ASRC Procedia: Global Perspectives in Science and Scholarship
type: channel
description: ASRC Procedia is a peer-reviewed, open-access conference proceedings journal that serves as the official publication outlet of the American Scholarly Research Conference (ASRC). Published from Florida, USA, the journal is dedicated to disseminating original research presented at academic and industry symposia held under the ASRC umbrella, including the flagship Global Research and Innovation Summit (GRIS). The journal promotes cross-disciplinary knowledge exchange by capturing diverse scholarly outputs across the domains of science, engineering, technology, health, environment, education, business, economics, public policy, and the social sciences.
language: en
publication name: ASRC Procedia: Global Perspectives in Science and Scholarship
items: Quantitative Evaluation of Machine Learning Models for Project Risk Prediction and Resource Optimization in Business Operations
items: Machine Learning–Based Consumer Behavior Prediction Models for E-Commerce Platforms: Enhancing Digital Financial Inclusion and Market Accessibility
items: AI-Driven Real-Time Methane Emissions Monitoring and Predictive Leak Detection Using Lidar and IOT Sensor Fusion in Upstream Oil and Gas Operations
items: Artificial Intelligence Driven Analytics for Market Entry Strategy, Digital Marketing Optimization, and Enterprise Workflow Transformation
items: Smart Occupational Safety Management Through IOT Sensor Networks, Machine Learning, and Real-Time Risk Assessment in Chemical Processing Plants
items: Advanced Computing Frameworks for Distributed Training, Deployment, and Monitoring of Artificial Intelligence and Machine Learning Models
items: Advanced Composite and Geopolymer Materials for Durable Water Supply and Sanitation Infrastructure: Performance, Sustainability, and Lifecycle Assessment
items: Predictive Analytics and KPI Dashboards for Enterprise Workflow Optimization
items: QUANTITATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DEFECT PREDICTION IN METAL ADDITIVE MANUFACTURING
items: Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems
items: AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems
items: Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking
items: AI-Driven Distribution Planning for Essential Goods in Underserved Communities: A Mixed Methods Framework for Access Optimization
items: Business Intelligence–Driven Risk Assessment and Portfolio Performance Analytics for Financial and Investment Institutions
items: Financial Vulnerability Mapping in Global Supply Chains: Implications for U.S. Trade Stability and Investment Risk
items: UAV-Based Hyperspectral and Thermal Signature Analytics for Early Detection of Soil Moisture Stress, Erosion Hotspots, and Flood Susceptibility
items: Quantitative Assessment of Predictive Analytics for Risk Management in U.S. Healthcare Finance Systems
items: Integrating Smart Sensor Systems and Digital Safety Dashboards for Real-Time Hazard Monitoring in High-Risk Industrial Facilities
items: Computational Modeling of Failure Mechanisms in Mechanical Systems: Applications For Energy and Industrial Sectors
items: INTEGRATING SMART SENSOR SYSTEMS AND DIGITAL SAFETY DASHBOARDS FOR REAL-TIME HAZARD MONITORING IN HIGH-RISK INDUSTRIAL FACILITIES
items: DATA-DRIVEN INDUSTRIAL ENGINEERING MODELS FOR OPTIMIZING WATER PURIFICATION AND SUPPLY CHAIN SYSTEMS IN THE U.S.
items: QUANTITATIVE ANALYSIS OF MECHANICAL TESTING AND VALVE PERFORMANCE IN THE OIL AND GAS SECTOR: ENSURING COMPLIANCE WITH ISO/IEC 17025 IN GLOBAL INDUSTRIAL INFRASTRUCTURE
items: FEDERATED LEARNING–DRIVEN REAL-TIME DISEASE SURVEILLANCE FOR SMART HOSPITALS USING MULTI-SOURCE HETEROGENEOUS HEALTHCARE DATA
items: AI-ENABLED DIGITAL TWIN FRAMEWORK FOR PREDICTIVE MAINTENANCE AND ENERGY OPTIMIZATION IN INDUSTRIAL SYSTEMS
items: ARTIFICIAL INTELLIGENCE IN DRIVEN DIGITAL TWIN FOR REAL-TIME TRAFFIC SIGNAL OPTIMIZATION AND TRANSPORTATION PLANNING
items: AI-ORCHESTRATED CYBER-PHYSICAL SYSTEMS FOR SUSTAINABLE INDUSTRY 5.0 MANUFACTURING AND SUPPLY CHAIN RESILIENCE
items: QUANTITATIVE ASSESSMENT OF AUTOMATION AND CONTROL STRATEGIES FOR PERFORMANCE OPTIMIZATION IN U.S. INDUSTRIAL PLANTS
items: AUTOMATING NIST 800-53 CONTROL IMPLEMENTATION: A CROSS-SECTOR REVIEW OF ENTERPRISE SECURITY TOOLKITS
items: RECENT DEVELOPMENTS AND CHALLENGES IN FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION
items: IMPLEMENTING SUSTAINABLE SUPPLY CHAIN PRACTICES IN GLOBAL APPAREL RETAIL: A SYSTEMATIC REVIEW OF CURRENT TRENDS

Quantitative Evaluation of Machine Learning Models for Project Risk Prediction and Resource Optimization in Business Operations
type: item
description: This study addresses the persistent problem that many business projects continue to suffer from schedule delays, budget overruns, resource conflicts, and weak decision quality because organizations often rely on fragmented forecasting methods and subjective judgment instead of data-driven predictive systems. Guided by this problem, the purpose of the study was to quantitatively evaluate whether machine learning model effectiveness improves project risk prediction and resource optimization in business operations, and whether these effects extend to operational efficiency and decision-making quality. The study adopted a quantitative, cross-sectional, case-based design grounded in enterprise project environments, drawing on 250 valid responses from professionals such as project managers, operations managers, business analysts, risk officers, and IT or analytics staff working across IT and digital transformation, operations improvement, construction, finance, and mixed project portfolios. Data were collected using a five-point Likert-scale questionnaire and analyzed through descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression in SPSS. The key variables were machine learning model effectiveness, project risk prediction, resource optimization, operational efficiency, and decision-making quality. The findings showed consistently positive perceptions across all constructs, with mean scores ranging from 3.98 to 4.12, including machine learning model effectiveness (M = 4.12, SD = 0.64), project risk prediction (M = 4.05, SD = 0.68), resource optimization (M = 3.98, SD = 0.71), operational efficiency (M = 4.09, SD = 0.66), and decision-making quality (M = 4.01, SD = 0.69). Reliability was strong, with Cronbach’s alpha values from 0.81 to 0.89. Inferential results confirmed that machine learning model effectiveness significantly predicted project risk prediction (β = .720, R² = .518, p < .001), project risk prediction significantly predicted r…
subject: Articles
  topic: section
subject: Machine Learning Model Effectiveness
  topic: keywords
subject: Project Risk Prediction
  topic: keywords
subject: Resource Optimization
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: Decision-Making Quality
  topic: keywords
creator: Kazi Rakib Hasan Saurav
rights: Copyright (c) 2025 Kazi Rakib Hasan Saurav (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2119
ending page: 2159
doi: 10.63125/01bg6n62

Machine Learning–Based Consumer Behavior Prediction Models for E-Commerce Platforms: Enhancing Digital Financial Inclusion and Market Accessibility
type: item
description: This study examines the growing problem that e-commerce platforms increasingly rely on machine learning based consumer behavior prediction models, yet limited empirical evidence explains whether these systems improve only commercial efficiency or also strengthen digital financial inclusion and market accessibility for consumers. The purpose of the study was therefore to investigate how machine learning based prediction models influence digital financial inclusion, market accessibility, and overall e-commerce platform performance within a quantitative, cross-sectional, case-based research design. Using purposive sampling, data were collected from 312 active online consumers drawn from cloud based and enterprise style e-commerce platform cases who had prior experience with personalized recommendations and digital payment systems. The key variables were machine learning based prediction models as the independent variable, digital financial inclusion and market accessibility as explanatory variables, and e-commerce platform performance as the dependent variable. Data were analyzed through descriptive statistics, reliability testing, correlation analysis, and multiple regression using SPSS. The findings showed consistently positive perceptions across all constructs, with mean scores of 3.94 for machine learning prediction models, 3.87 for digital financial inclusion, 4.01 for market accessibility, and 4.08 for platform performance. Reliability was strong, with Cronbach’s alpha ranging from 0.84 to 0.89 and 0.91 for the full instrument. Correlation analysis revealed significant positive relationships between machine learning prediction and digital financial inclusion (r = .61, p < .001), market accessibility (r = .66, p < .001), and platform performance (r = .72, p < .001). Regression results further showed that machine learning prediction significantly explained 37.2% of the variance in digital financial inclusion (β = .61, p < .001) and 43.6% of the variance in market a…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Consumer Behavior Prediction
  topic: keywords
subject: Digital Financial Inclusion
  topic: keywords
subject: Market Accessibility
  topic: keywords
subject: E-Commerce Platform Performance
  topic: keywords
creator: MD Hasibur Rahman Murad
rights: Copyright (c) 2025 MD Hasibur Rahman Murad (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2078
ending page: 2118
doi: 10.63125/pnz32s94

