Journal of Sustainable Development and Policy
type: channel
description: 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.
language: en
publication name: Journal of Sustainable Development and Policy
items: Intelligent Decision-Support Systems for Cross-Functional Workflow Optimization in Data-Driven Organizations
items: The Role of Cloud-Native Infrastructures in Supporting Autonomous and Uncrewed Systems (UXS) in Operations
items: Machine Learning–Enhanced PFAS Contaminant Fate and Transport Modeling for Optimized Groundwater Remediation Strategies at Industrial Sites
items: Data Preparation and Feature Engineering in High-Dimensional Predictive Modeling
items: Digital Technologies and IoT: Reshaping Financial Risk and Investment in Global Supply Chains
items: Machine Learning–Driven Optimization of Water Distribution Networks: Demand Forecasting, and Energy Efficiency Analysis
items: Quantitative Assessment of CRM-Based Business Intelligence on Customer Satisfaction and Retention: Evidence from Multi-Channel Service Operations
items: A GIS-Based Geospatial Risk Modeling Framework for Natural Gas Distribution Pipeline Infrastructure Integrity and Resilience
items: Deep-Learning Architectures for Predicting Cardiovascular Outcomes Using High Dimensional Medical Imaging Data
items: Topology-Optimized, 3D-Printed Thermal Management for Wide-Bandgap Power Electronics in High-Efficiency Drives
items: A Quantitative Financial Impact Assessment of Digital Trade Platforms on Export Performance, Capital Efficiency, and Market Competitiveness
items: Systematic Review of Calibration Technologies and their Impact on Safety in Global Critical Infrastructure
items: BLOCKCHAIN-ORCHESTRATED CYBER-PHYSICAL SUPPLY CHAIN NETWORKS WITH BYZANTINE FAULT TOLERANCE FOR MANUFACTURING ROBUSTNESS
items: DATA-DRIVEN PROCESS OPTIMIZATION IN AUTOMOTIVE MANUFACTURING A MACHINE LEARNING APPROACH TO WASTE REDUCTION AND QUALITY IMPROVEMENT
items: IOT-INTEGRATED DEEP NEURAL PREDICTIVE MAINTENANCE SYSTEM WITH VIBRATION-SIGNAL DIAGNOSTICS IN SMART FACTORIES
items: COMPUTATIONAL THERMO-FLUID DYNAMICS MODELING FOR PROCESS OPTIMIZATION IN HYDROGEN-INTEGRATED INDUSTRIAL HEAT SYSTEMS
items: INTELLIGENT CLIMATE RISK MODELING FOR ROBUST ENERGY RESILIENCE AND NATIONAL SECURITY
items: THE IMPACT OF SMART MATERIALS AND FIRE-RESISTANT STRUCTURES ON SAFETY IN U.S. PUBLIC INFRASTRUCTURE
items: AI-ENHANCED MIS PLATFORMS FOR STRATEGIC BUSINESS DECISION-MAKING IN SMES
items: ASSESSMENT OF DATA-DRIVEN VENDOR PERFORMANCE EVALUATION IN RETAIL SUPPLY CHAINS ANALYZING METRICS, SCORECARDS, AND CONTRACT MANAGEMENT TOOLS
items: SMART HYBRID MANUFACTURING: A COMBINATION OF ADDITIVE, SUBTRACTIVE, AND LEAN TECHNIQUES FOR AGILE PRODUCTION SYSTEMS
items: INTEGRATION OF IOT AND EDGE COMPUTING FOR LOW-LATENCY DATA ANALYTICS IN SMART CITIES AND IOT NETWORKS
items: IOT INTEGRATION IN INTELLIGENT LUBRICATION SYSTEMS FOR PREDICTIVE MAINTENANCE AND PERFORMANCE OPTIMIZATION IN ADVANCED MANUFACTURING INDUSTRIES
items: INTEGRATING INDUSTRIAL ENGINEERING AND PETROLEUM SYSTEMS WITH LINEAR PROGRAMMING MODEL FOR FUEL EFFICIENCY AND DOWNTIME REDUCTION
items: VEHICLE-TO-INFRASTRUCTURE (V2I) COMMUNICATION AND TRAFFIC INCIDENT REDUCTION: AN EMPIRICAL STUDY ACROSS U.S. HIGHWAY NETWORKS
items: INTEGRATION OF ARTIFICIAL INTELLIGENCE AND ADVANCED COMPUTING TO DEVELOP RESILIENT CYBER DEFENSE SYSTEMS
items: THE EFFECT OF PRODUCTION PLANNING EFFICIENCY ON DELIVERY TIMELINES IN U.S. APPAREL IMPORTS
items: THE INFLUENCE OF SECURE DATA SYSTEMS ON FRAUD DETECTION IN BUSINESS INTELLIGENCE APPLICATIONS
items: EMPIRICAL INSIGHTS INTO POWER BI APPLICATIONS FOR DYNAMIC FINANCIAL REPORTING AND PREDICTIVE ANALYSIS
items: SYSTEMATIC REVIEW OF STRESS AND BURNOUT INTERVENTIONS AMONG U.S. HEALTHCARE PROFESSIONALS USING ADVANCED COMPUTING APPROACHES

