ASRC Procedia: Global Perspectives in Science and Scholarship
ASRC Procedia is a peer-reviewed, open-access conference proceedings journal that serves as the official publication outlet of the American Scholarly Research Conference (ASRC). Published from Florida, USA, the journal is dedicated to disseminating original research presented at academic and industry symposia held under the ASRC umbrella, including the flagship Global Research and Innovation Summit (GRIS). The journal promotes cross-disciplinary knowledge exchange by capturing diverse scholarly outputs across the domains of science, engineering, technology, health, environment, education, business, economics, public policy, and the social sciences.
QUANTITATIVE ANALYSIS OF MECHANICAL TESTING AND VALVE PERFORMANCE IN THE OIL AND GAS SECTOR: ENSURING COMPLIANCE WITH ISO/IEC 17025 IN GLOBAL INDUSTRIAL INFRASTRUCTURE
This study addresses the problem that valve qualification decisions weaken when mechanical testing laboratories implement ISO/IEC 17025 unevenly, reducing confidence in test evidence. The purpose was to quantify ISO/IEC 17025 compliance maturity and test which compliance mechanisms predict valve performance assurance (VPA). A quantitative, cross-sectional survey was administered to a purposive sample of 214 professionals in an enterprise oil and gas testing case. The independent construct was ISO/IEC 17025 compliance, measured as D1 competence and authorization, D2 calibration and traceability, D3 method validation and SOP adherence, D4 internal audits and corrective actions, and D5 uncertainty and decision rules; the dependent construct was a composite VPA index. Analysis used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Compliance maturity (CI) was moderately high (M = 3.96/5) and VPA was moderately high (M = 3.88/5), with excellent reliability (α = 0.92 and α = 0.91). Overall compliance correlated strongly with VPA (r = 0.71, p < .001), and all dimensions were significant (r = 0.49 to 0.66, p < .001). The regression model explained 58% of VPA variance (R² = 0.58; F(5, 208) = 57.40, p < .001); D3 was the strongest predictor (β = 0.31, p < .001), followed by D1 (β = 0.24, p < .001) and D2 (β = 0.19, p = .001), while D4 and D5 showed smaller effects (p ≤ .050). Implications are to prioritize method control and SOP discipline, strengthen competency authorization and traceability, and improve audit closure and uncertainty-based decision rules for integrity decisions.
Copyright (c) 2023 Efat Ara Haque (Author)
FEDERATED LEARNING–DRIVEN REAL-TIME DISEASE SURVEILLANCE FOR SMART HOSPITALS USING MULTI-SOURCE HETEROGENEOUS HEALTHCARE DATA
Federated learning is increasingly promoted as a privacy-preserving way to use multi-source heterogeneous healthcare data for real-time disease surveillance in smart hospitals, but little is known about how its capabilities, data integration, governance, and organizational conditions jointly shape perceived surveillance performance. This study therefore aimed to quantify how federated learning capability, multi-source data integration, privacy and security assurance, and organizational readiness contribute to perceived surveillance effectiveness across cloud and enterprise smart hospital cases. A quantitative cross-sectional, case-based survey design was used, with 228 usable responses (87.7 percent response rate from 260 questionnaires) from clinicians, infection prevention staff, health informatics personnel, and IT/security professionals working in digitally advanced hospitals. Key variables included federated learning capability, multi-source integration, privacy and security assurance, organizational readiness, and perceived surveillance effectiveness, all measured on five-point Likert scales. The analysis plan comprised descriptive statistics, reliability testing, Pearson correlations, and multiple regression. Mean scores indicated generally high maturity (FLC 3.78, MSI 3.92, PSA 4.06, OR 3.84, SE 3.95), with strong internal consistency (Cronbach’s alpha 0.83–0.88). Correlations between surveillance effectiveness and predictors were all positive and significant, highest for organizational readiness (r = 0.72) and federated learning capability (r = 0.65). The regression model was significant and explained 61.3 percent of the variance in surveillance effectiveness (R² = 0.613), with organizational readiness (β = 0.29), federated learning capability (β = 0.24), privacy and security assurance (β = 0.21), and multi-source integration (β = 0.18) all making significant contributions. The findings imply that effective federated learning driven surveillance in smart hospitals depends not only on advanced models and integrated data, but also on robust privacy safeguards and high organizational readiness to act on analytic insights.
Multi-Source Heterogeneous Healthcare Data
Copyright (c) 2025 Arfan Uzzaman (Author)
AI-ENABLED DIGITAL TWIN FRAMEWORK FOR PREDICTIVE MAINTENANCE AND ENERGY OPTIMIZATION IN INDUSTRIAL SYSTEMS
Industrial organizations face growing pressure to improve asset reliability and energy efficiency at the same time, yet there is limited quantitative evidence on how AI enabled digital twins contribute to these outcomes. This study therefore examines how AI enabled digital twin capability relates to predictive maintenance effectiveness and energy optimization performance in real industrial enterprise environments. A quantitative, cross sectional, case-based survey design was applied to 220 professionals from discrete manufacturing, process industries and energy intensive utilities using digital twin and AI driven maintenance and energy management. Key latent variables were AI enabled digital twin capability, predictive maintenance effectiveness, energy optimization performance, AI analytics maturity and organizational readiness, measured with multi-item Likert scales showing strong internal consistency. The analysis plan combined descriptive statistics, correlation analysis and multiple regression with mediation and moderation tests. Results show moderately high perceived capability levels, with mean scores of 3.67 for digital twin capability, 3.79 for predictive maintenance and 3.61 for energy optimization on a five-point scale, and strong positive associations among the constructs. Digital twin capability significantly predicts predictive maintenance effectiveness (β = 0.58, R² = 0.53) and energy optimization performance (direct β = 0.29 within a model R² = 0.56), with predictive maintenance providing a significant partial mediation and AI analytics maturity and organizational readiness acting as important enablers. These findings indicate that AI enabled digital twins, supported by mature analytics and organizational readiness, act as strategic levers to advance predictive maintenance and industrial energy efficiency in Industry 4.0 enterprises.
Copyright (c) 2025 Saikat Sarkar (Author)
ARTIFICIAL INTELLIGENCE IN DRIVEN DIGITAL TWIN FOR REAL-TIME TRAFFIC SIGNAL OPTIMIZATION AND TRANSPORTATION PLANNING
This study examined the effectiveness of an artificial intelligence–driven digital twin system for real-time traffic signal optimization and transportation planning using a quantitative simulation-based approach. The digital twin replicated a congested urban corridor consisting of six signalized intersections and evaluated performance under baseline and AI-controlled conditions across 48 experimental scenarios covering peak, off-peak, and incident periods. A total of 192 simulation replications were analyzed to ensure statistical robustness. Descriptive results showed that the AI-based controller reduced average delay from 68.4 seconds per vehicle to 29.5 seconds, representing a 56.9% improvement. Average queue length declined from 19.6 vehicles to 8.4 vehicles, a 57.1% reduction, while throughput increased from 12.7 to 19.8 vehicles per minute, reflecting a 55.9% gain. Travel time reliability improved as the reliability index rose from 74.3% to 91.4%, indicating a more stable operating environment. Correlation analysis showed that the relationship between demand intensity and congestion severity weakened under AI control, with the demand-delay correlation decreasing from 0.74 to 0.48. Reliability and validity checks confirmed strong internal consistency, with simulation replications producing variability below 0.30 units across all key metrics. Criterion validity assessment showed that simulated baseline values differed from field observations by less than 7%. Collinearity diagnostics confirmed model suitability, with all VIF values below 2.3, ensuring stable regression estimates. Regression analysis showed that AI control significantly predicted improvements in delay (β = –0.62, p < .001), queue length (β = –0.58, p < .001), throughput (β = 0.49, p < .001), and reliability (β = 0.57, p < .001). All hypotheses were supported. The study demonstrated that integrating digital twin technology with reinforcement learning produced substantial quantitative benefits, establishing a strong empirical foundation for adopting AI-enhanced signal control in advanced transportation management systems.