AI-Driven Real-Time Methane Emissions Monitoring and Predictive Leak Detection Using Lidar and IOT Sensor Fusion in Upstream Oil and Gas Operations
type: item
description: This study addresses the persistent problem of delayed, fragmented, and insufficiently integrated methane leak detection in upstream oil and gas operations, where conventional inspection-based approaches often fail to capture spatially dispersed, intermittent, and high-risk emission events. The purpose of the research was to examine whether an AI-driven real-time methane monitoring framework using LiDAR and IoT sensor fusion can improve monitoring performance, predictive leak detection, trustworthiness, and deployment readiness in upstream environments. The study adopted a quantitative, cross-sectional, case-based design focused on cloud-enabled enterprise monitoring contexts in upstream oil and gas operations, using a purposive sample of 214 valid professional respondents drawn from methane monitoring, operations, compliance, maintenance, and intelligent systems roles. Key variables included LiDAR monitoring capability, IoT sensor fusion efficiency, AI predictive analytics capability, real-time methane monitoring performance, predictive leak detection effectiveness, leak response capability, sensor fusion readiness and trustworthiness, leak detection timeline advantage, and high-risk scenario confidence. Data were collected through a structured 5-point Likert-scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression modeling. The findings showed strong positive evaluations across all core constructs, including AI predictive analytics capability (M = 4.23, SD = 0.58), predictive leak detection effectiveness (M = 4.20, SD = 0.60), LiDAR monitoring capability (M = 4.18, SD = 0.61), and real-time methane monitoring performance (M = 4.15, SD = 0.63). Correlation results indicated significant positive relationships, including LiDAR with real-time monitoring performance (r = .742, p < .001), AI capability with predictive leak detection effectiveness (r = .768, p < .001), and real-time monitoring performa…
subject: Articles
  topic: section
subject: Methane Emissions Monitoring
  topic: keywords
subject: Predictive Leak Detection
  topic: keywords
subject: Lidar
  topic: keywords
subject: IOT Sensor Fusion
  topic: keywords
subject: Upstream Oil and Gas Operations
  topic: keywords
creator: Albert Anokye
rights: Copyright (c) 2025 Albert Anokye (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 2035
ending page: 2077
doi: 10.63125/yavd2f86

Artificial Intelligence Driven Analytics for Market Entry Strategy, Digital Marketing Optimization, and Enterprise Workflow Transformation
type: item
description: This study addressed the problem that many organizations deploy AI-driven analytics but still struggle to demonstrate measurable, cross-functional value beyond isolated functional gains, especially across strategic market-entry decisions, digital marketing optimization, and enterprise workflow transformation. The purpose was to test whether AI-Analytics Intensity (AAI), defined as the embeddedness of analytics in routine decision checkpoints and execution controls, predicts (1) Market Entry Strategy Effectiveness (MES), (2) Digital Marketing Optimization (DMO), and (3) Enterprise Workflow Transformation (EWT), and whether these domain outcomes jointly explain overall Performance Impact (PI) within an enterprise case setting. Using a quantitative, cross-sectional, case-study–based design with purposive sampling of analytics-exposed staff across strategy, marketing, operations, and information systems functions, the study analyzed N = 210 valid responses. Key variables were operationalized as five-point Likert composite constructs with strong reliability (AAI α = 0.88; MES α = 0.86; DMO α = 0.84; EWT α = 0.87; PI α = 0.89), supporting construct consistency for hypothesis testing. The analysis plan combined descriptives, Pearson correlations, and multiple regression modeling. Descriptively, respondents reported moderately high analytics embeddedness and outcomes (AAI M = 3.88, SD = 0.62; MES M = 3.74, SD = 0.66; DMO M = 3.92, SD = 0.58; EWT M = 3.81, SD = 0.60; PI M = 3.86, SD = 0.57). Correlations showed strong positive associations between AAI and MES (r = .58), DMO (r = .62), EWT (r = .60), and PI (r = .64), all p < .001, indicating that higher analytics intensity aligns with stronger strategic, marketing, operational, and overall performance outcomes. In the integrated regression predicting performance impact, MES (β = .24, p = .001), DMO (β = .31, p < .001), and EWT (β = .27, p < .001) all remained significant, explaining 52% of PI variance (R² = .52; F (3,206) = …
subject: Articles
  topic: section
subject: AI-Driven Analytics Intensity
  topic: keywords
subject: Market Entry Strategy Effectiveness
  topic: keywords
subject: Digital Marketing Optimization
  topic: keywords
subject: Enterprise Workflow Transformation
  topic: keywords
subject: Performance Impact
  topic: keywords
creator: Md Khaled Hossain
rights: Copyright (c) 2025 Md Khaled Hossain (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1999
ending page: 2034
doi: 10.63125/82gn4y08

Smart Occupational Safety Management Through IOT Sensor Networks, Machine Learning, and Real-Time Risk Assessment in Chemical Processing Plants
type: item
description: This study addresses the persistent problem that conventional occupational safety systems in chemical processing plants often rely on periodic inspection, fixed alarms, and post-incident review, which are insufficient for detecting rapidly evolving hazards in high-risk environments. The purpose of the research was to examine whether smart occupational safety management can be improved through the integration of IoT sensor networks, machine learning, and real-time risk assessment in chemical processing plants using a quantitative, cross-sectional, case-based design. Data were collected from 200 valid respondents drawn from enterprise plant cases, including operations, maintenance, safety and compliance, process control, and supervisory roles, out of 220 distributed questionnaires, yielding a 90.9% valid response rate. The key independent variables were IoT sensor networks, machine learning, and real-time risk assessment, while smart occupational safety management was the dependent variable. Analysis was conducted using SPSS through descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression. The findings showed high mean scores for all core constructs, including IoT sensor networks (M = 4.18, SD = 0.61), machine learning (M = 4.06, SD = 0.67), real-time risk assessment (M = 4.24, SD = 0.58), and smart occupational safety management (M = 4.29, SD = 0.55). Reliability was strong, with Cronbach’s alpha ranging from 0.84 to 0.90 and an overall instrument reliability of 0.91. Correlation results indicated significant positive associations with smart occupational safety management for IoT sensor networks (r = .71, p < .001), machine learning (r = .68, p < .001), and real-time risk assessment (r = .76, p < .001). The regression model was highly significant, F (3,196) = 145.27, p < .001, explaining 69% of the variance (R² = .69), with real-time risk assessment emerging as the strongest predictor (β = .38), followed by IoT sensor…
subject: Articles
  topic: section
subject: IoT Sensor Networks
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Real-Time Risk Assessment
  topic: keywords
subject: Occupational Safety Management
  topic: keywords
subject: Chemical Processing Plants
  topic: keywords
creator: Md Abubakar Siddique Akash
creator: Bhanu Prakash Sah
rights: Copyright (c) 2025 Md Abubakar Siddique Akash, Bhanu Prakash Sah (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1958
ending page: 1998
doi: 10.63125/dynnzy25

Advanced Computing Frameworks for Distributed Training, Deployment, and Monitoring of Artificial Intelligence and Machine Learning Models
type: item
description: This study addresses a persistent operational problem in distributed AI and ML: many organizations can scale model training, but still experience reliability and governance breakdowns because training, deployment, and monitoring are implemented as fragmented toolchains rather than an end-to-end lifecycle system. The purpose was to synthesize and compare advanced computing frameworks that support distributed training, deployment, and monitoring, and to quantify which framework patterns most strongly align with dependable production operations. A quantitative, cross-sectional, case-based design was applied via a structured literature review in which each eligible publication was treated as a case instance, spanning cloud and enterprise environments including cloud, on-premises clusters, and hybrid deployments. The sample comprised 32 case instances (n = 32). Key variables included framework category prevalence and Lifecycle Integration Score (LIS), dominant architectural patterns and evidence scores, effectiveness outcomes (scalability, deployment reliability, monitoring, traceability), monitoring-maturity indicators, and unresolved-gap severity. The analysis plan combined deductive and inductive coding with frequency, mean, and cross-tabulation summaries (reported as means, SDs, and shares), supported by spreadsheet tools and SPSS. Headline findings show that training-centric frameworks were most prevalent (11/32, 34.4%) but had lower integration (LIS M = 2.9), while end-to-end lifecycle platforms (8/32, 25.0%) achieved the highest integration (LIS M = 4.3) with 87.5% of cases scoring ≥4. The most common architecture was data-parallel training with collective all-reduce (62.5%), followed by orchestration-first deployment (56.3%) and observability-by-design (50.0%). Cross-sectionally, training scalability effectiveness scored highest (M = 4.1; 65.6% ≥4), while deployment reliability control (M = 3.7) and monitoring effectiveness (M = 3.6) lagged, indicating that opera…
subject: Articles
  topic: section
subject: Distributed AI/ML Frameworks
  topic: keywords
subject: Mlops Lifecycle Integration
  topic: keywords
subject: Distributed Training Scalability
  topic: keywords
subject: Model Monitoring And Observability
  topic: keywords
subject: Governance And Traceability
  topic: keywords
creator: Mohammad Robel Miah
rights: Copyright (c) 2025 Mohammad Robel Miah (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1922
ending page: 1957
doi: 10.63125/rxb2cb66

