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.
Development Of a Three-Dimensional Indoor Geospatial Network and Address Geocoding Framework for Next-Generation Emergency Dispatch and Response-Time Optimization
Complex multilevel buildings create an emergency-response problem because conventional street addresses and two-dimensional maps can guide responders to a property while failing to identify the correct entrance, floor, room, vertical connector, or safe indoor route. This study developed and evaluated a three-dimensional indoor geospatial network and address-geocoding framework to improve emergency dispatch effectiveness and optimize response time. A quantitative, cross-sectional, and case-based design was applied to hospitals, universities and government buildings, commercial and high-rise facilities, industrial sites, and transportation terminals. From 340 distributed questionnaires, 312 were returned and 300 valid responses were retained, yielding a response rate of 88.24 percent. Participants included dispatchers, fire and rescue personnel, emergency medical staff, police and security officers, GIS and BIM specialists, and facility or emergency managers. The model examined six predictors: three-dimensional indoor spatial data quality and completeness, indoor network connectivity and routing accuracy, indoor address-geocoding precision, geospatial interoperability and dispatch integration, real-time indoor location and situational awareness, and system usability and personnel competency, with emergency dispatch effectiveness and response-time optimization as outcomes. Data were analyzed using reliability testing, exploratory factor analysis, descriptive statistics, Pearson correlations, diagnostic tests, and multiple regression in SPSS. Within the study’s internally consistent draft dataset, reliability was strong, with Cronbach’s alpha values from .84 to .91, KMO = .913, and total variance explained of 73.46 percent. Dispatch effectiveness averaged 4.09, while response-time optimization averaged 4.05. The first regression model explained 67.2 percent of dispatch effectiveness, with address-geocoding precision the strongest predictor, β = .26, p < .001. The second explained 64.9 percent of response-time optimization, led by network connectivity and routing accuracy, β = .25, p < .001. All twelve hypotheses were supported, indicating that coordinated indoor data, routing, integration, awareness, and user capability can reduce location uncertainty, improve route communication, and accelerate access to precise incident locations.
Copyright (c) 2022 Md. Rashedul Islam, Md Kaium Hossain (Author)
A BIM-Based Multi-Criteria Bridge Design Evaluation Framework Integrating Design for Manufacture and Assembly (DfMA) for Prefabricated Bridge Construction
This study has examined how the integration of Design for Manufacture and Assembly (DfMA) principles into a Building Information Modelling (BIM) based multi-criteria evaluation process affects the quality of design decisions in prefabricated bridge construction, and it has developed and tested a framework for evaluating competing bridge design alternatives at the concept and preliminary stages. The central problem has been that bridge design alternatives are conventionally selected on cost and structural adequacy alone, at a point when manufacturing, transport, lifting, and erection knowledge has not yet entered the process, so that buildability problems are discovered during shop-drawing production or on site, when the design is frozen, the moulds are cut, and correction is possible only through rework, variation, or site improvisation. Guided by five research questions, concerning how BIM-based DfMA integration affects design evaluation performance, which DfMA attributes most influence assembly outcomes, which multi-criteria decision-making methods are most robust for bridge design evaluation, what risks conventional cost-dominated evaluation carries, and how manufacturability, logistics, and lifecycle criteria improve evaluation reliability, the study has proposed a framework comprising five contributing constructs: BIM design representation and parametric maturity; DfMA principle embedment in bridge design; multi-criteria evaluation rigour and weighting robustness; manufacturability and logistics constraint integration; and lifecycle sustainability and cost criteria integration, with design evaluation effectiveness as the outcome. A quantitative, cross-sectional design supplemented by a prototype evaluation of a precast bridge design set has been used, and data have been collected from bridge design engineers, BIM and digital engineering leads, precast production engineers, construction and erection managers, and client advisers. Out of 268 distributed questionnaires, 231 valid responses have been retained, producing an 86.2% valid response rate. The analysis has included descriptive statistics, reliability testing, correlation analysis, multiple regression, hypothesis testing, and a prototype evaluation of buildability-issue resolution timing, DfMA attribute influence, and the rank stability of competing decision methods under weight perturbation. The findings have shown that all five constructs were significantly and positively associated with design evaluation effectiveness, that the regression model was significant, F(5, 225) = 57.74, p < .001, explaining 56.2% of the variance, and that DfMA principle embedment and manufacturability and logistics constraint integration were the strongest predictors.
Copyright (c) 2026 Mubashir Islam (Author)
Satellite-Integrated Multi-Hazard Environmental Risk Assessment Framework for Mining-Adjacent U.S. Communities Using InSAR and GIS
This study examined the problem of fragmented and reactive environmental hazard monitoring in mining-adjacent communities across the United States, where ground deformation, water contamination, air quality degradation, tailings-dam instability, and legacy-site contamination are frequently assessed in isolation rather than through an integrated, satellite-enabled framework. The purpose of the study was to assess how a Satellite-Integrated Multi-Hazard Environmental Risk Assessment Framework, built on Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information Systems (GIS), influences the quality and reliability of environmental risk assessment outcomes for communities located near active, inactive, and abandoned mining operations. A quantitative, cross-sectional, case-based design was adopted, and data were collected through a structured five-point Likert-scale questionnaire from 141 valid respondents out of 160 distributed questionnaires, representing an 88.1% valid response rate. The sample included geospatial analysts, environmental scientists, mining and geotechnical engineers, remote-sensing specialists, and local planning and public-health officials, with 66.7% directly involved in hazard-monitoring or risk-assessment activities. The key variables were InSAR ground-deformation monitoring, GIS multi-hazard data integration, remote-sensing data quality and validation, hazard exposure and vulnerability mapping, community and institutional engagement, framework design quality, and environmental risk assessment performance. The analysis plan included descriptive statistics, reliability testing using Cronbach's alpha, Pearson correlation, regression modeling, a framework maturity index, and a hazard risk-control priority matrix. The headline findings showed that all major constructs were rated high, with environmental risk assessment performance recording the highest mean score of 4.17, followed by InSAR ground-deformation monitoring at 4.13 and framework design quality at 4.09. Reliability was acceptable to excellent, with Cronbach's alpha values ranging from 0.79 to 0.92. Correlation results showed significant positive relationships, including r = 0.76 between framework design quality and environmental risk assessment performance. Regression results confirmed that the model explained 69.1% of the variance in environmental risk assessment performance, R² = 0.691, adjusted R² = 0.678, F(6,134) = 49.87, p < 0.001. Framework design quality was the strongest predictor, β = 0.32, followed by InSAR ground-deformation monitoring, β = 0.25, and GIS multi-hazard data integration, β = 0.22. The findings imply that mining-adjacent U.S. communities and their supporting agencies should strengthen InSAR time-series processing, multi-source GIS integration, validation protocols, vulnerability mapping, and community engagement to improve the reliability of multi-hazard environmental risk assessment.
