Journal of Sustainable Development and Policy
Journal of Sustainable Development and Policy (JSDP) is a peer-reviewed, open-access, interdisciplinary academic journal committed to advancing research, discourse, and practical solutions on issues of sustainability, development, and public policy. The journal serves as a critical forum for scholars, policymakers, practitioners, and civil society actors to explore the dynamic interplay between environmental stewardship, economic resilience, and social equity in an era of accelerating global change. JSDP recognizes that sustainable development cannot be achieved through isolated interventions or sectoral silos. As such, the journal encourages interdisciplinary and cross-sectoral perspectives that bridge the gap between theory and practice, local experiences and global frameworks, and short-term solutions and long-term impacts. Our mission is to publish forward-thinking, empirically grounded, and policy-relevant research that fosters transformative change toward just, inclusive, and sustainable societies.
DATA-DRIVEN PROCESS OPTIMIZATION IN AUTOMOTIVE MANUFACTURING A MACHINE LEARNING APPROACH TO WASTE REDUCTION AND QUALITY IMPROVEMENT
Data-driven process optimization in automotive manufacturing was examined as a quantitative approach for reducing waste and improving product quality by linking integrated production, quality, maintenance, and batch-traceability data with measurable outcomes. The study was positioned through a structured review of 35 peer-reviewed papers and was executed as a retrospective case analysis using 48,720 eligible production-unit records joined by unit ID, station identity, and synchronized timestamps. Descriptive findings indicated scrap occurrence of 2.60% (n = 1,268), rework routing of 12.55% (n = 6,112), first-pass yield of 87.45% (n = 42,608), and defect occurrence of 6.15% (n = 2,994) at the selected inspection gate; defect cases were distributed across dimensional 36.0% (n = 1,077), weld 24.0% (n = 718), surface/paint 22.0% (n = 659), and functional 18.0% (n = 540) categories. Correlation analysis showed that rework hours per unit were positively associated with torque deviation (r = 0.46) and dimensional deviation (r = 0.41) and negatively associated with a sensor-based stability index (r = −0.38). Measurement reliability supported quantitative interpretation, including inspection label agreement of 0.97, defect-category consistency (κ = 0.78), and high repeatability for key trace-derived indicators (ICC = 0.89). Collinearity screening identified redundancy among correlated telemetry summaries, with maximum VIF reduced from 9.6 to 3.6 after consolidation. Regression results indicated higher defect odds with torque deviation (OR = 1.28, 95% CI [1.21, 1.35]) and dimensional deviation (OR = 1.42, 95% CI [1.31, 1.54]), while stability reduced defect odds (OR = 0.81, 95% CI [0.77, 0.85]). Block wise modeling showed improved explanatory power from context-only (adjusted R² = 0.08) to full models including process, equipment, and batch predictors (adjusted R² = 0.33), supporting an integrated quantitative framework for waste reduction and quality improvement.
Copyright (c) 2022 Md. Foysal Hossain, Subrato Sarker (Author)
IOT-INTEGRATED DEEP NEURAL PREDICTIVE MAINTENANCE SYSTEM WITH VIBRATION-SIGNAL DIAGNOSTICS IN SMART FACTORIES
This quantitative study examined an IoT-Integrated Deep Neural Predictive Maintenance System with Vibration-Signal Diagnostics in Smart Factories through an empirical evaluation informed by a structured review of 92 peer-reviewed journal articles and conference papers on vibration-based diagnostics, deep learning architectures, and industrial IoT deployment practices. Vibration data were acquired from 24 rotating assets operating across two smart-factory production lines at a sampling rate of 25.6 kHz and were segmented into 2.0-second windows with 50% overlap, producing 1,152,000 diagnostic windows and 38,400 prognostic sequences composed of 30 windows per sequence. The experimental design compared signal representation types (raw 1D, STFT, wavelet), model architecture families (1D CNN, temporal CNN, LSTM/GRU), inference placement (edge versus cloud), and data-quality conditions (baseline, noise, and missingness). Descriptive and inferential analyses showed that time–frequency representations consistently outperformed raw time-domain inputs for fault diagnosis and remaining useful life estimation. Wavelet-based cloud configurations achieved a macro F1 of 0.934 and PR-AUC of 0.953, compared with macro F1 of 0.881 and PR-AUC of 0.904 for raw 1D edge configurations. Prognostic accuracy followed a similar pattern, with wavelet-based cloud pipelines producing RUL MAE of 6.8 hours and RMSE of 9.9 hours, compared with 8.6 hours MAE and 12.4 hours RMSE for raw 1D edge pipelines. Mixed-effects regression confirmed statistically significant effects of representation type on diagnostic performance (wavelet versus raw, β = 0.041 for macro F1, p < 0.001) and significant degradation under missingness conditions (β = −0.034, p < 0.001). System-level analysis showed that cloud placement increased median latency by 102.6 ms, P95 latency by 181.2 ms, and bandwidth usage by 8.63 Mbps (p < 0.001), while sustaining higher throughput by 78.4 windows/s. Overall, the findings demonstrated that predictive accuracy and prognostic reliability were primarily governed by representation and architecture choices, whereas IoT placement chiefly determined latency and bandwidth behavior under realistic smart-factory streaming conditions.
Copyright (c) 2022 S. M. Habibullah, Md. Tahmid Farabe Shehun (Author)
COMPUTATIONAL THERMO-FLUID DYNAMICS MODELING FOR PROCESS OPTIMIZATION IN HYDROGEN-INTEGRATED INDUSTRIAL HEAT SYSTEMS
This study evaluated computational thermo-fluid dynamics modeling as a quantitative platform for process optimization in hydrogen-integrated industrial heat systems. A validated three-dimensional reacting CFD model was executed as a controlled numerical experiment across 60 design-of-experiments cases that spanned hydrogen volumetric shares from 0% to 100% (mean 52.3%, SD 30.1), oxidizer staging ratios from 0.50 to 0.90 (mean 0.70), swirl/mixing levels from 0.20 to 0.90 (mean 0.56), jet momentum ratios from 0.60 to 1.80 (mean 1.12), and recirculation rates from 0% to 25% (mean 11.8%). Extracted responses showed practical but non-uniform hydrogen effects: useful heat-transfer efficiency ranged from 71.2% to 86.5% (mean 79.8%, SD 4.1), load temperature spread increased markedly from 18.4°C to 72.9°C (mean 41.6°C, SD 13.8), peak wall temperature varied between 1125°C and 1328°C (mean 1224°C), chamber pressure drop rose from 185 to 412 Pa (mean 298 Pa), and NOx emission index spanned 0.38 to 1.54 g/MJ (mean 0.92 g/MJ). Correlation screening indicated strong positive associations between hydrogen share and NOx (r = 0.61), peak wall temperature (r = 0.52), and temperature spread (r = 0.47), while the link to efficiency was weak (r = 0.18). Reliability checks showed repeatability differences below 3% for all indicators and medium–fine mesh differences below 3.1%, confirming numerical stability. Multivariate regressions revealed that hydrogen share significantly increased NOx, wall hot spots, non-uniformity, and pressure drop, whereas oxidizer staging and swirl significantly mitigated these penalties; the hydrogen–staging interaction produced the largest NOx suppression. Feasible operating windows were therefore characterized by moderate-to-high hydrogen shares combined with elevated staging and swirl, preserving efficiency while meeting emissions, stability, pressure-loss, and material-temperature constraints.