Advanced Composite and Geopolymer Materials for Durable Water Supply and Sanitation Infrastructure: Performance, Sustainability, and Lifecycle Assessment
type: item
description: This study addresses the problem that water supply and sanitation (WASH) assets fail under coupled pressure, corrosion, and sewer environments, while sustainability appraisals often ignore service life and intervention frequency. The purpose was to benchmark, in a quantitative cross-sectional, case-based evidence synthesis, how advanced composite systems and geopolymer or alkali-activated materials compare on functional performance, durability, and lifecycle sustainability for WASH infrastructure decisions. The sample comprised 92 studies spanning water supply assets (n=38), sanitation assets (n=34), and cross-cutting lifecycle or durability assessments (n=20). Key variables were composite water-performance indicators (pressure/hoop integrity, leakage-risk proxy, ring stiffness, joint reliability, and creep resilience), geopolymer sanitation-durability indicators (acid resistance, sulfate resistance, MICC proxy, abrasion tolerance, transport resistance, and strength retention), and sustainability/implementation indicators (GWP per service year, boundary completeness, and decision-fit feasibility). The analysis plan coded each study on a 5-point Likert support scale and summarized results using descriptive statistics, frequency counts, and service-life normalized impact metrics. Headline findings show strong support for composites in water contexts: 78.9% of water-focused studies were supportive (mean=4.21/5, SD=0.73), with average pressure-capacity or stiffness gains of 18–32% and a 45% mean reduction in corrosion-related degradation rates versus conventional comparators. Sanitation evidence favored geopolymers/AAMs: 73.5% supportive (mean=4.08/5, SD=0.81), with 28–41% lower mass loss under acid or sulfate exposure, 72–85% retained compressive strength after acid conditioning (vs 55–68% for OPC), and about 30% lower surface recession in biogenic corrosion simulations. Lifecycle evidence was supportive but boundary-sensitive (70.0% supportive; mean=3.95/5), indicatin…
subject: Articles
  topic: section
subject: Advanced composites
  topic: keywords
subject: Geopolymer
  topic: keywords
subject: Alkali-activated materials
  topic: keywords
subject: Lifecycle assessment
  topic: keywords
subject: WASH infrastructure
  topic: keywords
creator: Palash Chandra Das
rights: Copyright (c) 2025 Palash Chandra Das (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1887
ending page: 1921
doi: 10.63125/z64akr77

Predictive Analytics and KPI Dashboards for Enterprise Workflow Optimization
type: item
description: This study addressed the persistent problem that many enterprises deploy predictive models and KPI dashboards in parallel, yet fail to convert model outputs into consistent, measurable workflow improvements because predictions are not embedded into governed, actionable decision routines. The purpose was to quantify, across applied enterprise cases, how integrating predictive analytics into KPI dashboards influences workflow performance, and which implementation conditions strengthen or weaken that impact. Using a quantitative, cross-sectional, case-based design, the study synthesized evidence from 52 eligible empirical and applied studies/cases (N = 52) and coded each case on adoption mechanisms, integration pathways, and outcome strength using a five-point Likert evidence scale to support comparable aggregation without overstating causality. Key variables included predictive analytics capabilities (e.g., delay and SLA-risk prediction), dashboard usage and KPI architecture, integration mechanisms (leading-indicator KPIs, threshold alerts, and routing or prioritization), governance conditions, and workflow outcomes (cycle time, SLA adherence, throughput, rework, decision timeliness). The analysis plan combined frequency statistics (cases and percentages), median and range synthesis for outcome deltas, and group comparisons of evidence scores by integration and governance quality. Overall workflow improvement evidence averaged M = 4.12 (SD = 0.71) across outcome-reporting cases (n = 47). Headline findings showed the clearest gains in time and compliance outcomes: cycle time decreased by a median 14% (typical range 8%–25%) and SLA adherence improved by a median +9 percentage points (range +4 to +18), while throughput improved by a median 11% (range 5%–20%) and rework decreased by a median 10% (range 6%–19%). Integration mattered: cases with explicit dashboard integration scored higher for performance improvement (M = 4.28) than cases without it (M = 3.41), and action-o…
subject: Articles
  topic: section
subject: Predictive analytics
  topic: keywords
subject: KPI dashboards
  topic: keywords
subject: Workflow optimization
  topic: keywords
subject: SLA risk prediction
  topic: keywords
subject: KPI governance
  topic: keywords
creator: Tanjina Binte Sohrab
rights: Copyright (c) 2025 Tanjina Binte Sohrab (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1848
ending page: 1886
doi: 10.63125/5x0d1702

QUANTITATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DEFECT PREDICTION IN METAL ADDITIVE MANUFACTURING
type: item
description: This study addresses the persistent challenge of predicting and controlling defects in metal additive manufacturing within an enterprise production environment, where porosity, lack of fusion, cracking, and geometric anomalies can increase scrap, rework, and qualification cost. The purpose was to quantify how technical and socio-technical factors jointly explain defect prediction effectiveness and deployment success, and to benchmark machine-learning models using risk-relevant performance measures. Using a quantitative, cross-sectional, case-based design, the sample combined (a) 162 usable survey responses from an enterprise AM facility workforce (quality engineering 32.1%, process engineering 26.5%, operations 23.5%, automation/data 17.9%; mean experience 6.8 years, SD 3.4) and (b) 312 inspected build or part cases verified via CT and metallography. Key variables included Data Quality, Feature Richness/Monitoring Capability, Process Window Stability, Technology Readiness, Interpretability/Trust, and Technology Acceptance Model constructs (Perceived Usefulness and Perceived Ease of Use), with the outcome measured as Defect Prediction Effectiveness and Deployment Success (Likert 1–5 composites). The analysis plan used descriptive statistics, reliability testing (Cronbach’s α = 0.82–0.90), Pearson correlations, and multiple regression, alongside build-wise model validation comparing Logistic Regression, SVM, Random Forest, and Gradient Boosting. Headline findings showed defects were dominated by porosity (46.8%) and lack of fusion (28.5%), with 12.9% classified as critical; drift-zone builds had higher defect prevalence (19.6%) than stable-zone builds (8.4%). Regression explained substantial outcome variance (R² = 0.61, Adj. R² = 0.59; F(7,154) = 34.78, p < .001), with significant effects for Data Quality (β = 0.19, p = .001), Interpretability/Trust (β = 0.21, p < .001), Perceived Usefulness (β = 0.29, p < .001), and other predictors. Gradient Boosting achieved the be…
subject: Articles
  topic: section
subject: Metal additive manufacturing
  topic: keywords
subject: Defect prediction
  topic: keywords
subject: Technology acceptance model
  topic: keywords
subject: Interpretability and trust
  topic: keywords
subject: Gradient boosting
  topic: keywords
creator: Md. Ashfaq Siddiquee
creator: Ashraful Islam
rights: Copyright (c) 2025 Md. Ashfaq Siddiquee, Ashraful Islam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1810
ending page: 1847
doi: 10.63125/3fkkwg05

Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems
type: item
description: Machine learning systems are increasingly deployed in U.S. regulated decision-support environments where predictive outputs must be accurate, interpretable, secure, privacy-preserving, and auditable. This study advanced an integrated quantitative assurance framework for evaluating explainable and secure machine learning in regulated contexts. Guided by a structured review of 118 peer-reviewed studies, a multi-phase experimental design was implemented incorporating predictive benchmarking, explanation fidelity and stability testing, adversarial robustness assessment, privacy inference evaluation, and human-in-the-loop experimentation. Baseline predictive accuracy across model families averaged 0.84 (SD = 0.04), with ensemble models reaching 0.86. Calibration error decreased from 0.041 in unconstrained models to 0.028 in constrained configurations. Under adversarial simulation, baseline models experienced a 14.2 percentage-point degradation in performance, whereas robustness-enhanced models limited degradation to 6.5 percentage points. Privacy controls reduced membership inference attack success rates from 0.64 (SD = 0.05) to 0.52 (SD = 0.04), with a modest 1.7% reduction in discrimination performance. Explanation fidelity reached 0.92 (SD = 0.02) for intrinsically interpretable models compared to 0.85 (SD = 0.04) for post hoc methods, and explanation stability variance decreased from 0.08 to 0.03 under enhanced configurations. In human-in-the-loop evaluation (N = 312; 6,240 trials), structured explanations increased decision accuracy from 0.72 (SD = 0.09) to 0.79 (SD = 0.07), improved confidence calibration from 0.74 to 0.82, and increased selective override behavior from 18.4% to 24.7%, while response time rose from 34.1 to 40.8 seconds. Regression models explained 34% of variance in decision accuracy and 38% in confidence calibration. Findings demonstrated that integrated evaluation of explain ability, robustness, and privacy produced measurable improvements in pre…
subject: Articles
  topic: section
subject: Explain ability
  topic: keywords
subject: Security
  topic: keywords
subject: Decision Support
  topic: keywords
subject: Robustness
  topic: keywords
subject: Privacy
  topic: keywords
creator: Md Arifur Rahman
creator: B. M. Taslimul Haque
rights: Copyright (c) 2023 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
date: 2023-05-29
publication date: 2023-05-29
starting page: 231
ending page: 273
doi: 10.63125/fmp86e72

AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems
type: item
description: This study examined intelligent condition monitoring and fault diagnosis in electrical power and control systems using a quantitative predictive analytics framework grounded in machine learning and construct-based modeling. A total of 268 responses were collected from professionals working in power distribution, industrial control, and power electronics environments, and after data screening 241 responses were retained for analysis, representing an 89.9% retention rate. The final sample was dominated by engineers (38.6%) and maintenance specialists (27.0%), with 73.9% reporting daily interaction with condition monitoring tools. Five constructs were evaluated: Condition Monitoring Effectiveness, Fault Detection Accuracy, Predictive Maintenance Capability, System Integration Quality, and Operational Performance Impact. Descriptive results indicated generally high perceptions of monitoring and diagnostic performance, with construct means ranging from 3.62 to 4.08 on a five-point scale and moderate dispersion (SD range = 0.58–0.72). Distribution diagnostics supported normality, with skewness values between −0.48 and −0.21 and kurtosis between −0.37 and 0.42. Internal consistency reliability was strong across constructs, with Cronbach’s alpha values ranging from .81 to .88 and two items removed during refinement due to weak item-total contribution. Multiple regression analysis was performed to evaluate the predictive influence of the independent constructs on Operational Performance Impact. The regression model was statistically significant, F(4, 236) = 67.84, p < .001, and explained 53.5% of the variance in the dependent construct (R² = .535; adjusted R² = .527), with independence of errors supported (Durbin–Watson = 1.94). Predictive Maintenance Capability demonstrated the strongest effect (β = .34, p < .001), followed by Condition Monitoring Effectiveness (β = .26, p < .001) and System Integration Quality (β = .19, p = .002), while Fault Detection Accuracy produced a …
subject: Articles
  topic: section
subject: Intelligent Monitoring
  topic: keywords
subject: Fault Diagnosis
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Power Systems
  topic: keywords
creator: Shamsul Arifeen
rights: Copyright (c) 2025 Shamsul Arifeen (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1778
ending page: 1809
doi: 10.63125/csjs7238

Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking
type: item
description: This study addresses the persistent problem in digital banking that traditional rule-based AML/KYC transaction monitoring generates high alert noise and inconsistent investigative outcomes, increasing compliance cost and residual regulatory exposure. The purpose was to quantify how machine learning enabled transaction monitoring capabilities contribute to suspicious activity detection and perceived compliance risk reduction in a cloud-enabled enterprise digital banking case context. Using a quantitative cross-sectional, case-based design, data were collected via a structured 5-point Likert survey from professionals embedded in AML operations, compliance oversight, KYC/CDD, risk, and AML technology functions (N = 168 valid responses; mean AML experience = 6.2 years, SD = 2.9; role distribution led by transaction monitoring analysts at 42.3%). Key variables included ML Detection Effectiveness (MDE), Data Quality and Feature Readiness (DQF), , Analyst Trust and Adoption (ATA), and three domain indices: Alert Quality and Workload Impact (AQWII), Explainability Audit Readiness and Model Governance Evidence (EARMGE), and Typology Coverage and Adaptability Results (TCAR), with Compliance Risk Reduction (CRR) as the dependent variable. The analysis plan applied descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression to estimate unique predictors of CRR. Results showed strong overall agreement that ML monitoring improved outcomes (CRR M = 4.14, SD = 0.52; MDE M = 4.18, SD = 0.54; AQWII M = 4.12, SD = 0.55), and all constructs met good to excellent internal consistency (α = 0.83–0.91). CRR correlated most strongly with MDE (r = 0.72, p < .001) and AQWII (r = 0.69, p < .001), indicating that detection strength and workload-relevant alert quality moved together with compliance benefit. The regression model was significant and explanatory (R² = 0.68; F(8,159) = 42.11, p < .001), with headline effects from MDE (β = 0.31, p < .…
subject: Articles
  topic: section
subject: AML/KYC
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Transaction Monitoring
  topic: keywords
subject: Suspicious Activity Detection
  topic: keywords
subject: Compliance Risk Reduction
  topic: keywords
creator: Sazzadul Islam
rights: Copyright (c) 2025 Sazzadul Islam (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1740
ending page: 1775
doi: 10.63125/r9c8q813

AI-Driven Distribution Planning for Essential Goods in Underserved Communities: A Mixed Methods Framework for Access Optimization
type: item
description: This study examined how underserved communities experience moderate to low and uneven access to essential goods due to last-mile distribution planning that is constrained by unreliable data, partial workflow embedding, and volatile operating conditions. The purpose was to quantify how AI-driven distribution planning capability improves access optimization within cloud and enterprise distribution contexts, while accounting for technology, organizational, and environmental conditions. Using a quantitative, cross-sectional, case-based design, a structured five-point Likert survey captured system-level perceptions from 312 valid respondents (78.0% usable) out of 400 invited and 320 completed, spanning planners/coordinators (22.4%), field and delivery staff (27.6%), retail or community distribution points (18.9%), NGO or partner actors (11.5%), and beneficiaries (19.6%). Access Optimization (AO) was modeled as the outcome, with AI Planning Capability (AIPC), Data Quality and Integration (DQI), Organizational Readiness and Embeddedness (ORG_READY), Environmental Support (ENV_SUPPORT), and Constraint Severity (CONSTRAINTS) as predictors. The analysis included reliability testing, descriptive statistics, Pearson correlations, and multiple regression with collinearity diagnostics. Measurement reliability was strong across constructs (AO α = .91; AIPC α = .88; DQI α = .86; ORG_READY α = .89; ENV_SUPPORT α = .82; CONSTRAINTS α = .79). Baseline access was constrained (AO M = 2.84, SD = 0.76) alongside high operational constraints (M = 3.74, SD = 0.62), moderate AI capability (AIPC M = 3.27, SD = 0.71), and moderate organizational readiness (M = 3.12, SD = 0.69). Equity profiling revealed meaningful disparity, with the lowest access cluster reporting AO M = 2.41 versus 3.18 in the highest access cluster (gap = 0.77; inequity index = 0.21). Bivariate results aligned with the conceptual model, showing positive associations between AO and AIPC (r = .52), DQI (r = .47), ORG_READY (r…
subject: Articles
  topic: section
subject: AI-Driven Distribution Planning
  topic: keywords
subject: Access Optimization
  topic: keywords
subject: Underserved Communities
  topic: keywords
subject: TOE Framework
  topic: keywords
subject: Data Quality and Organizational Readiness
  topic: keywords
creator: Md Shahab Uddin
rights: Copyright (c) 2025 Md Shahab Uddin (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1700
ending page: 1739
doi: 10.63125/chv6qf37

Business Intelligence–Driven Risk Assessment and Portfolio Performance Analytics for Financial and Investment Institutions
type: item
description: This study addresses a persistent challenge in financial and investment organizations where fragmented reporting, delayed reconciliation, and inconsistent risk definitions weaken portfolio oversight and slow decision-making. The purpose was to test whether business intelligence driven risk assessment capability (BIRA) improves portfolio performance analytics effectiveness (PPAE) and decision value in enterprise cloud analytics settings. A quantitative cross-sectional, case-based design surveyed N = 180 professionals across portfolio, risk, BI, and governance functions in an institutional portfolio case (mean experience 7.6 years, SD = 3.4). The dependent construct was PPAE, while five BIRA dimensions served as predictors: Integration and Timeliness (INT), Governance and Definition Stability (GOV), Automation and Consistency (AUTO), Usability and Interpretability (USE), and Cross-risk Coverage (XRISK). Decision outcomes were captured via the BI Risk-Portfolio Readiness Index (BRPRI), Decision Confidence Score (DCS), and Decision Timeliness Score (DTS). The analysis plan used data screening and descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression modeling. Results indicated moderate-to-high levels (BIRA M = 3.78, SD = 0.62; PPAE M = 3.71, SD = 0.65) with strong reliability (α = 0.89 for BIRA; α = 0.87 for PPAE). BIRA was strongly associated with PPAE (r = 0.68, p < .001), and the regression model explained 52% of PPAE variance (R² = 0.52; Adjusted R² = 0.50; F (5,174) = 38.1; p < .001). GOV (β = 0.29, p < .001) and INT (β = 0.25, p < .001) were the strongest predictors, followed by AUTO (β = 0.19, p = .002) and USE (β = 0.12, p = .041), while XRISK was not statistically decisive (β = 0.08, p = .118). Readiness results showed BRPRI M = 3.77 (SD = 0.58) with 55.0% high readiness, 38.9% moderate, and 6.1% low. Higher capability aligned with stronger decision confidence (DCS M = 3.74, SD = 0.67; r = 0.63, p < .001) …
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: Risk Assessment Capability
  topic: keywords
subject: Portfolio Performance Analytics
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Decision Confidence and Timeliness
  topic: keywords
creator: Shamsunnahar Chadni
rights: Copyright (c) 2025 Shamsunnahar Chadni (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1668
ending page: 1699
doi: 10.63125/827e2c29

