Intelligence
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Autonomous Logistics
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
creator: S. M. Habibullah
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-09-21
publication date: 2025-09-21
starting page: 01
ending page: 34
doi: 10.63125/p1q8yf46

THE ROLE OF CALIBRATION ENGINEERING IN STRENGTHENING RELIABILITY OF U.S. ADVANCED MANUFACTURING SYSTEMS THROUGH ARTIFICIAL INTELLIGENCE
type: item
description: This research addresses a persistent problem in advanced manufacturing: AI initiatives promise higher availability, yield, and effective capacity, yet their realized impact is constrained when the underlying measurement systems are weakly calibrated and uncertainty is not governed. The purpose is to quantify how AI-enabled calibration engineering relates to plant-level reliability and to specify the organizational and data conditions under which benefits materialize. We adopt a quantitative cross-sectional, case-based design spanning eight U.S. enterprise manufacturing cases and associated cloud and on-premise operational data sources, combining a structured survey of operations stakeholders with de-identified archival KPIs. The sample includes 402 respondents nested within sites and linked to CMMS, production counters, calibration certificates, GR&R summaries, and historian records. Key variables include an AI-Enabled Calibration Practices index capturing predictive interval setting, automated drift detection, AI-assisted GR&R, digital-twin utilization, and alerting workflows; moderators for data quality, operator training, and equipment age; and reliability outcomes constructed from MTBF, MTTR, availability, OEE, FPY, and DPPM. The analysis plan specifies descriptive profiling, correlation matrices, and multiple linear regressions with robust errors and site fixed effects, plus moderation tests and sensitivity checks. Headline findings show a positive association between AI-enabled calibration practices and reliability that strengthens when data quality and training are higher and attenuates as fleets age. Implications for managers are to institutionalize calibration metadata and uncertainty budgets as machine-readable context, enforce ingestion gates for decision-grade data, and stage capability building that pairs metrology governance with targeted training. A targeted literature review of 47 peer-reviewed studies substantiates the constructs and methods employe…
subject: Articles
  topic: section
subject: Calibration Engineering
  topic: keywords
subject: Measurement Uncertainty
  topic: keywords
subject: AI In Manufacturing
  topic: keywords
subject: Reliability
  topic: keywords
subject: OEE
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
creator: Efat Ara Haque
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-10-25
publication date: 2025-10-25
starting page: 820
ending page: 851
doi: 10.63125/0y0m8x22

PREDICTIVE NEURAL NETWORK MODELS FOR CYBERATTACK PATTERN RECOGNITION AND CRITICAL INFRASTRUCTURE VULNERABILITY ASSESSMENT
type: item
description: This study investigated the effectiveness of predictive neural network models in enhancing cyberattack detection and vulnerability assessment within critical infrastructure systems, addressing the limitations of traditional machine learning approaches in accuracy, adaptability, and operational performance. Drawing on a comprehensive review of 176 peer-reviewed studies published between 2015 and 2025, the research synthesized current advancements in machine learning, deep learning, and vulnerability analysis to develop and evaluate an integrated predictive framework. The empirical analysis was conducted on a large-scale, real-world dataset consisting of over 30 million network flow records, 12 million authentication and identity events, and more than 10,000 documented vulnerabilities from the energy, healthcare, and transportation sectors. The study employed convolutional neural networks (CNNs), gated recurrent units (GRUs), and hybrid CNN–GRU models, benchmarking them against logistic regression and random forest classifiers to measure improvements in detection accuracy, false positive reduction, vulnerability prioritization, and real-time performance. Findings revealed that neural network models consistently outperformed classical baselines, achieving AUC scores between 0.91 and 0.95 (compared to 0.84–0.87), reducing false positive rates by up to 38%, and improving precision by 12–17 percentage points at a recall of 0.90. Additionally, vulnerability prioritization accuracy improved substantially, with a 22–26% increase in top-100 exploited vulnerability hit rates and correlation coefficients above 0.86 with real-world exploitation events. Latency and throughput metrics demonstrated that CNN detectors processed samples in under 2 milliseconds, while hybrid models achieved event processing in less than 20 milliseconds, confirming their suitability for operational deployment. The study concludes that predictive neural network models offer a significant advancement in …
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Vulnerability Assessment
  topic: keywords
subject: Critical Infrastructure
  topic: keywords
subject: Cyberattack Detection
  topic: keywords
creator: Mst. Shahrin Sultana
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-10-20
publication date: 2025-10-20
starting page: 777
ending page: 819
doi: 10.63125/qp0de852

