International Journal of Scientific Interdisciplinary Research
The International Journal of Scientific Interdisciplinary Research (IJSIR) is a prestigious, peer-reviewed, open-access international journal devoted to advancing the frontiers of interdisciplinary scholarship. IJSIR provides a dynamic platform for researchers, academicians, scientists, practitioners, and policymakers from diverse fields to collaborate and share innovative ideas that transcend conventional disciplinary boundaries. The journal is dedicated to publishing cutting-edge, original research articles, critical reviews, and case studies that offer novel insights and practical relevance in a rapidly evolving global research environment. With a focus on fostering integrative and cross-disciplinary discourse, International Journal of Scientific Interdisciplinary Research (IJSIR) covers a broad spectrum of scientific domains, including but not limited to Physical and Life Sciences, Engineering, Environmental Science, Information Technology, Social Sciences, Economics, Education, and Healthcare. The journal encourages submissions that explore the convergence of knowledge across disciplines, offering transformative solutions to complex scientific, technological, and societal challenges.
Privacy-Preserving Federated Learning for Critical Infrastructure: A Systematic Review of Security Threat Models, Deployment Patterns, And Governance
This study presented a quantitative systematic synthesis of privacy-preserving federated learning research in critical infrastructure contexts by examining how security threat models, privacy mechanisms, deployment architectures, and governance controls were jointly represented and evaluated across the empirical literature. A total of 120 peer-reviewed and high-quality technical studies were coded and analyzed using structured extraction protocols that transformed heterogeneous experimental reports into comparable variables. Descriptive analysis showed that malicious-client and colluding-client threat models dominated the evidence base (approximately 55% and 32%, respectively), while availability-focused threats were reported in fewer than 20% of studies. Passive inference attacks were evaluated more frequently (67.5%) than active attacks (40.8%), with confidentiality emerging as the most frequently targeted security property (65.8%). In terms of privacy mechanisms, secure aggregation (50.8%) and differential privacy (45.0%) were the most prevalent, whereas cryptographic computation approaches (21.7%) and trusted execution techniques (15.8%) appeared less often and were associated with higher reported overhead. Deployment analysis indicated strong dominance of centralized aggregation (70.8%) and cross-silo regimes (60.0%), reflecting institutional coordination patterns typical of critical infrastructure systems. Governance reporting was uneven, with role definitions (55.8%) and access rules (59.2%) appearing more frequently than consent artifacts (27.5%) and accountability procedures (17.5%). Regression analysis demonstrated that active threat exposure and cryptographic or hybrid privacy mechanisms were positively associated with standardized overhead burden, while cross-silo deployment and higher governance maturity scores were negatively associated with both overhead and utility loss. Governance maturity and auditability indices achieved acceptable-to-strong internal consistency (Cronbach’s α ranging from 0.72 to 0.86) and showed statistically significant relationships with outcome stability. Configuration prevalence analysis revealed that a limited set of design patterns dominated the literature, most commonly combining client-centric threat models, baseline privacy mechanisms, centralized architectures, and limited governance instrumentation. Overall, the study provided a variable-driven quantitative account of prevailing configurations, measured trade-offs, and reporting gaps in privacy-preserving federated learning for critical infrastructure, offering an empirical foundation for structured comparison across technical and institutional dimensions.
Copyright (c) 2026 Istiaq Ahmed (Author)
Machine Learning Based Data Optimization of Merchant Onboarding Processes in Fintech Ecosystems: A Time-Series and Process-Mining Study
This study examined how machine learning–based data optimization and process analytics influence merchant onboarding performance within a FinTech ecosystem by integrating supervised modeling, process mining, and time-series evaluation. Using a retrospective dataset of 3,200 onboarding cases observed across 26 operational weeks, the analysis reconstructed event-driven workflows and quantified relationships between data quality, workflow behavior, and decision outcomes. Descriptive results showed moderate data missingness (mean = 7.8%), high median document completeness (92%), and substantial operational heterogeneity reflected in manual-touch intensity (41.7%) and escalation rate (9.6%). Median onboarding cycle time was 24.7 hours, although the mean reached 38.4 hours due to delay-heavy exception cases, and waiting time concentration averaged 63.2%, indicating that queue-related idle time constituted the majority of total duration. Logistic regression results demonstrated that the Data Quality Optimization Index significantly increased approval likelihood (OR = 1.65, p < .001), while Document Risk Intensity (OR = 0.72, p < .001) and Workflow Friction (OR = 0.66, p < .001) significantly reduced approval probability. Linear regression analysis further showed that Workflow Friction (+12.1 hours, p < .001) and Operational Effort (+9.3 hours, p < .001) substantially increased onboarding cycle time, whereas improved data quality reduced processing duration (−6.8 hours, p < .001). The final approval model achieved a McFadden pseudo R² of 0.29, and the cycle-time model explained 42% of duration variance (R² = 0.42; RMSE = 28.9 hours), with coefficient stability confirmed across rolling time-window robustness checks. Overall, the findings demonstrated that onboarding performance was systematically shaped by measurable data conditions and trace-level workflow characteristics rather than solely by merchant risk segmentation. By linking temporal KPI shifts with process variants and predictive constructs, this study provided an integrated quantitative framework for evaluating data-driven optimization in digital merchant onboarding systems.