Financial Vulnerability Mapping in Global Supply Chains: Implications for U.S. Trade Stability and Investment Risk
type: item
description: Financial vulnerability embedded within global supply chains represents a significant source of trade instability and investment risk in highly interconnected economies. This study quantitatively examined how exposure-weighted financial vulnerability across global supply chain networks was associated with U.S. trade stability and sector-level investment risk. The study was informed by a comprehensive review of more than 120 peer-reviewed articles and empirical studies, which guided construct development and analytical design. The empirical analysis utilized 1,000 sector-level panel observations, of which 61.2% were drawn from manufacturing sectors and 38.8% from non-manufacturing sectors, spanning multiple consecutive years. Descriptive results showed a mean financial vulnerability index of 0.47 (SD = 0.18), a mean trade stability index of 0.62 (SD = 0.21), and a mean investment risk index of 0.53 (SD = 0.24), indicating substantial dispersion across sectors. Correlation analysis revealed a negative association between financial vulnerability and trade stability (r = −0.41) and a positive association between financial vulnerability and investment risk (r = 0.48). Panel regression results demonstrated that exposure-weighted financial vulnerability was significantly associated with lower trade stability (β = −0.372, p < 0.001) and higher investment risk (β = 0.418, p < 0.001), controlling for industry size, trade intensity, and time effects. The trade stability model explained 46% of the variance in trade flow variability, while the investment risk model explained 49% of performance variability. Reliability analysis confirmed strong internal consistency for all composite constructs, with Cronbach’s alpha values ranging from 0.81 to 0.86. Overall, the findings quantitatively demonstrated that financial vulnerability embedded within global supply chains is a measurable and influential determinant of U.S. trade stability and investment risk.
subject: Articles
  topic: section
subject: Financial Vulnerability
  topic: keywords
subject: Global Supply Chains
  topic: keywords
subject: Trade Stability
  topic: keywords
subject: Investment Risk
  topic: keywords
creator: Mostafa Kamal
rights: Copyright (c) 2025 Mostafa Kamal (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1636
ending page: 1667
doi: 10.63125/42rd4x66

UAV-Based Hyperspectral and Thermal Signature Analytics for Early Detection of Soil Moisture Stress, Erosion Hotspots, and Flood Susceptibility
type: item
description: This study addresses the problem that soil moisture stress, erosion initiation, and localized flood exposure emerge at micro-scales that routine monitoring misses, delaying mitigation. The purpose was to test whether UAV-based hyperspectral and thermal signature analytics can deliver defensible early detection of soil moisture stress and improve erosion hotspot and flood susceptibility mapping using a quantitative, cross-sectional, case-based design. Across cloud and enterprise GIS deployment cases, the sample included 210 spatial zones within the case-study area, with five-point Likert composites for Soil Moisture Stress (SMS), Erosion Hotspot Concern (EHC), and Flood Susceptibility Perception (FSP). Key predictors were a hyperspectral moisture proxy (HSI_MoistureIndex), local thermal anomaly (TH_Anomaly), and terrain and hydrologic controls (slope, flow accumulation, distance to channel, and elevation). The analysis plan used descriptive statistics, Cronbach’s alpha, Pearson correlations, multivariate regression with sensor-fusion model comparison, risk-class summaries, hotspot agreement (Jaccard), and robustness checks. Results indicated moderate-to-high perceived risk (SMS M=3.62, SD=0.71; EHC M=3.48, SD=0.76; FSP M=3.55, SD=0.74) and strong construct reliability (alpha=0.86, 0.83, and 0.88). SMS increased with TH_Anomaly (r=0.58, p<.001) and decreased with HSI_MoistureIndex (r=-0.52, p<.001). Regression confirmed a fusion gain: the fused stress model achieved Adj. R²=0.58 and RMSE=0.44, outperforming hyperspectral-only (Adj. R²=0.42, RMSE=0.56) and thermal-only (Adj. R²=0.46, RMSE=0.53), with significant effects for TH_Anomaly (beta=0.41) and HSI_MoistureIndex (beta=-0.36). Erosion modeling achieved Adj. R²=0.54 with flow accumulation and slope as primary drivers, and flood susceptibility improved from Adj. R²=0.55 (terrain only) to 0.62 when thermal information was added. Spatial validation showed strong agreement for stress versus thermal hotspots (68% overla…
subject: Articles
  topic: section
subject: UAV Sensor Fusion
  topic: keywords
subject: Hyperspectral Imaging
  topic: keywords
subject: Thermal Infrared Anomaly
  topic: keywords
subject: Soil Moisture Stress
  topic: keywords
subject: Erosion And Flood Susceptibility Mapping
  topic: keywords
creator: Ratul Debnath
rights: Copyright (c) 2025 Ratul Debnath (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1603
ending page: 1635
doi: 10.63125/c2vtn214

Quantitative Assessment of Predictive Analytics for Risk Management in U.S. Healthcare Finance Systems
type: item
description: This study addresses the problem that U.S. healthcare finance workflows on cloud and enterprise platforms face denial, improper payment, and audit exposure risk, while predictive analytics is not always embedded into control decisions. The purpose was to test whether Predictive Analytics Capability (PAC) improves Risk Management Effectiveness (RME) and how Data Quality and Integration (DQI), Governance and Compliance Readiness (GCR), and Actionability (ACT) contribute. A quantitative cross sectional, case-based design surveyed an enterprise revenue cycle and payment integrity environment, producing N = 214 usable responses (89.2% completion). Measures were 1 to 5 Likert composites with strong reliability (Cronbach’s alpha: PAC .88, DQI .84, GCR .86, ACT .85, RME .90). Descriptives indicated high PAC (M = 3.74) and high RME (M = 3.81), alongside moderate data integration readiness (DQI M = 3.46). The analysis plan used descriptive statistics, Pearson correlations, and hierarchical multiple regression with role controls. All predictors were positively associated with RME (PAC r = .62; DQI r = .49; GCR r = .53; ACT r = .58; all p < .001). In regression, adding PAC increased explained variance by ΔR² = .33 (total R² = .39) with a large effect (β = .58, p < .001). The final model explained 51% of variance (R² = .51) and retained independent effects for PAC (β = .31, p < .001), ACT (β = .29, p < .001), GCR (β = .18, p = .006), and DQI (β = .12, p = .041). A risk value map showed the strongest analytics impact in denials and rework (M = 3.94) and fraud or waste or abuse screening (M = 3.86). Pipeline reliability was moderate (PRR M = 3.44) and separated outcomes (mean RME 3.28 at low PRR versus 4.09 at high PRR). Implications are that risk leaders should prioritize workflow actionability, governance traceability, and cross system linkage, and strengthen outcome tracking so predictive signals translate into sustained risk reduction in practice.
subject: Articles
  topic: section
subject: Predictive Analytics Capability
  topic: keywords
subject: Risk Management Effectiveness
  topic: keywords
subject: Healthcare Finance Systems
  topic: keywords
subject: Actionability
  topic: keywords
subject: Governance and Data Integration
  topic: keywords
creator: Rifat Chowdhury
rights: Copyright (c) 2025 Rifat Chowdhury (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1570
ending page: 1602
doi: 10.63125/x4cta041

Integrating Smart Sensor Systems and Digital Safety Dashboards for Real-Time Hazard Monitoring in High-Risk Industrial Facilities
type: item
description: This study addresses the problem that U.S. healthcare finance workflows on cloud and enterprise platforms face denial, improper payment, and audit exposure risk, while predictive analytics is not always embedded into control decisions. The purpose was to test whether Predictive Analytics Capability (PAC) improves Risk Management Effectiveness (RME) and how Data Quality and Integration (DQI), Governance and Compliance Readiness (GCR), and Actionability (ACT) contribute. A quantitative cross sectional, case-based design surveyed an enterprise revenue cycle and payment integrity environment, producing N = 214 usable responses (89.2% completion). Measures were 1 to 5 Likert composites with strong reliability (Cronbach’s alpha: PAC .88, DQI .84, GCR .86, ACT .85, RME .90). Descriptives indicated high PAC (M = 3.74) and high RME (M = 3.81), alongside moderate data integration readiness (DQI M = 3.46). The analysis plan used descriptive statistics, Pearson correlations, and hierarchical multiple regression with role controls. All predictors were positively associated with RME (PAC r = .62; DQI r = .49; GCR r = .53; ACT r = .58; all p < .001). In regression, adding PAC increased explained variance by ΔR² = .33 (total R² = .39) with a large effect (β = .58, p < .001). The final model explained 51% of variance (R² = .51) and retained independent effects for PAC (β = .31, p < .001), ACT (β = .29, p < .001), GCR (β = .18, p = .006), and DQI (β = .12, p = .041). A risk value map showed the strongest analytics impact in denials and rework (M = 3.94) and fraud or waste or abuse screening (M = 3.86). Pipeline reliability was moderate (PRR M = 3.44) and separated outcomes (mean RME 3.28 at low PRR versus 4.09 at high PRR). Implications are that risk leaders should prioritize workflow actionability, governance traceability, and cross system linkage, and strengthen outcome tracking so predictive signals translate into sustained risk reduction in practice.
subject: Articles
  topic: section
subject: Predictive analytics capability
  topic: keywords
subject: Risk management effectiveness
  topic: keywords
subject: Healthcare finance systems
  topic: keywords
subject: Actionability
  topic: keywords
subject: Governance and data integration
  topic: keywords
creator: Jahangir Shekh
rights: Copyright (c) 2025 Jahangir Shekh (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1533
ending page: 1569
doi: 10.63125/newtd389