Copyright (c) 2024 Saikat Sarkar (Author)
INTELLIGENT CLIMATE RISK MODELING FOR ROBUST ENERGY RESILIENCE AND NATIONAL SECURITY
This quantitative research investigates the integration of Artificial Intelligence (AI) into climate risk modeling to enhance national energy resilience and inform security planning. Climate risk modeling quantifies the probabilistic impacts of temperature anomalies, precipitation variability, and extreme weather events on energy systems, while AI provides the computational capacity to process multidimensional climatic and infrastructural datasets. Using a multi-site, longitudinal design and federated learning architecture, this study models the dynamic interdependencies among climate variables, grid performance, and defense-critical infrastructures. The results reveal strong correlations between temperature anomalies and outage frequency (r = 0.82, p < 0.01), as well as inverse relationships between precipitation variability and hydropower efficiency (r = –0.64, p < 0.05). Regression analysis indicates that climate predictors explain 71% of the variance in energy reliability outcomes (Adjusted R² = 0.71), which increases to 0.84 with the inclusion of AI-derived variables. Mediation testing demonstrates that energy resilience accounts for 42% of the indirect effect of climatic stressors on national security readiness. Reliability and validity assessments confirm strong internal consistency (Cronbach’s α > 0.85) and predictive accuracy exceeding 93%. These findings substantiate that AI-enhanced models outperform traditional statistical methods in forecasting energy system disruptions and quantifying resilience metrics. The research concludes that AI-driven climate modeling provides a scientifically verifiable foundation for anticipatory energy governance and national defense planning. Policy recommendations emphasize institutionalizing AI-based predictive analytics, establishing federated data-sharing infrastructures, and standardizing resilience metrics across critical energy networks. The study contributes to the evolving discipline of computational climate-security analytics by demonstrating how AI transforms environmental uncertainty into actionable intelligence for sustainable energy and national security systems.
Copyright (c) 2023 FNU Zulqarnain, Subrato Sarker (Author)
THE IMPACT OF SMART MATERIALS AND FIRE-RESISTANT STRUCTURES ON SAFETY IN U.S. PUBLIC INFRASTRUCTURE
This quantitative study examined how smart materials and fire-resistant structural systems influenced measurable fire-safety performance in U.S. public infrastructure. Using a cross-sectional comparative design grounded in performance-based fire engineering, the analysis evaluated bridges, tunnels, transit systems, and high-occupancy public buildings classified into baseline, smart-materials-only, fire-resistant-only, and combined-intervention conditions. Safety was operationalized as a multidimensional outcome family capturing structural survival and occupant tenability, including structural endurance time, residual load capacity after cooling, peak fire deflection, collapse-risk score, and evacuation tenability window. Descriptive results demonstrated a clear improvement gradient across intervention categories. Mean structural endurance increased from 78.4 minutes in baseline assets to 96.2 minutes in smart-materials-only assets and 104.7 minutes in fire-resistant-only assets, reaching 121.3 minutes in combined systems. Residual load capacity followed the same pattern, rising from 62.1% in baseline systems to 69.5% in smart-materials-only systems, 72.8% in fire-resistant-only systems, and 79.6% in combined systems. Peak fire deflection declined progressively from 41.7 mm in baseline assets to 35.2 mm under smart materials, 32.8 mm under fire-resistant structures, and 27.6 mm under combined assemblies. Collapse-risk scores were reduced from 0.34 in baseline systems to 0.27 in smart-materials-only systems, 0.24 in fire-resistant-only systems, and 0.18 in combined systems. Evacuation tenability windows expanded from 9.6 minutes in baseline assets to 11.4 minutes under smart materials, 12.2 minutes under fire-resistant structures, and 13.8 minutes under combined systems, with the largest tenability gains observed in tunnel, transit, and public-building assets. Inferential modeling confirmed that passive protection level and redundancy rating were the strongest predictors of structural endurance and collapse-risk reduction, while sensing responsiveness and compartment integrity were dominant predictors of tenability improvement. Interaction testing indicated statistically meaningful non-additive benefits for combined interventions, showing that integrated smart-material and fire-resistant assemblies corresponded to the most robust and stable safety outcomes across U.S. public infrastructure types.
Copyright (c) 2025 Masud Rana, Md Sarwar Hossain Shuvo (Author)
AI-ENHANCED MIS PLATFORMS FOR STRATEGIC BUSINESS DECISION-MAKING IN SMES
This study investigated how AI-enhanced management information systems (MIS) platforms influenced strategic business decision-making in small and medium-sized enterprises (SMEs) using a quantitative explanatory model grounded in a review of 40 peer-reviewed papers. Cross-sectional survey data were collected from 352 SMEs (70.4% usable response rate) representing services (33.5%), manufacturing (26.1%), retail/trade (21.0%), logistics (10.8%), and technology-enabled sectors (8.5%); 55.7% were small firms, 31.3% medium, and 13.1% micro. Descriptive results showed moderately high AI-MIS capability (M = 5.21, SD = 0.89), led by predictive analytics (M = 5.44) and automated insight/alerting (M = 5.36), while prescriptive recommendation was lower (M = 4.88). MIS integration quality was mid-range (M = 4.76, SD = 0.93), with breadth (M = 4.92) exceeding depth (M = 4.61). Managerial usage intensity was moderately high (M = 4.98), driven by usage frequency (M = 5.24) rather than scenario testing (M = 4.68). Strategic decision outcomes were positive overall (M = 5.06), strongest for decision speed (M = 5.28) and cross-functional alignment (M = 5.26), with weaker risk calibration (M = 4.72). Correlations supported theoretical associations: AI-MIS capability correlated with usage intensity (r = 0.71) and decision outcomes (r = 0.68), and integration quality correlated with decision outcomes (r = 0.60). Reliability and validity were strong (Cronbach’s α = 0.85–0.93; CR = 0.88–0.94; AVE = 0.52–0.68; HTMT ≤ 0.79), and collinearity was acceptable (VIF = 1.58–2.13). Regression results indicated significant direct effects of AI-MIS capability on decision outcomes (β = 0.41, t = 8.72, p < .001) and usage intensity (β = 0.58, t = 12.10, p < .001). Usage intensity predicted decision outcomes (β = 0.47, t = 10.24, p < .001) and partially mediated the capability–outcomes relationship (indirect β = 0.27, 95% CI [0.20, 0.35]; residual direct β = 0.14, p = .003). Integration quality independently improved decision outcomes (β = 0.26, p < .001) and moderated capability effects on usage (β = 0.12, p = .007) and outcomes (β = 0.11, p = .014). The final models explained 54% of variance in usage intensity and 62% in strategic decision outcomes.
Copyright (c) 2023 Aditya Dhanekula, M.A. Rony (Author)
ASSESSMENT OF DATA-DRIVEN VENDOR PERFORMANCE EVALUATION IN RETAIL SUPPLY CHAINS ANALYZING METRICS, SCORECARDS, AND CONTRACT MANAGEMENT TOOLS
This quantitative explanatory study assessed data-driven vendor performance evaluation in retail supply chains by integrating multi-KPI scorecards with downstream operational outcomes. The measurement architecture and variable logic were grounded in a structured review of 32 empirical papers on retail vendor evaluation, KPI taxonomies, scorecard construction, and outcome modeling, which informed the selection of delivery, quality, cost, and flexibility indicators. Operational data were compiled from ERP purchasing tables, WMS receipt logs, TMS carrier scans, POS demand records, supplier portal submissions, and claims databases. Of 312 vendors initially identified, 47 inactive vendors and 23 vendors with insufficient transaction histories were excluded, yielding 242 active vendors observed from January 2021 to December 2021. After data cleaning, 3,805 vendor–period records remained for analysis. Descriptive results showed strong average delivery performance (on-time delivery mean 91.4%, SD 6.8; order fill rate mean 94.1%, SD 5.2), while lead-time deviation displayed wider dispersion (mean 2.6 days, SD 1.9). Quality performance was generally stable but risk-concentrated (defect rate mean 1.9%, SD 1.4; return ratio mean 2.7%, SD 2.0), and flexibility exhibited the highest volatility (rush-order acceptance mean 76.8%, SD 14.9; recovery time mean 4.1 days, SD 3.3). Reliability tests supported dimensional consistency (Cronbach’s α 0.86 delivery, 0.83 quality, 0.79 cost, 0.76 flexibility; overall scorecard α 0.88). Correlation patterns confirmed KPI coherence (on-time delivery–fill rate r 0.74; defect rate–return ratio r 0.69) and strong alignment between vendor scores and retail outcomes (overall score–stockout r −0.58; overall score–shelf availability r 0.62). Driver models indicated that forecast variability (β −0.27, p < .001), distance (β −0.14, p = .010), and product complexity (β −0.17, p = .003) reduced vendor performance, while vendor size improved scores (β 0.18, p = .002). Outcome regressions demonstrated temporally ordered effects, with delivery strength reducing stockouts (β −0.36) and quality strength lowering return processing costs (β −0.29). Overall, the study showed that data-driven scorecards reliably differentiated vendor capability and explained meaningful variation in retail service and cost outcomes.