TRUSTWORTHY AI: EXPLAINABILITY & FAIRNESS IN LARGE-SCALE DECISION SYSTEMS
type: item
description: This study examined the critical roles of explain ability and fairness in advancing trustworthy artificial intelligence (AI) within large-scale decision systems. As AI technologies increasingly shape consequential decisions in domains such as healthcare, finance, employment, and judicial processes, ensuring transparency, equity, and legitimacy has become paramount. Drawing on a comprehensive review of 152 peer-reviewed studies, this research synthesized conceptual foundations, methodological advancements, and empirical findings to build a robust framework for understanding how explain ability and fairness jointly contribute to trustworthiness. A quantitative research design was employed, incorporating large-scale datasets and multi-phase statistical analyses to evaluate how explanation fidelity, stability, and sparsity influence comprehension, trust, and perceived fairness, and how fairness interventions impact model performance and equity outcomes. Results demonstrated that explanation fidelity significantly enhanced user comprehension, while stability strongly predicted trust, highlighting the importance of consistent and faithful explanations in shaping user confidence. Fairness metrics such as demographic parity and equal opportunity gaps were powerful predictors of perceived fairness, and reductions in these disparities substantially increased user acceptance of AI decisions. Interaction analyses revealed that combining counterfactual explanations with fairness constraints produced synergistic effects, improving both equity and trust without excessively compromising predictive performance. The study also quantified trade-offs, showing that fairness interventions slightly reduced accuracy but delivered substantial gains in legitimacy and social acceptability. Human-cantered outcomes such as trust and reliance were closely linked to technical measures, illustrating that the social impact of AI is deeply intertwined with its design. By integrating findings across …
subject: Articles
  topic: section
subject: Trustworthy Artificial Intelligence
  topic: keywords
subject: Explain ability
  topic: keywords
subject: Fairness
  topic: keywords
subject: Decision Systems
  topic: keywords
subject: Human-Cantered Outcomes
  topic: keywords
creator: Sai Srinivas Matta
creator: Manish Bolli
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-12-15
publication date: 2023-12-15
starting page: 54
ending page: 93
doi: 10.63125/3w9v5e52

ALIGNING FEDRAMP AND NIST FRAMEWORKS IN CLOUD-BASED GOVERNANCE MODELS: CHALLENGES AND BEST PRACTICES
type: item
description: This quantitative study investigates how aligning the Federal Risk and Authorization Management Program (FedRAMP) with the National Institute of Standards and Technology (NIST) frameworks influences governance effectiveness, security performance, and compliance outcomes in cloud-based environments. Drawing on an extensive review of 132 peer-reviewed studies, industry assessments, audit reports, and governance frameworks, the research examines the measurable impact of key alignment practices, including standardized infrastructure blueprints, policy-as-code enforcement, continuous monitoring discipline, evidence reuse maturity, shared responsibility matrix (SRM) clarity, privacy-by-design integration, and Zero Trust adoption. Data collected from 327 authorization boundaries across 26 organizations over a 24-month period were analyzed to quantify relationships between these governance practices and critical operational metrics such as authorization lead time, audit finding density, control coverage, remediation velocity, configuration drift, privacy incident rates, and privileged access events. The findings demonstrate that governance-by-design approaches significantly reduce authorization time and improve compliance consistency, while blocking policy-as-code enforcement lowers configuration drift and strengthens control reliability, especially in complex architectures. Mature continuous monitoring programs enhance remediation speed and reduce audit findings, and harmonized evidence reuse substantially decreases documentation workload and compliance overhead. Moreover, clear and regularly reviewed SRMs reduce gap incidence, privacy engineering maturity improves data protection and regulatory adherence, and Zero Trust implementations markedly lower privileged access incidents. Collectively, these results reveal that aligning FedRAMP and NIST frameworks is not merely a compliance exercise but a strategic governance approach that transforms cloud security, compliance, and…
subject: Articles
  topic: section
subject: FedRAMP
  topic: keywords
subject: NIST
  topic: keywords
subject: Cloud Governance
  topic: keywords
subject: Compliance
  topic: keywords
subject: Zero Trust
  topic: keywords
creator: Md Omar Faruq
creator: Md. Jobayer Ibne Saidur
rights: Copyright (c) 2022 Review of Applied Science and Technology
date: 2022-03-15
publication date: 2022-03-15
starting page: 01
ending page: 37
doi: 10.63125/vnkcwq87

REAL-TIME CYBER-PHYSICAL DEPLOYMENT AND VALIDATION OF H-DEABSF: MODEL PREDICTIVE CONTROL, AND DIGITAL-TWIN–DRIVEN PROCESS CONTROL IN SMART FACTORIES
type: item
description: The evolution of intelligent manufacturing has necessitated the development of integrated simulation and control frameworks capable of synchronizing physical operations with digital decision-making environments. This study introduces and empirically validates a Hybrid Discrete-Event and Agent-Based Simulation Framework (H-DEABSF) augmented by Model Predictive Control (MPC) and Digital Twin (DT) technologies for real-time cyber-physical process control in smart factory environments. The research addresses the limitations of single-paradigm simulation models by establishing a hybrid architecture that unifies the event-driven precision of Discrete-Event Simulation (DES) with the autonomous, adaptive decision-making capabilities of Agent-Based Simulation (ABS). Through the incorporation of MPC and real-time DT synchronization, the framework achieves continuous bidirectional communication between physical equipment and virtual models, enabling predictive decision support and dynamic reconfiguration under stochastic production conditions. A quantitative experimental design was employed using a cyber-physical testbed comprising interconnected programmable logic controllers, IIoT-enabled sensors, and a virtual simulation layer that replicates factory operations. Empirical data were collected across twelve operational trials under varying workload intensities and analyzed using descriptive, inferential, and multivariate statistical methods including ANOVA, regression, MANOVA, and correlation modeling. The hybrid configuration achieved a 22.8% increase in throughput efficiency, a 39% reduction in response latency, and a 96.2% predictive accuracy rate, outperforming traditional DES-only and ABS-only control architectures. Furthermore, fault recovery time decreased by 53%, while overall machine utilization improved by 11.7%, coupled with a 15.8% reduction in energy consumption, demonstrating the hybrid system’s efficiency and sustainability advantages. The integration of predic…
subject: Articles
  topic: section
subject: Hybrid Simulation
  topic: keywords
subject: Model Predictive Control (MPC)
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Cyber-Physical Systems (CPS)
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
creator: Sai Praveen Kudapa
creator: Md Kamruzzaman
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-10-09
publication date: 2025-10-09
starting page: 750
ending page: 776
doi: 10.63125/yrkm0057