Computational Modeling of Failure Mechanisms in Mechanical Systems: Applications For Energy and Industrial Sectors
type: item
description: This study examined computational modeling of failure mechanisms in mechanical systems operating within energy and industrial sector applications using a quantitative, explanatory case study design. A panel dataset comprising 412 component-level units embedded within 168 mechanical assets was analyzed across 9,214 time-indexed observation windows spanning an average observation horizon of 17.2 months. Failure severity was operationalized as a continuous composite outcome to preserve degradation gradients rather than binary failure states. Descriptive analysis showed that failure severity exhibited strong right-skewness, with a median value of 0.61 and a 90th percentile of 1.82, indicating concentration of degradation within a limited subset of operating windows. Multivariable mixed-effects regression was applied to quantify the influence of operational exposure and degradation constructs while accounting for asset-level heterogeneity. The final model achieved a marginal R² of 0.48 and a conditional R² of 0.62, confirming substantial explanatory power from both fixed effects and asset-level random effects. Condition monitoring indicators demonstrated the strongest association with failure severity (β = 0.356, p < 0.001), followed by cyclic loading (β = 0.287, p < 0.001) and thermal exposure intensity (β = 0.214, p < 0.001). Contact stress (β = 0.169, p < 0.001) and time-under-load (β = 0.132, p < 0.001) also retained statistically significant effects. Operational regime analysis showed that continuous-duty components exhibited higher severity than intermittent-duty units (β = 0.118, p < 0.001), while cyclic-duty operation also increased severity (β = 0.064, p = 0.040). Interaction testing revealed amplification effects for combined thermal exposure and cyclic loading (β = 0.072, p = 0.013), indicating non-additive degradation behavior. Robustness checks demonstrated coefficient stability within ±5% under alternative specifications, and time-based holdout validation r…
subject: Articles
  topic: section
subject: Mechanical Failure Modeling
  topic: keywords
subject: Reliability Analytics
  topic: keywords
subject: Degradation Severity
  topic: keywords
subject: Condition Monitoring
  topic: keywords
subject: Energy Systems
  topic: keywords
creator: Efat Ara Haque
rights: Copyright (c) 2023 Efat Ara Haque (Author)
date: 2023-05-29
publication date: 2023-05-29
starting page: 196
ending page: 230
doi: 10.63125/0nmn9h72

INTEGRATING SMART SENSOR SYSTEMS AND DIGITAL SAFETY DASHBOARDS FOR REAL-TIME HAZARD MONITORING IN HIGH-RISK INDUSTRIAL FACILITIES
type: item
description: This quantitative study examined the performance impact of integrating smart sensor systems with digital safety dashboards for real-time hazard monitoring in a high-risk industrial facility. The study adopted a facility-based observational design using system-generated monitoring records rather than survey data. A total of 3,600 monitoring episodes were analyzed, representing multiple facility zones, operational shifts, and hazard modalities, including gas, thermal, vibration, and pressure monitoring. Independent variables captured system configuration characteristics such as sensor coverage density, network protocol type, and dashboard update interval, while dependent variables represented monitoring performance outcomes, including detection latency, alarm accuracy, threshold exceedance frequency, and operator response time. Control variables accounted for operational context, including facility zone type, environmental baseline variability, equipment age category, and shift category. Descriptive results showed that sensor coverage density averaged 6.8 sensors per zone, dashboard update intervals averaged 4.6 seconds, and detection latency averaged 6.9 seconds with a standard deviation of 2.5 seconds. Alarm accuracy demonstrated a high mean value of 84.3%, while threshold exceedance frequency averaged 3.7 events per shift, indicating heterogeneous hazard dynamics across operational conditions. Operator response time, measured for episodes with valid interaction logs, averaged 18.6 seconds, reflecting variability in human-system interaction across shifts and zones. Reliability testing confirmed acceptable to good internal consistency for composite constructs, with Cronbach’s alpha values ranging from 0.76 to 0.88. Regression analysis revealed statistically significant relationships between system design variables and monitoring performance outcomes. Increased sensor coverage density was associated with reduced detection latency (B = −0.42, p < .001) and improved ala…
subject: Articles
  topic: section
subject: Smart Sensors
  topic: keywords
subject: Safety Dashboards
  topic: keywords
subject: Hazard Monitoring
  topic: keywords
subject: Industrial Safety
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
creator: Jahangir Shekh
creator: Rebeka Sultana Panna
rights: Copyright (c) 2025 Jahangir Shekh, Rebeka Sultana Panna (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1496
ending page: 1532
doi: 10.63125/bwzf0d29

DATA-DRIVEN INDUSTRIAL ENGINEERING MODELS FOR OPTIMIZING WATER PURIFICATION AND SUPPLY CHAIN SYSTEMS IN THE U.S.
type: item
description: This study addresses the problem that U.S. water purification enterprises are rapidly adopting cloud enabled analytics and enterprise information systems, yet purification stability and supply readiness remain inconsistent when analytics capability, data governance, industrial engineering discipline, and cross functional coordination are not integrated into one operational optimization approach. The purpose was to test a data driven industrial engineering capability framework by estimating how five predictors, Analytics Capability (AC), Data Quality and Governance (DQG), IE Process Optimization Maturity (IEM), Supply Chain Visibility and Coordination (SCV), and Operational Flexibility and Responsiveness (OFR), relate to three outcomes, Purification System Performance (PSP), Supply Chain Performance (SCP), and Overall Optimization Outcome (OOO). Using a quantitative cross sectional, case-based design, a structured five-point Likert survey was administered in cloud and enterprise system contexts across U.S. water purification operations, yielding 210 usable responses from operations, maintenance, quality, procurement, and analytics roles. Descriptive results showed moderate to high capability levels (IEM M=3.90, SD=0.58; AC M=3.84, SD=0.62), while SCV and OFR were comparatively lower (SCV M=3.65, SD=0.70; OFR M=3.59, SD=0.73); outcomes were above the midpoint (PSP M=3.88, SD=0.60; SCP M=3.62, SD=0.71; OOO M=3.79, SD=0.63). The analysis plan included data screening, reliability testing, Pearson correlation analysis, and three multiple regression models with multicollinearity checks (all VIF<2.0), and internal consistency was strong (Cronbach’s α=.82 to .91). Correlations supported the proposed relationships, including AC–PSP r=.58, IEM–PSP r=.61, SCV–SCP r=.63, DQG–OOO r=.52, PSP–OOO r=.66, and SCP–OOO r=.62 (all p<.001). Regression findings showed that PSP was explained at R²=.469 (F(4,205)=46.20, p<.001) with IEM (β=.33, p<.001) and AC (β=.24, p<.001) as the stronges…
subject: Articles
  topic: section
subject: Analytics Capability
  topic: keywords
subject: Data Quality and Governance
  topic: keywords
subject: Process Optimization Maturity
  topic: keywords
subject: Supply Chain Visibility
  topic: keywords
subject: Water Purification Optimization
  topic: keywords
creator: Md. Al Amin Khan
rights: Copyright (c) 2025 Md. Al Amin Khan (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1458
ending page: 1495
doi: 10.63125/s17rjm73

QUANTITATIVE ANALYSIS OF MECHANICAL TESTING AND VALVE PERFORMANCE IN THE OIL AND GAS SECTOR: ENSURING COMPLIANCE WITH ISO/IEC 17025 IN GLOBAL INDUSTRIAL INFRASTRUCTURE
type: item
description: This study addresses the problem that valve qualification decisions weaken when mechanical testing laboratories implement ISO/IEC 17025 unevenly, reducing confidence in test evidence. The purpose was to quantify ISO/IEC 17025 compliance maturity and test which compliance mechanisms predict valve performance assurance (VPA). A quantitative, cross-sectional survey was administered to a purposive sample of 214 professionals in an enterprise oil and gas testing case. The independent construct was ISO/IEC 17025 compliance, measured as D1 competence and authorization, D2 calibration and traceability, D3 method validation and SOP adherence, D4 internal audits and corrective actions, and D5 uncertainty and decision rules; the dependent construct was a composite VPA index. Analysis used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Compliance maturity (CI) was moderately high (M = 3.96/5) and VPA was moderately high (M = 3.88/5), with excellent reliability (α = 0.92 and α = 0.91). Overall compliance correlated strongly with VPA (r = 0.71, p < .001), and all dimensions were significant (r = 0.49 to 0.66, p < .001). The regression model explained 58% of VPA variance (R² = 0.58; F(5, 208) = 57.40, p < .001); D3 was the strongest predictor (β = 0.31, p < .001), followed by D1 (β = 0.24, p < .001) and D2 (β = 0.19, p = .001), while D4 and D5 showed smaller effects (p ≤ .050). Implications are to prioritize method control and SOP discipline, strengthen competency authorization and traceability, and improve audit closure and uncertainty-based decision rules for integrity decisions.
subject: Articles
  topic: section
subject: ISO/IEC 17025 compliance
  topic: keywords
subject: Mechanical testing laboratories
  topic: keywords
subject: Valve performance assurance
  topic: keywords
subject: Calibration and traceability
  topic: keywords
subject: Method validation and SOP adherence
  topic: keywords
creator: Efat Ara Haque
rights: Copyright (c) 2023 Efat Ara Haque (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1424
ending page: 1457
doi: 10.63125/a5c2w129