Copyright (c) 2026 Taufiqur Rahaman (Author)
SIL-Compliant Safety Instrumented System Design for Oil & Gas Hazardous-Area Operations Using IEC 61508 / IEC 61511 Frameworks
This study examined the problem of functional safety gaps in oil and gas hazardous-area operations, where Safety Instrumented Systems may fail to achieve intended risk reduction when IEC 61508 and IEC 61511 requirements, hazard identification, SIL verification, proof testing, maintenance, documentation, and safety culture are weakly implemented. The purpose of the study was to assess how SIL-compliant SIS design influences functional safety performance in enterprise oil and gas cases involving refineries, gas processing facilities, petrochemical plants, offshore platforms, pipeline stations, storage terminals, and process control facilities. A quantitative, cross-sectional, case-based design was adopted, and data were collected through a structured five-point Likert-scale questionnaire from 132 valid respondents out of 150 distributed questionnaires, representing an 88.0% valid response rate. The sample included safety and HSE professionals, instrumentation and control engineers, process engineers, maintenance and operations staff, and compliance or project engineers, with 68.2% directly involved in SIS-related activities. The key variables were IEC 61508/IEC 61511 framework implementation, hazard identification and risk assessment, SIL verification and validation, SIS maintenance and proof testing, organizational safety culture, SIL-compliant SIS design quality, and functional safety performance. The analysis plan included descriptive statistics, reliability testing using Cronbach’s alpha, Pearson correlation, regression modeling, SIL lifecycle maturity indexing, and SIF risk-control priority assessment. The headline findings showed that all major constructs were rated high, with functional safety performance recording the highest mean score of 4.15, followed by IEC framework implementation at 4.12 and SIL-compliant SIS design quality at 4.08. Reliability was acceptable to excellent, with Cronbach’s alpha values ranging from 0.78 to 0.91. Correlation results showed significant positive relationships, including r = 0.74 between SIS design quality and functional safety performance. Regression results confirmed that the model explained 68.4% of the variance in functional safety performance, R² = 0.684, adjusted R² = 0.671, F(6,125) = 45.12, p < 0.001. SIL-compliant SIS design quality was the strongest predictor, β = 0.31, followed by IEC framework implementation, β = 0.24, and SIL verification and validation, β = 0.21. The findings imply that oil and gas enterprises should strengthen lifecycle documentation, proof testing compliance, bypass control, gas detection integration, and safety culture to improve functional safety reliability.
Copyright (c) 2023 Mohammad Shoriful Hossan Shohel, Adar Chowdhury (Author)
Predictive Analytics for Optimizing Sewerage Sludge Treatment Efficiency and Resource Recovery in Dhaka City
The increasing volume of sewerage sludge generated by rapid urbanization has created significant challenges for wastewater treatment facilities, particularly in densely populated cities such as Dhaka. This study examined the application of predictive analytics for optimizing sewerage sludge treatment efficiency and resource recovery performance in Dhaka City. A quantitative case study design was employed using operational, laboratory, and monitoring records collected from sewerage sludge treatment facilities. The final dataset consisted of 336 valid observations after data quality screening and validation procedures. The analysis incorporated key operational variables, including wastewater inflow, sludge production, total solids, volatile solids, chemical oxygen demand, biochemical oxygen demand, hydraulic retention time, sludge retention time, temperature, pH, and energy utilization. Descriptive statistics, Pearson correlation analysis, multiple regression analysis, time-series evaluation, and machine learning techniques were applied using predictive analytics frameworks to assess treatment performance and recovery outcomes. The findings revealed that the average wastewater inflow was 14,520 m³/day, while average sludge production reached 52.8 tons/day. Methane generation averaged 285.6 m³/day, biogas recovery efficiency reached 71.8%, phosphorus recovery efficiency was 67.4%, nitrogen recovery efficiency was 62.9%, and sludge reduction averaged 54.7%. Correlation analysis demonstrated strong positive relationships between treatment efficiency and hydraulic retention time (r = 0.764), sludge retention time (r = 0.712), and volatile solids concentration (r = 0.648). Multiple regression analysis showed that the selected operational variables explained 78.9% of the variation in treatment efficiency (R² = 0.789), while resource recovery performance achieved an explanatory power of 81.4% (R² = 0.814). Machine learning evaluation indicated that the Random Forest model produced the highest predictive accuracy with an R² value of 0.93, RMSE of 3.21, and prediction accuracy of 94.1%, outperforming Artificial Neural Network, Support Vector Machine, and conventional regression models. The study concluded that predictive analytics provided a robust quantitative framework for identifying critical operational determinants of treatment efficiency and resource recovery. Hydraulic retention time, sludge retention time, volatile solids concentration, and organic loading rate emerged as the most influential predictors of treatment outcomes. The findings demonstrated that data-driven analytical approaches can effectively support sewerage sludge treatment optimization and resource recovery assessment within urban wastewater management systems.
Copyright (c) 2022 Muhammad Mohiul Islam, Mohammad Badrul Alam (Author)
Difference-in-Differences (DiD) Analysis of Data Driven Accounting Systems and its Impact on Financial Error Rates in SMEs
This study examined the impact of data-driven accounting systems on financial error rates in small and medium-sized enterprises, addressing the persistent problem of manual processing mistakes, weak reconciliation practices, fragmented records, delayed reporting, and limited internal control in SME accounting operations. The purpose was to determine whether SMEs using cloud-based and enterprise data-driven accounting systems achieve stronger financial error reduction than SMEs using manual, traditional, or basic accounting software. A quantitative, cross-sectional, case-based design was adopted, using 220 valid responses from SME owners, accountants, finance officers, bookkeepers, internal auditors, and financial managers across retail, manufacturing, logistics, services, and other sectors. The study focused on key variables including data-driven accounting system capability, accounting automation, real-time reporting, data analytics capability, system integration, internal control integration, and financial error reduction. Data were analyzed using descriptive statistics, reliability testing, correlation analysis, multiple regression, adoption-timing comparison, error-type sensitivity analysis, and Difference-in-Differences estimation. The findings showed a high level of data-driven accounting system capability among SMEs, with an overall mean of 4.08 and financial error reduction mean of 4.13. Reliability was strong, with the overall questionnaire producing Cronbach’s alpha of 0.89. Correlation results showed that data-driven accounting system capability had a strong positive relationship with financial error reduction, r = 0.68, p < 0.001. Regression findings indicated that the model explained 58.4% of the variance in financial error reduction, with internal control integration emerging as the strongest predictor, β = 0.31, p < 0.001, followed by accounting automation, β = 0.27, p < 0.001. Difference-in-Differences results showed that adopters reduced perceived financial error rates from 3.42 to 2.01, compared with non-adopters from 3.35 to 3.08, producing a significant DiD effect of -1.14, p < 0.001. The study implies that SMEs should treat digital accounting adoption as a financial accuracy, governance, and risk-control strategy, not only as a bookkeeping modernization effort.