Copyright (c) 2025 Md Sarwar Hossain Shuvo (Author)
AI-ORCHESTRATED CYBER-PHYSICAL SYSTEMS FOR SUSTAINABLE INDUSTRY 5.0 MANUFACTURING AND SUPPLY CHAIN RESILIENCE
This study examined the influence of AI-orchestrated cyber-physical systems (CPS) on sustainable Industry 5.0 manufacturing and supply chain resilience through a quantitative, multi-construct analytical model. Using data collected from 412 industrial respondents across manufacturing, logistics, and technology-driven organizations, the study evaluated relationships among CPS integration maturity, AI orchestration capability, sustainability performance, and supply chain resilience. Descriptive analysis showed strong adoption levels, with mean CPS maturity at 4.21, AI orchestration at 4.08, sustainability performance at 4.17, and resilience at 4.11 on a 5-point scale. Correlation results indicated significant positive associations among all major constructs, with AI orchestration exhibiting the strongest correlation with sustainability (r = 0.71) and resilience (r = 0.68). Reliability and validity tests confirmed strong measurement consistency, with Cronbach’s alpha values ranging from 0.89 to 0.94, and AVE values exceeding 0.63. Regression modeling revealed that CPS maturity significantly predicted AI orchestration (β = 0.62, p < 0.001), which in turn significantly enhanced sustainability (β = 0.58, p < 0.001) and resilience (β = 0.54, p < 0.001). Mediation analysis showed that AI orchestration mediated over 64% of the total effect of CPS maturity on sustainability and 59% on resilience. Moderation analysis further demonstrated that organizational readiness strengthened the AI-to-sustainability pathway (β = 0.27, p < 0.01), while environmental turbulence amplified the AI-to-resilience relationship (β = 0.24, p < 0.01). Overall, the findings demonstrated that AI-orchestrated CPS functioned as a central mechanism enabling real-time coordination, ecological optimization, and disruption-adaptive behavior, highlighting their essential role in achieving sustainable and resilient Industry 5.0 manufacturing ecosystems.
Copyright (c) 2025 Shaikh Shofiullah (Author)
QUANTITATIVE ASSESSMENT OF AUTOMATION AND CONTROL STRATEGIES FOR PERFORMANCE OPTIMIZATION IN U.S. INDUSTRIAL PLANTS
Industrial automation continues to redefine modern production systems through advanced control strategies, intelligent monitoring, and data-driven optimization. This study presents a comprehensive quantitative assessment of automation and control systems implemented across 126 U.S. industrial plants, encompassing manufacturing, petrochemicals, power generation, and materials processing. The research explores how automation sophistication measured through integration level, predictive maintenance capability, control responsiveness, real-time monitoring, and system adaptability translates into measurable improvements in operational efficiency, reliability, and sustainability. Findings demonstrate clear and statistically significant relationships between automation variables and industrial performance outcomes. Correlation coefficients revealed strong positive associations between automation integration and OEE (r = .71), energy efficiency (r = .57), and MTBF (r = .53), alongside strong negative correlations with downtime ratio (r = –.49). Predictive maintenance was particularly influential, showing strong correlations with MTBF (r = .68) and downtime reduction (r = –.64), underscoring the value of data-driven asset reliability frameworks. Regression models further quantified these effects. The OEE model produced an Adjusted R² of .724 (F(5,120) = 48.37, p < .001), with automation level (β = .42, p < .001) and predictive maintenance (β = .31, p < .01) emerging as the strongest predictors. Energy efficiency was similarly explained by automation integration, predictive maintenance, and system adaptability (Adjusted R² = .61; F(4,121) = 35.19, p < .001). Downtime ratio showed a strong inverse relationship to predictive maintenance (β = –.41, p < .001) and control responsiveness (β = –.22, p < .01), indicating that responsive and proactive systems are highly effective in preventing production disruptions. ANOVA confirmed significant performance differences across automation maturity levels (p < .001). Fully adaptive plants achieved the highest outcomes (mean OEE = 89.8%, downtime = 4.7%, energy efficiency = 0.82), significantly outperforming manual and semi-automated facilities. Robust diagnostic testing—Cronbach’s α (.87–.94), VIF (1.28–2.43), Durbin–Watson (2.03), and 5,000-bootstrap validation—verified reliability and model stability. Collectively, results demonstrate that U.S. industrial performance is strongly driven by a triad of automation integration, predictive intelligence, and control responsiveness.
Copyright (c) 2024 Jabed Hasan Tarek, Mohammad Shah Paran (Author)
AUTOMATING NIST 800-53 CONTROL IMPLEMENTATION: A CROSS-SECTOR REVIEW OF ENTERPRISE SECURITY TOOLKITS
This study addresses the persistent problem of manual, document-centric compliance that slows implementation of NIST SP 800-53 controls and produces uneven evidence quality across complex cloud estates. The purpose is to quantify how enterprise security toolkits operationalize automated control implementation and to identify which capabilities most strongly predict measurable compliance and operations outcomes. Using a quantitative, cross-sectional, case-based design, we analyze organization-level survey data and embedded evidence from cloud and enterprise cases spanning finance, healthcare, manufacturing, public sector, and education. Key variables include four predictors toolkit capability maturity, integration breadth, policy-as-code adoption, and infrastructure-as-code security adoption and five outcomes automation coverage percentage, time to compliance, audit pass rate, mean time to remediate, and false positive rate. The analysis plan specifies descriptives, correlation matrices, and multiple regressions with sector fixed effects and a regulatory pressure moderator, supported by robustness and diagnostic checks. Headline findings show capability maturity and integration breadth as the strongest, consistent predictors of higher automation coverage and shorter time to compliance, with policy-as-code and infrastructure-as-code adding incremental gains; audit pass rates rise where standardized, machine-generated evidence is produced, and false positives decline modestly as correlation and context enrichment improve. Implications for practice are clear, prioritize an integration roadmap that wires CI or CD, cloud control planes, identity, CMDB or ITSM, and SIEM or SOAR into a single evidence pipeline, enforce policies at merge and admission, and institutionalize evidence-as-code mapped to assessment objectives so compliance becomes continuous and verifiable rather than periodic and manual.