FEDERATED LEARNING–DRIVEN REAL-TIME DISEASE SURVEILLANCE FOR SMART HOSPITALS USING MULTI-SOURCE HETEROGENEOUS HEALTHCARE DATA
type: item
description: Federated learning is increasingly promoted as a privacy-preserving way to use multi-source heterogeneous healthcare data for real-time disease surveillance in smart hospitals, but little is known about how its capabilities, data integration, governance, and organizational conditions jointly shape perceived surveillance performance. This study therefore aimed to quantify how federated learning capability, multi-source data integration, privacy and security assurance, and organizational readiness contribute to perceived surveillance effectiveness across cloud and enterprise smart hospital cases. A quantitative cross-sectional, case-based survey design was used, with 228 usable responses (87.7 percent response rate from 260 questionnaires) from clinicians, infection prevention staff, health informatics personnel, and IT/security professionals working in digitally advanced hospitals. Key variables included federated learning capability, multi-source integration, privacy and security assurance, organizational readiness, and perceived surveillance effectiveness, all measured on five-point Likert scales. The analysis plan comprised descriptive statistics, reliability testing, Pearson correlations, and multiple regression. Mean scores indicated generally high maturity (FLC 3.78, MSI 3.92, PSA 4.06, OR 3.84, SE 3.95), with strong internal consistency (Cronbach’s alpha 0.83–0.88). Correlations between surveillance effectiveness and predictors were all positive and significant, highest for organizational readiness (r = 0.72) and federated learning capability (r = 0.65). The regression model was significant and explained 61.3 percent of the variance in surveillance effectiveness (R² = 0.613), with organizational readiness (β = 0.29), federated learning capability (β = 0.24), privacy and security assurance (β = 0.21), and multi-source integration (β = 0.18) all making significant contributions. The findings imply that effective federated learning driven surveillance in smart ho…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Smart Hospitals
  topic: keywords
subject: Real-Time Disease Surveillance
  topic: keywords
subject: Multi-Source Heterogeneous Healthcare Data
  topic: keywords
subject: Organizational Readiness
  topic: keywords
creator: Arfan Uzzaman
rights: Copyright (c) 2025 Arfan Uzzaman (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1390
ending page: 1423
doi: 10.63125/9jzvd439

AI-ENABLED DIGITAL TWIN FRAMEWORK FOR PREDICTIVE MAINTENANCE AND ENERGY OPTIMIZATION IN INDUSTRIAL SYSTEMS
type: item
description: Industrial organizations face growing pressure to improve asset reliability and energy efficiency at the same time, yet there is limited quantitative evidence on how AI enabled digital twins contribute to these outcomes. This study therefore examines how AI enabled digital twin capability relates to predictive maintenance effectiveness and energy optimization performance in real industrial enterprise environments. A quantitative, cross sectional, case-based survey design was applied to 220 professionals from discrete manufacturing, process industries and energy intensive utilities using digital twin and AI driven maintenance and energy management. Key latent variables were AI enabled digital twin capability, predictive maintenance effectiveness, energy optimization performance, AI analytics maturity and organizational readiness, measured with multi-item Likert scales showing strong internal consistency. The analysis plan combined descriptive statistics, correlation analysis and multiple regression with mediation and moderation tests. Results show moderately high perceived capability levels, with mean scores of 3.67 for digital twin capability, 3.79 for predictive maintenance and 3.61 for energy optimization on a five-point scale, and strong positive associations among the constructs. Digital twin capability significantly predicts predictive maintenance effectiveness (β = 0.58, R² = 0.53) and energy optimization performance (direct β = 0.29 within a model R² = 0.56), with predictive maintenance providing a significant partial mediation and AI analytics maturity and organizational readiness acting as important enablers. These findings indicate that AI enabled digital twins, supported by mature analytics and organizational readiness, act as strategic levers to advance predictive maintenance and industrial energy efficiency in Industry 4.0 enterprises.
subject: Articles
  topic: section
subject: AI Enabled Digital Twin
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Energy Optimization
  topic: keywords
subject: Industrial Systems
  topic: keywords
subject: Industry 4.0
  topic: keywords
creator: Saikat Sarkar
rights: Copyright (c) 2025 Saikat Sarkar (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1359
ending page: 1389
doi: 10.63125/8v1nwj69

ARTIFICIAL INTELLIGENCE IN DRIVEN DIGITAL TWIN FOR REAL-TIME TRAFFIC SIGNAL OPTIMIZATION AND TRANSPORTATION PLANNING
type: item
description: This study examined the effectiveness of an artificial intelligence–driven digital twin system for real-time traffic signal optimization and transportation planning using a quantitative simulation-based approach. The digital twin replicated a congested urban corridor consisting of six signalized intersections and evaluated performance under baseline and AI-controlled conditions across 48 experimental scenarios covering peak, off-peak, and incident periods. A total of 192 simulation replications were analyzed to ensure statistical robustness. Descriptive results showed that the AI-based controller reduced average delay from 68.4 seconds per vehicle to 29.5 seconds, representing a 56.9% improvement. Average queue length declined from 19.6 vehicles to 8.4 vehicles, a 57.1% reduction, while throughput increased from 12.7 to 19.8 vehicles per minute, reflecting a 55.9% gain. Travel time reliability improved as the reliability index rose from 74.3% to 91.4%, indicating a more stable operating environment. Correlation analysis showed that the relationship between demand intensity and congestion severity weakened under AI control, with the demand-delay correlation decreasing from 0.74 to 0.48. Reliability and validity checks confirmed strong internal consistency, with simulation replications producing variability below 0.30 units across all key metrics. Criterion validity assessment showed that simulated baseline values differed from field observations by less than 7%. Collinearity diagnostics confirmed model suitability, with all VIF values below 2.3, ensuring stable regression estimates. Regression analysis showed that AI control significantly predicted improvements in delay (β = –0.62, p < .001), queue length (β = –0.58, p < .001), throughput (β = 0.49, p < .001), and reliability (β = 0.57, p < .001). All hypotheses were supported. The study demonstrated that integrating digital twin technology with reinforcement learning produced substantial quantitative benefits, estab…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Traffic Signal Optimization
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Real-Time Control
  topic: keywords
creator: Md Sarwar Hossain Shuvo
rights: Copyright (c) 2025 Md Sarwar Hossain Shuvo (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1316
ending page: 1358
doi: 10.63125/dthvcp78

AI-ORCHESTRATED CYBER-PHYSICAL SYSTEMS FOR SUSTAINABLE INDUSTRY 5.0 MANUFACTURING AND SUPPLY CHAIN RESILIENCE
type: item
description: This study examined the influence of AI-orchestrated cyber-physical systems (CPS) on sustainable Industry 5.0 manufacturing and supply chain resilience through a quantitative, multi-construct analytical model. Using data collected from 412 industrial respondents across manufacturing, logistics, and technology-driven organizations, the study evaluated relationships among CPS integration maturity, AI orchestration capability, sustainability performance, and supply chain resilience. Descriptive analysis showed strong adoption levels, with mean CPS maturity at 4.21, AI orchestration at 4.08, sustainability performance at 4.17, and resilience at 4.11 on a 5-point scale. Correlation results indicated significant positive associations among all major constructs, with AI orchestration exhibiting the strongest correlation with sustainability (r = 0.71) and resilience (r = 0.68). Reliability and validity tests confirmed strong measurement consistency, with Cronbach’s alpha values ranging from 0.89 to 0.94, and AVE values exceeding 0.63. Regression modeling revealed that CPS maturity significantly predicted AI orchestration (β = 0.62, p < 0.001), which in turn significantly enhanced sustainability (β = 0.58, p < 0.001) and resilience (β = 0.54, p < 0.001). Mediation analysis showed that AI orchestration mediated over 64% of the total effect of CPS maturity on sustainability and 59% on resilience. Moderation analysis further demonstrated that organizational readiness strengthened the AI-to-sustainability pathway (β = 0.27, p < 0.01), while environmental turbulence amplified the AI-to-resilience relationship (β = 0.24, p < 0.01). Overall, the findings demonstrated that AI-orchestrated CPS functioned as a central mechanism enabling real-time coordination, ecological optimization, and disruption-adaptive behavior, highlighting their essential role in achieving sustainable and resilient Industry 5.0 manufacturing ecosystems.
subject: Articles
  topic: section
subject: AI Orchestration
  topic: keywords
subject: Cyber-Physical Systems
  topic: keywords
subject: Industry 5.0
  topic: keywords
subject: Sustainable Manufacturing
  topic: keywords
subject: Supply Chain Resilience
  topic: keywords
creator: Shaikh Shofiullah
rights: Copyright (c) 2025 Shaikh Shofiullah (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1278
ending page: 1315
doi: 10.63125/jwm2e278