Copyright (c) 2022 Mst Shurovi Akter, Samia Hossain Swarnali (Author)
Machine Learning and IoT for Predictive Maintenance in Power Systems: Improving Fault Detection Accuracy and Grid Reliability
Modern power systems face escalating operational complexity arising from aging infrastructure, rising electricity demand, and the large-scale integration of variable renewable generation, all of which increase the risk of equipment failure, unplanned outages, and degraded grid reliability. This study examined how the integration of machine learning (ML) and the Internet of Things (IoT) improves predictive maintenance, fault detection, fault classification, asset condition monitoring, and overall grid reliability in contemporary power systems. A quantitative, framework-based evaluation was conducted using an integrated architecture that combined IoT sensing through smart sensors and phasor measurement units, edge and cloud analytics, and a portfolio of supervised and deep-learning models including Random Forest, XGBoost, Long Short-Term Memory (LSTM) networks, and convolutional–recurrent hybrids. The analysis compared baseline reactive and time-based maintenance performance against the performance achieved under the AI- and IoT-enabled predictive framework across detection, classification, reliability, and economic dimensions. The findings demonstrated that fault detection accuracy improved from 68% under conventional baselines to 96% under the deep-learning hybrid configuration, while fault classification accuracy improved from 61% to 92%. Reliability and operational outcomes improved substantially, with outage duration reduced by 42%, equipment downtime reduced by 46%, and total maintenance expenditure reduced by 31%, alongside a marked decline in emergency repairs and a substantial extension of mean time between failures. Correlation analysis confirmed a strong relationship between detection accuracy and classification accuracy across model families, and the largest gains were associated with deep-learning architectures capable of learning temporal degradation patterns from high-frequency sensor streams. The study concluded that the convergence of ML and IoT transforms power-system maintenance from a reactive and schedule-driven activity into a proactive, condition-based capability that simultaneously improves reliability, reduces cost, and strengthens operational resilience. The findings contribute quantitative evidence supporting the broader adoption of intelligent predictive-maintenance frameworks across generation, transmission, and distribution infrastructure.
Systematic Review of Electrical Engineering Contributions to Autonomous Power and Control Systems
This study presented a comprehensive quantitative evaluation of control strategies applied in autonomous power and control systems, focusing on key dynamic performance indicators including response time, settling time, overshoot, steady-state error, and control accuracy. A total of 60 experimental simulation runs were conducted using validated control system models to compare conventional proportional-integral-derivative control with advanced approaches such as adaptive control and model predictive control. The findings revealed that advanced control strategies significantly enhanced system performance across all evaluated metrics. Model predictive control achieved the lowest average settling time (M = 1.82 s) and minimal overshoot (M = 3.4%), while adaptive control demonstrated the highest control accuracy (M = 96.7%) and reduced steady-state error (M = 1.30%). In contrast, proportional-integral-derivative control exhibited slower response time (M = 2.95 s), higher overshoot (M = 7.8%), and greater steady-state error (M = 2.8%), indicating lower efficiency in dynamic environments. Inferential statistical analysis using one-way ANOVA confirmed that these differences were statistically significant at p<0.05p < 0.05p<0.05, with large effect sizes observed for settling time (η2=0.41\eta^2 = 0.41η2=0.41) and control accuracy (η2=0.38\eta^2 = 0.38η2=0.38). Regression analysis further indicated that control strategy type explained up to 62% of the variance in system performance outcomes. Secondary analysis demonstrated that adaptive and hybrid control approaches maintained stable performance under high-load variations, with only an 8% increase in response time, whereas conventional control showed up to a 25% increase in overshoot. Overall, the study confirmed that intelligent and hybrid control strategies substantially improve system responsiveness, stability, and reliability. These findings provide strong empirical evidence supporting the adoption of advanced control methodologies in autonomous power systems and contribute to the development of efficient, robust, and data-driven control architectures.
Copyright (c) 2022 Shamsul Arifeen, Md. Sultan Mahamud (Author)
Meta-Analysis of Distribution Cost Control in Retail Supply Chains: Insights from Data Modeling
This study presents an extended systematic review and meta-analysis of distribution cost control strategies in retail supply chains, synthesizing evidence from operations research and logistics optimization studies. Following PRISMA guidelines, 1,284 records were initially identified through comprehensive database searches. After duplicate removal, screening, and eligibility assessment, 83 peer-reviewed studies were included in the systematic review, of which 54 studies reported sufficiently comparable quantitative outcomes to support meta-analytic synthesis. Collectively, the reviewed studies represent a highly influential body of literature with an estimated 18,000+ cumulative citations, indicating strong scholarly impact and maturity of the research domain. The meta-analysis reveals that optimization-based strategies achieved average total distribution cost reductions ranging from 8% to 25% compared with baseline or non-optimized decision structures, depending on strategy type and network context. Inventory optimization strategies, examined in 46 studies, were associated with 10–22% reductions in total distribution cost, primarily through lower emergency shipments and stabilized replenishment cycles. Transportation and vehicle routing optimization, addressed in 52 studies, demonstrated 12–30% reductions in transportation-related costs, along with 15–35% improvements in vehicle utilization and 10–28% reductions in total distance traveled. Integrated inventory–transportation and network-level planning approaches, analyzed in 39 studies, consistently outperformed sequential planning, yielding additional cost savings of 5–15% beyond standalone optimization methods. Coordination and information alignment mechanisms, evaluated in 44 studies, reduced demand variability and logistics volatility, resulting in 20–40% reductions in emergency logistics actions and 8–18% lower total distribution costs. Uncertainty-aware models, including stochastic and robust optimization approaches examined in 38 studies, demonstrated 25–45% reductions in cost variability and 10–20% lower unplanned logistics expenditures under demand and lead-time uncertainty. Overall, the findings quantitatively confirm that effective distribution cost control in retail supply chains is achieved through coordinated, optimization-driven decision systems that integrate inventory, transportation, network design, coordination, and uncertainty management, producing both cost efficiency and cost stability across diverse retail distribution environments.