PRESENT AND FUTURE OF DIGITAL BANKING SERVICES IN BANGLADESH
type: item
description: The rapid digital transformation of Bangladesh’s financial sector has positioned Mobile Financial Services (MFSs) as a cornerstone of financial inclusion and socio-economic empowerment. This study explores the evolution, barriers, and impacts of MFS adoption across households and small to medium enterprises (SMEs) in Bangladesh, employing a qualitative research design to capture the multifaceted experiences and insights of key industry professionals. The interpretivist framework guided the study, emphasizing the subjective understanding of participants actively engaged in the MFS ecosystem. Data were collected through semi-structured, in-depth interviews with five senior experts representing leading financial institutions, regulatory authorities, and fintech organizations. The selection of participants followed a purposive sampling approach, ensuring that each respondent possessed over a decade of experience in policy implementation, digital finance operations, or regulatory oversight. The research findings were derived through Braun and Clarke’s (2006) six-phase thematic analysis, which enabled the systematic identification of recurring themes such as technological accessibility, digital literacy, regulatory complexity, trust, and socio-economic empowerment. The analysis revealed that while Bangladesh has achieved substantial progress in digital financial inclusion—accounting for nearly 12% of the global mobile money accounts (The Daily Star, 2024) challenges such as high transaction fees, limited rural connectivity, and cybersecurity concerns persist. MFS platforms like bKash, Rocket, and Nagad have redefined household and business financial practices by enabling faster remittances, transparent transactions, and enhanced liquidity management. At the same time, gender disparities and digital literacy gaps continue to hinder the equitable distribution of benefits, highlighting the need for targeted financial education and regulatory reform. This study contributes to…
subject: Articles
  topic: section
subject: Mobile Financial Services (MFS)
  topic: keywords
subject: Digital Banking
  topic: keywords
subject: Financial Inclusion
  topic: keywords
subject: Socio-Economic Empowerment
  topic: keywords
subject: Bangladesh
  topic: keywords
creator: Md Hasinur Rahman
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-10-07
publication date: 2025-10-07
starting page: 725
ending page: 749
doi: 10.63125/k7tgr183

REAL-TIME ADAPTIVE MACHINE LEARNING FOR OPERATIONAL OPTIMIZATION ACROSS GLOBAL TRANSPORTATION, ENERGY, AND INDUSTRIAL INFRASTRUCTURE
type: item
description: This study investigates the role of real-time adaptive machine learning (AML) in optimizing operations across global transportation, energy, grid, and industrial infrastructures. The research adopts a quantitative, cross-sectional design, testing the central hypothesis that AML implementation significantly improves sectoral performance outcomes compared to traditional rule-based or static optimization methods. Four specific hypotheses were formulated: H1, AML improves transportation efficiency by reducing congestion and enhancing throughput; H2, AML increases energy forecast accuracy by reducing prediction errors such as mean absolute percentage error (MAPE); H3, AML strengthens grid stability by improving frequency and voltage regulation; and H4, AML enhances industrial reliability through predictive maintenance and downtime reduction. Data were drawn from secondary sources, including case studies, empirical reports, and international deployments, and analyzed through descriptive statistics, correlation testing, collinearity diagnostics, and multiple regression models. The findings provided consistent and statistically significant support for all four hypotheses. For transportation systems (H1), AML demonstrated a strong positive effect (β = .62, R² = .39, p < .01), confirming earlier evidence from adaptive traffic control deployments that machine learning-driven systems outperform fixed-time scheduling. For energy systems (H2), AML significantly reduced forecasting errors (β = .55, R² = .30, p < .01), aligning with prior literature on the superiority of ML-based models over conventional statistical methods. In terms of grid stability (H3), AML improved voltage and frequency regulation (β = .58, R² = .34, p < .01), reinforcing the argument that adaptive forecasting and real-time control are essential for resilient energy systems. Industrial systems (H4) exhibited the strongest association, with AML contributing to predictive maintenance accuracy and downtime reduct…
subject: Articles
  topic: section
subject: Adaptive Machine Learning
  topic: keywords
subject: Real-Time Optimization
  topic: keywords
subject: Transportation Systems
  topic: keywords
subject: Energy Infrastructure
  topic: keywords
subject: Industrial Operations
  topic: keywords
creator: Md Atiqur Rahman Khan
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-09-20
publication date: 2025-09-20
starting page: 697
ending page: 726
doi: 10.63125/7a4h2916