Copyright (c) 2023 Md. Jobayer Ibne Saidur, Md. Kamrul Khan (Author)
RECENT DEVELOPMENTS AND CHALLENGES IN FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION
This systematic review critically evaluates the advances and limitations of fracture mechanics–based fatigue life prediction approaches within the context of structural integrity assessment. Over the past five decades, fracture mechanics has evolved from a linear analytical framework into a multidisciplinary domain that integrates theoretical formulations, experimental validation, computational modeling, and data-driven analytics. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviewed and synthesized 214 peer-reviewed research papers published between 1972 and 2024. The analysis reveals that significant progress has been achieved in extending classical Linear Elastic Fracture Mechanics (LEFM) and Elastic–Plastic Fracture Mechanics (EPFM) toward nonlinear, probabilistic, and multi-scale modeling approaches that capture the complex behavior of materials under cyclic loading. Computational developments—particularly finite element analysis (FEA), extended finite element methods (XFEM), cohesive zone modeling, and continuum damage mechanics—have enabled detailed simulation of crack initiation and propagation under mixed-mode and variable amplitude stresses. Experimental advancements such as digital image correlation, acoustic emission monitoring, and thermographic analysis have enhanced the precision of crack growth measurement, enabling robust model calibration and validation. Moreover, the integration of Structural Health Monitoring (SHM), digital twin architectures, and machine learning has established a new paradigm for real-time fatigue prediction and adaptive maintenance in high-reliability structures. Despite these achievements, the review identifies persistent challenges including data inconsistency in fatigue thresholds, theoretical limitations under large-scale yielding, computational cost, and inadequate coupling of environmental and microstructural effects. The findings underscore the need for standardized testing methodologies, open-access fatigue databases, and harmonized probabilistic frameworks to improve predictive accuracy and cross-material applicability. Overall, this review concludes that fracture mechanics–based fatigue life prediction remains an indispensable yet continuously evolving foundation for structural integrity assessment, bridging traditional mechanics with digital innovation to ensure safer, more reliable, and sustainable engineering systems.
Copyright (c) 2025 Abul Kashem Mohammad Yahia, Mohammad Shahjalal (Author)
IMPLEMENTING SUSTAINABLE SUPPLY CHAIN PRACTICES IN GLOBAL APPAREL RETAIL: A SYSTEMATIC REVIEW OF CURRENT TRENDS
This study systematically examined the implementation of sustainable supply chain practices in global apparel retail through a comprehensive analysis of seventy-four peer-reviewed articles published between 2010 and 2022. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the research synthesized current trends, challenges, and innovations across environmental, social, economic, and technological dimensions of sustainability. The findings demonstrate that sustainability has evolved from a compliance-based obligation to a strategic imperative embedded within corporate decision-making, shaping profitability, competitiveness, and brand reputation. Environmental sustainability has advanced through the adoption of circular economy models, life-cycle assessments, and resource-efficient production systems that reduce emissions, water consumption, and textile waste. Social sustainability has improved through multi-stakeholder initiatives promoting labor rights, gender equity, and workplace safety, though persistent disparities remain in subcontracting tiers. The integration of digital technologies such as blockchain, artificial intelligence, and the Internet of Things has strengthened transparency, traceability, and data-driven accountability within apparel supply networks. Quantitative analytical frameworks—incorporating life-cycle costing, carbon accounting, and multi-criteria decision-making—have enhanced the empirical validation of sustainability performance. Furthermore, evolving consumer expectations and stakeholder engagement have reinforced sustainability as a collaborative responsibility, driving greater transparency and ethical governance. The study concludes that the convergence of technological innovation, environmental responsibility, and ethical labor governance defines a new paradigm in global apparel retail, where sustainability functions as both a moral obligation and a strategic instrument for long-term resilience.
Copyright (c) 2022 Md Mesbaul Hasan, Md. Tahmid Farabe Shehun (Author)
ARTIFICIAL INTELLIGENCE IN PRODUCT MARKETING: TRANSFORMING CUSTOMER EXPERIENCE AND MARKET SEGMENTATION
This study, titled AI-Driven Insights for Product Marketing: Enhancing Customer Experience and Refining Market Segmentation, explored the transformative influence of artificial intelligence (AI) on modern marketing practices through a systematic synthesis of 72 peer-reviewed papers published between 2013 and 2023. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the study examined how AI technologies including machine learning, natural language processing, neural networks, and predictive analytics reshaped data interpretation, customer engagement, and segmentation precision. The findings revealed that AI-driven marketing models enabled organizations to transition from intuition-based strategies to data-centric, adaptive, and predictive decision systems capable of delivering personalized experiences at scale. Across the reviewed literature, AI was found to enhance customer experience (CX) by integrating emotional intelligence and real-time feedback analytics, transforming traditional satisfaction metrics into dynamic, behavioral insights. Similarly, AI-based segmentation replaced static demographic categorizations with predictive micro-segmentation, allowing for continuous audience redefinition based on evolving contextual and behavioral patterns. The study also highlighted the emergence of algorithmic governance as an essential pillar for sustaining consumer trust, emphasizing fairness, transparency, and data privacy within AI-enabled marketing ecosystems. Ethical AI adoption, explainable models, and regulatory compliance were identified as vital determinants of credibility and brand reputation in digital environments. Furthermore, the interdisciplinary convergence of psychology, data science, and marketing theory positioned AI as both a technological and cognitive agent capable of understanding human intent and shaping responsive marketing strategies. Overall, the analysis concluded that AI-driven insights not only optimized marketing performance but also redefined the theoretical foundations of consumer engagement, establishing a new paradigm of intelligent, ethically governed, and culturally adaptive marketing systems that continuously evolve with global consumer behavior.
STATISTICAL ANALYSIS OF CYBER RISK EXPOSURE AND FRAUD DETECTION IN CLOUD-BASED BANKING ECOSYSTEMS
This study presented a comprehensive statistical examination of the interrelationships between cyber risk exposure, control maturity, and fraud detection efficiency within cloud-based banking ecosystems. The research aimed to quantify how exposure dimensions—specifically identity and access control, encryption and data security, network segmentation, monitoring and incident response, and governance compliance—affect the frequency and severity of fraudulent transactions across financial institutions operating in hybrid and public cloud environments. Drawing upon an extensive review of 127 empirical and theoretical papers published in the domains of cybersecurity analytics, cloud computing, and financial risk management, this study developed a robust quantitative framework to assess and predict the probability of fraud events. The dataset incorporated multi-institutional secondary data derived from transaction logs, cyber incident reports, and fraud management systems covering a 30-month observation period. Descriptive statistics, correlation matrices, and multivariate regression analyses were employed to identify the statistical strength, direction, and significance of relationships among variables, while hierarchical regression and reliability testing ensured model accuracy and construct consistency. The findings revealed that higher levels of cyber risk exposure were significantly associated with increased fraud frequency and greater financial loss severity, whereas greater control maturity exerted a strong negative influence, effectively mitigating exposure-induced vulnerabilities. The regression models exhibited high explanatory power (adjusted R² exceeding 0.60), confirming that exposure and control constructs jointly predict the likelihood and magnitude of fraud with strong statistical reliability. Furthermore, the study demonstrated that certain exposure subdimensions, particularly authentication integrity and encryption quality, contributed most prominently to fraud probability, emphasizing the operational importance of technical rigor and governance maturity. The comprehensive synthesis of prior research and empirical validation highlighted that risk management in cloud-based financial systems must transition from qualitative compliance-based practices toward quantitative, data-driven governance. The results offered both theoretical and practical implications for cybersecurity strategists, policy regulators, and financial institutions by providing a replicable model capable of evaluating cyber risk exposure and fraud dynamics through statistical inference. Overall, this study provided a measurable, evidence-based foundation for enhancing cyber resilience and fraud prevention in modern, cloud-enabled banking ecosystems.