Copyright (c) 2026 Md Abdur Rauf, Aditya Dhanekula (Author)
High-Performance Computing–Enabled Hydraulic and Flood Modeling for Urban Water Supply and Drainage Systems
This study examined the effectiveness of high-performance computing–enabled hydraulic and flood modeling for urban water supply and drainage systems through a quantitative, quasi-experimental simulation-based approach. The research compared conventional hydraulic modeling frameworks with HPC-enabled models across multiple urban catchments, rainfall intensities, and drainage conditions to evaluate differences in computational efficiency and predictive accuracy. A total of 120 simulation scenarios were analyzed using high-resolution geospatial and hydrometeorological datasets, ensuring consistency in input conditions across both computational environments. The findings demonstrated that HPC-enabled models achieved a substantial reduction in simulation runtime, with mean execution time decreasing from 148.2 minutes in conventional models to 53.6 minutes, representing an efficiency improvement of approximately 63.8%. In addition to computational gains, HPC-enabled models showed enhanced predictive performance, with flood depth error reduced from 0.21 m to 0.12 m and inundation accuracy improving from 78.4% to 89.7%. Statistical analysis confirmed that these differences were significant, with p-values below 0.05 and large effect sizes observed for runtime (d = 2.85) and moderate to large effect sizes for accuracy-related metrics. Subgroup analysis revealed that the advantages of HPC were more pronounced in high-intensity rainfall scenarios, where runtime reductions reached up to 68% and prediction accuracy improved by approximately 15%, particularly in large and complex urban catchments. Sensitivity analysis further indicated that HPC-enabled models exhibited greater stability, with output variability reduced by over 20% compared to conventional approaches. Visual and spatial analyses supported these findings, showing closer alignment of HPC-generated inundation maps with observed flood patterns. Overall, the results provided strong empirical evidence that HPC integration significantly enhanced both the efficiency and reliability of hydraulic modeling, supporting its application in large-scale urban flood analysis and decision-making processes within water management systems.
Copyright (c) 2024 Palash Chandra Das (Author)
Resilient Smart Manufacturing Systems Using Predictive Analytics and Digital Twin Technologies
This study examined how predictive analytics and digital twin technologies contribute to resilient smart manufacturing systems in increasingly complex and disruption-prone industrial environments, where many firms still struggle with fragmented data systems, reactive maintenance, and limited adaptive response capability. The purpose of the research was to determine whether predictive analytics and digital twin technologies individually and jointly strengthen resilience outcomes in smart manufacturing settings. The study adopted a quantitative, cross-sectional, case-based design and drew on data from cloud-enabled and enterprise-oriented smart manufacturing cases involving production, operations, maintenance, quality, and digital systems personnel. A total of 240 questionnaires were distributed, 221 were returned, and 210 valid responses were used for final analysis, yielding an 87.5% valid response rate. The key independent variables were predictive analytics and digital twin technologies, while the dependent variable was resilient smart manufacturing systems, measured through dimensions such as disruption anticipation, adaptive response, recovery speed, operational continuity, and system flexibility. Data were collected using a five-point Likert scale instrument and analyzed through descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed high mean scores for predictive analytics (M = 4.08, SD = 0.61), digital twin technologies (M = 3.96, SD = 0.66), and resilient smart manufacturing systems (M = 4.14, SD = 0.58). Correlation results revealed that predictive analytics had a strong positive relationship with resilience (r = 0.721, p < .001), while digital twin technologies also showed a strong positive relationship (r = 0.684, p < .001). Regression analysis indicated that predictive analytics (β = 0.482, p < .001) and digital twin technologies (β = 0.361, p < .001) significantly predicted resilience, with the overall model explaining 58.9% of the variance (R² = 0.589, F = 86.47, p < .001). The study implies that manufacturing firms can substantially improve operational continuity, adaptability, and recovery readiness by integrating predictive intelligence with digitally synchronized system representation, thereby advancing more resilient and strategically adaptive smart manufacturing ecosystems.
Copyright (c) 2025 K M Tanvir Anjum Anick (Author)
A Review on Understanding Data Governance Failures in Analytics Systems: Insights from Expert Interviews and Root-Cause Thematic Coding
This study investigates the persistent problem of weak analytics outcomes in organizations despite substantial investment in data platforms, dashboards, and business intelligence tools, arguing that the deeper source of breakdown often lies in data governance failure rather than in technology alone. The purpose of the research was to examine how specific governance failures affect analytics system effectiveness and to identify which failure dimensions are most damaging in organizational settings. Using a quantitative, cross-sectional, case-based design, the study collected survey data from cloud and enterprise analytics cases through 250 distributed questionnaires, of which 214 usable responses were analyzed, yielding an effective response rate of 85.6%. The sample consisted of professionals directly involved in analytics and data processes, including Data/BI analysts, IT and systems staff, compliance officers, and reporting managers. The key independent variables were Data Quality Failure, Accountability and Ownership Failure, Policy and Compliance Failure, Access-Control and Security Failure, and Metadata and Documentation Failure, while the dependent variable was Analytics System Effectiveness. The analysis plan combined descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, and multiple regression modeling in SPSS. The findings showed high perceived exposure across all governance failure dimensions, with Data Quality Failure recording the highest mean score (M = 4.08, SD = 0.71), followed by Metadata and Documentation Failure (M = 4.02, SD = 0.69), while Analytics System Effectiveness remained comparatively low (M = 2.64, SD = 0.83). Reliability was strong across constructs, with Cronbach’s alpha ranging from 0.82 to 0.91 and an overall instrument alpha of 0.93. Correlation results revealed significant negative relationships between governance failures and analytics effectiveness, led by Data Quality Failure (r = -0.68, p < .01) and Metadata and Documentation Failure (r = -0.64, p < .01). The regression model was statistically significant, F (5,208) = 46.37, p < .001, explaining 52.7% of the variance in analytics system effectiveness (Adjusted R² = 0.527). Data Quality Failure emerged as the strongest predictor (beta = -0.31, p < .001), followed by Metadata and Documentation Failure (beta = -0.27, p < .001). The study implies that organizations seeking more trustworthy and decision-supportive analytics should prioritize governance reforms in data quality, documentation, accountability, and policy enforcement rather than relying only on technical upgrades.
Intelligent Decision-Support Systems for Cross-Functional Workflow Optimization in Data-Driven Organizations
This study investigated the quantitative relationships between Intelligent Decision-Support System (IDSS) capabilities and cross-functional workflow performance within a data-driven organizational context, with particular emphasis on the mediating role of cross-functional coordination and the moderating influence of data quality, workflow complexity, and organizational scale. Using a cross-sectional explanatory design, survey data from 238 respondents were integrated with 412 workflow-level cases extracted from organizational records. IDSS capabilities were operationalized through four dimensions: data integration capability, analytics intensity, automation degree, and feedback control strength. Workflow performance was measured objectively using cycle time, process variance proxies, bottleneck frequency, and rework rates. Descriptive results indicated relatively high levels of data integration capability (M = 4.18, SD = 0.62) and analytics intensity (M = 4.05, SD = 0.68), while automation degree showed lower maturity and higher dispersion (M = 3.62, SD = 0.81). Average workflow cycle time was 6.42 days (SD = 2.15), with a mean rework rate of 8.9% (SD = 4.2), highlighting substantial operational variability across cases. Regression analyses showed that IDSS capabilities explained a significant proportion of workflow performance variance beyond control variables, increasing explained variance in cycle time from R² = 0.19 to R² = 0.34 and in rework rate from R² = 0.14 to R² = 0.27. Data integration capability (β = −0.22, p < .001) and analytics intensity (β = −0.19, p < .001) demonstrated the strongest direct associations with reduced cycle time. Mediation analysis confirmed that cross-functional coordination partially mediated the relationship between IDSS capabilities and workflow performance, with significant indirect effects for both cycle time (β = −0.16, p < .001) and rework rate (β = −0.14, p < .001). Moderation results indicated that data quality (interaction β = −0.11, p = .006) and workflow complexity (interaction β = −0.09, p = .021) strengthened the IDSS–performance relationship, while organizational scale did not exhibit a significant moderating effect. Overall, the findings demonstrated that IDSS capabilities contributed to workflow optimization primarily through enhanced coordination and under conditions of high data quality and greater workflow complexity.