MODELING OF AN AI-INTEGRATED PREDICTIVE FRAMEWORK FOR COASTAL ECOSYSTEM CARBON SEQUESTRATION AND WATER QUALITY ASSESSMENT
type: item
description: Coastal ecosystems such as mangroves, salt marshes, and seagrass meadows are critical to global climate regulation and ecological sustainability. They serve as highly efficient long-term carbon sinks, regulate water quality, and provide essential ecosystem services including shoreline stabilization and biodiversity support. Yet, these systems face unprecedented pressures from anthropogenic disturbance, land-use change, and climate-driven stressors such as sea-level rise and ocean acidification. Conventional monitoring and modeling frameworks, while valuable for mechanistic understanding, often lack the capacity to fully capture the nonlinear interactions, spatial heterogeneity, and temporal variability that define coastal ecosystems. To address this gap, this study develops and evaluates an AI-integrated predictive framework designed to enhance the assessment of carbon sequestration capacity and water quality dynamics in coastal environments. The framework leverages advanced machine learning and deep learning models, remote sensing technologies, and in situ ecological indicators to deliver high-resolution spatiotemporal predictions that link carbon flux, nutrient cycling, and pollutant dispersion to ecosystem performance. Findings from the meta-analysis and empirical validation reveal robust evidence that coastal vegetated ecosystems sequester significantly higher quantities of carbon compared to degraded or non-vegetated controls. Mangroves exhibited the largest effect sizes, with soil carbon densities frequently exceeding 1,000 Mg C ha⁻¹, while salt marshes demonstrated strong sediment-trapping efficiency and seagrass meadows provided moderate but significant contributions, heavily influenced by water clarity and hydrodynamics. For water quality, pooled results confirmed consistent associations between nutrient enrichment, elevated chlorophyll-a, and declining dissolved oxygen levels, with hypoxia severity most pronounced in stratified estuaries. Comparative asses…
subject: Articles
  topic: section
subject: Coastal Ecosystems
  topic: keywords
subject: Carbon Sequestration
  topic: keywords
subject: Water Quality Assessment
  topic: keywords
subject: Artificial Intelligence (AI) Framework
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Md Rashedul Islam
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-07-15
publication date: 2025-07-15
starting page: 669
ending page: 696
doi: 10.63125/z6h8ky18

DEVELOPMENT OF A PREDICTIVE SIMULATION MODEL FOR SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE ANALYSIS CONSIDERING ENVIRONMENTAL, TECHNICAL, AND ECONOMIC EFFICIENCY FACTORS
type: item
description: This study presents the development of a comprehensive predictive simulation model for solar photovoltaic (PV) system performance analysis by integrating environmental, technical, and economic efficiency factors into a unified framework. Recognizing that existing models often assess these domains in isolation, this research aimed to construct a holistic and modular approach capable of capturing the full causal chain from climatic variability through technical energy conversion to long-term financial viability. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure methodological rigor, transparency, and reproducibility. In total, 524 peer-reviewed articles published across the past two decades were examined, encompassing 142 environmental modeling studies, 167 technical performance studies, 123 techno-economic studies, 64 integration-focused studies, and 58 uncertainty and sensitivity analysis studies, representing a combined citation volume exceeding 50,000 scholarly references.The synthesis revealed that accurate environmental resource modeling—particularly solar irradiance transposition, thermal behavior estimation, and stochastic soiling dynamics—forms the foundational determinant of yield prediction accuracy. Detailed technical modeling of modules, inverters, balance-of-system losses, and geometric layout optimization emerged as critical for converting environmental inputs into realistic DC and AC power outputs. Economic modeling findings emphasized that metrics such as levelized cost of electricity, net present value, and internal rate of return are highly sensitive to performance deviations, underscoring the need to embed financial modules directly within performance simulations. The study also found that fully integrated models, which simultaneously link environmental, technical, and economic layers while embedding end-to-end uncertainty propagation and sensitivity asse…
subject: Articles
  topic: section
subject: Solar Photovoltaic Systems
  topic: keywords
subject: Predictive Simulation Modeling
  topic: keywords
subject: Environmental and Climatic Factors
  topic: keywords
subject: Technical Performance Analysis
  topic: keywords
subject: Techno-Economic Efficiency
  topic: keywords
creator: Md Akteruzzaman
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-07-15
publication date: 2025-07-15
starting page: 639
ending page: 668
doi: 10.63125/8ftjw526