Copyright (c) 2022 Md Nahid Hossain (Author)
THERMO-ECONOMIC MODELING OF HYDROGEN ENERGY INTEGRATION IN SMART FACTORIES
Here we investigate when and how hydrogen integration improves thermo-economic performance in smart factories by synthesizing international evidence and analyzing a multi-case plant dataset; a systematic review of 46 peer-reviewed studies scoped the technology, operations, and exergy–cost foundations that informed the empirical model, and the problem addressed is the lack of plant-resolved, tariff-aware evidence linking digital maturity and integration depth to levelized cost outcomes; the purpose is to quantify the relationships among renewable share to electrolysis, integration depth across electrolyzer, storage, and fuel cell, storage-to-load ratio, load variability, demand-response participation, and energy-management maturity with plant-level key performance indicators including levelized cost of hydrogen, energy cost intensity, exergy efficiency, CO₂ intensity, and reliability; the design is quantitative, cross-sectional, and case-based; the sample comprises 120 cloud or enterprise cases with EMS or SCADA telemetry aligned to audited tariff and financial records; the analysis plan includes descriptive statistics, correlation matrices, and multiple regression with sector fixed effects, robust errors, and a pre-specified interaction term testing whether energy-management maturity amplifies the benefit of renewable share; headline findings show that higher renewable share is the strongest single predictor of lower unit cost, deeper integration raises exergetic efficiency and lowers emissions, storage exhibits diminishing cost returns beyond roughly work-shift duration, and digital maturity significantly strengthens the cost reductions associated with renewable alignment; implications for practice are to prioritize renewable-aligned electrolysis before oversizing storage, right-size duration to variability fingerprints, invest in EMS maturity and governance, and deploy fuel-cell CHP where thermal and electrical peaks coincide to decarbonize without compromising reliability.
Copyright (c) 2022 Md Sanjid Khan, Zayadul Hasan (Author)
HIGH-PERFORMANCE COMPUTING FOR SCALING LARGE-SCALE LANGUAGE AND DATA MODELS IN ENTERPRISE APPLICATIONS
This quantitative study investigates the efficiency, scalability, and computational performance of high-performance computing (HPC) infrastructures in training and operationalizing large-scale language and data models within enterprise environments. HPC, characterized by parallel processing, distributed memory architectures, and high-bandwidth interconnects, serves as the backbone for scaling advanced artificial intelligence (AI) workloads. The research analyzed 120 performance observations across GPU-, TPU-, ASIC-, and hybrid-based clusters, focusing on metrics including throughput-per-dollar, inference latency, energy-per-token, time-to-recovery (TTR), and mean-time-between-failure (MTBF). Statistical analyses, encompassing correlation, regression, and structural equation modeling, revealed that scaling strategy and compute capacity were the most influential predictors of computational throughput (β = .45, p < .001 and β = .32, p < .001, respectively). Hybrid HPC configurations achieved optimal trade-offs between speed, reliability, and cost efficiency, outperforming homogeneous systems in energy proportionality and fault tolerance. The inclusion of sustainability parameters—such as energy optimization and adaptive checkpointing—significantly improved model explanatory power (ΔR² = .08, p < .001), demonstrating that sustainable computing practices reduce both operational costs and energy consumption. Reliability modeling confirmed that higher thermal efficiency and optimized interconnects enhanced system uptime and reduced recovery times. Validity testing (Cronbach’s α = .90; AVE > .63) established robust construct integrity across performance dimensions, while regression diagnostics verified predictor independence (VIF < 2.5). The findings conclude that performance, cost, and sustainability form an interdependent triad defining enterprise HPC efficiency. The study proposes a quantitative framework integrating energy optimization, parallel scaling, and reliability modeling to guide enterprise decision-making in AI deployment. It emphasizes that HPC-enabled AI scaling is not solely a technical enhancement but a strategic enabler of operational predictability, energy discipline, and long-term cost stability—advancing both computational science and enterprise digital transformation.
Copyright (c) 2023 Mst. Shahrin Sultana, Samia Akter (Author)
SYSTEMATIC REVIEW OF DATA SCIENCE APPLICATIONS IN PROJECT COORDINATION AND ORGANIZATIONAL TRANSFORMATION
This study investigates the quantitative relationships between data-science capability, coordination efficiency, and organizational transformation, emphasizing how analytics-driven decision systems enhance operational and strategic performance. Using a cross-sectoral dataset of 210 organizational observations, the analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the predictive influence of data-science capability and the mediating roles of data literacy and leadership alignment. The results reveal that data-science capability significantly predicts both coordination efficiency (β = 0.68, p < .001) and organizational transformation (β = 0.55, p < .001), explaining 46% and 59% of the respective variances. When mediators are included, the full model explains 67% of total variance, demonstrating that transformation outcomes depend on both technological and human enablers. Strong model-fit indices (CFI = 0.97, RMSEA = 0.037, SRMR = 0.033) confirm the reliability of the analytical framework. The study also establishes that data literacy (β_indirect = 0.23) and leadership alignment (β_indirect = 0.18) significantly strengthen the indirect pathways between analytics capability and transformation outcomes, indicating that human and managerial dimensions are integral to successful digital change. The findings highlight that coordination efficiency functions as a mechanism translating analytics maturity into process agility and transformation readiness. Overall, the study concludes that sustainable organizational transformation arises from the integration of advanced data-science capability, analytical culture, and leadership alignment. These results contribute to the growing empirical evidence that data-driven ecosystems serve as the foundation for strategic adaptability, operational precision, and continuous innovation in contemporary organizations.