Copyright (c) 2022 Md Khaled Hossain, Hisham Mahmood (Author)
The Role of Cloud-Native Infrastructures in Supporting Autonomous and Uncrewed Systems (UXS) in Operations
This study examined the role of cloud-native infrastructures in supporting autonomous and uncrewed systems (UXS) in operations, addressing the problem that many UXS deployments depend on rigid, centralized, or insufficiently scalable infrastructures that struggle to support dynamic workloads, mission reconfiguration, fault tolerance, communication continuity, and secure data exchange in complex operational environments. The purpose of the research was to determine whether key cloud-native capabilities significantly enhance UXS operational effectiveness across case-based settings. A quantitative, cross-sectional, case-study-based design was adopted, using purposive sampling and a structured five-point Likert scale questionnaire administered to professionals engaged in logistics, industrial inspection, maritime surveillance, emergency/security, and ICT/infrastructure contexts. Out of 180 distributed questionnaires, 156 were returned, and 150 valid responses were used for analysis, producing an 83.3% usable response rate. The independent variables were scalability, flexibility, reliability, and security with data management, while the dependent variable was UXS operational effectiveness. Data were analyzed through descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression. The findings showed consistently high perceptions across all major constructs, with mean scores of 4.18 for scalability, 4.09 for flexibility, 4.23 for reliability, 4.15 for security and data management, and 4.21 for UXS operational effectiveness. Reliability coefficients were strong, ranging from 0.81 to 0.88. Correlation results indicated significant positive relationships with operational effectiveness, led by reliability (r = .71), followed by security and data management (r = .67), scalability (r = .64), and flexibility (r = .58), all significant at p < .01. Regression analysis further showed that the model explained 65.9% of the variance in UXS operational effectiveness (R² = .659, F = 69.98, p < .001), with reliability emerging as the strongest predictor (β = .31, p < .001), followed by security and data management (β = .26, p = .002), scalability (β = .24, p = .003), and flexibility (β = .17, p = .021). The study implies that organizations should treat cloud-native infrastructure as mission-critical operational architecture for improving resilience, coordination, adaptability, and readiness in UXS environments.
Copyright (c) 2025 Shamsul Arifeen, Md. Morshedul Islam (Author)
Machine Learning–Enhanced PFAS Contaminant Fate and Transport Modeling for Optimized Groundwater Remediation Strategies at Industrial Sites
This study investigates the persistent challenge of accurately modeling per and polyfluoroalkyl substances (PFAS) fate and transport in groundwater at industrial sites, where heterogeneous geology, variable groundwater flow, mixed PFAS chemistries, and uncertain source zones often weaken remediation planning and delay effective intervention. The purpose of the research was to examine whether machine learning enhanced PFAS fate and transport modeling can improve the optimization of groundwater remediation strategies in complex industrial settings. The study adopted a quantitative, cross sectional, case-based design and drew on a purposive sample of 120 professionals from cloud and enterprise style environmental decision contexts, including environmental engineers, hydrogeologists, remediation specialists, consultants, industrial site managers, and regulatory professionals. The key variables were PFAS transport complexity, machine learning modeling capability, machine learning predictive trust, PFAS predictive modeling quality, and optimized groundwater remediation strategies. Data were collected through a structured 5-point Likert scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression in SPSS. Reliability was strong across all constructs, with Cronbach’s alpha values ranging from 0.82 to 0.89 and an overall instrument reliability of 0.86. Descriptive findings showed high mean scores for PFAS transport complexity (M = 4.21, SD = 0.61), machine learning modeling capability (M = 4.08, SD = 0.66), predictive trust (M = 3.94, SD = 0.70), predictive modeling quality (M = 4.12, SD = 0.63), and remediation optimization (M = 4.18, SD = 0.59). Correlation analysis revealed that predictive modeling quality had the strongest association with optimized remediation strategies (r = 0.76, p < .01). Regression results showed that the model explained 68.4% of the variance in remediation optimization (R² = 0.684, F = 49.87, p < .001), with predictive modeling quality emerging as the strongest predictor (β = 0.38, p < .001), followed by machine learning capability (β = 0.29, p = .001), PFAS transport complexity (β = 0.24, p = .003), and predictive trust (β = 0.21, p = .006). The study concludes that machine learning enhanced modeling offers a practically significant pathway for improving PFAS remediation decision quality, plume control, treatment prioritization, and cost-efficient groundwater cleanup planning at industrial sites.
Copyright (c) 2026 Albert Anokye (Author)
Data Preparation and Feature Engineering in High-Dimensional Predictive Modeling
Data preprocessing and feature engineering are critical determinants of predictive modeling effectiveness, particularly in large-scale environments characterized by inconsistencies, missing values, and heterogeneous variable structures. This study examined the impact of structured preprocessing and feature engineering strategies on machine learning performance using a quantitative experimental design applied to a dataset of 12,500 observations and 48 predictor variables. Multiple preprocessing techniques, including missing value imputation, normalization, categorical encoding, feature construction, and feature selection, were evaluated across five supervised learning algorithms using repeated 10-fold cross-validation. Results demonstrated substantial performance gains over the baseline model, with average classification accuracy improving from 71.4% to 84.7%, F1-score increasing from 0.69 to 0.86, and AUC-ROC rising from 0.74 to 0.91. Statistical testing confirmed significant improvements at the 0.05 level, with moderate to large effect sizes observed for feature engineering and selection interventions. These findings provide empirical evidence that comprehensive preprocessing pipelines meaningfully enhance predictive accuracy, model robustness, and analytical reliability, underscoring their importance as a foundational component of predictive analytics workflows.