Intelligent Decision-Support Systems for Cross-Functional Workflow Optimization in Data-Driven Organizations
type: item
description: 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 …
subject: Articles
  topic: section
subject: Intelligent Decision-Support Systems
  topic: keywords
subject: Workflow Optimization
  topic: keywords
subject: Cross-Functional Coordination
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Analytics Capability
  topic: keywords
creator: Md Khaled Hossain
creator: Hisham Mahmood
rights: Copyright (c) 2022 Md Khaled Hossain, Hisham Mahmood (Author)
date: 2022-06-29
publication date: 2022-06-29
starting page: 168
ending page: 207
doi: 10.63125/dsfg3k24

The Role of Cloud-Native Infrastructures in Supporting Autonomous and Uncrewed Systems (UXS) in Operations
type: item
description: 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 effectiven…
subject: Articles
  topic: section
subject: Cloud-native infrastructure
  topic: keywords
subject: Autonomous and uncrewed systems
  topic: keywords
subject: Operational effectiveness
  topic: keywords
subject: Reliability
  topic: keywords
subject: Mission readiness
  topic: keywords
creator: Shamsul Arifeen
creator: Md. Morshedul Islam
rights: Copyright (c) 2025 Shamsul Arifeen, Md. Morshedul Islam (Author)
date: 2025-10-27
publication date: 2025-10-27
starting page: 82
ending page: 125
doi: 10.63125/vntbqq40

Machine Learning–Enhanced PFAS Contaminant Fate and Transport Modeling for Optimized Groundwater Remediation Strategies at Industrial Sites
type: item
description: 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 …
subject: Articles
  topic: section
subject: PFAS
  topic: keywords
subject: Groundwater Remediation
  topic: keywords
subject: Machine Learning Modeling
  topic: keywords
subject: Contaminant Fate and Transport
  topic: keywords
subject: Industrial Sites
  topic: keywords
creator: Albert Anokye
rights: Copyright (c) 2026 Albert Anokye (Author)
date: 2026-03-24
publication date: 2026-03-24
starting page: 34
ending page: 72
doi: 10.63125/e3x2tt57

Data Preparation and Feature Engineering in High-Dimensional Predictive Modeling
type: item
description: 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.
subject: Articles
  topic: section
subject: Data Preprocessing
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Analytics
  topic: keywords
creator: B M Taslimul Haque
creator: Md. Arifur Rahman
rights: Copyright (c) 2024 B M Taslimul Haque, Md. Arifur Rahman (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 205
ending page: 244
doi: 10.63125/5txtr530

Digital Technologies and IoT: Reshaping Financial Risk and Investment in Global Supply Chains
type: item
description: 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 equ…
subject: Articles
  topic: section
subject: IoT Adoption
  topic: keywords
subject: Digital Monitoring
  topic: keywords
subject: Financial Risk
  topic: keywords
subject: Supply Chain
  topic: keywords
subject: Investment Efficiency
  topic: keywords
creator: Mahfuj Ahmed Ruzel
creator: Md. Mehedi Hasan
rights: Copyright (c) 2023 Mahfuj Ahmed Ruzel, Md. Mehedi Hasan (Author)
date: 2023-12-28
publication date: 2023-12-28
starting page: 297
ending page: 345
doi: 10.63125/nbv6ka16

Machine Learning–Driven Optimization of Water Distribution Networks: Demand Forecasting, and Energy Efficiency Analysis
type: item
description: 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 = 0.7…
subject: Articles
  topic: section
subject: Machine Learning Adoption
  topic: keywords
subject: Water Distribution Networks
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Network Optimization
  topic: keywords
creator: Palash Chandra Das
rights: Copyright (c) 2023 Palash Chandra Das (Author)
date: 2023-12-24
publication date: 2023-12-24
starting page: 257
ending page: 296
doi: 10.63125/jdxq0819

