ASRC Procedia: Global Perspectives in Science and Scholarship
ASRC Procedia is a peer-reviewed, open-access conference proceedings journal that serves as the official publication outlet of the American Scholarly Research Conference (ASRC). Published from Florida, USA, the journal is dedicated to disseminating original research presented at academic and industry symposia held under the ASRC umbrella, including the flagship Global Research and Innovation Summit (GRIS). The journal promotes cross-disciplinary knowledge exchange by capturing diverse scholarly outputs across the domains of science, engineering, technology, health, environment, education, business, economics, public policy, and the social sciences.
Artificial Intelligence Driven Analytics for Market Entry Strategy, Digital Marketing Optimization, and Enterprise Workflow Transformation
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) = 74.3; p < .001), with DMO the strongest unique predictor. A supporting traceability analysis further clarified mechanisms: personalization/targeting analytics most strongly predicted DMO (β = .34, p < .001), while automation/decision-rule enablement most strongly predicted EWT (β = .33, p < .001).
Copyright (c) 2025 Md Khaled Hossain (Author)
Smart Occupational Safety Management Through IOT Sensor Networks, Machine Learning, and Real-Time Risk Assessment in Chemical Processing Plants
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 networks (β = .31) and machine learning (β = .27). The study implies that chemical plants should adopt integrated, data-driven safety architectures to strengthen hazard detection, emergency readiness, and reliability-oriented plant safety performance.
Copyright (c) 2025 Md Abubakar Siddique Akash, Bhanu Prakash Sah (Author)
Advanced Computing Frameworks for Distributed Training, Deployment, and Monitoring of Artificial Intelligence and Machine Learning Models
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 operational dependability remains constrained by lifecycle linkages. Monitoring maturity was strongest for service observability (p95/p99 latency and tracing, M = 3.9) but weaker for label-scarce performance degradation tracking (M = 3.4). The most severe gaps were interoperability fragmentation (M = 4.3; 75.0% ≥4) and unclear incident ownership (M = 4.2; 71.9% ≥4), implying that organizations should prioritize integrated platforms with traceability, governance linkage, and label-scarce monitoring proxies to translate scaling gains into stable operations.
Copyright (c) 2025 Mohammad Robel Miah (Author)
Advanced Composite and Geopolymer Materials for Durable Water Supply and Sanitation Infrastructure: Performance, Sustainability, and Lifecycle Assessment
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), indicating an average 22% reduction in GWP per service year for geopolymer pathways and a 17% reduction in cumulative emissions for composite rehabilitation versus full replacement, while decision-fit scores were 4.12/5 for composites and 3.89/5 for geopolymers. These findings imply that utilities should match material choice to exposure regime, require QA for joints and curing, and adopt impact-per-service-year criteria in procurement.
Copyright (c) 2025 Palash Chandra Das (Author)
Predictive Analytics and KPI Dashboards for Enterprise Workflow Optimization
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-oriented dashboards outperformed descriptive dashboards (median cycle-time reduction 16% vs 9%). Practical implications are that organizations should treat predictive indicators as governed KPIs with clear ownership, refresh cadence, thresholds, and intervention playbooks, while addressing top barriers such as data-quality inconsistency (57.7% of cases; negative impact M = 4.35) and system integration gaps (50.0%; negative impact M = 4.21).
Copyright (c) 2025 Tanjina Binte Sohrab (Author)
QUANTITATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DEFECT PREDICTION IN METAL ADDITIVE MANUFACTURING
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 best predictive performance (Accuracy 0.89, F1 0.82, ROC-AUC 0.92) and the lowest false negative rate (10.8%), supporting risk-aware deployment. Implications are that enterprise defect prediction success depends not only on algorithms, but also on robust data governance, monitoring coverage, stable process windows, and explainable outputs that strengthen trust and workflow adoption.
Copyright (c) 2025 Md. Ashfaq Siddiquee, Ashraful Islam (Author)
Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems
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 predictive validity, interpretability reliability, security resilience, and human decision performance within regulated systems.