Copyright (c) 2022 Md. Abdur Rauf, Zamal Haider Shish (Author)
SMART HYBRID MANUFACTURING: A COMBINATION OF ADDITIVE, SUBTRACTIVE, AND LEAN TECHNIQUES FOR AGILE PRODUCTION SYSTEMS
Smart Hybrid Manufacturing (SHM)—the coordinated integration of additive, subtractive, lean, and smart control capabilities—has emerged as a central pathway for enabling responsive, high-quality, and resource-efficient production in modern manufacturing systems. Yet, empirical evidence explaining how these multidimensional capability foundations contribute to productivity, quality, efficiency, and agility remains limited. This study addresses this gap by developing and testing a comprehensive capability–performance model using data from a heterogeneous sample of industrial plants and hybrid manufacturing cells operating across multiple sectors, complexity classes, and volatility conditions. The research examines how additive and subtractive process maturity, lean–smart integration governance, and cyber-physical smart control jointly shape agile production system performance (APSP), while also assessing the mediating role of smart control capability and the moderating role of lean maturity. Additional multi-group comparisons evaluate whether SHM performance impacts differ across hybridization architectures and environmental turbulence. Measurement model evaluation demonstrated strong psychometric robustness. All constructs achieved acceptable levels of internal consistency (Cronbach’s α ≥ 0.85; CR ≥ 0.90), convergent validity (AVE ≥ 0.70), and discriminant validity across Fornell–Larcker, HTMT, and CFA criteria. The overall measurement structure supported a multidimensional representation of SHM capability, as well as second-order factors for SHM Capability (SHMC) and Agile Production System Performance (APSP). Model fit indices (CFI = 0.957; TLI = 0.948; RMSEA = 0.045; SRMR = 0.037) indicated strong alignment between data and the hypothesized measurement structure. Structural equation modeling results provided strong support for the hypothesized relationships. SHM Capability exhibited a significant and substantial effect on APSP (β = 0.62), explaining more than half of the observed variance in agile system performance. Each SHM sub-dimension demonstrated its expected directional relationship with specific performance outcomes: additive maturity predicted agility, subtractive finishing predicted quality, and lean–smart integration predicted productivity and efficiency. Smart Control Maturity partially mediated the SHM → quality relationship, confirming that in-situ sensing and closed-loop correction serve as essential mechanisms for enabling conformance and reducing variability. Lean maturity significantly moderated the SHM → productivity and SHM → agility relationships, indicating that SHM benefits are amplified under strong pull-flow discipline and structured continuous improvement.
Copyright (c) 2023 Md Al Amin Khan, Md Mesbaul Hasan (Author)
INTEGRATION OF IOT AND EDGE COMPUTING FOR LOW-LATENCY DATA ANALYTICS IN SMART CITIES AND IOT NETWORKS
This quantitative, cross-sectional, case-based study investigates how the integration of Internet of Things (IoT) infrastructures with edge computing architectures enhances low-latency data analytics and improves the quality of smart city services. Although smart city ecosystems continue to expand globally, a persistent challenge lies in their reliance on cloud-centric analytics models, which often struggle to satisfy stringent latency, reliability, and responsiveness requirements associated with time-sensitive public services such as traffic control, emergency response, environmental monitoring, and utility management. The central problem addressed in this study is that traditional cloud-dependent analytics pipelines frequently introduce processing delays and network congestion, thereby constraining the ability of municipalities and smart service operators to deliver real-time, high-quality services. This study therefore aims to provide empirical evidence, grounded in real IoT edge deployment scenarios across cloud and enterprise environments, regarding how integration quality and underlying infrastructure conditions shape latency outcomes and perceived service performance. Data were collected using a structured questionnaire administered to professionals directly involved in smart city, IoT systems design, and network infrastructure projects. Out of 250 distributed surveys, 200 valid responses were obtained, yielding an 80 percent usable response rate suitable for inferential analyses. Key constructs—including IoT Edge Integration Quality, Network Infrastructure Quality, System Reliability, Low Latency Analytics Performance, Smart City Service Quality, Adoption or Optimization Intention, and Perceived Integration Challenges—were measured using multi-item Likert scales. Analytical procedures included descriptive statistics, reliability and validity testing, Pearson correlations, and multiple regression modeling to examine predictive relationships among the variables. The regression models accounted for 56 percent of the variance in low-latency analytics performance and 61 percent of the variance in smart city service quality. IoT edge integration quality (β = 0.39), system reliability (β = 0.26), and network infrastructure quality (β = 0.17) emerged as significant predictors of low-latency analytics, underscoring the combined importance of architectural coherence, dependable system behavior, and communication efficiency. Furthermore, low-latency analytics performance demonstrated a strong positive effect on smart city service quality (β = 0.37), highlighting latency reduction as a key mechanism for improving user experience, operational responsiveness, and service effectiveness.
Copyright (c) 2023 Md Harun-Or-Rashid Mollah, Mst. Shahrin Sultana, Sai Praveen Kudapa (Author)
IOT INTEGRATION IN INTELLIGENT LUBRICATION SYSTEMS FOR PREDICTIVE MAINTENANCE AND PERFORMANCE OPTIMIZATION IN ADVANCED MANUFACTURING INDUSTRIES
This study investigates how the integration of Internet of Things (IoT) technologies into intelligent lubrication systems enhances predictive maintenance capability and supports performance optimization within advanced manufacturing industries. The research addresses a critical empirical gap: although Maintenance 4.0 frameworks widely highlight the strategic role of smart lubrication and sensor-embedded subsystems, there remains insufficient quantitative evidence regarding how IoT-enabled lubrication solutions influence plant-level operational outcomes such as equipment availability, unplanned downtime, throughput stability, and overall equipment effectiveness (OEE). To respond to this gap, the study develops and empirically tests a conceptual model that links IoT integration, predictive maintenance effectiveness, and multidimensional performance outcomes in cloud-connected, data-driven manufacturing environments. A quantitative, cross-sectional, case-based survey design was adopted, utilizing a structured five-point Likert questionnaire administered to maintenance, reliability, production, and engineering professionals working in plants that have deployed IoT-driven lubrication and condition monitoring technologies. A total of 210 usable responses were collected, reflecting an effective response rate of 80.8 percent and representing diverse industrial contexts with varying levels of digital maturity. The model incorporated key variables including IoT integration in intelligent lubrication systems, predictive maintenance effectiveness, operational performance outcomes, and user acceptance—recognized in technology adoption theory as a critical enabler of system effectiveness. Data analysis involved multiple stages, beginning with descriptive statistics and reliability assessments to verify internal consistency across constructs, followed by Pearson correlation analysis to establish initial relationships among variables. A series of multiple regression models and mediation–moderation analyses, supplemented with bootstrapping procedures, were employed to rigorously test the hypothesized relationships. Results indicated that IoT integration exerted a strong and statistically significant positive effect on predictive maintenance effectiveness (β = 0.66, R² = 0.54), confirming that advanced, sensor-enabled lubrication systems materially enhance a plant’s capability to anticipate equipment failures and prevent lubrication-related anomalies. Further, IoT integration and predictive maintenance jointly accounted for 63 percent of the variance in multidimensional performance outcomes, highlighting the centrality of intelligent lubrication systems as a technological lever for improving OEE and reliability-centered performance metrics. Mediation results showed that predictive maintenance partially mediated the IoT–performance relationship (indirect effect = 0.34), indicating that IoT-generated condition data translate into performance gains primarily through improved diagnostic and prognostic capabilities. Additionally, user acceptance significantly moderated the IoT–predictive maintenance pathway, demonstrating that even sophisticated digital lubrication and monitoring architectures require high levels of user readiness, trust, and operational engagement to deliver their full value.