Quantitative Assessment of CRM-Based Business Intelligence on Customer Satisfaction and Retention: Evidence from Multi-Channel Service Operations
type: item
description: 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, …
subject: Articles
  topic: section
subject: CRM-based business intelligence
  topic: keywords
subject: Customer satisfaction
  topic: keywords
subject: Customer retention
  topic: keywords
subject: Multi-channel service operations
  topic: keywords
subject: Predictive customer insight
  topic: keywords
creator: Zakia Afroz
creator: Rukaiya Khatun Moury
rights: Copyright (c) 2024 Zakia Afroz, Rukaiya Khatun Moury (Author)
date: 2024-06-02
publication date: 2024-06-02
starting page: 01
ending page: 42
doi: 10.63125/hjd22x72

A GIS-Based Geospatial Risk Modeling Framework for Natural Gas Distribution Pipeline Infrastructure Integrity and Resilience
type: item
description: 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 system…
subject: Articles
  topic: section
subject: GIS
  topic: keywords
subject: Distribution Integrity Management Program
  topic: keywords
subject: Pipeline Risk Modeling
  topic: keywords
subject: Infrastructure Integrity
  topic: keywords
subject: Spatial Risk Analysis
  topic: keywords
subject: Gas Distribution Systems
  topic: keywords
creator: Ratul Debnath
rights: Copyright (c) 2026 Ratul Debnath (Author)
date: 2026-03-12
publication date: 2026-03-12
starting page: 01
ending page: 33
doi: 10.63125/6z18x885

Deep-Learning Architectures for Predicting Cardiovascular Outcomes Using High Dimensional Medical Imaging Data
type: item
description: 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 performanc…
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Cardiovascular Outcomes
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Prediction Modeling
  topic: keywords
subject: Multi-Center
  topic: keywords
creator: Jinnat Ara
creator: Samiha Binte Abdullah
rights: Copyright (c) 2024 Jinnat Ara, Samiha Binte Abdullah (Author)
date: 2024-09-29
publication date: 2024-09-29
starting page: 134
ending page: 166
doi: 10.63125/vrgee960

Topology-Optimized, 3D-Printed Thermal Management for Wide-Bandgap Power Electronics in High-Efficiency Drives
type: item
description: 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…
subject: Articles
  topic: section
subject: Topology Optimization
  topic: keywords
subject: Additive Manufacturing
  topic: keywords
subject: Thermal Management
  topic: keywords
subject: Wide-Bandgap Power Electronics
  topic: keywords
subject: Adoption Readiness
  topic: keywords
creator: S. M. Habibullah
creator: Zaheda Khatun
rights: Copyright (c) 2022 S. M. Habibullah, Zaheda Khatun (Author)
date: 2022-06-28
publication date: 2022-06-28
starting page: 134
ending page: 167
doi: 10.63125/p8m2p864

A Quantitative Financial Impact Assessment of Digital Trade Platforms on Export Performance, Capital Efficiency, and Market Competitiveness
type: item
description: 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-m…
subject: Articles
  topic: section
subject: Digital Trade Platforms
  topic: keywords
subject: Firm Performance
  topic: keywords
subject: Export Competitiveness
  topic: keywords
subject: Econometric Analysis
  topic: keywords
subject: Cross-Border E-Commerce
  topic: keywords
creator: Mostafa Kamal
creator: Md Tohidul Islam
rights: Copyright (c) 2022 Mostafa Kamal, Md Tohidul Islam (Author)
date: 2022-09-08
publication date: 2022-09-08
starting page: 01
ending page: 26
doi: 10.63125/pt5v9517

Systematic Review of Calibration Technologies and their Impact on Safety in Global Critical Infrastructure
type: item
description: 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…
subject: Articles
  topic: section
subject: Calibration Technologies
  topic: keywords
subject: Safety
  topic: keywords
subject: Critical Infrastructure
  topic: keywords
subject: Measurement Reliability
  topic: keywords
subject: Risk Management
  topic: keywords
creator: Efat Ara Haque
rights: Copyright (c) 2024 Efat Ara Haque (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 174
ending page: 204
doi: 10.63125/cznpnr41