Copyright (c) 2023 Md. Arifur Rahman, B. M. Taslimul Haque (Author)
AI-Driven Condition Monitoring and Fault Detection in Electrical Power and Industrial Control Systems
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 smaller but significant contribution (β = .14, p = .018). Multicollinearity was not problematic (VIF range = 1.31–1.58). Overall, the findings confirmed that operational performance outcomes were significantly associated with the effectiveness of monitoring, diagnostic accuracy, predictive maintenance capability, and integration quality, providing quantitative evidence that structured machine learning–driven condition monitoring systems contribute meaningfully to measurable operational performance improvement in electrical power and control environments.
Copyright (c) 2025 Shamsul Arifeen (Author)
Machine Learning–Based AML/KYC Transaction Monitoring for Suspicious Activity Detection and Compliance Risk Reduction in Digital Banking
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 < .001) and AQWII (β = 0.23, p < .001), alongside meaningful contributions from ATA (β = 0.18, p = .002), DQF (β = 0.15, p = .007), SIA (β = 0.14, p = .015), MET (β = 0.12, p = .029), EARMGE (β = 0.13, p = .018), and TCAR (β = 0.11, p = .042).
Copyright (c) 2025 Sazzadul Islam (Author)
AI-Driven Distribution Planning for Essential Goods in Underserved Communities: A Mixed Methods Framework for Access Optimization
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 = .55), and ENV_SUPPORT (r = .32), and a negative association with CONSTRAINTS (r = −.41). The regression model was significant and explanatory (R² = .45; Adj. R² = .44; F (5,306) = 49.8, p < .001; VIF 1.22–2.05), with ORG_READY as the strongest predictor (β = .29), followed by AIPC (β = .24) and DQI (β = .18). Results indicate that AI-enabled planning can improve access, but realized gains depend on data integration, organizational embedding, and targeted interventions to narrow subarea gaps under persistent constraints.
Data Quality and Organizational Readiness
Copyright (c) 2025 Md Shahab Uddin (Author)
Business Intelligence–Driven Risk Assessment and Portfolio Performance Analytics for Financial and Investment Institutions
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) and faster decisions (DTS M = 3.66, SD = 0.69; r = 0.58, p < .001). The findings imply that organizations should prioritize governed, stable definitions and timely integration, then reinforce automation and interpretability, and institutionalize BRPRI-based maturity tracking to sustain risk-informed portfolio management.
Copyright (c) 2025 Shamsunnahar Chadni (Author)
Financial Vulnerability Mapping in Global Supply Chains: Implications for U.S. Trade Stability and Investment Risk
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.
Copyright (c) 2025 Mostafa Kamal (Author)
UAV-Based Hyperspectral and Thermal Signature Analytics for Early Detection of Soil Moisture Stress, Erosion Hotspots, and Flood Susceptibility
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% overlap; J=0.52) and flood susceptibility versus flow corridors (71% overlap; J=0.55), while sensitivity tests reduced the fused model from Adj. R²=0.58 to 0.49 without TH_Anomaly and to 0.50 without HSI_MoistureIndex. High-risk classes covered 22 to 28% of zones and consistently aligned with Likert means above 4.0. Implications are that sensor-fusion UAV analytics, operationalized through cloud and enterprise workflows, can support earlier irrigation triage, prioritized erosion control in convergent high-slope zones, and more defensible flood preparedness zoning with auditable quantitative evidence.
Erosion And Flood Susceptibility Mapping
Copyright (c) 2025 Ratul Debnath (Author)
Quantitative Assessment of Predictive Analytics for Risk Management in U.S. Healthcare Finance Systems
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.
Copyright (c) 2025 Rifat Chowdhury (Author)
Integrating Smart Sensor Systems and Digital Safety Dashboards for Real-Time Hazard Monitoring in High-Risk Industrial Facilities
Copyright (c) 2025 Jahangir Shekh (Author)
Computational Modeling of Failure Mechanisms in Mechanical Systems: Applications For Energy and Industrial Sectors
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 resulted in a modest 6.2% increase in prediction error. Overall, the findings established a statistically stable and interpretable framework linking monitoring-derived degradation signals and core exposure mechanisms to continuous failure severity within operational mechanical systems.