Copyright (c) 2023 Zobayer Eusufzai (Author)
INTEGRATING INDUSTRIAL ENGINEERING AND PETROLEUM SYSTEMS WITH LINEAR PROGRAMMING MODEL FOR FUEL EFFICIENCY AND DOWNTIME REDUCTION
This study addresses the persistent operational challenges of high fuel consumption and costly downtime in petroleum refinery environments, focusing on how the coordinated application of industrial engineering practices and linear programming (LP)–based optimisation can jointly improve these outcomes. Refinery operations typically involve complex, multistage conversion processes that are prone to inefficiencies arising from suboptimal process routing, equipment degradation, and inconsistent production planning. Motivated by these systemic issues, the present research adopts a quantitative cross-sectional, case-based approach conducted in a single enterprise refinery, integrating perceptual and objective data sources. Survey responses were collected from a diverse sample of 120 refinery personnel—including engineers, operators, maintenance technicians, and managerial staff—capturing organisational practices and capabilities related to production engineering and operational management. These perceptual constructs were complemented with plant-level operational records, including historical data on fuel consumption, throughput volumes, and downtime frequency and duration, thereby enabling a comprehensive evaluation of both human-centred practices and technical performance indicators. The study focused on four key explanatory variables: Industrial Engineering Practices, Energy-Management Capability, Maintenance and Reliability Practices, and LP-Based Optimisation Capability. Each construct was operationalised using multiple items measured on five-point Likert scales to reflect employees’ assessments of procedural integration, resource utilisation, reliability culture, and modelling proficiency. The outcome variables were derived directly from refinery performance data: a Fuel-Efficiency Index calculated from total fuel consumed relative to throughput, and a Downtime Ratio reflecting the share of operational hours lost to equipment or process failures. The analytical strategy proceeded in several stages. First, descriptive statistics established baseline distributions of the constructs and performance metrics. Second, Pearson correlation analyses identified initial associations between practice constructs and operational outcomes. Third, multiple regression models were estimated to quantify the individual and combined predictive effects of the four practice constructs on fuel efficiency and downtime. The regression findings indicated that all four explanatory constructs significantly predicted the Fuel-Efficiency Index, collectively explaining 39 percent of its variance (R² = 0.39). This suggests that production planning discipline, energy-conscious operating behaviour, robust maintenance routines, and modelling capability have measurable and complementary effects on refinery energy performance. For the Downtime Ratio, the analysis revealed that Maintenance and Reliability Practices, Industrial Engineering Practices, and LP-Based Optimisation Capability were statistically significant predictors, jointly explaining 42 percent of the variance (R² = 0.42). These results reinforce the influential role of reliability-centred maintenance, structured engineering interventions, and model-informed decision-making in reducing operational disruptions and system bottlenecks.
Copyright (c) 2023 Sabuj Kumar Shil (Author)
VEHICLE-TO-INFRASTRUCTURE (V2I) COMMUNICATION AND TRAFFIC INCIDENT REDUCTION: AN EMPIRICAL STUDY ACROSS U.S. HIGHWAY NETWORKS
Vehicle-to-Infrastructure (V2I) communication has emerged as a critical component of intelligent transportation systems designed to enhance roadway safety, operational efficiency, and real-time decision-making across highway networks. This empirical study examines the extent to which V2I-enabled information exchange—particularly involving roadside units, traffic management centers, and connected vehicle systems—contributes to measurable reductions in traffic incidents across major U.S. highways. Drawing on multi-state datasets that integrate incident logs, roadway sensor feeds, and V2I communication records, the analysis assesses correlations between V2I deployment density and decreases in crash frequency, severity, and secondary collisions. Advanced statistical modeling and spatiotemporal analysis reveal that highways with mature V2I infrastructure experience significantly improved driver hazard awareness, reduced response lag for incident management teams, and smoother traffic flow patterns under high-volume conditions. Additionally, the findings highlight regional disparities in V2I effectiveness influenced by infrastructure investment levels, network design, and operational integration with legacy systems. The study contributes to the growing body of empirical evidence demonstrating the tangible safety benefits of connected transportation ecosystems and underscores the importance of policy alignment, sustained infrastructure funding, and interoperable communication standards for maximizing V2I’s impact on national roadway safety outcomes. Beyond its empirical contributions, this study reinforces growing national and international evidence supporting the safety benefits of connected transportation ecosystems. It highlights the critical need for continued infrastructure funding, harmonized policy frameworks, and interoperable communication standards to ensure that V2I technologies operate cohesively across jurisdictions and platforms. Ultimately, the expanded insights offered here illustrate how robust V2I deployment not only mitigates crash risks but also strengthens the overall resilience and efficiency of the transportation network, positioning connected vehicle technologies as an essential component of future roadway safety strategies.
Vehicle-to-Infrastructure (V2I) Communication
Copyright (c) 2025 Md Asfaquar Rahman (Author)
INTEGRATION OF ARTIFICIAL INTELLIGENCE AND ADVANCED COMPUTING TO DEVELOP RESILIENT CYBER DEFENSE SYSTEMS
This quantitative cross-sectional, case-based study investigates a critical challenge facing digitally intensive organizations: although many firms invest heavily in artificial intelligence and advanced computing platforms for cyber defense, there is limited empirical clarity on how these capabilities translate into improved cyber resilience. To address this gap, the study tested the relationships between AI-enabled analytics—operating across cloud, big data, and edge-computing infrastructures—and two organizational outcomes: cyber defense process capability and cyber resilience. Data were gathered through a structured survey of 210 cybersecurity professionals working in cloud-intensive enterprises, resulting in a 75 percent usable response rate from 280 distributed questionnaires. Three latent variables—AI advanced-computing integration capability, cyber defense process capability, and cyber resilience—were measured using reliable five-point Likert scales with high internal consistency (α = 0.89 to 0.92). The analysis followed a staged approach using descriptive statistics, Pearson correlations, and hierarchical multiple regression with organizational size, sector, and regulatory exposure included as controls. Correlation results showed strong and meaningful associations: AI integration was positively related to cyber defense process capability (r = 0.58) and to cyber resilience (r = 0.49), while cyber defense process capability demonstrated a substantial positive relationship with resilience (r = 0.62). Regression findings further revealed that AI integration had a significant direct effect on resilience (β = 0.38, p < .01). When cyber defense process capability was added to the model, the effect of AI integration remained positive but decreased (β = 0.21), while process capability emerged as a strong predictor of resilience (β = 0.47), producing a model that explained 45 percent of variance (R² = 0.45). Overall, the results suggest that AI-enabled analytics and scalable computing infrastructures contribute to stronger cyber resilience, particularly when embedded within mature monitoring, detection, and incident-response processes. These findings indicate that CISOs and security leaders should jointly prioritize the development of AI-driven architectures and the governance of cyber defense workflows. Additionally, the validated measurement model and empirically supported relationships generated by this study offer a foundation for further research on the mechanisms through which AI enhances organizational resilience.
Copyright (c) 2023 Saba Ashfaq, Shaikat Biswas, Tonoy Kanti Chowdhury (Author)
THE EFFECT OF PRODUCTION PLANNING EFFICIENCY ON DELIVERY TIMELINES IN U.S. APPAREL IMPORTS
This study investigated the effect of production planning efficiency on delivery timelines in U.S. apparel imports, drawing on quantitative analysis supported by a comprehensive dataset of 1,864 purchase orders across multiple sourcing regions. The research examined whether planning efficiency—captured through schedule adherence, capacity utilization accuracy, work-in-progress stability, and production cycle consistency—served as a significant predictor of on-time shipment performance, days late, and lead-time variance. Logistic regression results showed that production planning efficiency substantially improved delivery outcomes, increasing the likelihood of on-time shipment by 2.18 times for every unit increase in planning performance. Linear regression further revealed that higher planning efficiency reduced shipment delays by an average of 0.46 days and lowered lead-time variance by 39%, even when controlling for order quantity, style complexity, seasonality, and shipping mode. Additional findings indicated that style complexity and large order volumes contributed to greater delays; however, planning efficiency remained a consistently significant predictor across all conditions. Air shipments demonstrated superior punctuality compared with sea shipments, yet planning performance continued to exert a strong influence independent of transportation mode. Collectively, the findings confirm that production planning efficiency is a critical operational capability that directly improves the timeliness and predictability of U.S. apparel imports. The results underscore the need for supplier factories to strengthen planning systems and for U.S. importers to prioritize planning-related metrics in vendor evaluations. This study contributes to a deeper understanding of the operational drivers of delivery performance and highlights planning efficiency as a central determinant of supply chain reliability in global apparel sourcing.