BLOCKCHAIN-ORCHESTRATED CYBER-PHYSICAL SUPPLY CHAIN NETWORKS WITH BYZANTINE FAULT TOLERANCE FOR MANUFACTURING ROBUSTNESS
type: item
description: 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 varian…
subject: Articles
  topic: section
subject: Blockchain orchestration
  topic: keywords
subject: Byzantine fault tolerance
  topic: keywords
subject: Cyber-physical supply chains
  topic: keywords
subject: Manufacturing
  topic: keywords
subject: Robustness
  topic: keywords
subject: Consensus performance
  topic: keywords
creator: S. M. Habibullah
creator: Aditya Dhanekula
rights: Copyright (c) 2023 S. M. Habibullah, Aditya Dhanekula (Author)
date: 2023-09-28
publication date: 2023-09-28
starting page: 34
ending page: 72
doi: 10.63125/057vwc78

DATA-DRIVEN PROCESS OPTIMIZATION IN AUTOMOTIVE MANUFACTURING A MACHINE LEARNING APPROACH TO WASTE REDUCTION AND QUALITY IMPROVEMENT
type: item
description: 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.
subject: Articles
  topic: section
subject: Data-driven optimization
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Automotive manufacturing
  topic: keywords
subject: Waste reduction
  topic: keywords
subject: Quality improvement
  topic: keywords
creator: Md. Foysal Hossain
creator: Subrato Sarker
rights: Copyright (c) 2022 Md. Foysal Hossain, Subrato Sarker (Author)
date: 2022-06-28
publication date: 2022-06-28
starting page: 87
ending page: 133
doi: 10.63125/2hk0qd38

IOT-INTEGRATED DEEP NEURAL PREDICTIVE MAINTENANCE SYSTEM WITH VIBRATION-SIGNAL DIAGNOSTICS IN SMART FACTORIES
type: item
description: 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…
subject: Articles
  topic: section
subject: Iot Integration
  topic: keywords
subject: Deep Neural Networks
  topic: keywords
subject: Vibration Diagnostics
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Smart Factories
  topic: keywords
creator: S. M. Habibullah
creator: Md. Tahmid Farabe Shehun
rights: Copyright (c) 2022 S. M. Habibullah, Md. Tahmid Farabe Shehun (Author)
date: 2022-09-30
publication date: 2022-09-30
starting page: 35
ending page: 83
doi: 10.63125/6jjq1p95

COMPUTATIONAL THERMO-FLUID DYNAMICS MODELING FOR PROCESS OPTIMIZATION IN HYDROGEN-INTEGRATED INDUSTRIAL HEAT SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: Hydrogen combustion
  topic: keywords
subject: Thermo-fluid CFD
  topic: keywords
subject: Process optimization
  topic: keywords
subject: Industrial heat
  topic: keywords
subject: NOx control
  topic: keywords
creator: Saikat Sarkar
rights: Copyright (c) 2024 Saikat Sarkar (Author)
date: 2024-09-28
publication date: 2024-09-28
starting page: 87
ending page: 133
doi: 10.63125/8rm6bc88

INTELLIGENT CLIMATE RISK MODELING FOR ROBUST ENERGY RESILIENCE AND NATIONAL SECURITY
type: item
description: 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-securit…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Climate Risk Modeling
  topic: keywords
subject: Energy Resilience
  topic: keywords
subject: National Security Analytics
  topic: keywords
subject: Quantitative Environmental Forecasting
  topic: keywords
creator: FNU Zulqarnain
creator: Subrato Sarker
rights: Copyright (c) 2023 FNU Zulqarnain, Subrato Sarker (Author)
date: 2023-12-23
publication date: 2023-12-23
starting page: 218
ending page: 256
doi: 10.63125/jmer2r39

THE IMPACT OF SMART MATERIALS AND FIRE-RESISTANT STRUCTURES ON SAFETY IN U.S. PUBLIC INFRASTRUCTURE
type: item
description: 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 predict…
subject: Articles
  topic: section
subject: Smart materials
  topic: keywords
subject: Fire-resistant structures
  topic: keywords
subject: Public infrastructure
  topic: keywords
subject: Fire safety
  topic: keywords
subject: Quantitative modeling
  topic: keywords
creator: Masud Rana
creator: Md Sarwar Hossain Shuvo
rights: Copyright (c) 2025 Masud Rana, Md Sarwar Hossain Shuvo (Author)
date: 2024-09-27
publication date: 2024-09-27
starting page: 44
ending page: 86
doi: 10.63125/ygr1yk30