QUANTITATIVE ASSESSMENT OF AUTOMATION AND CONTROL STRATEGIES FOR PERFORMANCE OPTIMIZATION IN U.S. INDUSTRIAL PLANTS
type: item
description: Industrial automation continues to redefine modern production systems through advanced control strategies, intelligent monitoring, and data-driven optimization. This study presents a comprehensive quantitative assessment of automation and control systems implemented across 126 U.S. industrial plants, encompassing manufacturing, petrochemicals, power generation, and materials processing. The research explores how automation sophistication measured through integration level, predictive maintenance capability, control responsiveness, real-time monitoring, and system adaptability translates into measurable improvements in operational efficiency, reliability, and sustainability. Findings demonstrate clear and statistically significant relationships between automation variables and industrial performance outcomes. Correlation coefficients revealed strong positive associations between automation integration and OEE (r = .71), energy efficiency (r = .57), and MTBF (r = .53), alongside strong negative correlations with downtime ratio (r = –.49). Predictive maintenance was particularly influential, showing strong correlations with MTBF (r = .68) and downtime reduction (r = –.64), underscoring the value of data-driven asset reliability frameworks. Regression models further quantified these effects. The OEE model produced an Adjusted R² of .724 (F(5,120) = 48.37, p < .001), with automation level (β = .42, p < .001) and predictive maintenance (β = .31, p < .01) emerging as the strongest predictors. Energy efficiency was similarly explained by automation integration, predictive maintenance, and system adaptability (Adjusted R² = .61; F(4,121) = 35.19, p < .001). Downtime ratio showed a strong inverse relationship to predictive maintenance (β = –.41, p < .001) and control responsiveness (β = –.22, p < .01), indicating that responsive and proactive systems are highly effective in preventing production disruptions. ANOVA confirmed significant performance differences across automatio…
subject: Articles
  topic: section
subject: Industrial Automation
  topic: keywords
subject: Control Strategies
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Quantitative Assessment
  topic: keywords
subject: Process Control Systemse
  topic: keywords
creator: Jabed Hasan Tarek
creator: Mohammad Shah Paran
rights: Copyright (c) 2024 Jabed Hasan Tarek, Mohammad Shah Paran (Author)
date: 2024-05-17
publication date: 2024-05-17
starting page: 169
ending page: 205
doi: 10.63125/eqfz8220

AUTOMATING NIST 800-53 CONTROL IMPLEMENTATION: A CROSS-SECTOR REVIEW OF ENTERPRISE SECURITY TOOLKITS
type: item
description: This study addresses the persistent problem of manual, document-centric compliance that slows implementation of NIST SP 800-53 controls and produces uneven evidence quality across complex cloud estates. The purpose is to quantify how enterprise security toolkits operationalize automated control implementation and to identify which capabilities most strongly predict measurable compliance and operations outcomes. Using a quantitative, cross-sectional, case-based design, we analyze organization-level survey data and embedded evidence from cloud and enterprise cases spanning finance, healthcare, manufacturing, public sector, and education. Key variables include four predictors toolkit capability maturity, integration breadth, policy-as-code adoption, and infrastructure-as-code security adoption and five outcomes automation coverage percentage, time to compliance, audit pass rate, mean time to remediate, and false positive rate. The analysis plan specifies descriptives, correlation matrices, and multiple regressions with sector fixed effects and a regulatory pressure moderator, supported by robustness and diagnostic checks. Headline findings show capability maturity and integration breadth as the strongest, consistent predictors of higher automation coverage and shorter time to compliance, with policy-as-code and infrastructure-as-code adding incremental gains; audit pass rates rise where standardized, machine-generated evidence is produced, and false positives decline modestly as correlation and context enrichment improve. Implications for practice are clear, prioritize an integration roadmap that wires CI or CD, cloud control planes, identity, CMDB or ITSM, and SIEM or SOAR into a single evidence pipeline, enforce policies at merge and admission, and institutionalize evidence-as-code mapped to assessment objectives so compliance becomes continuous and verifiable rather than periodic and manual.
subject: Articles
  topic: section
subject: NIST SP 800-53
  topic: keywords
subject: Controls as code
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: SIEM
  topic: keywords
subject: CSPM or CNAPP
  topic: keywords
subject: Integration breadth
  topic: keywords
subject: Automation coverage
  topic: keywords
subject: Time to compliance
  topic: keywords
subject: Audit pass rate
  topic: keywords
subject: Infrastructure as code
  topic: keywords
subject: Policy as code
  topic: keywords
creator: Md. Jobayer Ibne Saidur
creator: Md. Kamrul Khan
rights: Copyright (c) 2023 Md. Jobayer Ibne Saidur, Md. Kamrul Khan (Author)
date: 2023-04-29
publication date: 2023-04-29
starting page: 160
ending page: 195
doi: 10.63125/prkw8r07

RECENT DEVELOPMENTS AND CHALLENGES IN FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION
type: item
description: This systematic review critically evaluates the advances and limitations of fracture mechanics–based fatigue life prediction approaches within the context of structural integrity assessment. Over the past five decades, fracture mechanics has evolved from a linear analytical framework into a multidisciplinary domain that integrates theoretical formulations, experimental validation, computational modeling, and data-driven analytics. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviewed and synthesized 214 peer-reviewed research papers published between 1972 and 2024. The analysis reveals that significant progress has been achieved in extending classical Linear Elastic Fracture Mechanics (LEFM) and Elastic–Plastic Fracture Mechanics (EPFM) toward nonlinear, probabilistic, and multi-scale modeling approaches that capture the complex behavior of materials under cyclic loading. Computational developments—particularly finite element analysis (FEA), extended finite element methods (XFEM), cohesive zone modeling, and continuum damage mechanics—have enabled detailed simulation of crack initiation and propagation under mixed-mode and variable amplitude stresses. Experimental advancements such as digital image correlation, acoustic emission monitoring, and thermographic analysis have enhanced the precision of crack growth measurement, enabling robust model calibration and validation. Moreover, the integration of Structural Health Monitoring (SHM), digital twin architectures, and machine learning has established a new paradigm for real-time fatigue prediction and adaptive maintenance in high-reliability structures. Despite these achievements, the review identifies persistent challenges including data inconsistency in fatigue thresholds, theoretical limitations under large-scale yielding, computational cost, and inadequate coupling of environmental and microstructural effects. The findings undersco…
subject: Articles
  topic: section
subject: Fracture Mechanics
  topic: keywords
subject: Fatigue Life Prediction
  topic: keywords
subject: Structural Integrity Assessment
  topic: keywords
subject: Crack Growth Modeling
  topic: keywords
subject: Probabilistic Fatigue Analysis
  topic: keywords
creator: Abul Kashem Mohammad Yahia
creator: Mohammad Shahjalal
rights: Copyright (c) 2025 Abul Kashem Mohammad Yahia, Mohammad Shahjalal (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 1202
ending page: 1237
doi: 10.63125/qgtnp591

IMPLEMENTING SUSTAINABLE SUPPLY CHAIN PRACTICES IN GLOBAL APPAREL RETAIL: A SYSTEMATIC REVIEW OF CURRENT TRENDS
type: item
description: This study systematically examined the implementation of sustainable supply chain practices in global apparel retail through a comprehensive analysis of seventy-four peer-reviewed articles published between 2010 and 2022. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the research synthesized current trends, challenges, and innovations across environmental, social, economic, and technological dimensions of sustainability. The findings demonstrate that sustainability has evolved from a compliance-based obligation to a strategic imperative embedded within corporate decision-making, shaping profitability, competitiveness, and brand reputation. Environmental sustainability has advanced through the adoption of circular economy models, life-cycle assessments, and resource-efficient production systems that reduce emissions, water consumption, and textile waste. Social sustainability has improved through multi-stakeholder initiatives promoting labor rights, gender equity, and workplace safety, though persistent disparities remain in subcontracting tiers. The integration of digital technologies such as blockchain, artificial intelligence, and the Internet of Things has strengthened transparency, traceability, and data-driven accountability within apparel supply networks. Quantitative analytical frameworks—incorporating life-cycle costing, carbon accounting, and multi-criteria decision-making—have enhanced the empirical validation of sustainability performance. Furthermore, evolving consumer expectations and stakeholder engagement have reinforced sustainability as a collaborative responsibility, driving greater transparency and ethical governance. The study concludes that the convergence of technological innovation, environmental responsibility, and ethical labor governance defines a new paradigm in global apparel retail, where sustainability functions as both a moral obligation and a strategic instrument for long-term res…
subject: Articles
  topic: section
subject: Sustainable Supply Chain Management
  topic: keywords
subject: Global Apparel Retail
  topic: keywords
subject: Environmental Sustainability
  topic: keywords
subject: Social Responsibility
  topic: keywords
subject: Corporate Governance
  topic: keywords
creator: Md Mesbaul Hasan
creator: Md. Tahmid Farabe Shehun
rights: Copyright (c) 2022 Md Mesbaul Hasan, Md. Tahmid Farabe Shehun (Author)
date: 2022-04-29
publication date: 2022-04-29
starting page: 332
ending page: 363
doi: 10.63125/nen7vd57