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.
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)
HIGH-PERFORMANCE COMPUTING FOR SCALING LARGE-SCALE LANGUAGE AND DATA MODELS IN ENTERPRISE APPLICATIONS
This quantitative study investigates the efficiency, scalability, and computational performance of high-performance computing (HPC) infrastructures in training and operationalizing large-scale language and data models within enterprise environments. HPC, characterized by parallel processing, distributed memory architectures, and high-bandwidth interconnects, serves as the backbone for scaling advanced artificial intelligence (AI) workloads. The research analyzed 120 performance observations across GPU-, TPU-, ASIC-, and hybrid-based clusters, focusing on metrics including throughput-per-dollar, inference latency, energy-per-token, time-to-recovery (TTR), and mean-time-between-failure (MTBF). Statistical analyses, encompassing correlation, regression, and structural equation modeling, revealed that scaling strategy and compute capacity were the most influential predictors of computational throughput (β = .45, p < .001 and β = .32, p < .001, respectively). Hybrid HPC configurations achieved optimal trade-offs between speed, reliability, and cost efficiency, outperforming homogeneous systems in energy proportionality and fault tolerance. The inclusion of sustainability parameters—such as energy optimization and adaptive checkpointing—significantly improved model explanatory power (ΔR² = .08, p < .001), demonstrating that sustainable computing practices reduce both operational costs and energy consumption. Reliability modeling confirmed that higher thermal efficiency and optimized interconnects enhanced system uptime and reduced recovery times. Validity testing (Cronbach’s α = .90; AVE > .63) established robust construct integrity across performance dimensions, while regression diagnostics verified predictor independence (VIF < 2.5). The findings conclude that performance, cost, and sustainability form an interdependent triad defining enterprise HPC efficiency. The study proposes a quantitative framework integrating energy optimization, parallel scaling, and reliability modeling to guide enterprise decision-making in AI deployment. It emphasizes that HPC-enabled AI scaling is not solely a technical enhancement but a strategic enabler of operational predictability, energy discipline, and long-term cost stability—advancing both computational science and enterprise digital transformation.
Copyright (c) 2023 Mst. Shahrin Sultana, Samia Akter (Author)
SYSTEMATIC REVIEW OF DATA SCIENCE APPLICATIONS IN PROJECT COORDINATION AND ORGANIZATIONAL TRANSFORMATION
This study investigates the quantitative relationships between data-science capability, coordination efficiency, and organizational transformation, emphasizing how analytics-driven decision systems enhance operational and strategic performance. Using a cross-sectoral dataset of 210 organizational observations, the analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the predictive influence of data-science capability and the mediating roles of data literacy and leadership alignment. The results reveal that data-science capability significantly predicts both coordination efficiency (β = 0.68, p < .001) and organizational transformation (β = 0.55, p < .001), explaining 46% and 59% of the respective variances. When mediators are included, the full model explains 67% of total variance, demonstrating that transformation outcomes depend on both technological and human enablers. Strong model-fit indices (CFI = 0.97, RMSEA = 0.037, SRMR = 0.033) confirm the reliability of the analytical framework. The study also establishes that data literacy (β_indirect = 0.23) and leadership alignment (β_indirect = 0.18) significantly strengthen the indirect pathways between analytics capability and transformation outcomes, indicating that human and managerial dimensions are integral to successful digital change. The findings highlight that coordination efficiency functions as a mechanism translating analytics maturity into process agility and transformation readiness. Overall, the study concludes that sustainable organizational transformation arises from the integration of advanced data-science capability, analytical culture, and leadership alignment. These results contribute to the growing empirical evidence that data-driven ecosystems serve as the foundation for strategic adaptability, operational precision, and continuous innovation in contemporary organizations.
Copyright (c) 2021 Md. Wahid Zaman Raj, Momena Akter (Author)
THE ROLE OF AI-ENABLED INFORMATION SECURITY FRAMEWORKS IN PREVENTING FRAUD IN U.S. HEALTHCARE BILLING SYSTEMS
This quantitative study investigated the role of AI-enabled information security frameworks in preventing fraud within U.S. healthcare billing systems, addressing a critical challenge in safeguarding financial integrity and improving payment accuracy. The research aimed to evaluate how the integration of artificial intelligence with established security controls influences technical detection performance, operational efficiency, and financial outcomes in healthcare organizations. A total of 126 peer-reviewed studies and industry reports published over the past decade were systematically reviewed to construct the theoretical foundation, guide the selection of variables, and inform the research design. The study utilized a large multi-payer dataset encompassing over 12 million claims from Medicare, Medicaid, and commercial payers, along with organizational-level measures of security maturity, AI capabilities, and governance quality. Descriptive analysis revealed that organizations implementing mature AI-enabled frameworks exhibited significantly lower fraud incidence, reduced improper payment rates, and shorter detection latency compared to those relying on traditional systems. Correlation analysis indicated strong negative associations between framework maturity and fraud-related outcomes and positive associations between AI capability indices and operational metrics such as workload yield and recovery ratios. Reliability and validity assessments confirmed the robustness of the measurement constructs, while collinearity diagnostics indicated no significant multicollinearity among predictors. Multiple regression analyses demonstrated that framework maturity, logging completeness, access control strength, and AI capability were significant predictors of improved detection performance and financial recovery, explaining a substantial proportion of variance across key outcomes. Subgroup analyses further revealed that the effectiveness of AI-enabled frameworks was moderated by organizational size, payer type, and enforcement intensity. The findings underscored the critical importance of integrating AI with strong governance, comprehensive logging, and secure access controls to build resilient fraud prevention systems.