Copyright (c) 2023 Efat Ara Haque (Author)
INTEGRATING SMART SENSOR SYSTEMS AND DIGITAL SAFETY DASHBOARDS FOR REAL-TIME HAZARD MONITORING IN HIGH-RISK INDUSTRIAL FACILITIES
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 alarm accuracy (B = 1.12, p < .001). Wireless network protocols were associated with higher detection latency (B = 0.78, p < .001) and lower alarm accuracy (B = −2.46, p < .001) compared with wired configurations. Longer dashboard update intervals were linked to increased detection latency and reduced alarm accuracy, while also contributing to longer operator response times (p < .05).
Copyright (c) 2025 Jahangir Shekh, Rebeka Sultana Panna (Author)
DATA-DRIVEN INDUSTRIAL ENGINEERING MODELS FOR OPTIMIZING WATER PURIFICATION AND SUPPLY CHAIN SYSTEMS IN THE U.S.
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 strongest predictors; SCP was explained at R²=.430 (F(4,205)=39.10, p<.001) dominated by SCV (β=.46, p<.001); and OOO was explained at R²=.520 (F(3,206)=73.40, p<.001) by PSP (β=.39, p<.001), SCP (β=.31, p<.001), and DQG (β=.17, p=.002). Overall, the model indicates that targeted improvements in process optimization maturity and embedded analytics can lift purification performance, investments in supply chain visibility can stabilize supply outcomes, and stronger data governance can amplify integrated optimization results across enterprise and cloud enabled environments.
Copyright (c) 2025 Md. Al Amin Khan (Author)
QUANTITATIVE ANALYSIS OF MECHANICAL TESTING AND VALVE PERFORMANCE IN THE OIL AND GAS SECTOR: ENSURING COMPLIANCE WITH ISO/IEC 17025 IN GLOBAL INDUSTRIAL INFRASTRUCTURE
This study addresses the problem that valve qualification decisions weaken when mechanical testing laboratories implement ISO/IEC 17025 unevenly, reducing confidence in test evidence. The purpose was to quantify ISO/IEC 17025 compliance maturity and test which compliance mechanisms predict valve performance assurance (VPA). A quantitative, cross-sectional survey was administered to a purposive sample of 214 professionals in an enterprise oil and gas testing case. The independent construct was ISO/IEC 17025 compliance, measured as D1 competence and authorization, D2 calibration and traceability, D3 method validation and SOP adherence, D4 internal audits and corrective actions, and D5 uncertainty and decision rules; the dependent construct was a composite VPA index. Analysis used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Compliance maturity (CI) was moderately high (M = 3.96/5) and VPA was moderately high (M = 3.88/5), with excellent reliability (α = 0.92 and α = 0.91). Overall compliance correlated strongly with VPA (r = 0.71, p < .001), and all dimensions were significant (r = 0.49 to 0.66, p < .001). The regression model explained 58% of VPA variance (R² = 0.58; F(5, 208) = 57.40, p < .001); D3 was the strongest predictor (β = 0.31, p < .001), followed by D1 (β = 0.24, p < .001) and D2 (β = 0.19, p = .001), while D4 and D5 showed smaller effects (p ≤ .050). Implications are to prioritize method control and SOP discipline, strengthen competency authorization and traceability, and improve audit closure and uncertainty-based decision rules for integrity decisions.