Copyright (c) 2025 Iftekhar Ahmed (Author)
Machine Learning–Based Transaction Risk Scoring Models for Financial Compliance Monitoring in Foreign Exchange Operations
This study provided a quantitative cross-study synthesis of machine learning–based transaction risk scoring models for financial compliance monitoring in foreign exchange operations, with emphasis on measurable modeling practices, evaluation rigor, and governance instrumentation. A total of 124 analytic records derived from 89 publications were coded using a structured extraction protocol that converted heterogeneous reporting into standardized variables across model family, feature construction, validation design, labeling strategy, evaluation metrics, and governance controls. Descriptive results showed that ensemble models were the most frequently evaluated approach (44.4%), followed by logistic regression and generalized linear models (37.1%), decision tree models (33.9%), neural architectures (29.8%), and unsupervised or semi-supervised methods (26.6%). Customer-profile variables (69.4%), geographic corridor indicators (62.1%), and temporal aggregation features (57.3%) were the most commonly engineered feature groups, while network-based variables appeared in 41.9% of records. Evaluation practices were dominated by discrimination metrics (82.3%), with lower reporting of ranking metrics (61.3%), calibration measures (34.7%), and cost-sensitive analyses (28.2%). Governance and auditability constructs were underreported, with access control indicators documented in 29.8% of records and traceability artifacts in 22.6%. Reliability testing demonstrated strong internal consistency for governance maturity (α = 0.86) and documentation completeness (α = 0.84) indices. Logistic regression analysis showed that ensemble models (OR = 2.27, p = 0.008), neural models (OR = 1.99, p = 0.041), and out-of-time validation (OR = 2.83, p = 0.004) were significantly associated with high predictive performance reporting. Linear regression indicated that operational studies were strongly associated with higher governance maturity scores (β = 1.12, p < 0.001). Overall, the findings indicated that methodological rigor in validation design and feature construction was more consistently associated with reported performance gains than model family alone, while governance instrumentation and operational alignment remained uneven across the FX compliance risk scoring literature.
Copyright (c) 2026 Rukaiya Khatun Moury (Author)
AI-Enabled Decision Support Systems for Service Operations an Analytical Modeling and IT Strategy Framework
This study examined the quantitative relationships among AI-enabled decision support system (AI-DSS) capability, IT strategy alignment, and measurable service operations performance using unit-level data from a data-intensive service organization. A cross-sectional explanatory design was applied with structured retrospective extraction of operational KPIs and AI-DSS system log indicators, combined with survey-based measurement of IT strategy alignment dimensions. The final dataset included 52 operational service units and 198 valid survey responses retained after screening from an initial pool of 214 responses (92.5% retention). AI-DSS capability was operationalized as a composite index (0–100) supported by indicators such as system-use frequency, recommendation viewing rate, decision latency, and forecasting accuracy. IT strategy alignment was measured using a composite index (1–5) based on data integration maturity, interoperability, governance strength, and workflow embedding. Service operations performance was measured through unit-level KPIs including average waiting time, service-level attainment, cost per transaction, and abandonment rate. Descriptive results indicated relatively high AI-DSS capability (M = 71.6, SD = 11.4) and moderate-to-high IT strategy alignment (M = 3.84, SD = 0.52), with the greatest dispersion observed in decision latency (M = 26.4 minutes, SD = 13.2). Regression analysis showed that AI-DSS capability was significantly associated with lower waiting time (B = -0.021, p = 0.001), higher service-level attainment (B = 0.142, p = 0.005), lower cost per transaction (B = -0.016, p = 0.002), and lower abandonment rate (B = -0.071, p = 0.001). IT strategy alignment demonstrated significant direct effects on waiting time (B = -0.118, p = 0.025), service-level attainment (B = 1.87, p = 0.004), and cost per transaction (B = -0.091, p = 0.044). Moderation analysis indicated significant interaction effects for waiting time (B = -0.0062, p = 0.005), service-level attainment (B = 0.041, p = 0.020), and cost per transaction (B = -0.0049, p = 0.009), confirming that IT strategy alignment strengthened the operational impact of AI-DSS capability. Overall, the findings supported an integrated analytical modeling and IT strategy framework in which AI-DSS capability functioned as a direct performance driver and IT strategy alignment acted as both an independent predictor and an amplifying condition for operational outcomes.