Copyright (c) 2023 Md. Hasan Imam, Ashraful Islam (Author)
THE INFLUENCE OF SECURE DATA SYSTEMS ON FRAUD DETECTION IN BUSINESS INTELLIGENCE APPLICATIONS
This study investigated the influence of secure data systems on fraud detection performance within business intelligence (BI) applications through a quantitative analysis. The empirical phase examined five core security dimensions—access control maturity, encryption coverage, monitoring completeness, pipeline security, and data governance strength—across 220 organizations using BI-driven fraud detection. Descriptive analysis showed moderate to high implementation of security features, with mean scores ranging from 4.8 to 5.4 on a seven-point scale. Fraud detection performance indicators demonstrated substantial variability, with detection accuracy averaging 78.4%, false-positive rates averaging 22.7%, and time-to-detection ranging from 1 to 72 hours. Correlation analysis revealed strong positive associations between monitoring completeness and detection accuracy (r = .52), as well as between pipeline security and output consistency (r = .54). Regression analysis indicated that secure data system features explained 39% additional variance in detection accuracy beyond organizational controls. Monitoring completeness (β = .28), pipeline security (β = .24), and governance strength (β = .22) emerged as the strongest predictors. Access control maturity demonstrated a significant negative effect on false-positive rates (β = –.26), while encryption coverage showed meaningful influence on faster time-to-detection (β = .19). Mediation analysis found that security culture partially strengthened relationships between key security features and detection outcomes, whereas BI integration complexity weakened them. Overall, the findings demonstrated that secure data systems play a decisive role in enhancing fraud detection accuracy, stability, and responsiveness within BI environments, highlighting their importance as both protective and performance-enabling components of organizational analytics.
Copyright (c) 2024 Hozyfa Shafa, Mst. Shahrin Sultana (Author)
EMPIRICAL INSIGHTS INTO POWER BI APPLICATIONS FOR DYNAMIC FINANCIAL REPORTING AND PREDICTIVE ANALYSIS
This study addresses a persistent problem in finance analytics, namely the lack of quantitative, finance specific evidence on how technical quality, user proficiency, and interactive design jointly translate self service BI into better forecasts and decisions. The purpose is to estimate the contribution of Power BI enabled dynamic reporting and embedded predictive analysis to decision quality, and to identify the conditions under which these benefits are strongest. Using a quantitative, cross sectional, case based design, we sampled 208 professionals across 29 cloud and enterprise cases that have used governed Power BI environments for at least six months. Key variables include information quality, system quality, dashboard interactivity, user training and proficiency, organizational support, dynamic reporting effectiveness, predictive performance, and decision quality. The analysis plan specified hierarchical OLS models with HC3 robust errors, moderation by organizational support, and bias corrected bootstrap mediation from reporting to decision quality via predictive performance, with assumption checks and sensitivity tests. Headline findings show that dashboard interactivity, information quality, user proficiency, and system quality uniquely predict dynamic reporting effectiveness, with organizational support adding a direct effect and amplifying returns to proficiency and interactivity. Higher reporting effectiveness is associated with meaningfully better forecast accuracy, approximately a 0.9 percentage point reduction in MAPE per one unit increase in reporting effectiveness, and both reporting effectiveness and predictive performance explain decision quality, with a significant indirect effect through predictive performance. Implications for practice emphasize governed self service: invest early in role targeted training, codify semantic and design standards, and maintain visible sponsorship so finance teams consistently realize faster, more accurate, and more auditable decisions.
Copyright (c) 2023 Md Redwanul Islam (Author)
SYSTEMATIC REVIEW OF STRESS AND BURNOUT INTERVENTIONS AMONG U.S. HEALTHCARE PROFESSIONALS USING ADVANCED COMPUTING APPROACHES
This systematic review investigates how advanced computing technologies are transforming the detection and management of stress and burnout among U.S. healthcare professionals, conditions that remain pervasive and systemically embedded within the nation’s healthcare infrastructure. Adhering to PRISMA guidelines, the study systematically analyzed 92 empirical works published between 2013 and 2024 across databases including PubMed, Scopus, Web of Science, IEEE Xplore, ScienceDirect, and PsycINFO, emphasizing interventions utilizing artificial intelligence (AI), machine learning (ML), wearable sensors, predictive analytics, and digital therapeutics. The synthesis revealed that burnout prevalence remains critically high, particularly among nurses and frontline clinicians, driven by workload intensity, emotional fatigue, and administrative burden. Machine learning and AI-based predictive models demonstrated accuracies between 78% and 94% in identifying burnout risk through multimodal data integration, encompassing electronic health records, physiological signals, and communication patterns. Wearable and biosignal monitoring systems, capturing heart rate variability, electrodermal activity, and sleep metrics, achieved approximately 85% accuracy in detecting stress and enabled real-time interventions that reduced physiological arousal. AI-driven behavioral interventions, such as adaptive cognitive-behavioral therapy platforms, chatbots, and virtual reality relaxation modules, reduced self-reported stress levels by nearly 28% and enhanced emotional regulation and resilience. Institutional analytics and decision-support systems using predictive dashboards improved workforce retention by up to 22%, linking data-informed workload management with organizational well-being. Ethical implementation emerged as a critical determinant of technology acceptance, emphasizing compliance with HIPAA standards, transparency in data use, informed consent, and mitigation of algorithmic bias. Collectively, the findings underscore a paradigm shift from reactive, self-reported burnout management to proactive, computationally enabled frameworks that merge predictive intelligence with real-time behavioral support. By embedding these technologies within healthcare infrastructures, institutions can move toward sustainable, ethically governed systems that prioritize psychological well-being alongside operational efficiency, marking a transformative advance in the integration of digital health analytics into occupational wellness and resilience management.
Copyright (c) 2024 Pankaz Roy Sarkar, Sai Praveen Kudapa (Author)
A REVIEW ON SUSTAINABLE BUILDING MATERIALS AND THEIR ROLE IN ENHANCING U.S. GREEN INFRASTRUCTURE GOALS
This study presented a comprehensive systematic review of sustainable building materials and their role in enhancing U.S. green infrastructure goals, focusing on their environmental performance, structural functionality, and policy integration within national sustainability frameworks. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review examined 78 peer-reviewed papers published over the past two decades, encompassing a wide range of material innovations, life-cycle assessments, and infrastructure applications. The objective of this research was to synthesize scientific, technical, and policy-based evidence that explains how sustainable materials contribute to the design, implementation, and resilience of green infrastructure systems in the United States. The reviewed studies collectively demonstrated that low-carbon cementitious binders, bio-based composites, and recycled materials significantly reduced embodied carbon and energy while improving durability, water management, and heat mitigation performance. Quantitative analyses across the selected papers revealed that the substitution of traditional construction materials with sustainable alternatives reduced greenhouse gas emissions by up to 70% and extended the service life of pavements, roofs, and stormwater systems through enhanced resilience to environmental stressors. Moreover, the integration of permeable pavements, green roofs, and recycled aggregates supported improved hydrological balance, pollutant filtration, and thermal regulation across urban landscapes. The study further found that U.S. federal and state-level programs—such as the EPA Green Infrastructure Program and the FHWA Sustainable Pavements initiative—had facilitated innovation through research funding, performance guidelines, and policy incentives, although challenges remained in data standardization, cost variability, and regional material supply chains. The synthesis emphasized the importance of interagency collaboration, interdisciplinary research, and transparent life-cycle reporting to accelerate market transformation and policy coherence. Overall, the review concluded that sustainable building materials play a pivotal role in advancing the ecological, economic, and social dimensions of green infrastructure, serving as critical enablers of climate resilience, resource efficiency, and long-term urban sustainability in the United States.