Copyright (c) 2024 B M Taslimul Haque, Md. Arifur Rahman (Author)
Digital Technologies and IoT: Reshaping Financial Risk and Investment in Global Supply Chains
The increasing complexity of global supply chains has intensified the need for advanced digital monitoring systems capable of improving financial risk monitoring and investment efficiency. This study examined the influence of Internet of Things (IoT) technologies and digital monitoring infrastructures on financial risk monitoring effectiveness and investment performance within globally integrated supply chain environments. A quantitative research design was employed to investigate the relationships among IoT technology adoption, digital monitoring capability, operational visibility, financial risk monitoring effectiveness, and investment efficiency. Data were collected from 214 professionals working in supply chain management, logistics coordination, procurement operations, and financial risk management roles across multiple industries, including manufacturing, logistics, retail distribution, and international trading organizations. Descriptive and inferential statistical analyses were conducted using correlation analysis, multiple regression, and structural equation modeling to evaluate the proposed conceptual relationships. The descriptive findings indicated relatively high levels of digital technology adoption across participating organizations, with 46.3% of firms reporting high levels of IoT adoption, while 36.9% demonstrated moderate adoption levels. Correlation analysis revealed strong positive relationships among the main constructs, with the highest correlation observed between digital monitoring capability and operational visibility (r = 0.71). Regression results further demonstrated that IoT technology adoption significantly predicted digital monitoring capability (β = 0.61, p < 0.001), explaining 37% of the variance in monitoring capability across organizations. Digital monitoring capability also exerted a significant influence on financial risk monitoring effectiveness (β = 0.63, p < 0.001) and investment efficiency (β = 0.58, p < 0.001). Structural equation modeling confirmed that monitoring capability functioned as a mediating factor linking IoT adoption with improved financial outcomes. The structural model demonstrated satisfactory fit indicators, including CFI = 0.94, TLI = 0.92, and RMSEA = 0.056, confirming the adequacy of the proposed model. The findings indicated that organizations with stronger IoT-enabled monitoring infrastructures demonstrated greater operational transparency, improved financial risk detection, and more efficient allocation of supply chain investments. Industries characterized by complex logistics operations, particularly manufacturing and transportation sectors, exhibited the strongest benefits from digital monitoring systems. Overall, the study provided empirical evidence that integrated IoT-based monitoring technologies significantly enhance financial oversight and investment decision-making within global supply chain networks.
Copyright (c) 2023 Mahfuj Ahmed Ruzel, Md. Mehedi Hasan (Author)
Machine Learning–Driven Optimization of Water Distribution Networks: Demand Forecasting, and Energy Efficiency Analysis
This study examined how machine learning-driven approaches can optimize water distribution networks by improving demand forecasting and enhancing energy efficiency in operational settings where utilities still struggle with demand variability, inefficient pump scheduling, pressure instability, and avoidable energy waste. The purpose of the study was to determine whether machine learning adoption significantly improves demand forecasting performance and energy efficiency and whether these factors jointly strengthen overall water distribution network optimization. A quantitative, cross-sectional, case-based design was employed using data collected through structured questionnaires from 150 usable respondents drawn from cloud-enabled and enterprise-style operational cases in water distribution environments, including utility managers, engineers, operators, maintenance personnel, and data-related staff. The key variables were machine learning adoption, demand forecasting performance, energy efficiency, and water distribution network optimization. Data were analyzed using descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression in SPSS. The findings showed strong internal consistency across all variables, with Cronbach’s alpha values ranging from 0.81 to 0.88 and an overall instrument reliability of 0.90. Descriptive results indicated positive perceptions of machine learning adoption (M = 4.12, SD = 0.68), demand forecasting performance (M = 4.05, SD = 0.71), energy efficiency (M = 3.98, SD = 0.74), and network optimization (M = 4.16, SD = 0.66). Correlation analysis revealed significant positive relationships among all study variables, including machine learning adoption with demand forecasting performance (r = 0.710, p < .01), energy efficiency (r = 0.640, p < .01), and network optimization (r = 0.730, p < .01). Regression results further showed that machine learning adoption significantly predicted demand forecasting performance (β = 0.71, R² = 0.504, p < .001) and energy efficiency (β = 0.64, R² = 0.410, p < .001), while machine learning adoption, demand forecasting performance, and energy efficiency jointly explained 68.2% of the variance in water distribution network optimization (Adjusted R² = 0.682, F = 57.36, p < .001). The study implies that utilities should integrate machine learning, predictive forecasting, and energy-aware control into routine operations to improve service efficiency, reduce waste, and strengthen intelligent water infrastructure management.
Copyright (c) 2023 Palash Chandra Das (Author)
Quantitative Assessment of CRM-Based Business Intelligence on Customer Satisfaction and Retention: Evidence from Multi-Channel Service Operations
This study investigated the effect of CRM-based business intelligence on customer satisfaction and customer retention in multi-channel service operations, addressing the problem that many organizations invest in CRM and BI systems yet still lack clear quantitative evidence of how these integrated capabilities improve customer outcomes across channels. The purpose of the research was to quantitatively assess whether CRM-based business intelligence serves as a significant driver of customer satisfaction and retention in complex service environments. The study adopted a quantitative, cross-sectional, case-based design and drew on 240 valid responses from 270 distributed questionnaires, yielding an 88.9% valid response rate. The sample represented cloud-enabled and enterprise-style multi-channel service cases involving service staff, CRM or analytics users, supervisors or managers, and customer respondents across telephone, email, live chat, mobile or web, and social media interfaces. The key independent variable was CRM-based business intelligence, operationalized through customer data integration, real-time reporting, predictive customer insight, and personalized service intelligence, while the dependent variables were customer satisfaction and customer retention. Analysis was conducted using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression modeling. Findings showed high mean scores for CRM-based business intelligence (M = 4.08, SD = 0.61), customer satisfaction (M = 4.01, SD = 0.64), and customer retention (M = 3.94, SD = 0.67). Reliability was strong, with Cronbach’s alpha values of 0.89 for CRM-based business intelligence, 0.87 for customer satisfaction, and 0.85 for customer retention. CRM-based business intelligence was strongly correlated with customer satisfaction (r = 0.68, p < .001) and customer retention (r = 0.61, p < .001). Regression results confirmed significant positive effects on customer satisfaction (β = 0.71, t = 11.42, R² = 0.504, p < .001) and customer retention (β = 0.64, t = 9.87, R² = 0.412, p < .001). Live chat (M = 4.16) and mobile or web channels (M = 4.12) showed the highest effectiveness. The study implies that enterprise and cloud-based service organizations should strengthen intelligence-driven CRM capabilities to improve service personalization, channel integration, and long-term customer relationship performance.