FEDERATED LEARNING–DRIVEN REAL-TIME DISEASE SURVEILLANCE FOR SMART HOSPITALS USING MULTI-SOURCE HETEROGENEOUS HEALTHCARE DATA
Federated learning is increasingly promoted as a privacy-preserving way to use multi-source heterogeneous healthcare data for real-time disease surveillance in smart hospitals, but little is known about how its capabilities, data integration, governance, and organizational conditions jointly shape perceived surveillance performance. This study therefore aimed to quantify how federated learning capability, multi-source data integration, privacy and security assurance, and organizational readiness contribute to perceived surveillance effectiveness across cloud and enterprise smart hospital cases. A quantitative cross-sectional, case-based survey design was used, with 228 usable responses (87.7 percent response rate from 260 questionnaires) from clinicians, infection prevention staff, health informatics personnel, and IT/security professionals working in digitally advanced hospitals. Key variables included federated learning capability, multi-source integration, privacy and security assurance, organizational readiness, and perceived surveillance effectiveness, all measured on five-point Likert scales. The analysis plan comprised descriptive statistics, reliability testing, Pearson correlations, and multiple regression. Mean scores indicated generally high maturity (FLC 3.78, MSI 3.92, PSA 4.06, OR 3.84, SE 3.95), with strong internal consistency (Cronbach’s alpha 0.83–0.88). Correlations between surveillance effectiveness and predictors were all positive and significant, highest for organizational readiness (r = 0.72) and federated learning capability (r = 0.65). The regression model was significant and explained 61.3 percent of the variance in surveillance effectiveness (R² = 0.613), with organizational readiness (β = 0.29), federated learning capability (β = 0.24), privacy and security assurance (β = 0.21), and multi-source integration (β = 0.18) all making significant contributions. The findings imply that effective federated learning driven surveillance in smart hospitals depends not only on advanced models and integrated data, but also on robust privacy safeguards and high organizational readiness to act on analytic insights.
Multi-Source Heterogeneous Healthcare Data
Copyright (c) 2025 Arfan Uzzaman (Author)
AI-ENABLED DIGITAL TWIN FRAMEWORK FOR PREDICTIVE MAINTENANCE AND ENERGY OPTIMIZATION IN INDUSTRIAL SYSTEMS
Industrial organizations face growing pressure to improve asset reliability and energy efficiency at the same time, yet there is limited quantitative evidence on how AI enabled digital twins contribute to these outcomes. This study therefore examines how AI enabled digital twin capability relates to predictive maintenance effectiveness and energy optimization performance in real industrial enterprise environments. A quantitative, cross sectional, case-based survey design was applied to 220 professionals from discrete manufacturing, process industries and energy intensive utilities using digital twin and AI driven maintenance and energy management. Key latent variables were AI enabled digital twin capability, predictive maintenance effectiveness, energy optimization performance, AI analytics maturity and organizational readiness, measured with multi-item Likert scales showing strong internal consistency. The analysis plan combined descriptive statistics, correlation analysis and multiple regression with mediation and moderation tests. Results show moderately high perceived capability levels, with mean scores of 3.67 for digital twin capability, 3.79 for predictive maintenance and 3.61 for energy optimization on a five-point scale, and strong positive associations among the constructs. Digital twin capability significantly predicts predictive maintenance effectiveness (β = 0.58, R² = 0.53) and energy optimization performance (direct β = 0.29 within a model R² = 0.56), with predictive maintenance providing a significant partial mediation and AI analytics maturity and organizational readiness acting as important enablers. These findings indicate that AI enabled digital twins, supported by mature analytics and organizational readiness, act as strategic levers to advance predictive maintenance and industrial energy efficiency in Industry 4.0 enterprises.
Copyright (c) 2025 Saikat Sarkar (Author)
ARTIFICIAL INTELLIGENCE IN DRIVEN DIGITAL TWIN FOR REAL-TIME TRAFFIC SIGNAL OPTIMIZATION AND TRANSPORTATION PLANNING
This study examined the effectiveness of an artificial intelligence–driven digital twin system for real-time traffic signal optimization and transportation planning using a quantitative simulation-based approach. The digital twin replicated a congested urban corridor consisting of six signalized intersections and evaluated performance under baseline and AI-controlled conditions across 48 experimental scenarios covering peak, off-peak, and incident periods. A total of 192 simulation replications were analyzed to ensure statistical robustness. Descriptive results showed that the AI-based controller reduced average delay from 68.4 seconds per vehicle to 29.5 seconds, representing a 56.9% improvement. Average queue length declined from 19.6 vehicles to 8.4 vehicles, a 57.1% reduction, while throughput increased from 12.7 to 19.8 vehicles per minute, reflecting a 55.9% gain. Travel time reliability improved as the reliability index rose from 74.3% to 91.4%, indicating a more stable operating environment. Correlation analysis showed that the relationship between demand intensity and congestion severity weakened under AI control, with the demand-delay correlation decreasing from 0.74 to 0.48. Reliability and validity checks confirmed strong internal consistency, with simulation replications producing variability below 0.30 units across all key metrics. Criterion validity assessment showed that simulated baseline values differed from field observations by less than 7%. Collinearity diagnostics confirmed model suitability, with all VIF values below 2.3, ensuring stable regression estimates. Regression analysis showed that AI control significantly predicted improvements in delay (β = –0.62, p < .001), queue length (β = –0.58, p < .001), throughput (β = 0.49, p < .001), and reliability (β = 0.57, p < .001). All hypotheses were supported. The study demonstrated that integrating digital twin technology with reinforcement learning produced substantial quantitative benefits, establishing a strong empirical foundation for adopting AI-enhanced signal control in advanced transportation management systems.