Copyright (c) 2024 Md. Mominul Haque, Syed Zaki Uddin (Author)
EXPLAINABLE REINFORCEMENT LEARNING FOR HIGH-STAKES DECISION SYSTEMS DEVELOPING INTERPRETABLE RL MODELS FOR AUTONOMOUS VEHICLES, HEALTHCARE, OR FINANCE
The study on Explainable Reinforcement Learning (XRL) for High-Stakes Decision Systems: Developing Interpretable RL Models for Autonomous Vehicles, Healthcare, or Finance had been conducted to investigate how interpretability in reinforcement learning enhances performance, trust, and accountability in critical decision-making environments. This research had reviewed and synthesized findings from 126 peer-reviewed papers spanning the past decade, focusing on the integration of explain ability mechanisms into reinforcement learning models applied to safety-critical and ethically sensitive domains. The study aimed to identify quantitative relationships between key explain ability constructs—fidelity, stability, and comprehensibility—and measurable human or system outcomes such as decision accuracy, response time, trust calibration, and accountability perception. Using a mixed quantitative framework, the research combined simulation-based performance data, human-centered evaluation metrics, and statistical modeling to assess how explainable RL architectures perform compared to non-explainable counterparts. The findings revealed that explainable reinforcement learning models consistently outperformed traditional opaque systems across all three domains. In autonomous vehicles, explanations improved driver response times and reduced intervention rates; in healthcare, they enhanced clinician confidence and treatment decision accuracy; and in finance, they improved risk-adjusted returns and investor trust. Regression and correlation analyses demonstrated that explanation fidelity strongly predicted decision accuracy, while explanation stability and comprehensibility were significant predictors of trust and accountability. Furthermore, repeated-measures ANOVA confirmed statistically significant improvements in user trust and performance under explainable conditions, supported by large effect sizes. The study also identified several persistent challenges, including the trade-off between interpretability and performance, variability in user comprehension, and limitations in real-time explanation delivery. Overall, the review and empirical analysis provided a comprehensive understanding of how explainable reinforcement learning contributes to safer, more transparent, and ethically accountable AI-driven decision systems. The insights derived from the 126 reviewed studies establish a robust foundation for developing future XRL frameworks capable of balancing performance optimization with human interpretability in complex, high-stakes environments such as autonomous vehicles, clinical systems, and financial analytics.
Copyright (c) 2022 Sai Srinivas Matta, Manish Bolli (Author)
THE ROLE OF AI-ENABLED CUSTOMER SEGMENTATION IN DRIVING BRAND PERFORMANCE ON ONLINE RETAIL PLATFORMS
This study examines the role of artificial intelligence (AI)–enabled customer segmentation in enhancing brand performance on online retail platforms, addressing how data-driven audience design translates into measurable marketplace outcomes. Using a quantitative, cross-sectional, case-based design, the research analyzes relationships among AI-enabled segmentation capability, personalization quality, customer engagement, data governance strength, and platform-based brand performance, controlling for firm size, category, advertising spend, tenure, and price tier. Data were gathered through structured five-point Likert-scale surveys from 200 brand-side professionals responsible for e-commerce and performance marketing within a focal marketplace ecosystem. Statistical analyses—including reliability and validity tests, Pearson correlations, hierarchical OLS regressions with robust (HC3) errors, and bootstrapped mediation and moderation models—reveal that AI-enabled segmentation capability has a strong positive effect on brand performance (β = .31, p < .001), explaining an additional 10% of variance beyond structural controls. The relationship is partially mediated by personalization quality and customer engagement, with significant indirect effects (AISC → PQ → BP = .09; AISC → CE → BP = .06, 95% CI excluding zero), indicating that improved relevance and deeper interactions are key pathways through which capability drives performance. Moreover, data governance moderates this relationship (β = .14, p < .01), showing that segmentation under stronger consent, access, and quality controls yields steeper performance gains than under weaker governance. Descriptive findings indicate moderate-to-high maturity across firms (AISC M = 3.78; PQ M = 3.58; BP M = 3.62 on a 1–5 scale), with governance showing the widest dispersion (M = 3.36, SD = 0.82). Overall, the results establish that AI-enabled segmentation enhances brand outcomes when supported by experiential excellence and disciplined data stewardship. The study contributes to marketing analytics and dynamic capability theory by demonstrating that segmentation, personalization, engagement, and governance function as interdependent levers of brand performance, and it recommends that firms institutionalize segmentation as a continuously refreshed, governance-anchored process to maximize platform returns.
Copyright (c) 2024 Abdul Hye, Mohammad Shoeb Abdullah (Author)
AI-BASED LANGUAGE EDUCATION PLATFORMS: A SYSTEMATIC ANALYSIS OF EDTECH TOOLS FOR ENGLISH PROFICIENCY
This study addresses a practical problem in higher education and enterprise deployments: institutions are adopting AI-based language learning platforms without clear, quantified evidence of which features most strongly relate to measurable English proficiency. The purpose is to estimate feature–outcome relationships in authentic settings. Using a quantitative, cross-sectional, case-based design, we analyze multi-institution data from cloud-hosted, enterprise-grade platforms (writing focused, speaking focused, and integrated skills). The sample comprises linked survey, telemetry, and assessment records, with proficiency indicators mapped to CEFR bands or rubric scores. Key variables include engagement intensity, feedback specificity, feedback immediacy, adaptivity breadth, and spacing quality, with controls for baseline proficiency, prior exposure, study time, device access, and demographics. The analysis plan includes descriptive statistics, zero-order correlations, multiple linear regression with case fixed effects and heteroskedasticity-robust errors, moderation by motivation, subgroup contrasts by platform modality, and spline checks for nonlinearity. Headline findings indicate that feedback specificity shows the largest unique association with proficiency (about 0.17 SD per 1 SD increase), followed by engagement (≈0.13), spacing quality (≈0.10), feedback immediacy (≈0.09), and adaptivity breadth (≈0.08). Motivation positively moderates the engagement–proficiency link, speaking cases benefit most from fast feedback and spaced practice, and writing cases benefit most from granular, itemized feedback. Returns to weekly minutes flatten beyond the upper quintile, suggesting calibrated rather than maximal practice. Implications for policy and practice include engineering actionable feedback at scale, setting latency service targets, exposing spacing indices, and aligning dashboards with revision uptake and error convergence to guide instruction and procurement.
Copyright (c) 2024 Elmoon Akhter (Author)
COMPARATIVE ANALYSIS OF BI SYSTEMS IN THE U.S. AND EUROPE: LESSONS IN DATA GOVERNANCE AND PREDICTIVE ANALYTICS
This study conducted a comprehensive comparative analysis of Business Intelligence (BI) systems in the United States and Europe, emphasizing the interrelationships among data governance maturity, regulatory rigor, organizational data culture, and predictive analytics integration. The purpose of the research was to evaluate how governance frameworks and institutional structures shaped predictive accuracy, model reliability, and overall BI system efficiency across differing regulatory and cultural environments. A total of 132 peer-reviewed academic papers, industry reports, and empirical studies published over the last decade were systematically reviewed to establish the conceptual foundation, measurement framework, and analytical design of this study. Drawing upon this extensive literature base, a cross-sectional quantitative model was developed and applied to 420 organizations operating within multiple sectors, including finance, healthcare, manufacturing, technology, and retail. The analysis employed structural equation modeling (SEM) to test direct, indirect, and moderated effects among governance maturity, regulatory rigor, data culture, and predictive integration. The findings demonstrated that governance maturity significantly and positively influenced predictive accuracy, model reliability, and BI system efficiency, confirming its role as the central determinant of BI performance. Predictive analytics integration was identified as a partial mediator, indicating that governance maturity indirectly enhanced analytical precision through systematic predictive adoption. Regulatory rigor moderated these relationships, with European firms showing stronger governance-performance linkages due to higher compliance obligations, while U.S. firms exhibited greater agility and innovation capacity. Additionally, organizational data culture emerged as a critical enabler that strengthened BI adoption and improved the operationalization of governance principles. The study revealed that effective BI performance stemmed from the alignment of structural, technological, and cultural systems rather than from technological investment alone. The comparative findings underscored that European organizations excelled in data reliability and compliance consistency, whereas U.S. firms demonstrated superior scalability, speed, and analytical flexibility. Together, these outcomes provided valuable lessons for developing hybrid BI governance models that balance regulatory accountability with innovation agility. The research offered both empirical and conceptual contributions by integrating governance theory, analytics capability, and institutional context into a unified framework, advancing understanding of how transatlantic organizations can harmonize governance maturity and predictive intelligence for sustainable data-driven performance.