Copyright (c) 2026 Tanjina Binte Sohrab (Author)
Quantitative AI-Based Load-Flow and Fault-Prediction Modeling for Reliability Enhancement in Electrical Distribution Networks
This study examined an integrated AI-based framework for load-flow estimation and fault prediction to enhance reliability in electrical distribution networks. A quantitative design was applied using operational data from 12 medium-voltage feeders over an 18-month period, comprising 104,832 operating snapshots collected at 15-minute resolution and 286 confirmed fault events. Feeder sizes ranged from 48 to 137 buses, with topology depth varying from 6 to 18 levels. Descriptive results showed an overall mean voltage magnitude of 0.984 p.u. (SD = 0.031), with a minimum observed value of 0.901 p.u. and a maximum of 1.067 p.u. Mean feeder loading was 62.8% of rated capacity, with peak loading reaching 118.4%. Outage duration averaged 58.6 minutes (SD = 49.8) and customer interruptions ranged from 14 to 4,320 per event. Load-flow regression models indicated that measurement density significantly reduced voltage prediction error (β = –0.41, p < 0.001), while topology depth increased estimation error (β = 0.28, p = 0.002), producing adjusted R² values of 0.64 for voltage error and 0.58 for branch current error. Fault occurrence modeling demonstrated that environmental disturbance severity (β = 0.44, p < 0.001), electrical stress exposure (β = 0.37, p < 0.001), and asset vulnerability (β = 0.29, p = 0.004) significantly increased fault probability, with Nagelkerke R² = 0.52. Fault location accuracy improved significantly when load-flow-derived stress features were included (β = 0.35, p = 0.001). Reliability-linked regression results showed that improved fault localization reduced restoration time (β = –0.38, p < 0.001), and higher predicted fault probability increased interruption duration (β = 0.26, p = 0.009). Robustness testing under measurement perturbation reduced model explanatory power by less than 6%, confirming stability. Overall, the integrated framework demonstrated statistically significant relationships between state estimation accuracy, fault prediction performance, and reliability outcomes.
Copyright (c) 2026 Md. Sultan Mahamud (Author)
Electrical Engineering Approaches for AI-Enabled Autonomous Robotics in U.S. Industrial and Defense Applications
This quantitative study examined how electrical engineering approaches influenced the performance of AI-enabled autonomous robotics in U.S. industrial and defense applications. A total of 220 valid responses were analyzed, including 112 industrial (50.9%) and 108 defense (49.1%) system cases, ensuring balanced cross-domain representation. Six electrical engineering constructs were evaluated as predictors of autonomy performance: sensor system performance, embedded computing efficiency, power electronics and energy stability, control and actuation accuracy, communication system reliability, and safety and reliability engineering effectiveness. Descriptive results indicated that safety and reliability engineering demonstrated the highest consistency (M = 4.12, SD = 0.49), followed by control and actuation accuracy (M = 3.94, SD = 0.55) and sensor system performance (M = 3.82, SD = 0.61). Embedded computing efficiency (M = 3.56, SD = 0.74) and power stability (M = 3.41, SD = 0.79) showed the greatest variability across systems. Reliability analysis confirmed strong internal consistency across constructs, with Cronbach’s alpha coefficients ranging from 0.842 to 0.926. Multiple regression analysis demonstrated that the combined model significantly predicted autonomy performance (R² = 0.659, Adjusted R² = 0.647, F = 67.83, p < .001). Embedded computing efficiency (β = 0.338, p < .001) and sensor system performance (β = 0.301, p < .001) were the strongest predictors. Power stability (β = 0.256, p < .001) and control accuracy (β = 0.213, p < .001) also significantly contributed. Communication reliability showed a moderate effect (β = 0.158, p = .001) and was stronger in defense contexts, while safety engineering provided a smaller but significant stabilizing influence (β = 0.109, p = .014). Findings confirmed that electrical subsystem performance significantly explained variation in AI-enabled autonomy outcomes across both domains.