Copyright (c) 2025 Md Sarwar Hossain Shuvo (Author)
AI-ORCHESTRATED CYBER-PHYSICAL SYSTEMS FOR SUSTAINABLE INDUSTRY 5.0 MANUFACTURING AND SUPPLY CHAIN RESILIENCE
This study examined the influence of AI-orchestrated cyber-physical systems (CPS) on sustainable Industry 5.0 manufacturing and supply chain resilience through a quantitative, multi-construct analytical model. Using data collected from 412 industrial respondents across manufacturing, logistics, and technology-driven organizations, the study evaluated relationships among CPS integration maturity, AI orchestration capability, sustainability performance, and supply chain resilience. Descriptive analysis showed strong adoption levels, with mean CPS maturity at 4.21, AI orchestration at 4.08, sustainability performance at 4.17, and resilience at 4.11 on a 5-point scale. Correlation results indicated significant positive associations among all major constructs, with AI orchestration exhibiting the strongest correlation with sustainability (r = 0.71) and resilience (r = 0.68). Reliability and validity tests confirmed strong measurement consistency, with Cronbach’s alpha values ranging from 0.89 to 0.94, and AVE values exceeding 0.63. Regression modeling revealed that CPS maturity significantly predicted AI orchestration (β = 0.62, p < 0.001), which in turn significantly enhanced sustainability (β = 0.58, p < 0.001) and resilience (β = 0.54, p < 0.001). Mediation analysis showed that AI orchestration mediated over 64% of the total effect of CPS maturity on sustainability and 59% on resilience. Moderation analysis further demonstrated that organizational readiness strengthened the AI-to-sustainability pathway (β = 0.27, p < 0.01), while environmental turbulence amplified the AI-to-resilience relationship (β = 0.24, p < 0.01). Overall, the findings demonstrated that AI-orchestrated CPS functioned as a central mechanism enabling real-time coordination, ecological optimization, and disruption-adaptive behavior, highlighting their essential role in achieving sustainable and resilient Industry 5.0 manufacturing ecosystems.
Copyright (c) 2025 Shaikh Shofiullah (Author)
QUANTITATIVE ASSESSMENT OF AUTOMATION AND CONTROL STRATEGIES FOR PERFORMANCE OPTIMIZATION IN U.S. INDUSTRIAL PLANTS
Industrial automation continues to redefine modern production systems through advanced control strategies, intelligent monitoring, and data-driven optimization. This study presents a comprehensive quantitative assessment of automation and control systems implemented across 126 U.S. industrial plants, encompassing manufacturing, petrochemicals, power generation, and materials processing. The research explores how automation sophistication measured through integration level, predictive maintenance capability, control responsiveness, real-time monitoring, and system adaptability translates into measurable improvements in operational efficiency, reliability, and sustainability. Findings demonstrate clear and statistically significant relationships between automation variables and industrial performance outcomes. Correlation coefficients revealed strong positive associations between automation integration and OEE (r = .71), energy efficiency (r = .57), and MTBF (r = .53), alongside strong negative correlations with downtime ratio (r = –.49). Predictive maintenance was particularly influential, showing strong correlations with MTBF (r = .68) and downtime reduction (r = –.64), underscoring the value of data-driven asset reliability frameworks. Regression models further quantified these effects. The OEE model produced an Adjusted R² of .724 (F(5,120) = 48.37, p < .001), with automation level (β = .42, p < .001) and predictive maintenance (β = .31, p < .01) emerging as the strongest predictors. Energy efficiency was similarly explained by automation integration, predictive maintenance, and system adaptability (Adjusted R² = .61; F(4,121) = 35.19, p < .001). Downtime ratio showed a strong inverse relationship to predictive maintenance (β = –.41, p < .001) and control responsiveness (β = –.22, p < .01), indicating that responsive and proactive systems are highly effective in preventing production disruptions. ANOVA confirmed significant performance differences across automation maturity levels (p < .001). Fully adaptive plants achieved the highest outcomes (mean OEE = 89.8%, downtime = 4.7%, energy efficiency = 0.82), significantly outperforming manual and semi-automated facilities. Robust diagnostic testing—Cronbach’s α (.87–.94), VIF (1.28–2.43), Durbin–Watson (2.03), and 5,000-bootstrap validation—verified reliability and model stability. Collectively, results demonstrate that U.S. industrial performance is strongly driven by a triad of automation integration, predictive intelligence, and control responsiveness.