DATA-DRIVEN BUSINESS ANALYSIS: A COMPREHENSIVE ANALYSIS OF PREDICTIVE ANALYTICS IN PRICING STRATEGIES, MARKETING DECISIONS AND OPERATIONAL EFFICIENCY
type: item
description: In today’s dynamic and competitive business environment, organizations face increasing pressure to make marketing decisions that are both timely and effective. Traditional intuition-based approaches are no longer sufficient to understand complex consumer behavior and market trends. This research paper explores how business analytics, through predictive and descriptive statistical models, can optimize marketing decisions. Using a real-world-inspired case study of 1,000 taco delivery orders across multiple U.S. cities, the study applies descriptive statistics, correlation analysis, and regression modeling to uncover insights into customer behavior, pricing strategies, and operational efficiency. Findings reveal that toppings and taco size significantly influence pricing, while weekend orders lead to higher customer tips. Delivery inefficiencies were also identified as critical areas for optimization. Beyond the food delivery industry, the implications of this study extend to marketing professionals and managers across sectors such as retail, corporate services, healthcare, energy, and finance. For example, retailers can leverage analytics to refine product assortments and promotional campaigns, healthcare organizations can optimize patient engagement strategies, energy firms can forecast demand more accurately, and financial institutions can enhance customer segmentation and risk assessment. Business analytics tools such as Tableau, Power BI, R, Python, SAS, and advanced Excel modeling can further support these applications, enabling professionals to translate raw data into actionable insights. Future research could expand the scope of this study by incorporating larger and more diverse datasets, integrating real-time data streams, or applying advanced techniques such as machine learning, sentiment analysis, or predictive demand modeling. This would provide deeper insights into consumer behavior and allow for more adaptive decision-making. Ultimately, the findings hig…
subject: Articles
  topic: section
subject: Business Analytics
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Prescriptive Analytics
  topic: keywords
subject: Marketing Strategy
  topic: keywords
subject: Customer Segmentation
  topic: keywords
creator: HM Imran
creator: Md Mujahidul Islam
creator: Sudman Sharar Shaharum
creator: Md. Rasel Ahmed
creator: Anika Hossain Orthy
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-09-14
publication date: 2025-09-14
starting page: 616
ending page: 638
doi: 10.63125/d2d96554

ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWINS FOR ENERGY EFFICIENCY IN SMART GRIDS
type: item
description: This PRISMA-guided systematic review synthesizes and critically evaluates how artificial intelligence (AI)-enabled digital twins (DTs) contribute to advancing energy efficiency in modern smart grids, spanning assets, feeders, microgrids, and system-level operations. A comprehensive database search and two-stage screening process identified 103 peer-reviewed studies that were assessed for context, twin architecture, AI methods, data pipelines, evaluation metrics, deployment maturity, and risk of bias. Evidence was systematically organized by the functional roles of DTs—monitoring, forecasting, optimization, and control—as well as by grid layers, revealing the growing integration of physics-informed surrogates, graph neural estimators, and reinforcement learning frameworks with data fabrics, semantic standards, and edge–cloud architectures. Quantitative synthesis demonstrates consistent and reproducible efficiency improvements when DTs mediate AI decisions against calibrated models: median feeder-level technical loss reductions of approximately five percent, median peak-demand reductions of nearly six percent, voltage compliance improvements of about twelve and a half percentage points, and renewable curtailment avoidance in the range of seven to nine percent relative to transparent baselines. These benefits are most concentrated when loop latencies are sub-second, particularly under control cycles closing within 300 milliseconds, and when DT deployments embed semantic interoperability, co-simulation, and uncertainty-aware decision-making with human-in-the-loop oversight. At the asset level, health-oriented DTs for transformers, breakers, cables, and wind turbines deliver measurable value through predictive maintenance that reduces inefficiencies and mitigates outages, with efficiency gains fully realized only when diagnostic outputs are integrated into scheduling, reconfiguration, and Volt/VAR optimization routines. Collectively, these findings advance the discourse …
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Smart Grid
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Volt VAR Optimization
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
creator: Manam Ahmed
creator: Md Rabbi Khan
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-08-05
publication date: 2025-08-05
starting page: 580
ending page: 615
doi: 10.63125/12kp9w74

HYBRID MACHINE LEARNING–DRIVEN FINANCIAL FORECASTING MODELS: INTEGRATING LSTM, PROPHET, AND XGBOOST FOR ENHANCED STOCK PRICE AND RISK PREDICTION
type: item
description: This review synthesizes research on hybrid machine learning for equity price and risk forecasting, focusing on combinations of LSTM, Prophet style additive models, and XGBoost. We used a PRISMA guided protocol covering 2015 to 2025 across Scopus, Web of Science, IEEE Xplore, ACM Digital Library, SSRN, and arXiv, with eligibility requiring equity focus, out of sample evaluation that respects time order, and explicit hybridization or risk components. After screening and full text assessment with reasons coded exclusions, the final qualitative synthesis comprised 110 studies. Across this evidence, deliberately engineered hybrids consistently outperform single learners on point accuracy, directional reliability, and risk calibration. Normalized comparisons in one day ahead settings show typical reductions in RMSE near 9 percent and gains in directional accuracy around five to six percentage points, with tighter Value at Risk coverage under quantile aware training. Benefits persist under strict rolling origin validation with nested tuning, and widen during turbulent regimes where dynamic weighting and residual correction add stability. The literature also emphasizes explainability and governance, recommending component plots for structural layers, Shapley value attributions for tree ensembles, and ablations that quantify each module's marginal value. Drawing these threads together, we outline a blueprint that decomposes trend and seasonality with a structural layer, models nonlinear temporal dynamics with an LSTM, learns interaction rich signals with XGBoost, and combines outputs using out of fold stacking and calibrated risk heads. This evidence-based specification offers decision grade forecasts and tail risk estimates for equity markets for deployment.
subject: Articles
  topic: section
subject: Hybrid forecasting
  topic: keywords
subject: LSTM
  topic: keywords
subject: Prophet
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Equity markets
  topic: keywords
subject: Stacking
  topic: keywords
subject: Residual correction
  topic: keywords
subject: Dynamic weighting
  topic: keywords
subject: Walk forward validation
  topic: keywords
subject: Value at Risk
  topic: keywords
subject: PRISMA
  topic: keywords
creator: Md Shah Ali Dolon
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-05-25
publication date: 2025-05-25
starting page: 01
ending page: 34
doi: 10.63125/nr1j8527