Copyright (c) 2021 Omar Muhammad Faruk, Mst. Shahrin Sultana (Author)
MACHINE LEARNING-BASED PREDICTIVE MODELING FOR ASSESSING BRIDGE LOAD CAPACITY USING REAL-TIME SENSOR DATA
This study develops and evaluates a real-time, data-driven framework for predicting bridge load capacity by integrating Internet-of-Things (IoT) sensor streams with advanced machine learning. A multi-site dataset of ~200k five-minute records from 48 bridges across temperate, tropical, and continental climates was compiled, cleaned (k-NN imputation; IQR outlier filtering), normalized, and time-synchronized. Models—Random Forest (RF), Gradient Boosting (GBM), Support Vector Regression, and Deep Neural Networks—were trained using blocked time-series cross-validation (80/20 split; five folds) and benchmarked with MAE, RMSE, and R2R^2R2. Ensemble approaches consistently outperformed single learners. RF achieved the best balance of accuracy and stability (MAE ≈ 12.4 kN; RMSE ≈ 18.1 kN; R2R^2R2 ≈ 0.958; fold SD < 2.1%), with an RF–GBM blend reaching R2R^2R2 ≈ 0.962 and 11% lower residual skewness. Incorporating environmental covariates (temperature, humidity) and dynamic features (vibration, deflection rate) improved accuracy by ~10% over structural-only baselines. Sensitivity and correlation analyses identified strain and deflection as dominant predictors (strain rrr ≈ 0.88; deflection rrr ≈ 0.80), with temperature exerting material-dependent moderating effects, particularly in steel bridges. Real-time deployment tests demonstrated operational feasibility with sub-2 s inference latency (RF 1.82 s average), >99% system uptime, and superior accuracy under dynamic loading (RF MAPE ≈ 3.6%). Sliding-window retraining (7-day refresh) mitigated temporal drift and reduced error by ~6–7% relative to static models. Early-warning simulations showed high detection reliability for load-exceedance events (RF true-positive rate 97.2% with low false alarms). Findings establish that harmonized sensing plus ensemble learning yields accurate, robust, and responsive estimates of bridge load capacity, advancing structural health monitoring from periodic inspection toward continuous, anticipatory asset management and providing a reproducible blueprint for physics-aware, data-driven infrastructure decision support.
Copyright (c) 2025 Md Mazharul Islam (Author)
AI-ENHANCED DATA SCIENCE APPROACHES FOR OPTIMIZING USER ENGAGEMENT IN U.S. DIGITAL MARKETING CAMPAIGNS
This quantitative study investigates how artificial intelligence (AI)–enhanced data science methodologies can optimize user engagement within the rapidly evolving landscape of U.S. digital marketing campaigns. Drawing from an extensive review of 312 peer-reviewed journal articles, conference papers, and industry reports published over the past two decades, the research synthesizes theoretical foundations, empirical findings, and methodological advancements to construct a comprehensive framework for engagement optimization. The study explores how predictive analytics, causal inference, reinforcement learning, and creative intelligence can be integrated to transform marketing strategies from static, rule-based approaches into adaptive, data-driven systems that respond dynamically to user behavior. Predictive models are examined for their ability to identify and forecast engagement drivers across behavioral, contextual, and creative dimensions, while causal inference techniques are evaluated for isolating incremental effects and distinguishing true marketing impact from mere correlations. Reinforcement learning is analyzed as a sequential decision-making mechanism capable of optimizing the timing, sequencing, and delivery of marketing interventions to maximize long-term engagement and customer value. Creative intelligence, encompassing natural language processing and computer vision, is investigated for its role in designing emotionally resonant and contextually relevant content that enhances user interaction. Additionally, the study addresses the critical influence of governance, privacy, and fairness considerations on engagement strategies, demonstrating how ethical and regulatory compliance can coexist with performance optimization. The findings reveal that AI-driven approaches significantly outperform traditional methods across key engagement metrics, including click-through rates, dwell time, conversions, and retention, while simultaneously strengthening trust, inclusivity, and user experience. This research contributes to both academic scholarship and marketing practice by offering an integrated, evidence-based framework that advances the theoretical understanding of engagement as a dynamic, multidimensional construct and provides practical strategies for leveraging AI in competitive digital ecosystems.
Copyright (c) 2024 Sai Praveen Kudapa (Author)
DEPLOYMENT OF AI-SUPPORTED STRUCTURAL HEALTH MONITORING SYSTEMS FOR IN-SERVICE BRIDGES USING IOT SENSOR NETWORKS
This study investigates the deployment of artificial intelligence (AI)–supported structural health monitoring (SHM) systems for in-service bridges using Internet of Things (IoT) sensor networks, a rapidly advancing domain that merges cutting-edge sensing, communication, and data analytics to improve infrastructure safety and durability. Traditional bridge inspections and early wired SHM systems have faced persistent limitations, including manual data interpretation, latency in damage detection, and scalability barriers due to cost and complexity. Recent breakthroughs in wireless IoT technologies, multi-modal sensor fusion, and AI-based analytics now offer the potential for continuous, automated, and highly reliable monitoring of structural integrity across diverse bridge typologies and environmental conditions. To critically synthesize the state of knowledge, this systematic review analyzed 146 peer-reviewed papers published between 2000 and 2022 spanning civil engineering, computer science, and information systems. The review explored key dimensions: (a) the historical evolution and objectives of SHM in bridge engineering; (b) IoT sensor modalities and network architectures enabling large-scale monitoring; (c) AI techniques for damage classification, anomaly detection, and signal feature engineering; (d) system reliability and data integrity strategies including calibration, drift compensation, and cybersecurity; (e) deployment challenges and scalability considerations across steel and concrete bridges; (f) comparative field case studies and lessons from global smart infrastructure programs; and (g) emerging research directions such as digital twins, blockchain data provenance, climate-resilient AI, and hybrid human–AI decision systems. The findings indicate that AI-enhanced SHM significantly improves predictive damage detection, reduces false alarms, and supports timely maintenance decisions, especially when combined with multi-sensor IoT networks and robust data governance. However, challenges remain in standardization, model retraining under concept drift, cost–benefit justification for large-scale deployment, and regulatory acceptance of AI-informed safety decisions. The review also highlights knowledge gaps around extreme climate resilience, secure and scalable data management, and human oversight frameworks for trustworthy AI. By consolidating insights across 146 studies, this work provides an integrated roadmap for researchers, engineers, and policymakers aiming to advance the next generation of smart, resilient, and cost-effective bridge monitoring systems.
Copyright (c) 2022 Md Ismail Hossain (Author)
IMPLEMENTATION OF THE DEVELOPED SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE MODEL IN REAL-WORLD APPLICATIONS FOR SUSTAINABLE ENERGY OPTIMIZATION
This study presents an implementation-based investigation into the deployment and real-world validation of a developed solar photovoltaic (PV) system performance model designed to support sustainable energy optimization. Unlike prior works that primarily focused on theoretical formulations or simulation-driven analyses, this research emphasizes the direct application and operational evaluation of the model under diverse environmental and infrastructural conditions. The implementation was carried out across multiple grid-connected PV installations varying in scale, configuration, and climatic exposure to capture a broad spectrum of operational challenges. The framework involved integrating real-time data acquisition systems to continuously monitor solar irradiance, module temperature, ambient conditions, electrical output, and system losses, which were then processed through the developed model for real-time performance estimation. Empirical validation involved comparing model-predicted energy yields with measured field data over a 12-month operational period, incorporating seasonal variability, shading impacts, and maintenance-induced downtimes. The results showed that the implemented model achieved high predictive accuracy, with mean absolute percentage error consistently below 5%, while also identifying site-specific inefficiencies such as temperature-induced derating, mismatch losses, and inverter clipping. The implementation further demonstrated that coupling model predictions with adaptive control strategies enhanced energy harvesting efficiency, reduced curtailment, and improved system reliability. In addition to performance validation, the study ensured compliance with international standards such as IEC 61724 for performance monitoring and IEC 61853 for module characterization, enhancing the replicability and interoperability of the model in heterogeneous energy environments. This practical deployment bridges the gap between conceptual model design and field-based energy management by translating complex algorithms into operational decision-support tools. The findings affirm the model’s readiness for large-scale adoption, offering a scalable and adaptive framework to optimize PV system performance, improve return on investment, and accelerate the transition to sustainable energy systems in real-world contexts.