Copyright (c) 2024 Jabed Hasan Tarek, Mohammad Shah Paran (Author)
AUTOMATING NIST 800-53 CONTROL IMPLEMENTATION: A CROSS-SECTOR REVIEW OF ENTERPRISE SECURITY TOOLKITS
This study addresses the persistent problem of manual, document-centric compliance that slows implementation of NIST SP 800-53 controls and produces uneven evidence quality across complex cloud estates. The purpose is to quantify how enterprise security toolkits operationalize automated control implementation and to identify which capabilities most strongly predict measurable compliance and operations outcomes. Using a quantitative, cross-sectional, case-based design, we analyze organization-level survey data and embedded evidence from cloud and enterprise cases spanning finance, healthcare, manufacturing, public sector, and education. Key variables include four predictors toolkit capability maturity, integration breadth, policy-as-code adoption, and infrastructure-as-code security adoption and five outcomes automation coverage percentage, time to compliance, audit pass rate, mean time to remediate, and false positive rate. The analysis plan specifies descriptives, correlation matrices, and multiple regressions with sector fixed effects and a regulatory pressure moderator, supported by robustness and diagnostic checks. Headline findings show capability maturity and integration breadth as the strongest, consistent predictors of higher automation coverage and shorter time to compliance, with policy-as-code and infrastructure-as-code adding incremental gains; audit pass rates rise where standardized, machine-generated evidence is produced, and false positives decline modestly as correlation and context enrichment improve. Implications for practice are clear, prioritize an integration roadmap that wires CI or CD, cloud control planes, identity, CMDB or ITSM, and SIEM or SOAR into a single evidence pipeline, enforce policies at merge and admission, and institutionalize evidence-as-code mapped to assessment objectives so compliance becomes continuous and verifiable rather than periodic and manual.
Copyright (c) 2023 Md. Jobayer Ibne Saidur, Md. Kamrul Khan (Author)
RECENT DEVELOPMENTS AND CHALLENGES IN FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION
This systematic review critically evaluates the advances and limitations of fracture mechanics–based fatigue life prediction approaches within the context of structural integrity assessment. Over the past five decades, fracture mechanics has evolved from a linear analytical framework into a multidisciplinary domain that integrates theoretical formulations, experimental validation, computational modeling, and data-driven analytics. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviewed and synthesized 214 peer-reviewed research papers published between 1972 and 2024. The analysis reveals that significant progress has been achieved in extending classical Linear Elastic Fracture Mechanics (LEFM) and Elastic–Plastic Fracture Mechanics (EPFM) toward nonlinear, probabilistic, and multi-scale modeling approaches that capture the complex behavior of materials under cyclic loading. Computational developments—particularly finite element analysis (FEA), extended finite element methods (XFEM), cohesive zone modeling, and continuum damage mechanics—have enabled detailed simulation of crack initiation and propagation under mixed-mode and variable amplitude stresses. Experimental advancements such as digital image correlation, acoustic emission monitoring, and thermographic analysis have enhanced the precision of crack growth measurement, enabling robust model calibration and validation. Moreover, the integration of Structural Health Monitoring (SHM), digital twin architectures, and machine learning has established a new paradigm for real-time fatigue prediction and adaptive maintenance in high-reliability structures. Despite these achievements, the review identifies persistent challenges including data inconsistency in fatigue thresholds, theoretical limitations under large-scale yielding, computational cost, and inadequate coupling of environmental and microstructural effects. The findings underscore the need for standardized testing methodologies, open-access fatigue databases, and harmonized probabilistic frameworks to improve predictive accuracy and cross-material applicability. Overall, this review concludes that fracture mechanics–based fatigue life prediction remains an indispensable yet continuously evolving foundation for structural integrity assessment, bridging traditional mechanics with digital innovation to ensure safer, more reliable, and sustainable engineering systems.