Copyright (c) 2024 Zakia Afroz, Rukaiya Khatun Moury (Author)
A GIS-Based Geospatial Risk Modeling Framework for Natural Gas Distribution Pipeline Infrastructure Integrity and Resilience
Distribution pipeline systems constitute a critical component of national energy infrastructure, supplying natural gas to residential, commercial, and industrial consumers across extensive urban and rural networks. Ensuring the safety and reliability of these systems requires continuous monitoring, risk assessment, and proactive integrity management strategies. This study develops a Geographic Information Systems (GIS)–based analytical framework for implementing a Distribution Integrity Management Program (DIMP) aligned with regulatory requirements under 49 CFR Part 192 Subpart P. The proposed framework integrates spatial pipeline datasets, environmental risk indicators, infrastructure characteristics, and operational history into a unified geospatial risk modeling environment. Pipeline integrity is operationalized as a quantitative risk index derived from threat indicators including corrosion susceptibility, excavation damage potential, natural force hazards, and material degradation conditions. The analytical dataset includes spatially segmented pipeline assets linked with attribute information such as installation year, pipe material, diameter, operating pressure, and environmental exposure conditions. Spatial modeling procedures include geospatial risk weighting, hotspot detection, and predictive threat scoring using GIS-based statistical modeling techniques. Risk indicators are combined through a weighted scoring algorithm to produce segment-level integrity risk scores across the pipeline network. Preliminary modeling results demonstrate that integrating GIS spatial analytics with distribution integrity management workflows significantly improves the identification of high-risk pipeline segments and enhances decision-making for inspection prioritization, maintenance planning, and regulatory compliance. The findings demonstrate that GIS-driven integrity management frameworks provide scalable and data-driven tools for managing complex pipeline distribution systems. By combining spatial analytics with regulatory integrity management programs, the proposed framework supports improved infrastructure resilience, risk mitigation, and public safety outcomes across natural gas distribution networks.
Distribution Integrity Management Program
Copyright (c) 2026 Ratul Debnath (Author)
Deep-Learning Architectures for Predicting Cardiovascular Outcomes Using High Dimensional Medical Imaging Data
This quantitative study examined deep-learning architectures for predicting cardiovascular outcomes using high-dimensional medical imaging data within a retrospective, multi-center observational cohort framework. Imaging examinations were treated as baseline predictors, and clinically documented cardiovascular events were operationalized as outcome labels using predefined event windows and censoring logic. The unit of analysis was the individual patient, and one index imaging examination was retained per patient to ensure independence of observations. The final analytic sample included 1,248 patients drawn consecutively from four clinical sites, with a mean age of 57.6 years (SD = 12.9) and 56.7% male representation (n = 708). Imaging modalities included cardiac MRI (51.0%), CT/CT angiography (26.0%), and cine echocardiography (23.1%). Composite constructs were derived to represent structural imaging risk, functional imaging dynamics, tissue characterization, and clinical risk covariates, with all constructs standardized prior to modeling. Reliability analysis demonstrated satisfactory internal consistency across constructs, with final Cronbach’s alpha values ranging from 0.80 to 0.88. Regression analyses were conducted using stepwise specifications, beginning with clinical controls and expanding to imaging constructs and an integrated imaging–clinical score. Structural imaging risk (β = 0.287, p < .001), functional imaging dynamics (β = 0.214, p = .001), and tissue characterization (β = 0.246, p < .001) were each significantly associated with cardiovascular event occurrence. The integrated imaging–clinical score produced the strongest adjusted association (β = 0.521, 95% CI [0.407, 0.635], p < .001) and yielded the best model fit (AIC reduced from 914.7 to 889.2; pseudo R² increased from 0.167 to 0.193). Model diagnostics indicated acceptable multicollinearity (max VIF = 2.18) and adequate calibration (calibration slope = 0.97; intercept = 0.03). Holdout performance demonstrated strong discrimination (AUC = 0.82) and low probabilistic error (Brier score = 0.098), with specificity of 0.90 and sensitivity of 0.63 at a 0.50 threshold. Robustness checks, including bootstrap resampling and subgroup stratification, produced consistent estimates. Overall, the findings indicated that multi-domain imaging constructs significantly predicted cardiovascular outcomes, and integrated imaging–clinical modeling provided the strongest and most stable predictive evidence.
Copyright (c) 2024 Jinnat Ara, Samiha Binte Abdullah (Author)
Topology-Optimized, 3D-Printed Thermal Management for Wide-Bandgap Power Electronics in High-Efficiency Drives
This study addressed a persistent thermal bottleneck in wide-bandgap (WBG) power electronics used in high-efficiency drives: as power density rises, localized hotspots and junction-to-coolant resistance increasingly constrain reliability, allowable switching performance, and practical adoption of advanced cooling hardware. The purpose was to quantitatively evaluate whether topology-optimized, 3D-printed thermal-management architectures are perceived as both high-impact and implementable under real manufacturing and integration constraints, and to explain adoption readiness using a quantitative, cross-sectional, case-based design. Data were collected in one time snapshot from a cross-functional sample (N = 132) evaluating enterprise drive integration cases (air-cooled 43.9% and liquid-cooled 56.1%) within industrial-grade, digitally engineered workflows (including enterprise and cloud-enabled collaboration for design/assessment contexts). Key independent variables included topology optimization quality (TOQ), perceived implementation ease (PIE, with an AM Feasibility Index proxy), thermal integration quality (TIQ), and design complexity (DC); key outcome variables were perceived thermal usefulness (PTU), thermal performance improvement (TPI), reliability expectation (RE), and adoption readiness (ARI). The analysis plan applied descriptive statistics, reliability testing (Cronbach’s α: PTU = 0.86, PIE = 0.83, TIQ = 0.81, TOQ = 0.78, DC = 0.74, ARI = 0.80), Pearson correlations, and multiple regression models. Headline results showed high perceived value (PTU M = 4.21, SD = 0.52) and favorable readiness (ARI M = 3.89, SD = 0.59), with feasibility positive but more constrained (PIE M = 3.71, SD = 0.63; AFI = 72.6/100, SD = 10.8). Correlations supported the core relationships: TOQ–TPI (r = 0.52, p < .001), PIE–ARI (r = 0.58, p < .001), and DC–ARI (r = −0.41, p < .001). Regression explained substantial variance in outcomes: TPI (R² = .48) was predicted by TOQ (β = 0.29, p = .002), TIQ (β = 0.25, p = .006), and PTU (β = 0.31, p < .001), with DC reducing TPI (β = −0.18, p = .021); ARI (R² = .52) was driven most by PIE/AFI (β = 0.36, p < .001) and PTU (β = 0.27, p = .001), while DC reduced readiness (β = −0.22, p = .006). A bottleneck attribution map identified the dominant constraint as module-to-cooler interface/TIM resistance (M = 4.12), followed by baseplate/cold-plate spreading limits (M = 3.86), implying that adoption gains will depend as much on repeatable interface control and inspectable manufacturability as on geometry innovation. These findings imply that organizations pursuing WBG drive densification should pair topology optimization with design-for-additive-manufacturing gates (especially inspection/QA and powder/support removal) and standardized interface procedures to convert thermal promise into repeatable, deployable performance.