Copyright (c) 2021 Md. Wahid Zaman Raj, Momena Akter (Author)
THE ROLE OF AI-ENABLED INFORMATION SECURITY FRAMEWORKS IN PREVENTING FRAUD IN U.S. HEALTHCARE BILLING SYSTEMS
This quantitative study investigated the role of AI-enabled information security frameworks in preventing fraud within U.S. healthcare billing systems, addressing a critical challenge in safeguarding financial integrity and improving payment accuracy. The research aimed to evaluate how the integration of artificial intelligence with established security controls influences technical detection performance, operational efficiency, and financial outcomes in healthcare organizations. A total of 126 peer-reviewed studies and industry reports published over the past decade were systematically reviewed to construct the theoretical foundation, guide the selection of variables, and inform the research design. The study utilized a large multi-payer dataset encompassing over 12 million claims from Medicare, Medicaid, and commercial payers, along with organizational-level measures of security maturity, AI capabilities, and governance quality. Descriptive analysis revealed that organizations implementing mature AI-enabled frameworks exhibited significantly lower fraud incidence, reduced improper payment rates, and shorter detection latency compared to those relying on traditional systems. Correlation analysis indicated strong negative associations between framework maturity and fraud-related outcomes and positive associations between AI capability indices and operational metrics such as workload yield and recovery ratios. Reliability and validity assessments confirmed the robustness of the measurement constructs, while collinearity diagnostics indicated no significant multicollinearity among predictors. Multiple regression analyses demonstrated that framework maturity, logging completeness, access control strength, and AI capability were significant predictors of improved detection performance and financial recovery, explaining a substantial proportion of variance across key outcomes. Subgroup analyses further revealed that the effectiveness of AI-enabled frameworks was moderated by organizational size, payer type, and enforcement intensity. The findings underscored the critical importance of integrating AI with strong governance, comprehensive logging, and secure access controls to build resilient fraud prevention systems.
Copyright (c) 2025 Mohammad Mushfequr Rahaman (Author)
INTELLIGENT DECISION SUPPORT IN SMART GOVERNANCE: LEVERAGING AI AND BIG DATA ANALYTICS FOR PUBLIC SECTOR EFFICIENCY AND TRANSPARENCY
Smart governance represents a transformational shift in public administration, characterized by the integration of artificial intelligence (AI) and big data analytics (BDA) to optimize decision-making, improve operational efficiency, and enhance transparency. However, existing research has not sufficiently established whether these technologies directly translate into measurable public value outcomes, nor how data governance influences this translation. This study empirically examines the extent to which AI adoption and big data analytics capability (BDAC) contribute to administrative efficiency and organizational transparency across public-sector agencies, and whether data governance acts as a moderating mechanism that conditions these effects. A quantitative, cross-sectional, multi-case research design was implemented across five public agencies utilizing cloud-enabled infrastructures and analytics-driven decision environments. Using purposive sampling, 268 respondents who were actively engaged in data, IT, or management functions completed a validated Likert-scale survey measuring AI adoption, BDAC, data governance strength, administrative efficiency, and transparency. Hierarchical ordinary least squares (OLS) regression with agency-clustered robust standard errors was employed to estimate main effects, while moderation analysis tested data governance as a structural amplifier. Control variables included agency size, budget band, IT maturity, service domain, and fixed effects to isolate contextual variation. The results demonstrate that BDAC is the strongest predictor of both administrative efficiency and transparency, indicating that analytic capability—not mere technology deployment—is the key determinant of performance outcomes in the public sector. AI adoption is positively associated with both outcomes, though to a lesser extent. Crucially, data governance significantly moderates the impact of both AI adoption and BDAC on transparency, suggesting that governance structures such as auditability, stewardship, documentation standards, and data lineage are essential for converting internal analytics into externally verifiable public value. The moderating effect on efficiency is present but less pronounced. This study advances smart governance theory by validating a capability-dominant model and positioning data governance as the enabling mechanism that transforms technical assets into accountable governance outcomes. Practically, it provides a strategic implementation roadmap emphasizing capability maturation, governance integration, and the intentional design of transparency as a measurable performance output.
Copyright (c) 2025 Anisur Rahman (Author)
SYSTEMATIC REVIEW OF SUSTAINABLE CIVIL ENGINEERING PRACTICES AND THEIR INFLUENCE ON INFRASTRUCTURE COMPETITIVENESS
This systematic review examines how sustainable civil engineering practices influence infrastructure competitiveness across the full project life cycle, integrating environmental performance with decision-relevant outcomes in cost, schedule, productivity, quality, resilience, finance, and user benefits. Following a PRISMA-aligned protocol, we searched multidisciplinary databases, screened records with dual independent reviewers, and extracted standardized data on sector, life-cycle stage, intervention type, comparators, metrics, and study quality. The final synthesis encompasses 115 peer-reviewed studies covering materials and low-carbon mix strategies, circular economy and end-of-life pathways, BIM and digital twins with IoT sensing, low-impact and off-site construction, green procurement and performance-based contracting, and risk-informed operations and maintenance. Across the corpus, sustainable practices were most consistently associated with life-cycle cost advantages and schedule reliability when boundaries extended beyond first cost, when QA and performance specifications were enforced, and when digital coordination reduced rework and variance. Modularization and lean logistics yielded notable on-site duration and waste reductions, while condition-based maintenance and resilience-aware planning improved availability and recovery. Financing signals, though smaller in volume, aligned with asset-level results, indicating modest cost-of-capital benefits where transparency and verification were strong. Neutral or mixed effects clustered where sustainability was specified late, supply chains lacked maturity, or digital adoption was not paired with clear roles and data governance. Overall, the evidence supports treating sustainability as a performance discipline that, when embedded early and measured on whole-life terms, reliably co-delivers competitiveness outcomes alongside environmental goals.
Copyright (c) 2022 Syed Zaki Uddin (Author)
HIGH-PERFORMANCE COMPUTING ARCHITECTURES FOR TRAINING LARGE-SCALE TRANSFORMER MODELS IN CYBER-RESILIENT APPLICATIONS
The exponential growth of transformer-based architectures has fundamentally reshaped the landscape of artificial intelligence, enabling breakthroughs across domains such as natural language processing, cybersecurity analytics, and autonomous decision systems. However, training and deploying these models at scale demand robust high-performance computing (HPC) infrastructures capable of managing massive computational loads, distributed data pipelines, and cyber-resilient workflows. This systematic review explores the convergence of HPC technologies and large-scale transformer model training with a particular focus on security and fault-tolerant applications. A total of 126 peer-reviewed studies published between 2018 and 2022 were analyzed using structured screening and thematic synthesis techniques. The review identifies emerging trends in GPU- and TPU-based parallelism, memory optimization strategies, model sharding techniques, and secure distributed training frameworks that enhance system resilience against data breaches and adversarial interference. Furthermore, the study discusses advances in federated learning architectures, hybrid cloud–edge HPC environments, and AI-driven workload orchestration that collectively contribute to cyber-resilient computational ecosystems. The findings highlight the increasing emphasis on integrating fault tolerance, encryption-aware resource allocation, and autonomous security auditing within large-scale model training workflows. This work provides a consolidated framework for understanding how high-performance computing infrastructures are evolving to support the scalability, reliability, and security of transformer models in mission-critical and cyber-sensitive environments.
Copyright (c) 2022 Md Mohaiminul Hasan, Md Muzahidul Islam (Author)
DATA-DRIVEN QUALITY ASSURANCE SYSTEMS FOR FOOD SAFETY IN LARGE-SCALE DISTRIBUTION CENTERS
Ensuring food safety within large-scale distribution centers has become a critical priority in modern supply chain management, particularly as global logistics networks grow increasingly complex and data-intensive. This study examines the design, implementation, and optimization of data-driven quality assurance (QA) systems that leverage advanced analytics, Internet of Things (IoT) sensors, and machine learning algorithms to monitor and control food safety parameters in real time. By integrating predictive data models with automated quality inspection frameworks, organizations can significantly reduce contamination risks, improve traceability, and maintain regulatory compliance across diverse storage and transportation environments. The study systematically reviews 112 peer-reviewed papers published between 2017 and 2022, identifying key technological trends such as blockchain-enabled traceability, AI-based anomaly detection, temperature and humidity monitoring via IoT networks, and cloud-based decision support systems for risk assessment. Findings reveal that data-driven QA architectures not only enhance operational transparency but also enable proactive responses to deviations in food quality, thereby minimizing waste and ensuring consumer safety. The paper further highlights the challenges associated with data integration, cybersecurity, and scalability when deploying such systems across multinational logistics networks. Ultimately, this review provides a comprehensive framework for developing resilient, intelligent, and adaptive QA systems that align with evolving global standards for food safety in large-scale distribution centers.