Copyright (c) 2025 Abul Kashem Mohammad Yahia, Mohammad Shahjalal (Author)
IMPLEMENTING SUSTAINABLE SUPPLY CHAIN PRACTICES IN GLOBAL APPAREL RETAIL: A SYSTEMATIC REVIEW OF CURRENT TRENDS
This study systematically examined the implementation of sustainable supply chain practices in global apparel retail through a comprehensive analysis of seventy-four peer-reviewed articles published between 2010 and 2022. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the research synthesized current trends, challenges, and innovations across environmental, social, economic, and technological dimensions of sustainability. The findings demonstrate that sustainability has evolved from a compliance-based obligation to a strategic imperative embedded within corporate decision-making, shaping profitability, competitiveness, and brand reputation. Environmental sustainability has advanced through the adoption of circular economy models, life-cycle assessments, and resource-efficient production systems that reduce emissions, water consumption, and textile waste. Social sustainability has improved through multi-stakeholder initiatives promoting labor rights, gender equity, and workplace safety, though persistent disparities remain in subcontracting tiers. The integration of digital technologies such as blockchain, artificial intelligence, and the Internet of Things has strengthened transparency, traceability, and data-driven accountability within apparel supply networks. Quantitative analytical frameworks—incorporating life-cycle costing, carbon accounting, and multi-criteria decision-making—have enhanced the empirical validation of sustainability performance. Furthermore, evolving consumer expectations and stakeholder engagement have reinforced sustainability as a collaborative responsibility, driving greater transparency and ethical governance. The study concludes that the convergence of technological innovation, environmental responsibility, and ethical labor governance defines a new paradigm in global apparel retail, where sustainability functions as both a moral obligation and a strategic instrument for long-term resilience.
Copyright (c) 2022 Md Mesbaul Hasan, Md. Tahmid Farabe Shehun (Author)
ARTIFICIAL INTELLIGENCE IN PRODUCT MARKETING: TRANSFORMING CUSTOMER EXPERIENCE AND MARKET SEGMENTATION
This study, titled AI-Driven Insights for Product Marketing: Enhancing Customer Experience and Refining Market Segmentation, explored the transformative influence of artificial intelligence (AI) on modern marketing practices through a systematic synthesis of 72 peer-reviewed papers published between 2013 and 2023. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the study examined how AI technologies including machine learning, natural language processing, neural networks, and predictive analytics reshaped data interpretation, customer engagement, and segmentation precision. The findings revealed that AI-driven marketing models enabled organizations to transition from intuition-based strategies to data-centric, adaptive, and predictive decision systems capable of delivering personalized experiences at scale. Across the reviewed literature, AI was found to enhance customer experience (CX) by integrating emotional intelligence and real-time feedback analytics, transforming traditional satisfaction metrics into dynamic, behavioral insights. Similarly, AI-based segmentation replaced static demographic categorizations with predictive micro-segmentation, allowing for continuous audience redefinition based on evolving contextual and behavioral patterns. The study also highlighted the emergence of algorithmic governance as an essential pillar for sustaining consumer trust, emphasizing fairness, transparency, and data privacy within AI-enabled marketing ecosystems. Ethical AI adoption, explainable models, and regulatory compliance were identified as vital determinants of credibility and brand reputation in digital environments. Furthermore, the interdisciplinary convergence of psychology, data science, and marketing theory positioned AI as both a technological and cognitive agent capable of understanding human intent and shaping responsive marketing strategies. Overall, the analysis concluded that AI-driven insights not only optimized marketing performance but also redefined the theoretical foundations of consumer engagement, establishing a new paradigm of intelligent, ethically governed, and culturally adaptive marketing systems that continuously evolve with global consumer behavior.