Copyright (c) 2025 Mohammad Mushfequr Rahaman (Author)
INTELLIGENT DECISION SUPPORT IN SMART GOVERNANCE: LEVERAGING AI AND BIG DATA ANALYTICS FOR PUBLIC SECTOR EFFICIENCY AND TRANSPARENCY
Smart governance represents a transformational shift in public administration, characterized by the integration of artificial intelligence (AI) and big data analytics (BDA) to optimize decision-making, improve operational efficiency, and enhance transparency. However, existing research has not sufficiently established whether these technologies directly translate into measurable public value outcomes, nor how data governance influences this translation. This study empirically examines the extent to which AI adoption and big data analytics capability (BDAC) contribute to administrative efficiency and organizational transparency across public-sector agencies, and whether data governance acts as a moderating mechanism that conditions these effects. A quantitative, cross-sectional, multi-case research design was implemented across five public agencies utilizing cloud-enabled infrastructures and analytics-driven decision environments. Using purposive sampling, 268 respondents who were actively engaged in data, IT, or management functions completed a validated Likert-scale survey measuring AI adoption, BDAC, data governance strength, administrative efficiency, and transparency. Hierarchical ordinary least squares (OLS) regression with agency-clustered robust standard errors was employed to estimate main effects, while moderation analysis tested data governance as a structural amplifier. Control variables included agency size, budget band, IT maturity, service domain, and fixed effects to isolate contextual variation. The results demonstrate that BDAC is the strongest predictor of both administrative efficiency and transparency, indicating that analytic capability—not mere technology deployment—is the key determinant of performance outcomes in the public sector. AI adoption is positively associated with both outcomes, though to a lesser extent. Crucially, data governance significantly moderates the impact of both AI adoption and BDAC on transparency, suggesting that governance structures such as auditability, stewardship, documentation standards, and data lineage are essential for converting internal analytics into externally verifiable public value. The moderating effect on efficiency is present but less pronounced. This study advances smart governance theory by validating a capability-dominant model and positioning data governance as the enabling mechanism that transforms technical assets into accountable governance outcomes. Practically, it provides a strategic implementation roadmap emphasizing capability maturation, governance integration, and the intentional design of transparency as a measurable performance output.
Copyright (c) 2025 Anisur Rahman (Author)
SYSTEMATIC REVIEW OF SUSTAINABLE CIVIL ENGINEERING PRACTICES AND THEIR INFLUENCE ON INFRASTRUCTURE COMPETITIVENESS
This systematic review examines how sustainable civil engineering practices influence infrastructure competitiveness across the full project life cycle, integrating environmental performance with decision-relevant outcomes in cost, schedule, productivity, quality, resilience, finance, and user benefits. Following a PRISMA-aligned protocol, we searched multidisciplinary databases, screened records with dual independent reviewers, and extracted standardized data on sector, life-cycle stage, intervention type, comparators, metrics, and study quality. The final synthesis encompasses 115 peer-reviewed studies covering materials and low-carbon mix strategies, circular economy and end-of-life pathways, BIM and digital twins with IoT sensing, low-impact and off-site construction, green procurement and performance-based contracting, and risk-informed operations and maintenance. Across the corpus, sustainable practices were most consistently associated with life-cycle cost advantages and schedule reliability when boundaries extended beyond first cost, when QA and performance specifications were enforced, and when digital coordination reduced rework and variance. Modularization and lean logistics yielded notable on-site duration and waste reductions, while condition-based maintenance and resilience-aware planning improved availability and recovery. Financing signals, though smaller in volume, aligned with asset-level results, indicating modest cost-of-capital benefits where transparency and verification were strong. Neutral or mixed effects clustered where sustainability was specified late, supply chains lacked maturity, or digital adoption was not paired with clear roles and data governance. Overall, the evidence supports treating sustainability as a performance discipline that, when embedded early and measured on whole-life terms, reliably co-delivers competitiveness outcomes alongside environmental goals.
