Review of Applied Science and Technology
Review of Applied Science and Technology (RAST) is an international, peer-reviewed, open-access journal dedicated to publishing high-impact research and review articles in all areas of applied science and engineering technology. RAST serves as a critical platform for researchers, innovators, and practitioners aiming to translate scientific knowledge into practical solutions for industrial, environmental, technological, and societal advancement. With a commitment to academic rigor, technological innovation, and interdisciplinary collaboration, the journal bridges the gap between theoretical research and its practical implementation in diverse sectors, including energy, materials, information technology, mechanical systems, chemical processes, biomedical engineering, and sustainable development.
AUTOMATION AND RISK MITIGATION IN HEALTHCARE CLAIMS: POLICY AND COMPLIANCE IMPLICATIONS
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 of automation (interaction β = .14), and organizations with higher maturity exhibited substantially fewer audit exceptions (IRR = 0.86). Taken together, these results clarify the operational mechanisms through which automation delivers measurable value in cloud and enterprise claims ecosystems. For leaders overseeing digital claims transformation, the evidence suggests that automation alone is insufficient; instead, the most reliable reductions in denial friction, audit exposure, and payment-integrity risk emerge when automation is paired with strong compliance routines, explicit governance of business rules and machine-assisted review logic, and disciplined data-protection controls. These organizational capabilities appear to function synergistically, producing more consistent, transparent, and defensible outcomes at scale.
Copyright (c) 2023 Review of Applied Science and Technology
FEDERATED LEARNING-DRIVEN PREDICTIVE QUALITY ANALYTICS AND SUPPLY CHAIN OPTIMIZATION IN DISTRIBUTED MANUFACTURING NETWORKS
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 expedites decreased by 18 percent within six months. Implications are managerial and methodological: treat governance as the control plane, institutionalize integration routines, and use federated, privacy-preserving analytics to convert earlier defect detection into reliable service and leaner buffers without centralizing data.
Copyright (c) 2021 Review of Applied Science and Technology
QUANTITATIVE ASSESSMENT OF SMART CITY IOT INTEGRATION FOR REDUCING URBAN INFRASTRUCTURE VULNERABILITIES
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 resilience. This study provides empirical evidence for policymakers and urban engineers to design scalable, data-driven strategies for strengthening infrastructure reliability and adaptive capacity in smart cities worldwide.
Copyright (c) 2024 Review of Applied Science and Technology
HUMAN-CENTERED INTERFACES IN INDUSTRIAL CONTROL SYSTEMS: A REVIEW OF USABILITY AND VISUAL FEEDBACK MECHANISMS
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 synergistic nature of usability and perceptual clarity in human–machine interaction. Collectively, the findings advance an empirically validated framework linking interface design attributes to cognitive and behavioral outcomes in safety-critical operations. The study concludes that integrating usability optimization, visual feedback rationalization, and standardized measurement systems can measurably enhance operator awareness, reduce cognitive load, and improve performance reliability across diverse ICS domains, while calling for longitudinal, multi-modal research to capture dynamic and team-level adaptations in future studies.
Copyright (c) 2022 Review of Applied Science and Technology
INTEGRATION OF LEAN SIX SIGMA AND ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWIN TECHNOLOGIES FOR SMART MANUFACTURING SYSTEMS
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 methodologies with intelligent simulation and predictive analytics produced significant, quantifiable enhancements in manufacturing performance.
Copyright (c) 2025 Review of Applied Science and Technology
INFORMATION SYSTEM-BASED DECISION SUPPORT TOOLS: A SYSTEMATIC REVIEW OF STRATEGIC APPLICATIONS IN SERVICE-ORIENTED ENTERPRISES
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 advantage within global service enterprises.
A META-ANALYSIS OF LEAN MERCHANDISING STRATEGIES IN FASHION RETAIL: GLOBAL INSIGHTS FROM THE POST-PANDEMIC ERA
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.
AI-DRIVEN ANOMALY DETECTION FOR DATA LOSS PREVENTION AND SECURITY ASSURANCE IN ELECTRONIC HEALTH RECORDS
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 validation, and insufficient interpretability in deep learning models, which continue to constrain generalizability and adoption. Overall, the findings underscored that AI-driven anomaly detection offers a robust, adaptive, and evidence-based mechanism for safeguarding patient data, ensuring regulatory compliance, and reinforcing institutional trust in healthcare’s digital infrastructure. By transforming static, rule-based monitoring into dynamic, learning-oriented assurance systems, artificial intelligence demonstrated the potential to redefine how healthcare organizations achieve sustained data security and operational resilience in an increasingly interconnected clinical environment.
PREDICTIVE ANALYTICS IN SUPPLY CHAIN MANAGEMENT A REVIEW OF BUSINESS ANALYST-LED OPTIMIZATION TOOLS
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 competency frameworks, and continuous model validation to sustain predictive performance and organizational adaptability within dynamic global supply chains.