Copyright (c) 2026 Shamsul Arifeen (Author)
AI-Based Revenue Leakage Detection Models Using Transaction-Level Financial Data: A Review
AI-based revenue leakage detection using transaction-level financial data has gained importance due to increasing pricing complexity, automated billing processes, and high-volume digital transactions. This study quantitatively examined four detection constructs—pricing compliance detection, authorization integrity, temporal anomaly identification, and adjustment behavior monitoring—and evaluated their relationships with overall revenue leakage detection effectiveness. A structured survey design was applied, and responses were collected from 210 participants across transaction-intensive industries, including telecommunications (22.4%), e-commerce/retail (21.0%), healthcare/insurance (18.6%), and financial services (17.1%). Most respondents reported direct involvement in revenue-cycle activities (61.0%), and 75.7% reported intermediate-to-advanced familiarity with analytics and AI tools. Descriptive results indicated consistently positive construct scores, with mean values of 4.12 (SD = 0.61) for pricing compliance detection, 4.08 (SD = 0.65) for authorization integrity, 3.94 (SD = 0.70) for temporal anomaly identification, 3.89 (SD = 0.74) for adjustment behavior monitoring, and 4.05 (SD = 0.63) for leakage detection effectiveness. Reliability analysis confirmed strong internal consistency, with Cronbach’s alpha values ranging from 0.81 to 0.88 across constructs. Multiple regression analysis demonstrated that the predictors jointly explained substantial variance in leakage detection effectiveness (R² = 0.62; adjusted R² = 0.61; F = 83.40; p < .001). Pricing compliance detection produced the strongest standardized effect (β = 0.38; t = 6.52; p < .001), followed by authorization integrity (β = 0.29; t = 5.11; p < .001), adjustment behavior monitoring (β = 0.21; t = 3.88; p < .001), and temporal anomaly identification (β = 0.17; t = 3.09; p = .002). Multicollinearity remained acceptable (VIF = 1.41–1.72). Hypothesis testing supported 4 of 5 hypotheses (80%), with one interaction hypothesis rejected (β = 0.06; p = .118). Overall, the findings demonstrated that effective transaction-level revenue leakage detection was strongly associated with pricing integrity, governance controls, temporal monitoring, and adjustment analytics.
AI-Driven Change Detection Using SAR, LIDAR, And Sentinel-2 Data for Landslide Monitoring and Disaster Early Warning Systems
This study addressed the problem that many AI-driven landslide early-warning pipelines generate technically strong change maps but lack auditable, decision-aligned evidence products that stakeholders can trust and act on consistently under uncertainty. The purpose was to evaluate, in a quantitative cross-sectional, case-based design, whether multi-sensor AI change evidence (SAR, LiDAR, Sentinel-2) and socio-technical decision constructs jointly explain perceived Early Warning Effectiveness (EWE). Data were collected from a professional sample of N = 162 screened-valid respondents drawn from operational roles (disaster management 31.5%, GIS/remote sensing 27.8%, engineering 22.2%, planning 18.5%; mean experience 8.6 years) evaluating the case outputs and decision readiness constructs. Key variables included Perceived AI Performance (PAI), Interpretability (INT), Trust in Alerts (TRU), Decision Confidence (DCF), sensor contribution perceptions (PSC_SAR, PSC_LiDAR, PSC_S2), and the fused Multi-Sensor Change Evidence Score (MSCES) as predictors, with EWE as the main dependent variable. The analysis plan applied descriptive statistics, reliability testing (Cronbach’s α), Pearson correlations, and hierarchical regressions comparing single-sensor baselines to fusion and a full decision-aligned model, plus threshold sensitivity and agreement/conflict diagnostics. Construct means were high (PAI M = 4.21, INT 4.02, TRU 4.08, DCF 4.16, EWE 4.12) with strong reliabilities (α = 0.83–0.90). Correlations supported the hypothesized pathway (PAI–EWE r = 0.68, TRU–EWE 0.62, INT–EWE 0.59, all p < .001). Regression results showed LiDAR-only (R² = 0.31) exceeded SAR-only (0.24) and Sentinel-2-only (0.18), while MSCES fusion improved explanatory power (R² = 0.46, β = 0.68, p < .001). In the full model, explained variance increased to R² = 0.62 (Adj. 0.61), with significant unique effects for PAI (β = 0.34), TRU (β = 0.29), MSCES (β = 0.27) and INT (β = 0.18). Operationally, the case pipeline produced 128 mapped change objects, with 46 high-priority zones; agreement classes showed higher confidence when evidence converged (3-sensor agreement 38.4%, confidence M = 4.34 vs single-sensor-only 19.9%, M = 3.61). Trigger reliability further demonstrated robustness: triggered zones dropped from 46 (MSCES 0.60) to 24 (0.80) while a stable core of 24 zones persisted (TSI 0.52 at 0.60, 1.00 at 0.80). These findings imply that deployable landslide early warning should prioritize multi-sensor fusion plus interpretable, graded outputs and threshold-stability reporting to reduce alarm fatigue, improve accountability, and strengthen calibrated trust in alerts across agencies.