Copyright (c) 2025 Md Akteruzzaman (Author)
QUANTITATIVE RISK ASSESSMENT OF MEGA REAL ESTATE PROJECTS: A MONTE CARLO SIMULATION APPROACH
This systematic literature review synthesizes how Monte Carlo simulation is designed, implemented, and interpreted for quantitative risk assessment in mega real estate projects. Following a PRISMA-aligned protocol, we searched Scopus, Web of Science, ASCE Library, ScienceDirect, IEEE Xplore, and Emerald Insight, complemented by Taylor & Francis Online and snowballing, screened records in two stages, and extracted standardized methodological variables. The final sample comprises 115 studies, analyzed across four decision-shaping themes: input distributions, dependence modeling, sampling discipline, and integration of cost, schedule, and finance. Most studies adopt triangular or PERT-style inputs, with lognormal alternatives common, while heavy-tailed forms remain underused despite escalation and long-lead exposures. Explicit dependence is unevenly treated, many models assume independence, whereas rank correlation, common drivers, or copulas reveal wider tails. Latin hypercube sampling dominates, quasi Monte Carlo appears in a minority, and only a subset justifies run size against precision targets. Where models link schedule to cost and propagate into cash flow and financing metrics such as debt service coverage ratio, credible bands widen and breach probabilities rise, improving alignment with lender and board thresholds. Sensitivity analysis is frequent yet mixed in rigor, with global and tail-focused measures offering clearer mitigation leverage than tornado charts, and validation by hindcasting or exceedance tests remains rare but decisive for credibility. We conclude that governance-ready practice requires tail-aware input fitting, co-movement, variance-efficient designs with accuracy goals, integrated cost, schedule, and finance modeling, and transparent sensitivity and validation protocols that yield auditable percentile contingencies and covenant risk statements for stage-gate decisions.
Copyright (c) 2022 MKA Shahinoor Rahman Jahid (Author)
PROCESS OPTIMIZATION USING SIX SIGMA STRATEGY IN GARMENT MANUFACTURING
Increasing competition among developed countries makes it increasingly pressing for the garment industry in developing economies such as Bangladesh to produce high-quality, defect-free products at competitive prices in order to remain globally viable. As one of the largest contributors to Bangladesh’s economy, the garment sector faces constant pressure to balance quality with efficiency while responding to fluctuating international demand. Within this context, the present study investigates the effectiveness of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology in optimizing the cutting process at Epyllion Group, a leading garment manufacturing company in Bangladesh. The choice of focusing on the cutting process was strategic, as this stage plays a critical role in determining dimensional accuracy, fabric utilization, and overall product quality. Even small errors in this stage can cascade into large-scale defects, increasing rework rates and operational costs.The research systematically identified inefficiencies that contributed to frequent production defects and heightened expenses. Among the most significant issues were fabric misalignment, which caused irregular cuts and waste; bundle mislabeling, which created confusion and delayed workflows; and inconsistent fabric tension, which compromised uniformity in cut panels. These problems were compounded by inadequate operator skills and limited preventive maintenance practices. To address these challenges, the study employed a structured data collection framework supplemented by analytical tools such as Pareto analysis and cause-and-effect diagrams. These techniques allowed the research team to isolate the root causes of the majority of defects and to design targeted solutions. The interventions included operator training programs aimed at enhancing technical precision, machine calibration and maintenance schedules to ensure equipment reliability, the installation of automated tension controllers to standardize material handling, and the introduction of standardized operating procedures to reduce process variation. The impact of these measures was significant and measurable. Results following implementation revealed a 61.5% reduction in defect rates, which directly improved product quality and reduced waste. Machine downtime was cut by 66.7%, reflecting the success of preventive maintenance and calibration measures. Similarly, the product rework rate dropped by 55.6%, which not only lowered costs but also improved throughput and delivery reliability. Additional improvements were observed in measurement accuracy, which increased by 25%, and operator training scores, which rose by 38.5%, indicating that human capital development played a key role in sustaining process improvements. These results demonstrate the combined value of technological upgrades and workforce empowerment, showing that quality improvements in garment manufacturing require both structural and behavioral change.
Copyright (c) 2025 Moin Uddin Mojumder, Hamza Saad (Author)
DIGITAL TWIN TECHNOLOGY IN UTILITY INFRASTRUCTURE MANAGEMENT: OPPORTUNITIES AND CHALLENGES
This meta-analysis presents a comprehensive evaluation of the implementation, performance outcomes, and sectoral maturity of digital twin (DT) technology across two critical utility infrastructure domains: electricity and water. Drawing on 122 peer-reviewed empirical studies published between 2010 and 2024—with a cumulative citation count exceeding 10,000—the study assesses the efficacy of DTs in enhancing operational performance, reducing system downtime, improving cost efficiency, and bolstering infrastructure resilience. The findings indicate that the electricity sector demonstrates the highest degree of DT maturity, characterized by widespread adoption in smart grid optimization, real-time asset monitoring, fault prediction, and renewable energy integration. In contrast, the water sector, while moderately advanced, has achieved significant progress through the deployment of digital twins in hydraulic simulation, leak detection, stormwater forecasting, and wastewater treatment automation. Quantitative evidence reveals average downtime reductions ranging from 15% to 45%, alongside cost savings of up to 30% through predictive maintenance and optimized energy and resource use. Regional benchmarking highlights Europe and Asia as leaders in digital twin innovation, supported by robust regulatory frameworks, significant investment in smart infrastructure, and advanced ICT ecosystems. In contrast, utilities in emerging economies continue to face constraints related to legacy infrastructure, limited digital readiness, and fragmented policy environments. The study also identifies emerging opportunities shaping the next generation of DT deployment, including the integration of artificial intelligence for autonomous operational control, cloud-based Digital Twin-as-a-Service (DTaaS) platforms for scalable adoption, and strategic alignment with sustainability initiatives such as climate resilience, net-zero emissions, and smart city governance. Despite these advancements, persistent challenges remain in achieving data interoperability, ensuring cybersecurity resilience, and facilitating equitable access to DT solutions across regions. This meta-analysis not only consolidates the empirical knowledge base but also provides a forward-looking roadmap for enhancing the role of digital twins as a transformative technology in sustainable utility infrastructure management.
Copyright (c) 2025 Mohammad Hasan Imam (Author)
DATA ANALYTICS FOR STRATEGIC BUSINESS DEVELOPMENT: A SYSTEMATIC REVIEW ANALYZING ITS ROLE IN INFORMING DECISIONS, OPTIMIZING PROCESSES, AND DRIVING GROWTH
This meta-analysis offers a comprehensive synthesis of empirical evidence on the strategic role of data analytics in business development, with particular emphasis on its contributions to informed decision-making, operational process optimization, financial planning, risk mitigation, and customer-centric growth. Drawing from a dataset of 112 peer-reviewed empirical studies published between 2010 and 2024, the study employs a meta-analytic methodology following PRISMA guidelines to ensure methodological rigor and analytical depth. The research systematically categorizes analytics into descriptive, predictive, and prescriptive models, evaluating their implementation across key business functions including marketing, finance, operations, supply chain, human resources, and strategic management. Findings demonstrate that organizations with high analytics maturity achieve significantly better performance outcomes in terms of profitability, process efficiency, decision quality, and return on investment compared to their lower-maturity counterparts. Firms that integrate analytics across departments and align it with enterprise strategy report the greatest benefits, including improved responsiveness to market dynamics, enhanced customer engagement, and more effective capital allocation. The study further reveals that enabling factors such as executive sponsorship, cross-functional data integration, workforce analytics literacy, and robust governance frameworks are critical to unlocking the full potential of analytics. Comparative evaluation of theoretical models—including the Resource-Based View (RBV), Dynamic Capabilities Theory, Information Processing Theory (IPT), and the Technology–Organization–Environment (TOE) framework—provides insight into the mechanisms through which analytics capabilities translate into sustained competitive advantage. The analysis also identifies gaps related to construct standardization, sectoral applicability, and scalability, offering methodological and strategic recommendations for future research and practice. Ultimately, this study affirms that data analytics is not merely a technological function but a transformative, enterprise-wide capability that drives sustainable value creation, operational excellence, and strategic agility in an increasingly data-driven global economy. The findings contribute to a deeper understanding of how organizations can harness analytics to navigate complexity, enhance performance, and foster innovation.
Copyright (c) 2025 Md Musfiqur Rahman (Author)