MACHINE LEARNING-BASED PROCESS MINING FOR ANOMALY DETECTION AND QUALITY ASSURANCE IN HIGH-THROUGHPUT MANUFACTURING ENVIRONMENTS
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-monitoring systems can strengthen defect prevention, streamline production decision-making, and support real-time process optimization in smart manufacturing environments. The results contributed to advancing data-driven quality engineering and reinforced the role of AI-powered analytics in modern industrial process control.
SWARM INTELLIGENCE-BASED AUTONOMOUS LOGISTICS FRAMEWORK WITH EDGE AI FOR INDUSTRY 4.0 MANUFACTURING ECOSYSTEMS
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.
THE ROLE OF CALIBRATION ENGINEERING IN STRENGTHENING RELIABILITY OF U.S. ADVANCED MANUFACTURING SYSTEMS THROUGH ARTIFICIAL INTELLIGENCE
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 employed.
PREDICTIVE NEURAL NETWORK MODELS FOR CYBERATTACK PATTERN RECOGNITION AND CRITICAL INFRASTRUCTURE VULNERABILITY ASSESSMENT
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 cybersecurity by capturing nonlinear relationships, modelling IT–OT dependencies, and integrating attack detection with vulnerability prioritization. These results extend the existing literature by providing a unified, scalable, and proactive defence framework for protecting critical infrastructure from evolving cyber threats and demonstrate the transformative potential of deep learning in the next generation of cybersecurity systems.
TRUSTWORTHY AI: EXPLAINABILITY & FAIRNESS IN LARGE-SCALE DECISION SYSTEMS
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 technical, ethical, and behavioural dimensions, this study contributed new empirical evidence and theoretical insights into how explain ability and fairness shape trustworthy AI. The results provide a comprehensive foundation for designing, evaluating, and governing AI systems that are transparent, equitable, and socially aligned in large-scale decision-making contexts.
ALIGNING FEDRAMP AND NIST FRAMEWORKS IN CLOUD-BASED GOVERNANCE MODELS: CHALLENGES AND BEST PRACTICES
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 operational performance. The study offers actionable insights and best practices for organizations seeking to optimize governance maturity and resilience, demonstrating how integrated alignment strategies can reduce risk, enhance accountability, and support secure, scalable cloud adoption in complex regulatory landscapes.
REAL-TIME CYBER-PHYSICAL DEPLOYMENT AND VALIDATION OF H-DEABSF: MODEL PREDICTIVE CONTROL, AND DIGITAL-TWIN–DRIVEN PROCESS CONTROL IN SMART FACTORIES
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 predictive control and digital twin feedback enhanced both operational adaptability and stability, ensuring robust performance across variable manufacturing conditions. The results substantiate that H-DEABSF constitutes a validated and scalable architecture for intelligent process optimization, fusing simulation modeling, predictive analytics, and cyber-physical synchronization into a single self-regulating control ecosystem. This research contributes a significant advancement to the domain of smart manufacturing by providing empirical evidence of how hybrid simulation frameworks can operationalize the core principles of Industry 4.0, promoting data-driven autonomy, resilience, and sustainable production intelligence in next-generation industrial systems.
PRESENT AND FUTURE OF DIGITAL BANKING SERVICES IN BANGLADESH
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 the growing body of literature on digital financial transformation in developing economies by offering grounded insights into how MFS reshapes Bangladesh’s socio-economic fabric. The findings underscore the importance of balancing innovation with inclusive policy frameworks to ensure sustainability and trust within the digital financial ecosystem. The research advocates for a multi-dimensional strategy encompassing digital literacy development, regulatory modernization, and cost optimization to maximize MFS’s potential in achieving Bangladesh’s broader developmental goals under the “Smart Bangladesh Vision 2041.”
REAL-TIME ADAPTIVE MACHINE LEARNING FOR OPERATIONAL OPTIMIZATION ACROSS GLOBAL TRANSPORTATION, ENERGY, AND INDUSTRIAL INFRASTRUCTURE
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 reduction (β = .64, R² = .41, p < .01), extending previous findings that industrial Internet of Things (IIoT) applications are particularly responsive to adaptive learning techniques. Overall, the results demonstrate that AML is a significant predictor of operational optimization across all four domains, with industrial reliability and transportation efficiency showing the strongest gains. These findings advance the literature by moving beyond simulation-based validations and providing empirical, cross-sectoral evidence of AML’s transformative role in infrastructure optimization.
MODELING OF AN AI-INTEGRATED PREDICTIVE FRAMEWORK FOR COASTAL ECOSYSTEM CARBON SEQUESTRATION AND WATER QUALITY ASSESSMENT
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 assessments demonstrated that AI-driven models, including random forests, gradient boosting, and recurrent neural networks, outperformed traditional statistical and process-based frameworks, achieving lower RMSE values, higher predictive power, and stronger capacity to capture nonlinear thresholds. Integrated analyses revealed that improvements in water quality, such as reduced nutrient loading and enhanced optical clarity, directly supported higher carbon burial rates in seagrass meadows and marsh soils, highlighting the reciprocal benefits of ecosystem management interventions. These findings reinforce the interconnected nature of blue carbon and water quality services, validating AI-enhanced approaches as critical assets for adaptive coastal governance, restoration planning, and international sustainability initiatives.