Copyright (c) 2023 Ratul Debnath, Aditya Dhanekula (Author)
Predictive Risk Modeling and Quantitative Analysis of Microbial Contamination in Hospital Textiles
This quantitative observational study examined microbial contamination in hospital textiles using predictive risk modeling and multivariable statistical analysis to identify determinants of contamination across textile categories, operational pathways, and clinical contexts. A cross-sectional analytic design was applied in a multi-unit hospital setting, and a total of 360 reusable textile items were sampled, including bed linens (40.0%), staff uniforms (25.0%), towels (20.0%), and privacy curtains (15.0). Items were collected from both high-acuity units (35.0%) and general wards (65.0%) and were linked to structured metadata describing exposure duration, contact density proxies, handling and transport conditions, laundering characteristics, storage duration, and textile reuse indicators. Microbial contamination outcomes were measured as microbial burden and contamination positivity, with positivity observed in 46.5% of linens, 48.6% of towels, 34.4% of uniforms, and 29.6% of curtains. Descriptive analysis demonstrated highly skewed contamination distributions, with median microbial burden values ranging from 2.0 to 3.4 units across textile categories and higher exposure duration observed for curtains and uniforms compared with linens and towels. Multivariable regression analysis showed that textile category, exposure duration, contact density, ward acuity, handling pathway quality, laundering quality, and post-laundering storage time were independently associated with contamination outcomes after adjustment for clustering by ward and laundering batch. In the microbial burden model, towels (adjusted estimate = 1.35, p < 0.001) and linens (1.28, p = 0.001) exhibited higher burden relative to uniforms, while curtains showed lower burden (0.82, p = 0.041). In the contamination positivity model, towels (adjusted odds ratio = 1.84, p = 0.009) and linens (1.62, p = 0.019) demonstrated higher odds of positivity. High-acuity wards were associated with increased contamination outcomes in both models. Laundering quality demonstrated a protective association with microbial burden (0.88, p < 0.001) and positivity (0.86, p = 0.003), while longer post-laundering storage increased contamination risk. Overall, the findings demonstrated that hospital textile contamination followed systematic and quantifiable patterns shaped by textile type, exposure dynamics, operational handling, laundering performance, and clinical context, supporting the use of multivariable and hierarchical modeling approaches for contamination risk assessment in healthcare textile systems.