ARTIFICIAL INTELLIGENCE DRIVEN HEALTHCARE FINANCIAL MANAGEMENT TOWARD SUSTAINABLE DEVELOPMENT
type: item
description: Artificial Intelligence (AI) has emerged as a transformative force in reshaping both the operational and strategic dimensions of healthcare financial management (HFM). As healthcare systems worldwide face persistent challenges related to cost containment, efficient resource allocation, and sustainable service delivery, AI-driven tools present novel approaches to enhance decision-making, streamline administrative functions, and optimize financial performance. By leveraging machine learning, natural language processing, robotic process automation, and predictive analytics, AI integrates diverse clinical, financial, and operational datasets to provide actionable insights that extend beyond the capabilities of conventional financial frameworks. This study explores the integration of AI into HFM with a particular emphasis on sustainable development, where financial efficiency must align with equitable healthcare access, social accountability, and the long-term resilience of health systems. Empirical findings indicate that AI improves operational efficiency by reducing billing errors, lowering denial rates, and accelerating reimbursement processes, while simultaneously contributing to global sustainability goals by minimizing systemic inefficiencies, lowering administrative overhead, and promoting fair allocation of financial and medical resources. Furthermore, AI applications strengthen risk pooling, budgetary forecasting, and expenditure control, thereby enhancing financial protection for both institutions and patients. Issues of algorithmic fairness, data privacy, and explain ability are particularly relevant in ensuring that AI-supported financial decision-making maintains trust across stakeholders and avoids perpetuating inequities. The convergence of AI and HFM provides a structured pathway for healthcare organizations to achieve fiscal responsibility while advancing the sustainability agenda, ensuring that economic growth, social well-being, and environmental stewa…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Healthcare Financial Management (HFM)
  topic: keywords
subject: Sustainable Development
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Cost Optimization
  topic: keywords
creator: Janifer Nahar
creator: Md Ashrafuzzaman
creator: Md Majadul Islam Jim
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-08-15
publication date: 2025-08-15
starting page: 557
ending page: 579
doi: 10.63125/abzg7892

ONLINE RETAIL: PURCHASING OCCASIONAL GIFTS THROUGH ONLINE CHANNELS IS A PREVALENT TREND IN THE UNITED KINGDOM
type: item
description: This study develops and evaluates a predictive model for online gift purchasing behavior in the United Kingdom, utilizing transaction-level data collected from a UK-based online retailer over the period of December 2010 to December 2011. Drawing upon multiple linear regression, the research investigates the extent to which variables such as product type, purchase timing, and unit price influence the quantity of items purchased. The resulting model achieved a highly robust R² value of 0.98599, underscoring its accuracy in capturing consumer behavior within an e-commerce setting. Notably, the model successfully reflects seasonal fluctuations in consumer demand, with particularly pronounced surges during key holiday periods such as Christmas. These temporal spikes emphasize the strong role of cultural and economic factors in shaping online purchasing trends. The findings further reveal distinctive shifts in product popularity across the year, with specific items, such as the White Hanging Heart T-Light Holder, emerging as dominant choices during peak periods. Such patterns demonstrate not only the cyclical nature of gift-oriented consumption but also the importance of symbolic and aesthetic product attributes in influencing purchase decisions. By highlighting both temporal rhythms and product-specific dynamics, the research provides valuable insights into how consumer preferences evolve across different seasonal contexts. In addition to offering empirical evidence of behavioral patterns, this study integrates broader economic and sociocultural perspectives, thereby bridging theoretical frameworks from consumer behavior research with practical retail analytics. The model’s predictive strength enhances its utility for retailers, who may employ these insights to refine inventory management, forecast demand, and design targeted marketing strategies aligned with consumer motivations. Overall, the study contributes to the growing body of literature on predictive modeling in …
subject: Articles
  topic: section
subject: E-Commerce
  topic: keywords
subject: Online Retail
  topic: keywords
subject: Consumer Purchasing Behaviours
  topic: keywords
subject: Seasonal Sales Trends Analysis
  topic: keywords
subject: Multiple Linear Regression
  topic: keywords
subject: Predictive Modelling
  topic: keywords
creator: MD Asadul Hoque
creator: Mohammad Anowarul Kabir
creator: MD. Salsabil Tahseen
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-08-20
publication date: 2025-08-20
starting page: 527
ending page: 556
doi: 10.63125/ks7x2r28