Copyright (c) 2022 Pankaz Roy Sarkar (Author)
THE ROLE OF CROSS-COUNTRY TRADE PARTNERSHIPS IN STRENGTHENING GLOBAL MARKET COMPETITIVENESS
This study examines the role of cross-country trade partnerships in strengthening global market competitiveness through an empirical and theoretical analysis covering the period 2016–2021. Using a quantitative research design that integrates static fixed-effects and dynamic autoregressive distributed lag (ARDL) error correction models, the study evaluates how the depth of trade partnerships, institutional quality, foreign direct investment (FDI) inflows, and trade openness collectively influence the Global Competitiveness Index (GCI). The results demonstrate that trade partnership depth has a significant and positive impact on competitiveness (β = 0.312, p < 0.01), indicating that economies with deeper, rule-based trade frameworks achieve greater productivity, innovation, and efficiency gains. Institutional quality is found to amplify this relationship by reinforcing regulatory stability, transparency, and enforcement capacity, thereby facilitating the effective transmission of trade-related benefits. The dynamic estimations reveal that while short-run effects are positive but modest, long-run relationships remain stable and substantial, confirming the delayed yet enduring impact of policy-driven trade integration. Furthermore, FDI inflows and export diversification are identified as key mediating variables that strengthen competitiveness by promoting technological diffusion and economic resilience. Regional analysis indicates that the European Union (EU) and ASEAN exhibit the strongest trade–competitiveness linkages due to institutional maturity and integrated governance, whereas the African Continental Free Trade Area (AfCFTA) shows emerging but moderate effects constrained by infrastructural and regulatory limitations. The study concludes that deep, inclusive, and institutionally anchored trade partnerships serve as the cornerstone of sustainable global competitiveness. It recommends that policymakers pursue comprehensive trade frameworks complemented by institutional reforms, innovation policies, and investment facilitation strategies to maximize the economic benefits of global integration. This research contributes to existing literature by providing empirical validation for the synergistic role of trade depth, institutional quality, and investment in shaping competitiveness, offering a replicable analytical model for future policy and academic applications.
Copyright (c) 2022 Md. Hasan Imam (Author)
MACHINE LEARNING AND SECURE DATA PIPELINES FOR ENHANCING PATIENT SAFETY IN ELECTRONIC HEALTH RECORD (EHR) AMONG U.S. HEALTHCARE PROVIDERS
This quantitative study investigates the integrated role of machine learning (ML) performance, secure data pipelines, governance maturity, and interoperability infrastructures in improving patient safety outcomes within Electronic Health Record (EHR) environments among U.S. healthcare providers. Drawing upon data from 22 institutions encompassing over 1.26 million de-identified patient records, the research sought to determine the extent to which algorithmic accuracy and data governance collectively predict measurable safety improvements. The study employed a multi-variable framework featuring descriptive statistics, correlation analysis, confirmatory factor analysis (CFA), and multiple linear regression modeling. Patient safety was measured using standardized Agency for Healthcare Research and Quality (AHRQ) indicators, while predictors included ML accuracy metrics (AUC-ROC, F1-score), Secure Data Pipeline Index (SDPI), Governance Maturity Score (GMS), and Interoperability Index (I²). Results indicated a strong explanatory power for the overall regression model (R² = 0.694; Adjusted R² = 0.673; F = 38.45; p < .001), confirming that the combined predictors accounted for nearly 70% of the variance in patient safety scores. ML predictive accuracy demonstrated the strongest individual contribution (β = 0.46, p < .001), followed by the Secure Data Pipeline Index (β = 0.32, p < .01), Governance Maturity (β = 0.27, p < .05), and Interoperability (β = 0.28, p < .01). Reliability analysis yielded Cronbach’s α values above 0.80 for all constructs, confirming internal consistency, while CFA results supported strong construct validity (CFI = 0.948, RMSEA = 0.054). These findings suggest that technological precision, data security, and governance oversight must co-evolve to achieve sustainable patient safety gains. The study concludes that healthcare institutions integrating ML analytics with secure, interoperable, and well-governed infrastructures experience superior safety performance, reinforcing the need for a socio-technical model of digital health reliability. Implications extend to policymakers and administrators seeking to align data-driven innovation with regulatory compliance, ethical governance, and long-term clinical resilience.
Copyright (c) 2024 Saba Ashfaq, Md. Sakib Hasan Hriday (Author)
BLOCKCHAIN-ENABLED SECURE MEDICAL BILLING SYSTEMS: QUANTITATIVE ANALYSIS OF TRANSACTION INTEGRITY
The integration of blockchain technology into medical billing systems presents a transformative approach to addressing long-standing challenges in data security, transparency, and transaction integrity within healthcare finance. This quantitative study examines the implementation and performance of blockchain-enabled medical billing frameworks, focusing on their capacity to ensure immutability, traceability, and fraud prevention in electronic health payment transactions. By employing a mixed-method analytical model that integrates transaction latency metrics, cryptographic hash validation rates, and decentralized ledger consistency indices across 152 sampled healthcare billing networks, the study quantifies the impact of blockchain on billing accuracy and system resilience. Results indicate that blockchain-driven systems demonstrate a statistically significant 38% reduction in billing discrepancies and a 45% improvement in data reconciliation efficiency compared to traditional electronic billing infrastructures. Furthermore, smart contract automation within Ethereum-based frameworks reduced claim-processing times by up to 62%, mitigating human error and unauthorized modifications. The research also highlights how consensus algorithms, particularly Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT), enhance the verification process for medical transactions without compromising processing speed. These findings underscore blockchain’s potential to redefine financial trust structures within healthcare ecosystems by ensuring end-to-end transactional integrity, auditability, and compliance with data privacy regulations such as HIPAA and GDPR. The study concludes that blockchain-enabled secure billing systems can significantly strengthen institutional accountability and patient trust while establishing a robust foundation for interoperable, tamper-proof financial data management across global healthcare networks.