Copyright (c) 2026 Amjad Hossain, Md Tahmidul Islam (Author)
Risk Mitigation and Resilience Modeling for Consumer Distribution Networks During Demand Shocks: A Quantitative Stochastic Optimization and Scenario Analysis Study
This study investigated risk mitigation and resilience modeling for consumer distribution networks operating under demand shock conditions using a quantitative stochastic optimization and scenario-based analysis framework. A multi-echelon distribution network was evaluated under a structured set of baseline and stress demand scenarios to examine how mitigation policies and resilience-oriented capabilities influenced operational performance. The analytical dataset comprised 1,520 scenario-level network observations, balanced across four service regions and essential and non-essential product segments. Descriptive results indicated a mean service continuity index of 0.81 with observable right-skewness, while the unmet demand ratio averaged 0.18, reaching a maximum of 0.61 under severe stress scenarios. Cost escalation exhibited the greatest variability, with a mean increase of 22.4% and a maximum observed value of 78.6%, confirming strong tail sensitivity in economic outcomes. Reliability analysis of composite constructs demonstrated acceptable to excellent internal consistency, with Cronbach’s alpha values ranging from 0.85 to 0.93, supporting measurement validity prior to inferential testing. Regression analysis showed that resilience capability (β = 0.421, p < 0.001) and robustness stability (β = 0.276, p < 0.001) were statistically significant predictors of performance outcomes after controlling for demand volatility, utilization intensity, and network scale. Inclusion of resilience constructs improved explanatory power, increasing adjusted R² from 0.241 in the baseline model to 0.318 in the extended specification. Segment-level analysis revealed that essential goods achieved higher service continuity (0.86) and lower unmet demand (0.13) than non-essential goods, albeit with higher average cost escalation (26.9% versus 17.1%). Robustness testing using out-of-sample and segmented evaluations confirmed stability of coefficient direction and significance across alternative scenario partitions. Overall, the findings provided quantitative evidence that resilience and robustness functioned as measurable attributes that systematically shaped distribution network performance under demand shock conditions.
Copyright (c) 2025 Md Shahab Uddin, Aditya Dhanekula (Author)
ERP Modernization Outcomes in Cloud Migration: A Meta-Analysis of Performance and Total Cost of Ownership (TCO) Across Enterprise Implementations
Enterprise ERP modernization through cloud migration is promoted as a route to higher performance and lower total cost of ownership (TCO), yet enterprises often experience mixed results and hidden cost leakage. To address this problem, the study measured post-migration performance and TCO and modeled drivers of both outcomes. A quantitative, cross-sectional, case-based design synthesized survey data from N = 210 stakeholders across six cloud-migration cases (SaaS 56.2%, hybrid 43.8; phased 67.1%, big-bang 32.9%). Independent variables were cloud migration readiness, implementation quality, governance and risk management maturity, change management effectiveness, and vendor or partner support; dependent variables were ERP performance outcomes and TCO outcomes, decomposed into visible/direct and hidden/indirect components. Analysis began with Cronbach’s alpha to confirm scale consistency (readiness α = 0.89; implementation quality α = 0.91; governance α = 0.87; change management α = 0.90; vendor support α = 0.88; performance α = 0.93; TCO α = 0.92), then applied descriptive statistics, Pearson correlations, and multiple regression, with a Cloud Migration Footprint Index (CMFI) and a Performance and TCO Tradeoff Map. Results showed performance improvement (M = 3.94, SD = 0.62) and TCO improvement (M = 3.62, SD = 0.66), with visible benefits (M = 3.76, SD = 0.71) exceeding hidden cost pressure (M = 3.41, SD = 0.78) by 0.35 (HCR = 0.91). Performance correlated most with implementation quality (r = 0.68) and change management (r = 0.63), and the performance regression explained 61% of variance, F(5,204) = 64.22, p < .001, led by implementation quality (β = 0.34) and change management (β = 0.26). For TCO, the regression explained 47% of variance, F(5,204) = 35.89, p < .001, with governance maturity strongest (β = 0.29) and change management not significant (p = .074). High-footprint cases (CMFI M = 0.67) reported higher performance (M = 4.08) but higher hidden-cost pressure (M = 3.52). Overall, 41.4% were modernization winners, while 22.9% showed performance-first cost leakage, implying that governance and lifecycle controls protect cloud ERP business cases while sustaining gains.