THE ROLE OF ARTIFICIAL INTELLIGENCE-POWERED CHATBOTS AS A MARKETING TOOL IN U.S. BANKING: ENHANCING CUSTOMER SATISFACTION AND SERVICE EFFICIENCY
type: item
description: The integration of artificial intelligence (AI)-powered chatbots into the U.S. banking industry has transformed the way financial institutions engage with customers and deliver services, offering a scalable, intelligent alternative to traditional service channels. This study investigates the strategic role of AI chatbots not only as service automation tools but also as instruments of digital marketing aimed at enhancing customer satisfaction, promoting financial products, and improving operational efficiency. Utilizing a mixed-methods research design, the study combines quantitative data from a structured survey of 400 banking customers with qualitative insights derived from in-depth interviews with 20 banking professionals. Findings indicate that chatbot responsiveness, 24/7 availability, and personalization significantly influence customer satisfaction, with over 72% of users reporting favorable experiences due to fast, accessible service. Chatbots were also shown to be effective in delivering context-aware product recommendations, with 58% of respondents recalling promotional content and 31% acting on these suggestions—demonstrating chatbots’ capacity to function as real-time marketing agents. Moreover, operational cost analyses revealed that chatbot deployment led to a 30–35% reduction in call center volume and reduced average service costs per interaction, affirming their value in cost optimization strategies. The study also highlights limitations, including concerns related to data transparency, interpretative accuracy, and trust, especially among older and less digitally adept users. However, institutions that implemented hybrid support models and emphasized data ethics achieved higher levels of customer trust and satisfaction. The research aligns with and extends existing literature by framing chatbots as integral components of customer experience management, digital personalization, and strategic marketing in modern banking. Ultimately, AI chatbots are not …
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Chatbots
  topic: keywords
subject: US Banking Sectors
  topic: keywords
subject: Customer Satisfaction
  topic: keywords
subject: Service Efficiency
  topic: keywords
subject: Virtual Assistant
  topic: keywords
subject: Digital Banking
  topic: keywords
creator: Kazi Shakhawat Hossain
creator: Faysal Ahmed
creator: Md Hasanur Rahman
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2025-07-09
publication date: 2025-07-09
starting page: 501
ending page: 526
doi: 10.63125/2hp9jw50

EXAMINING THE EFFECT OF AI-POWERED PERSONALIZATION ON CUSTOMER LOYALTY: A META-ANALYSIS OF E-COMMERCE STUDIES
type: item
description: This systematic review explores the evolving intersection between artificial intelligence (AI)-powered personalization and customer loyalty within digital commerce ecosystems. As e-commerce platforms increasingly deploy AI technologies to tailor user experiences, understanding the mechanisms through which personalization fosters loyalty has become both a strategic and scholarly imperative. Drawing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study critically analyzed 52 peer-reviewed articles published between 2012 and 2025, spanning disciplines such as marketing, information systems, computer science, and consumer psychology. The review synthesized findings from both experimental and observational studies, incorporating diverse methodologies including structural equation modeling, log-file analytics, and meta-analytic techniques. The results confirm that AI-driven personalization significantly enhances both transactional and emotional dimensions of customer loyalty, especially when implemented through deep learning algorithms, hybrid recommender systems, and real-time behavioral adaptation. Psychological factors such as perceived relevance, trust, emotional bonding, and user satisfaction emerged as crucial mediators in the personalization–loyalty relationship. Additionally, ethical considerations—such as algorithmic transparency, data privacy compliance, and user control—were found to directly influence trust and long-term platform engagement. The review also highlights the importance of contextual and lifecycle-aware personalization, as well as the need for cultural sensitivity and localization when implementing personalization strategies across global markets. This study contributes to the literature by offering a multi-dimensional framework that integrates technological, psychological, ethical, and cultural perspectives on personalization. It challenges the notion of personalization as a static marketing too…
subject: Articles
  topic: section
subject: AI personalization
  topic: keywords
subject: customer loyalty
  topic: keywords
subject: e-commerce
  topic: keywords
subject: recommendation systems
  topic: keywords
subject: data privacy
  topic: keywords
creator: Mohammad Shoeb Abdullah
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-06-15
publication date: 2025-06-15
starting page: 309
ending page: 338
doi: 10.63125/780spe97

AI-DRIVEN PREDICTIVE MAINTENANCE FOR HIGH-VOLTAGE X-RAY CT TUBES: A MANUFACTURING PERSPECTIVE
type: item
description: High-voltage X-ray computed tomography (CT) tubes are critical components in advanced medical imaging, industrial inspection, and non-destructive evaluation systems. These vacuum-based devices operate under extreme electrical, thermal, and mechanical stress, making them highly susceptible to gradual degradation and sudden failure. Unplanned downtime of CT tubes can result in significant operational disruptions, financial loss, and safety risks. Traditional maintenance strategies—such as reactive or preventive maintenance—often fall short in anticipating complex failure mechanisms, especially in high-throughput environments. This study addresses this gap by proposing and validating a predictive maintenance framework powered by artificial intelligence (AI) and designed specifically for high-voltage CT tube systems within industrial manufacturing contexts. The research adopted a hybrid experimental-computational methodology, combining simulated sensor data with real-world failure records to develop and evaluate machine learning and deep learning models. A dataset comprising 18,000 multivariate sensor sequences—including filament current, cathode temperature, vacuum pressure, and rotor vibration—was used to train five predictive models: random forest, support vector machine (SVM), convolutional neural network (CNN), long short-term memory (LSTM), and autoencoder-based anomaly detection. Feature extraction was performed using signal processing techniques, and model performance was assessed using accuracy, F1-score, remaining useful life (RUL) prediction error, and inference latency under real-time constraints. Additionally, a Raspberry Pi-based edge computing prototype was developed to validate real-time deployment feasibility, and a centralized monitoring dashboard was created to visualize health status and facilitate technician interaction. Results showed that LSTM models outperformed other algorithms in temporal degradation forecasting, achieving a ±5% error in RUL es…
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: High-Voltage X-ray CT Tubes
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Manufacturing Analytics
  topic: keywords
subject: Remaining Useful Life (RUL) Estimation
  topic: keywords
creator: Md Mahamudur Rahaman Shamim
rights: Copyright (c) 2024 Review of Applied Science and Technology
date: 2024-04-05
publication date: 2024-04-05
starting page: 40
ending page: 67
doi: 10.63125/npwqxp02