DEVELOPMENT OF A PREDICTIVE SIMULATION MODEL FOR SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE ANALYSIS CONSIDERING ENVIRONMENTAL, TECHNICAL, AND ECONOMIC EFFICIENCY FACTORS
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 assessment, reduced prediction error from approximately ±12% to ±5% and improved investment decision reliability. Overall, this study contributes a robust conceptual and methodological foundation for developing predictive PV simulation models that are technically precise, economically credible, and transferable across diverse climatic and market contexts.
DATA-DRIVEN BUSINESS ANALYSIS: A COMPREHENSIVE ANALYSIS OF PREDICTIVE ANALYTICS IN PRICING STRATEGIES, MARKETING DECISIONS AND OPERATIONAL EFFICIENCY
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 highlight that adopting analytics can significantly enhance decision quality by improving forecasting accuracy, optimizing resource allocation, and reducing inefficiencies. In marketing contexts, efficiency improvements may translate into shorter campaign cycles, increased ROI, and better alignment of products or services with customer preferences.
ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWINS FOR ENERGY EFFICIENCY IN SMART GRIDS
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 beyond conceptual taxonomies by providing a reproducible, evidence-based blueprint for AI-enabled DT design: one that couples probabilistic and calibrated forecasting with latency-hardened voltage and topology control, links diagnostics to operations in a closed loop, and enforces transparency, explanation, and safety guardrails.
HYBRID MACHINE LEARNING–DRIVEN FINANCIAL FORECASTING MODELS: INTEGRATING LSTM, PROPHET, AND XGBOOST FOR ENHANCED STOCK PRICE AND RISK PREDICTION
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.
ARTIFICIAL INTELLIGENCE DRIVEN HEALTHCARE FINANCIAL MANAGEMENT TOWARD SUSTAINABLE DEVELOPMENT
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 stewardship remain interconnected. By situating AI within the broader discourse on digital transformation in healthcare, this research contributes to the understanding that sustainable development requires not only technological innovation but also robust governance mechanisms that safeguard equity, transparency, and institutional trust.
ONLINE RETAIL: PURCHASING OCCASIONAL GIFTS THROUGH ONLINE CHANNELS IS A PREVALENT TREND IN THE UNITED KINGDOM
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 e-commerce by presenting a comprehensive and adaptable framework that underscores the complexity and dynamism of the online gifting market in the UK.
THE ROLE OF ARTIFICIAL INTELLIGENCE-POWERED CHATBOTS AS A MARKETING TOOL IN U.S. BANKING: ENHANCING CUSTOMER SATISFACTION AND SERVICE EFFICIENCY
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 only redefining how services are delivered but also reshaping the strategic landscape of customer engagement and brand communication in the competitive and evolving digital financial ecosystem.
EXAMINING THE EFFECT OF AI-POWERED PERSONALIZATION ON CUSTOMER LOYALTY: A META-ANALYSIS OF E-COMMERCE STUDIES
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 tool and repositions it as a dynamic, systemic strategy for fostering sustainable customer relationships. Key recommendations include the implementation of adaptive personalization systems, integration of ethical design, and the promotion of cross-functional collaboration. The findings offer valuable insights for researchers, platform developers, and marketers aiming to design intelligent, user-centric personalization systems that enhance loyalty in competitive digital environments.
AI-DRIVEN PREDICTIVE MAINTENANCE FOR HIGH-VOLTAGE X-RAY CT TUBES: A MANUFACTURING PERSPECTIVE
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 estimation and offering a 24–48 hour predictive lead time. Multisensor data fusion significantly improved detection accuracy and model stability across diverse operating scenarios. The autoencoder demonstrated exceptional performance in detecting novel and rare fault patterns without prior labeling, with a 96% detection rate and low false positive incidence. Edge deployment tests confirmed low-latency model inference suitable for real-time applications, while dashboard integration improved decision-making efficiency and technician trust in AI outputs. Overall, the proposed framework enabled proactive intervention, reduced maintenance overhead, and extended CT tube operational uptime. These findings highlight the strategic value of AI-enhanced predictive maintenance in optimizing industrial reliability, aligning with the broader goals of Industry 4.0 and smart manufacturing ecosystems.
COMPREHENSIVE INSIGHTS INTO CO₂ CAPTURE: TECHNOLOGICAL PROGRESS AND CHALLENGES
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 removal, but their high energy requirements and material costs present substantial barriers to economic scalability. Hybrid systems, integrating complementary mechanisms, emerged as effective configurations for industrial decarbonization, though they entail higher capital investment and control complexity. Lifecycle assessment across all technologies revealed that environmental performance is highly dependent on energy source, material durability, and system integration. The review concludes that no single technology is universally optimal; rather, selection and implementation must be context-specific, guided by techno-economic evaluations, environmental trade-offs, and long-term system integration potential.
THE IMPACT OF HUMAN-MACHINE INTERACTION ON ENGLISH PRONUNCIATION AND FLUENCY: CASE STUDIES USING AI SPEECH ASSISTANTS
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.