STATISTICAL ANALYSIS OF CYBER RISK EXPOSURE AND FRAUD DETECTION IN CLOUD-BASED BANKING ECOSYSTEMS
This study presented a comprehensive statistical examination of the interrelationships between cyber risk exposure, control maturity, and fraud detection efficiency within cloud-based banking ecosystems. The research aimed to quantify how exposure dimensions—specifically identity and access control, encryption and data security, network segmentation, monitoring and incident response, and governance compliance—affect the frequency and severity of fraudulent transactions across financial institutions operating in hybrid and public cloud environments. Drawing upon an extensive review of 127 empirical and theoretical papers published in the domains of cybersecurity analytics, cloud computing, and financial risk management, this study developed a robust quantitative framework to assess and predict the probability of fraud events. The dataset incorporated multi-institutional secondary data derived from transaction logs, cyber incident reports, and fraud management systems covering a 30-month observation period. Descriptive statistics, correlation matrices, and multivariate regression analyses were employed to identify the statistical strength, direction, and significance of relationships among variables, while hierarchical regression and reliability testing ensured model accuracy and construct consistency. The findings revealed that higher levels of cyber risk exposure were significantly associated with increased fraud frequency and greater financial loss severity, whereas greater control maturity exerted a strong negative influence, effectively mitigating exposure-induced vulnerabilities. The regression models exhibited high explanatory power (adjusted R² exceeding 0.60), confirming that exposure and control constructs jointly predict the likelihood and magnitude of fraud with strong statistical reliability. Furthermore, the study demonstrated that certain exposure subdimensions, particularly authentication integrity and encryption quality, contributed most prominently to fraud probability, emphasizing the operational importance of technical rigor and governance maturity. The comprehensive synthesis of prior research and empirical validation highlighted that risk management in cloud-based financial systems must transition from qualitative compliance-based practices toward quantitative, data-driven governance. The results offered both theoretical and practical implications for cybersecurity strategists, policy regulators, and financial institutions by providing a replicable model capable of evaluating cyber risk exposure and fraud dynamics through statistical inference. Overall, this study provided a measurable, evidence-based foundation for enhancing cyber resilience and fraud prevention in modern, cloud-enabled banking ecosystems.
Copyright (c) 2022 Md Nahid Hossain (Author)
THERMO-ECONOMIC MODELING OF HYDROGEN ENERGY INTEGRATION IN SMART FACTORIES
Here we investigate when and how hydrogen integration improves thermo-economic performance in smart factories by synthesizing international evidence and analyzing a multi-case plant dataset; a systematic review of 46 peer-reviewed studies scoped the technology, operations, and exergy–cost foundations that informed the empirical model, and the problem addressed is the lack of plant-resolved, tariff-aware evidence linking digital maturity and integration depth to levelized cost outcomes; the purpose is to quantify the relationships among renewable share to electrolysis, integration depth across electrolyzer, storage, and fuel cell, storage-to-load ratio, load variability, demand-response participation, and energy-management maturity with plant-level key performance indicators including levelized cost of hydrogen, energy cost intensity, exergy efficiency, CO₂ intensity, and reliability; the design is quantitative, cross-sectional, and case-based; the sample comprises 120 cloud or enterprise cases with EMS or SCADA telemetry aligned to audited tariff and financial records; the analysis plan includes descriptive statistics, correlation matrices, and multiple regression with sector fixed effects, robust errors, and a pre-specified interaction term testing whether energy-management maturity amplifies the benefit of renewable share; headline findings show that higher renewable share is the strongest single predictor of lower unit cost, deeper integration raises exergetic efficiency and lowers emissions, storage exhibits diminishing cost returns beyond roughly work-shift duration, and digital maturity significantly strengthens the cost reductions associated with renewable alignment; implications for practice are to prioritize renewable-aligned electrolysis before oversizing storage, right-size duration to variability fingerprints, invest in EMS maturity and governance, and deploy fuel-cell CHP where thermal and electrical peaks coincide to decarbonize without compromising reliability.
Copyright (c) 2022 Md Sanjid Khan, Zayadul Hasan (Author)