COMPREHENSIVE INSIGHTS INTO CO₂ CAPTURE: TECHNOLOGICAL PROGRESS AND CHALLENGES
type: item
description: In response to the urgent global imperative to mitigate anthropogenic climate change, carbon dioxide (CO₂) capture technologies have become critical components of decarbonization strategies across the energy, industrial, and infrastructure sectors. This systematic review provides a comprehensive and comparative analysis of current CO₂ capture methodologies, drawing from 98 peer-reviewed studies published between 2010 and 2023. The review encompasses the full spectrum of capture technologies, including chemical absorption, physical adsorption, membrane separation, cryogenic methods, direct air capture (DAC), and hybrid or integrated systems. A structured search and selection process was conducted using the PRISMA 2020 framework, retrieving literature from major databases such as Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect. Key performance metrics—including capture efficiency, regeneration energy, operational scalability, environmental impact, and lifecycle emissions—were synthesized thematically and compared across technologies. Chemical absorption, particularly using monoethanolamine (MEA) and blended amines, remains the most mature technology, demonstrating high removal efficiency but incurring significant energy and environmental costs due to solvent degradation, corrosion, and thermal regeneration. Solid adsorption systems utilizing porous materials such as zeolites, activated carbon, and metal-organic frameworks (MOFs) showed promising low-energy alternatives, although scale-up constraints and moisture sensitivity limit current deployment. Membrane-based systems exhibited operational simplicity and adaptability in high-pressure environments but were limited by fouling and the permeability–selectivity trade-off. Cryogenic separation demonstrated high-purity CO₂ recovery and viability in niche applications, yet was hindered by its thermodynamic intensity and infrastructure demands. DAC technologies offered unique potential for atmospheric CO₂ re…
subject: Articles
  topic: section
subject: CO₂ Capture Technologies
  topic: keywords
subject: Carbon Utilization
  topic: keywords
subject: Chemical Absorption
  topic: keywords
subject: Direct Air Capture (DAC)
  topic: keywords
subject: Climate Change Mitigation
  topic: keywords
creator: Tamanna Rashid
creator: Dipongkar Ray Sobuj
rights: Copyright (c) 2023 Review of Applied Science and Technology
date: 2023-08-25
publication date: 2023-08-25
starting page: 113
ending page: 141
doi: 10.63125/9p690n14

THE IMPACT OF HUMAN-MACHINE INTERACTION ON ENGLISH PRONUNCIATION AND FLUENCY: CASE STUDIES USING AI SPEECH ASSISTANTS
type: item
description: This systematic review investigates the impact of human-machine interaction, specifically through AI-powered speech assistants, on the development of English pronunciation and fluency among second language learners. Drawing on 54 peer-reviewed studies published between 2005 and 2023, the review synthesizes findings from experimental, quasi-experimental, longitudinal, and mixed-method research that explore how tools such as Siri, Alexa, Google Assistant, and ELSA Speak influence oral language performance. The review follows the PRISMA 2020 guidelines to ensure methodological transparency, with eligibility criteria focusing on studies that employed measurable outcomes related to segmental and suprasegmental pronunciation features, temporal fluency metrics, acoustic analysis, and learner perceptions. The results reveal consistent improvements in phoneme articulation, word stress, intonation, speech rate, and pause reduction after sustained AI-mediated practice. Learners also demonstrated increased metacognitive awareness, greater confidence, and behavioral shifts toward self-regulated learning, particularly when interpreting implicit feedback from AI systems. Furthermore, the adaptability of AI speech tools across cultural contexts and learning settings highlights their scalability and pedagogical value. This review affirms the growing role of AI speech assistants as effective, accessible, and motivational tools for enhancing spoken English proficiency and offers recommendations for their integration into second language instruction and learner autonomy frameworks.
subject: Articles
  topic: section
subject: AI Speech Assistants
  topic: keywords
subject: English Pronunciation
  topic: keywords
subject: Second Language Acquisition
  topic: keywords
subject: Human-Machine Interaction
  topic: keywords
subject: Fluency Development
  topic: keywords
creator: Elmoon Akhter
rights: Copyright (c) 2025 Review of Applied Science and Technology
date: 2025-07-25
publication date: 2025-07-25
starting page: 473
ending page: 500
doi: 10.63125/1wyj3p84