Copyright (c) 2024 Md. Tarek Hasan, Md.Kamrul Khan (Author)
HYBRID DISCRETE-EVENT AND AGENT-BASED SIMULATION FRAMEWORK (H-DEABSF) FOR DYNAMIC PROCESS CONTROL IN SMART FACTORIES
This paper introduces a Hybrid Discrete-Event and Agent-Based Simulation Framework (H-DEABSF) designed for dynamic process control in smart factories. The framework integrates the advantages of discrete-event simulation (DES)—noted for its efficiency in modeling system flows and queues—with the adaptability and autonomy of agent-based simulation (ABS), which captures decentralized decision-making and interactions among heterogeneous entities. By combining these paradigms, the H-DEABSF addresses the limitations of each when applied in isolation, enabling both macroscopic process optimization and microscopic behavior modeling. The proposed framework is specifically developed to support the evolving needs of Industry 4.0, where factories must continuously adapt to fluctuating demand, real-time disruptions, and resource constraints. It leverages smart sensors, IoT-enabled devices, and cyber-physical systems to feed real-time data into the hybrid model, ensuring that simulations reflect operational realities. Through dynamic control loops, the H-DEABSF facilitates adaptive scheduling, predictive maintenance, and production-line reconfiguration, thereby enhancing responsiveness and resilience. A case study in a digital twin–enabled smart factory environment demonstrates the applicability of H-DEABSF for dynamic production scheduling under stochastic conditions. The results show improvements in system throughput, reduction of idle time, and optimized allocation of resources when compared to conventional single-method simulation models. Furthermore, the integration of human operators as autonomous agents highlights the framework’s ability to capture socio-technical interactions critical in real-world factory operations. This research contributes to the field of smart manufacturing by providing a comprehensive simulation framework that enhances real-time decision-making and supports sustainable operational strategies. It also offers an extensible platform for future integration with machine learning algorithms, enabling data-driven decision support for next-generation intelligent factories.
Copyright (c) 2024 Md Kamruzzaman, Sai Praveen Kudapa (Author)
IMPLEMENTATION OF AI-INTEGRATED IOT SENSOR NETWORKS FOR REAL-TIME STRUCTURAL HEALTH MONITORING OF IN-SERVICE BRIDGES
This quantitative study investigates the implementation and performance of artificial intelligence (AI)-integrated Internet of Things (IoT) sensor networks for real-time structural health monitoring (SHM) of in-service bridges. A dataset of 62 bridges, representing diverse structural types (steel, concrete, composite), environmental exposures (urban, rural, marine), and traffic conditions, was analyzed to understand deployment attributes, system performance, and predictors of structural condition. The study examined sensor modalities (accelerometers, GNSS, vision, and fiber Bragg gratings), data transmission networks (LoRa, ZigBee, 5G, Wi-Fi/Ethernet), and key performance indicators including sensor accuracy, AI detection precision, transmission latency, and the Bridge Health Index (BHI). Analytical procedures followed a rigorous, multi-step process: descriptive statistics profiled the asset base and technology adoption; assumption checks validated data quality and model suitability (normality, homoscedasticity, multicollinearity); correlation analysis explored variable relationships; and multiple regression models tested predictive drivers of bridge health. Results showed strong uptake of AI-enabled systems (61%) and robust sensing performance with accelerometer error ~1.8% and fiber Bragg grating error ~8 με. AI detection precision averaged 92%, while transmission latency varied substantially across networks (median: 21 ms for 5G vs. 180 ms for LoRa). The final regression model explained 64% of BHI variance (adjusted R² = 0.61). Both AI detection precision (β = 0.29, p = .001) and sensor accuracy (β = 0.27, p = .003) were strong positive predictors of BHI, while latency negatively impacted structural condition (β = −0.31, p = .001). Control variables such as bridge age, heavy traffic, and marine exposure were associated with lower BHI scores, highlighting the need to consider environmental and operational stressors. Hierarchical modeling confirmed that AI precision adds significant explanatory power beyond sensing and network performance, and interaction analyses revealed that robust AI can partially offset slower data networks, while sensor calibration is especially valuable in marine contexts. This work advances SHM practice by quantifying AI’s added predictive value, benchmarking network performance under field conditions, and clarifying environment–technology interactions.
Copyright (c) 2024 Md Ismail Hossain (Author)
THE ROLE OF BUSINESS ANALYTICS IN ESG-ORIENTED BRAND COMMUNICATION: A SYSTEMATIC REVIEW OF DATA-DRIVEN STRATEGIES
This study addresses the persistent gap between what firms say about sustainability and what stakeholders trust, by examining how business analytics (BA) capabilities shape ESG-oriented brand communication effectiveness. Drawing on a systematic review of 112 peer-reviewed papers to ground constructs and measures, and a quantitative, cross-sectional, case-based design across cloud and enterprise platform contexts, we analyze firm-level cases (n = 238) sampled from multiple industries and roles. Key variables include BA capability (data governance, skills, tooling, integration, experimentation), advanced analytics use (predictive modeling, causal testing, automation), message transparency/quality (specificity, balance, verifiability, clarity), ESG performance, and ESG communication effectiveness (clarity, credibility, trust impact, engagement intent). The preregistered analysis plan comprises scale reliability checks, descriptives, zero-order correlations, and hierarchical OLS with robust errors, including moderation tests for BA capability × ESG performance and robustness specifications (fixed effects, clustered SEs, rank-based models). Headline findings indicate that BA capability is positively associated with communication effectiveness, advanced analytics adds incremental explanatory power, transparency/quality shows a strong, independent association, and the BA payoff is steeper when underlying ESG performance is higher, implying analytics amplifies credible performance rather than substituting for it. Managerially, results recommend sequencing investments from governed data pipelines and experiment infrastructure to a transparency checklist and assurance linkages, so that what is measured, modeled, and messaged remains verifiable across channels and markets. Collectively, the evidence positions analytics-enabled transparency as an institutional capability that reliably improves the clarity, credibility, and stakeholder impact of ESG communication in cloud and enterprise settings.
Copyright (c) 2025 Rakibul Hasan (Author)
AI-POWERED BI DASHBOARDS IN OPERATIONS: A COMPARATIVE ANALYSIS FOR REAL-TIME DECISION SUPPORT
This study addresses a practical problem in operations: decision latency and variable decision quality when teams rely on visually dense dashboards under high data volume, velocity, and variability. The purpose is to quantify how AI-integrated business intelligence dashboards support real-time decision making across organizations. Using a quantitative, cross-sectional, case-based design, we analyze six production cloud and enterprise cases in manufacturing, logistics, healthcare operations, tech-enabled services, retail fulfillment, and utilities, with 168 active users as respondents. Key variables include an AI Integration Index (forecasting, anomaly detection, prescriptive recommendations, natural-language interaction, explainability), user perceptions (perceived usefulness, interpretability, trust, workload), outcomes (decision latency, decision accuracy or confidence), and contextual controls (data quality, dashboard tenure, organization size, training, analytics proficiency). The analysis plan combines descriptive statistics and correlations with multivariate regression using HC3-robust errors and case fixed effects, mediation tests via bootstrap for perceived usefulness, and moderation tests for data quality. Headline findings show that higher AI integration is associated with materially faster decisions and higher confidence, with perceived usefulness transmitting much of the effect on confidence and dependable data quality strengthening the speed benefits; interface workload relates to slower action. Implications are concrete for architects and managers: prioritize pipeline timeliness and semantic clarity, expose compact on-demand explanations and uncertainty cues, control alert and visual clutter, and connect predictions to guarded prescriptive actions. The literature review synthesizes 57 peer-reviewed papers to ground constructs, measures, and mechanisms used in this comparative evaluation.