Copyright (c) 2022 S. M. Habibullah, Zaheda Khatun (Author)
A Quantitative Financial Impact Assessment of Digital Trade Platforms on Export Performance, Capital Efficiency, and Market Competitiveness
This study examined the quantitative relationship between digital trade platform adoption and firm-level trade performance, operational efficiency, and financial outcomes within a digitally mediated cross-border trade context. Using a non-experimental, explanatory research design grounded in observational econometrics, the analysis integrated firm-level administrative trade records, financial statements, and structured survey data to assess how platform participation shaped export behavior under varying firm, sectoral, and destination-market conditions. The unit of analysis was the firm-year, and the final analytical sample consisted of exporting firms operating across multiple sectors with heterogeneous levels of digital engagement. The findings showed that firms participating in digital trade platforms demonstrated significantly higher export engagement compared with non-platform firms. Platform-active firms recorded, on average, a 22–28 percent higher export intensity and served approximately 1.6 more foreign destination markets than non-adopters. Export participation rates were also higher among platform users, with digitally active firms exhibiting export entry probabilities nearly 18 percent greater than firms relying solely on conventional channels. Operational efficiency indicators reflected moderate but consistent improvements, as platform adopters reported shorter average order-fulfillment cycles and lower order-processing variability relative to non-adopters. Financial performance results were more heterogeneous; while revenue growth rates were higher among platform-active firms by approximately 9–12 percent, profitability measures showed smaller and less uniform gains, with margin improvements concentrated among larger and more experienced exporters. The analysis further revealed pronounced heterogeneity across firm size, export experience, sector, and destination-market characteristics. Small and medium-sized enterprises experienced stronger extensive-margin gains in export participation, whereas larger firms captured more stable efficiency and financial benefits. Digitally compatible and standardized product sectors exhibited stronger platform-linked outcomes than highly regulated or logistics-intensive sectors. Destination-market conditions moderated results significantly, as firms exporting to markets with higher logistics reliability and digital payment penetration realized stronger performance effects than those targeting institutionally weaker destinations. Overall, the study concluded that digital trade platforms primarily functioned as access-enabling mechanisms that reshaped export participation and market reach, while realized efficiency and financial outcomes depended on complementary firm capabilities, sectoral suitability, and destination-market enabling conditions.
Copyright (c) 2022 Mostafa Kamal, Md Tohidul Islam (Author)
Systematic Review of Calibration Technologies and their Impact on Safety in Global Critical Infrastructure
Calibration technologies play a critical yet often understated role in maintaining safety across global critical infrastructure systems that depend on accurate, stable, and trustworthy measurements for monitoring, control, and protection. This systematic review examines how calibration technologies influence safety performance across multiple infrastructure domains, including industrial and process facilities, energy and utility systems, nuclear infrastructure, transportation networks, and other safety-critical socio-technical systems. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, an initial pool of 1,247 records was identified through structured database searches and supplementary manual screening. After removing 386 duplicate records and excluding 727 articles during title and abstract screening, 134 peer-reviewed and technical papers were retained for full analysis. These studies span more than 25 years of publication, represent research conducted in over 30 countries, and collectively account for approximately 19,800 scholarly citations, indicating a robust and mature evidence base.Numerical synthesis of the findings shows that 109 of the 134 reviewed studies (81.3%) explicitly identify calibration as a prerequisite for the reliable functioning of safety-critical instrumentation, alarms, and control systems. Instrument degradation and drift are addressed in 97 studies (72.4%), with 63 studies (47.0%) documenting that drift-related measurement errors can remain latent for extended operational periods before detection. Calibration interval determination is discussed in 88 studies (65.7%), of which 54 studies (61.4%) report limitations of fixed time-based scheduling in adequately controlling safety risk. Advanced calibration approaches, including online monitoring, analytical redundancy, and model-based drift detection, are examined in 61 studies (45.5%); however, 44 of these studies (72.1%) emphasize that such approaches are most effective when used in combination with direct, traceable calibration rather than as standalone solutions. Safety incident analyses are included in 67 studies (50.0%), with 49 studies (73.1%) identifying measurement or calibration-related failures as contributing factors to unsafe conditions, near-miss events, or incident escalation. Additionally, 85 studies (63.4%) highlight deficiencies in calibration documentation, traceability, or governance as recurring precursors to measurement failure. Overall, the numerical findings confirm that calibration technologies function as foundational socio-technical safety enablers, with measurable influence on safety margins, barrier integrity, and decision reliability across interconnected global critical infrastructure systems.
Copyright (c) 2024 Efat Ara Haque (Author)
BLOCKCHAIN-ORCHESTRATED CYBER-PHYSICAL SUPPLY CHAIN NETWORKS WITH BYZANTINE FAULT TOLERANCE FOR MANUFACTURING ROBUSTNESS
This study quantitatively examined how blockchain orchestration and Byzantine fault tolerance (BFT) were associated with manufacturing robustness in cyber-physical supply chain networks under varying workload and fault conditions. A controlled, scenario-based experimental design was implemented using network-run–level simulation outputs and system log data, enabling systematic comparison across three coordination regimes: centralized coordination, non-BFT blockchain coordination, and BFT-enabled blockchain orchestration. The analytic sample comprised 360 validated network runs spanning low, medium, and high event loads and low, moderate, and elevated fault intensities. Manufacturing robustness was operationalized using downtime probability, throughput stability, schedule deviation, recovery-time behavior, service level variance, and inventory oscillation indicators, while consensus performance and data integrity were modeled as explanatory mechanisms. Descriptive findings showed that centralized coordination achieved the highest mean throughput (920.3 TPS) and lowest mean confirmation latency (1.12 seconds) but exhibited higher downtime probability (5.20%) and longer recovery time (49.0 minutes) under fault stress. BFT-enabled orchestration demonstrated lower throughput (547.0 TPS) and higher confirmation latency (4.71 seconds) but achieved superior robustness outcomes, including lower downtime probability (2.80%), reduced schedule deviation (14.3 minutes), and faster recovery (31.3 minutes). Correlation analysis indicated strong associations between consensus performance and robustness, with deadline adherence negatively correlated with downtime probability (r = −0.66) and recovery time (r = −0.65). Data integrity metrics were also strongly related to robustness, as data completeness showed a negative correlation with downtime probability (r = −0.61). Hierarchical regression results revealed that coordination regime and BFT configuration explained 31% of the variance in manufacturing robustness, which increased to 63% when consensus performance and data integrity were included. Mediation analysis showed that consensus performance and data integrity jointly accounted for a substantial portion of the architecture effect, with a total indirect effect of −0.21. Interaction models further indicated that the robustness advantages of BFT-enabled orchestration strengthened under higher workload and fault intensity. Overall, the findings demonstrated that manufacturing robustness in cyber-physical supply chains was shaped by coordination architecture through measurable performance and integrity mechanisms, providing quantitative evidence on how distributed trust and fault tolerance influenced operational stability under stress.
Copyright (c) 2023 S. M. Habibullah, Aditya Dhanekula (Author)
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)