AI-ENHANCED MIS PLATFORMS FOR STRATEGIC BUSINESS DECISION-MAKING IN SMES
type: item
description: 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 independe…
subject: Articles
  topic: section
subject: AI-Enhanced MIS
  topic: keywords
subject: SMEs
  topic: keywords
subject: Strategic Decisions
  topic: keywords
subject: Managerial Usage
  topic: keywords
subject: Integration Quality
  topic: keywords
creator: Aditya Dhanekula
creator: M.A. Rony
rights: Copyright (c) 2023 Aditya Dhanekula, M.A. Rony (Author)
date: 2023-06-27
publication date: 2023-06-27
starting page: 01
ending page: 42
doi: 10.63125/km3fhs48

ASSESSMENT OF DATA-DRIVEN VENDOR PERFORMANCE EVALUATION IN RETAIL SUPPLY CHAINS ANALYZING METRICS, SCORECARDS, AND CONTRACT MANAGEMENT TOOLS
type: item
description: 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,…
subject: Articles
  topic: section
subject: Vendor Performance
  topic: keywords
subject: KPI Scorecards
  topic: keywords
subject: Retail Supply Chains
  topic: keywords
subject: Contract Governance
  topic: keywords
subject: Outcome Modeling
  topic: keywords
creator: Md. Abdur Rauf
creator: Zamal Haider Shish
rights: Copyright (c) 2022 Md. Abdur Rauf, Zamal Haider Shish (Author)
date: 2022-12-27
publication date: 2022-12-27
starting page: 71
ending page: 116
doi: 10.63125/2a641k35

SMART HYBRID MANUFACTURING: A COMBINATION OF ADDITIVE, SUBTRACTIVE, AND LEAN TECHNIQUES FOR AGILE PRODUCTION SYSTEMS
type: item
description: 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…
subject: Articles
  topic: section
subject: Smart Hybrid Manufacturing
  topic: keywords
subject: Additive–Subtractive Integration
  topic: keywords
subject: Lean Agility
  topic: keywords
subject: Cyber-Physical Control
  topic: keywords
subject: Production Performance
  topic: keywords
creator: Md Al Amin Khan
creator: Md Mesbaul Hasan
rights: Copyright (c) 2023 Md Al Amin Khan, Md Mesbaul Hasan (Author)
date: 2023-12-28
publication date: 2023-12-28
starting page: 174
ending page: 217
doi: 10.63125/7rb1zz78

INTEGRATION OF IOT AND EDGE COMPUTING FOR LOW-LATENCY DATA ANALYTICS IN SMART CITIES AND IOT NETWORKS
type: item
description: 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 variable…
subject: Articles
  topic: section
subject: IOT Edge Integration
  topic: keywords
subject: Low Latency Analytics
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Fog and Edge Computing
  topic: keywords
subject: Smart City Service Quality
  topic: keywords
creator: Md Harun-Or-Rashid Mollah
creator: Mst. Shahrin Sultana
creator: Sai Praveen Kudapa
rights: Copyright (c) 2023 Md Harun-Or-Rashid Mollah, Mst. Shahrin Sultana, Sai Praveen Kudapa (Author)
date: 2023-09-28
publication date: 2023-09-28
starting page: 01
ending page: 33
doi: 10.63125/004h7m29

IOT INTEGRATION IN INTELLIGENT LUBRICATION SYSTEMS FOR PREDICTIVE MAINTENANCE AND PERFORMANCE OPTIMIZATION IN ADVANCED MANUFACTURING INDUSTRIES
type: item
description: 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 medi…
subject: Articles
  topic: section
subject: Internet Of Things
  topic: keywords
subject: Intelligent Lubrication Systems
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Advanced Manufacturing Performance
  topic: keywords
creator: Zobayer Eusufzai
rights: Copyright (c) 2023 Zobayer Eusufzai (Author)
date: 2023-12-29
publication date: 2023-12-29
starting page: 140
ending page: 173
doi: 10.63125/zybrmx69

INTEGRATING INDUSTRIAL ENGINEERING AND PETROLEUM SYSTEMS WITH LINEAR PROGRAMMING MODEL FOR FUEL EFFICIENCY AND DOWNTIME REDUCTION
type: item
description: 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 stra…
subject: Articles
  topic: section
subject: Industrial Engineering
  topic: keywords
subject: Petroleum Refinery Operations
  topic: keywords
subject: Fuel Efficiency
  topic: keywords
subject: Downtime Reduction
  topic: keywords
subject: Linear Programming Model
  topic: keywords
creator: Sabuj Kumar Shil
rights: Copyright (c) 2023 Sabuj Kumar Shil (Author)
date: 2023-12-28
publication date: 2023-12-28
starting page: 108
ending page: 139
doi: 10.63125/v7d6a941