Copyright (c) 2023 Razia Sultana (Author)
ARTIFICIAL INTELLIGENCE-DRIVEN FINANCIAL ANALYTICS MODELS FOR PREDICTING MARKET RISK AND INVESTMENT DECISIONS IN U.S. ENTERPRISES
Smart manufacturing depends on stable tribological interfaces. This systematic review synthesizes how Advanced Lubrication Management Systems (ALMS) affect equipment longevity and operational efficiency by integrating online oil and film-state sensing, automated dosing and filtration, contamination control, and connections to CMMS, MES, and digital twins. Following PRISMA 2020, we searched databases for records from 2010 to 2025, screened against predefined criteria, and appraised quality. In total, 115 studies met inclusion and we extracted outcomes for reliability, efficiency, energy, and fluids. Across 64 reliability-reporting papers, median gains were notable: mean time between failures increased by 19 percent and failure or hazard rates declined by 23 percent, underpinned by typical two-step improvements in ISO 4406 cleanliness codes and 35 percent lower moisture. Operational performance improved across 74 studies: Overall Equipment Effectiveness rose by 5.6 points, driven by availability (+3.4), with additional gains in performance (+1.7) and quality (+0.5); unplanned downtime fell by 27 percent and mean time to repair by 14 percent. Energy intensity decreased by 8.5 percent and by 12 percent in dip or splash-lubricated gearboxes; lubricant consumption fell by 22 percent and drain intervals extended by 35 percent. Economic reporting indicated a 13-month payback and first-year return of 86 percent. Moderator analyses show larger, steadier effects when ALMS are closed-loop and digitally integrated, when cleanliness and moisture targets are sustained, and when oil-state sensing is paired with contact-level regime detection. Findings position lubrication as a governed, data-driven control problem that advances reliability, throughput, energy efficiency, and sustainability in smart manufacturing.
Copyright (c) 2025 Md Arman Hossain (Author)
IOT-DRIVEN IMPLEMENTATION OF AI PREDICTIVE MODELS FOR REAL-TIME PERFORMANCE ENHANCEMENT OF PEROVSKITE AND TANDEM PHOTOVOLTAIC SYSTEMS
This systematic review synthesizes how Internet of Things infrastructures and artificial intelligence predictive models enhance real-time operation of perovskite and tandem photovoltaic systems. Following a prospectively registered protocol and the PRISMA framework, we searched major scholarly databases, screened records with two independent reviewers, and extracted commensurate metrics for quantitative aggregation alongside structured narrative synthesis. In total, 115 articles met the eligibility criteria and were included in the final synthesis. Findings highlight four operational layers. For forecasting and nowcasting, multimodal pipelines that fuse plant telemetry with all sky imagery achieved error reductions relative to persistence baselines, and attention or graph based temporal models improved skill on multi hour horizons; practical latency was reported with gateway inference suitable for supervisory control. For fault and anomaly diagnostics, deep classifiers and segmentation models operating on infrared, electroluminescence, photoluminescence, and SCADA streams delivered high discriminative performance and supported explainable overlays for technician workflows. Degradation and remaining useful life estimation benefited from physics informed or Bayesian models that combine electrical and thermal or optical channels, improving early warning and calibration over purely data driven regressors. Finally, controller guidance for maximum power point tracking and thermal regulation increasingly leverages edge aware architectures while secure data fabrics align with IEC 61850 and FAIR principles. Across these layers, perovskite and tandem aware features reduce bias under heat and spectral variability and help close the gap between laboratory devices and fielded assets. The review also offers a taxonomy and decision matrix linking sensing, models, and deployment choices to operational objectives.
Copyright (c) 2025 Zayadul Hasan (Author)
THE IMPACT OF DATA-DRIVEN DECISION SUPPORT SYSTEMS ON GOVERNANCE AND POLICY IMPLEMENTATION IN U.S. INSTITUTIONS
This systematic review examines how data driven decision support systems shape the day-to-day realization of public policy in U.S. institutions by synthesizing evidence on their effectiveness, efficiency, compliance and risk management, transparency and accountability, and equity. Guided by a preregistered protocol and PRISMA procedures, we screened multidisciplinary databases and grey sources, applied calibrated two stage eligibility checks, and extracted implementation context, study design, and outcome metrics into a structured coding frame. In total, 115 studies were reviewed and included in the final analytic corpus. Across sectors such as health and human services, justice and public safety, education, benefits administration, and inspections and transportation, we find that decision support systems consistently improve proximal outcomes when analytic signals are coupled to executable protocols inside operational workflows, with frequent gains in guideline adherence, cycle time, backlog reduction, and error rates. Programs that embed documentation, rationale capture, and monitoring show stronger compliance and quicker remediation, while equity performance is most durable where pre-launch subgroup audits and scheduled post launch checks are routine. Moderator analyses highlight that leadership sponsorship, formal data governance, provenance controls, human in the loop checkpoints, role specific training, and scheduled model monitoring are reliable predictors of sustained benefits, especially when tools are fully integrated into systems of record rather than used as standalone dashboards. We conclude that value from decision support is contingent on socio technical completeness that connects trustworthy data pipelines to intelligible models, workflow embedded actions, and accountable governance.
Copyright (c) 2025 Khairul Alam Talukder (Author)
ARTIFICIAL INTELLIGENCE-ENHANCED PREDICTIVE ANALYTICS FOR DEMAND FORECASTING IN U.S. RETAIL SUPPLY CHAINS
This systematic review synthesizes evidence on artificial intelligence enhanced predictive analytics for demand forecasting in U.S. retail supply chains, with a focus on decision relevance and deployment realism. Guided by PRISMA, we searched major multidisciplinary databases for 2015 to 2025, screened records in two stages, assessed leakage risk and baseline adequacy, and extracted harmonized metrics for point accuracy, probabilistic calibration, and inventory outcomes. The final analytic corpus comprises 95 peer-reviewed studies. Across comparable evaluations, AI models consistently outperformed strong statistical baselines, yielding median WAPE reductions of roughly 7 to 9 percent, with larger gains under cross-series training and promotion rich contexts. Feature discipline mattered: encoding price and promotion depth, holiday proximity, and identifier representations delivered an additional 3 to 6 percent improvement. Structure added value: hierarchical and cross-temporal reconciliation contributed about 4 percent error reduction and improved quantile coverage, while spatiotemporal learners reduced store-day errors by about 6 percent in geographically correlated demand. Probabilistic outputs translated into operations, enabling about 12 percent safety stock reduction at fixed service or roughly 3.5 percentage point fill rate gains at fixed inventory. Deployment practices shaped realized value: drift monitors, champion challenger governance, and accountable human overrides shortened post-shock recovery, cut stockouts by about 11 percent, and modestly increased inventory turns. We integrate these findings into a practical selection framework that aligns data realism, global modeling, calibrated quantiles, structural reconciliation, and MLOps guardrails to deliver coherent forecasts that are auditable and economically meaningful for U.S. retail planning. Implications for researchers and practitioners are discussed.
Copyright (c) 2025 Md. Rabiul Karim (Author)