Copyright (c) 2022 Syed Zaki Uddin (Author)
HIGH-PERFORMANCE COMPUTING ARCHITECTURES FOR TRAINING LARGE-SCALE TRANSFORMER MODELS IN CYBER-RESILIENT APPLICATIONS
The exponential growth of transformer-based architectures has fundamentally reshaped the landscape of artificial intelligence, enabling breakthroughs across domains such as natural language processing, cybersecurity analytics, and autonomous decision systems. However, training and deploying these models at scale demand robust high-performance computing (HPC) infrastructures capable of managing massive computational loads, distributed data pipelines, and cyber-resilient workflows. This systematic review explores the convergence of HPC technologies and large-scale transformer model training with a particular focus on security and fault-tolerant applications. A total of 126 peer-reviewed studies published between 2018 and 2022 were analyzed using structured screening and thematic synthesis techniques. The review identifies emerging trends in GPU- and TPU-based parallelism, memory optimization strategies, model sharding techniques, and secure distributed training frameworks that enhance system resilience against data breaches and adversarial interference. Furthermore, the study discusses advances in federated learning architectures, hybrid cloud–edge HPC environments, and AI-driven workload orchestration that collectively contribute to cyber-resilient computational ecosystems. The findings highlight the increasing emphasis on integrating fault tolerance, encryption-aware resource allocation, and autonomous security auditing within large-scale model training workflows. This work provides a consolidated framework for understanding how high-performance computing infrastructures are evolving to support the scalability, reliability, and security of transformer models in mission-critical and cyber-sensitive environments.
Copyright (c) 2022 Md Mohaiminul Hasan, Md Muzahidul Islam (Author)
DATA-DRIVEN QUALITY ASSURANCE SYSTEMS FOR FOOD SAFETY IN LARGE-SCALE DISTRIBUTION CENTERS
Ensuring food safety within large-scale distribution centers has become a critical priority in modern supply chain management, particularly as global logistics networks grow increasingly complex and data-intensive. This study examines the design, implementation, and optimization of data-driven quality assurance (QA) systems that leverage advanced analytics, Internet of Things (IoT) sensors, and machine learning algorithms to monitor and control food safety parameters in real time. By integrating predictive data models with automated quality inspection frameworks, organizations can significantly reduce contamination risks, improve traceability, and maintain regulatory compliance across diverse storage and transportation environments. The study systematically reviews 112 peer-reviewed papers published between 2017 and 2022, identifying key technological trends such as blockchain-enabled traceability, AI-based anomaly detection, temperature and humidity monitoring via IoT networks, and cloud-based decision support systems for risk assessment. Findings reveal that data-driven QA architectures not only enhance operational transparency but also enable proactive responses to deviations in food quality, thereby minimizing waste and ensuring consumer safety. The paper further highlights the challenges associated with data integration, cybersecurity, and scalability when deploying such systems across multinational logistics networks. Ultimately, this review provides a comprehensive framework for developing resilient, intelligent, and adaptive QA systems that align with evolving global standards for food safety in large-scale distribution centers.
Copyright (c) 2022 Pankaz Roy Sarkar (Author)
THE ROLE OF CROSS-COUNTRY TRADE PARTNERSHIPS IN STRENGTHENING GLOBAL MARKET COMPETITIVENESS
This study examines the role of cross-country trade partnerships in strengthening global market competitiveness through an empirical and theoretical analysis covering the period 2016–2021. Using a quantitative research design that integrates static fixed-effects and dynamic autoregressive distributed lag (ARDL) error correction models, the study evaluates how the depth of trade partnerships, institutional quality, foreign direct investment (FDI) inflows, and trade openness collectively influence the Global Competitiveness Index (GCI). The results demonstrate that trade partnership depth has a significant and positive impact on competitiveness (β = 0.312, p < 0.01), indicating that economies with deeper, rule-based trade frameworks achieve greater productivity, innovation, and efficiency gains. Institutional quality is found to amplify this relationship by reinforcing regulatory stability, transparency, and enforcement capacity, thereby facilitating the effective transmission of trade-related benefits. The dynamic estimations reveal that while short-run effects are positive but modest, long-run relationships remain stable and substantial, confirming the delayed yet enduring impact of policy-driven trade integration. Furthermore, FDI inflows and export diversification are identified as key mediating variables that strengthen competitiveness by promoting technological diffusion and economic resilience. Regional analysis indicates that the European Union (EU) and ASEAN exhibit the strongest trade–competitiveness linkages due to institutional maturity and integrated governance, whereas the African Continental Free Trade Area (AfCFTA) shows emerging but moderate effects constrained by infrastructural and regulatory limitations. The study concludes that deep, inclusive, and institutionally anchored trade partnerships serve as the cornerstone of sustainable global competitiveness. It recommends that policymakers pursue comprehensive trade frameworks complemented by institutional reforms, innovation policies, and investment facilitation strategies to maximize the economic benefits of global integration. This research contributes to existing literature by providing empirical validation for the synergistic role of trade depth, institutional quality, and investment in shaping competitiveness, offering a replicable analytical model for future policy and academic applications.
Copyright (c) 2022 Md. Hasan Imam (Author)