VEHICLE-TO-INFRASTRUCTURE (V2I) COMMUNICATION AND TRAFFIC INCIDENT REDUCTION: AN EMPIRICAL STUDY ACROSS U.S. HIGHWAY NETWORKS
type: item
description: 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 insi…
subject: Articles
  topic: section
subject: Vehicle-to-Infrastructure (V2I) Communication
  topic: keywords
subject: Traffic Incident Reduction
  topic: keywords
subject: Intelligent Transportation Systems
  topic: keywords
subject: Connected Vehicle Technology
  topic: keywords
subject: U.S. Highway Safety Analysis
  topic: keywords
creator: Md Asfaqur Rahman
rights: Copyright (c) 2025 Md Asfaquar Rahman (Author)
date: 2025-10-27
publication date: 2025-10-27
starting page: 38
ending page: 81
doi: 10.63125/c1wm0t92

INTEGRATION OF ARTIFICIAL INTELLIGENCE AND ADVANCED COMPUTING TO DEVELOP RESILIENT CYBER DEFENSE SYSTEMS
type: item
description: 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 s…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Advanced Computing
  topic: keywords
subject: Cyber Resilience
  topic: keywords
subject: Cyber Defense
  topic: keywords
subject: Security Analytics
  topic: keywords
creator: Saba Ashfaq
creator: Shaikat Biswas
creator: Tonoy Kanti Chowdhury
rights: Copyright (c) 2023 Saba Ashfaq, Shaikat Biswas, Tonoy Kanti Chowdhury (Author)
date: 2023-12-27
publication date: 2023-12-27
starting page: 74
ending page: 107
doi: 10.63125/rxyc6y88

THE EFFECT OF PRODUCTION PLANNING EFFICIENCY ON DELIVERY TIMELINES IN U.S. APPAREL IMPORTS
type: item
description: 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.
subject: Articles
  topic: section
subject: Production Planning Efficiency
  topic: keywords
subject: Delivery Timelines
  topic: keywords
subject: Apparel Imports
  topic: keywords
subject: Supply Chain Performance
  topic: keywords
subject: Operational Reliability
  topic: keywords
creator: Md. Hasan Imam
creator: Ashraful Islam
rights: Copyright (c) 2023 Md. Hasan Imam, Ashraful Islam (Author)
date: 2023-12-27
publication date: 2023-12-27
starting page: 35
ending page: 73
doi: 10.63125/sg472m51

THE INFLUENCE OF SECURE DATA SYSTEMS ON FRAUD DETECTION IN BUSINESS INTELLIGENCE APPLICATIONS
type: item
description: 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.
subject: Articles
  topic: section
subject: Secure Data Systems
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Pipeline Security
  topic: keywords
creator: Hozyfa Shafa
creator: Mst. Shahrin Sultana
rights: Copyright (c) 2024 Hozyfa Shafa, Mst. Shahrin Sultana (Author)
date: 2024-12-27
publication date: 2024-12-27
starting page: 133
ending page: 173
doi: 10.63125/8ee0eq13

EMPIRICAL INSIGHTS INTO POWER BI APPLICATIONS FOR DYNAMIC FINANCIAL REPORTING AND PREDICTIVE ANALYSIS
type: item
description: 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…
subject: Articles
  topic: section
subject: Power BI
  topic: keywords
subject: Dynamic Reporting Effectiveness
  topic: keywords
subject: Predictive Performance
  topic: keywords
subject: Decision Quality
  topic: keywords
subject: Business Intelligence Governance
  topic: keywords
creator: Md Redwanul Islam
rights: Copyright (c) 2023 Md Redwanul Islam (Author)
date: 2023-12-24
publication date: 2023-12-24
starting page: 01
ending page: 34
doi: 10.63125/crtama94

SYSTEMATIC REVIEW OF STRESS AND BURNOUT INTERVENTIONS AMONG U.S. HEALTHCARE PROFESSIONALS USING ADVANCED COMPUTING APPROACHES
type: item
description: 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. …
subject: Articles
  topic: section
subject: Stress
  topic: keywords
subject: Burnout
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Pankaz Roy Sarkar
creator: Sai Praveen Kudapa
rights: Copyright (c) 2024 Pankaz Roy Sarkar, Sai Praveen Kudapa (Author)
date: 2024-12-24
publication date: 2024-12-24
starting page: 101
ending page: 132
doi: 10.63125/9mx2fc43