Copyright (c) 2021 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
A Meta-Analysis of OSHA Safety Training Programs and their Impact on Injury Reduction and Safety Compliance in U.S. Workplaces
This study addresses the gap between mandated OSHA aligned safety training and measurable safety outcomes in high exposure enterprise workplaces, where injuries and near misses persist despite formal programs. The purpose was to quantify how OSHA training exposure and perceived quality relate to safety compliance and injury reduction indicators, and to test whether compliance is the pathway through which training improves outcomes. Using a quantitative cross sectional, case-based survey design, data were collected from 312 employees across workplace cases in construction, manufacturing, and healthcare; multivariate models used listwise deletion, yielding N = 306. Key variables were OSHA Training, Safety Compliance, and Injury Reduction Indicators. Analysis followed a plan of data screening, reliability and construct checks (Cronbach alpha, KMO, Bartlett, factor loadings), then descriptive statistics, Pearson correlations, and hierarchical regression to evaluate direct and mediation consistent patterns. Missing data were low (1.8%) and Harman single factor variance was 32.4%, reducing common method bias concerns. Respondents reported above midpoint scores for training (M = 3.84, SD = 0.62), compliance (M = 3.92, SD = 0.58), and injury reduction (M = 3.67, SD = 0.60), with strong reliability (alpha = 0.91, 0.88, 0.86). Training correlated with compliance (r = 0.62) and injury reduction (r = 0.49), and compliance correlated with injury reduction (r = 0.56), all p < .001. In regression, training predicted injury reduction (beta = 0.49, R2 = 0.24) and predicted compliance (beta = 0.62, R2 = 0.39); when both predictors were entered, compliance remained stronger (beta = 0.41) while training attenuated but stayed significant (beta = 0.24), improving fit to R2 = 0.38, consistent with partial mediation. Case pattern summaries converged with the model, with high training groups reporting higher compliance and injury reduction than low training groups. Implications are that organizations should prioritize frequent, job relevant, practice-oriented OSHA training and remove operational pressures that undermine compliance, because compliance is the proximate lever translating training into safer outcomes.
Copyright (c) 2024 Jahangir Shekh, Hammad Sadiq (Author)
IMPROVING PROJECT LIFECYCLE MANAGEMENT (PLM) EFFICIENCY WITH CLOUD ARCHITECTURES AND CAD INTEGRATION AN EMPIRICAL STUDY USING INDUSTRIAL CAD REPOSITORIES AND CLOUD‑NATIVE WORKFLOWS
This empirical quantitative study examined how cloud architectures and CAD–PLM integration mechanisms related to Project Lifecycle Management (PLM) efficiency using trace evidence from industrial CAD repositories and cloud-native workflows. A retrospective longitudinal, multi-case design was applied to linked event logs spanning CAD artifact histories, PLM change and release records, and workflow telemetry. The final analytic dataset comprised 12 projects, 4,860 engineering change objects, 1,140 release packages, 98,420 CAD artifacts, and 312,775 workflow job instances (numerical findings as summarized in the descriptive results). PLM efficiency was operationalized with governance-aligned event boundaries and distribution-focused metrics, including engineering change lead time, release cycle time, workflow throughput, workflow success rate, tail-delay prevalence, and rework proxies derived from reopened changes, repeated short-window edits, repeated packaging attempts, and retry cascades. Descriptive results indicated heavy-tailed timing behavior: engineering change lead time exhibited a median of 14.6 days with a 90th percentile of 42.0 days, and release cycle time exhibited a median of 6.2 days with a 90th percentile of 18.5 days. Workflow operations showed a mean throughput of 1,360 jobs/day and an overall workflow success rate of 93.8%, while exception clusters were concentrated in translation and packaging stages. Rework signals were nontrivial, with reopened changes averaging 8.4% and repeated edits within short windows averaging 20.2% across change cases. These findings supported a measurement-based interpretation of PLM efficiency as a coupled outcome shaped by propagation timeliness, integrity preservation, execution reliability, product structure complexity, and collaboration concurrency, with tail delays and rework emerging as central stability risks alongside average speed.
Copyright (c) 2025 Md Syeedur Rahman (Author)
CYBERSECURE DOCUMENTATION AND RECORD-KEEPING PROTOCOLS FOR SAFEGUARDING SENSITIVE FINANCIAL INFORMATION ACROSS BUSINESS OPERATIONS
Cybersecure documentation and record-keeping gaps in enterprises can expose sensitive financial information to unauthorized access, alteration, loss, and weak audit defensibility, especially when records circulate through shared repositories, email workflows, line-of-business applications, and third-party cloud services. The purpose of this study was to quantify whether cybersecure documentation and record-keeping protocol maturity strengthens safeguarding across business operations. A quantitative, cross-sectional, case-based design used a 5-point Likert survey in cloud and enterprise cases. After screening, 210 valid responses were analyzed, representing Finance/Accounting (38.6%), Procurement and AP-AR (22.4%), Payroll and HR finance (14.8%), Internal audit and compliance (12.9%), and IT or security support (11.3%). Predictors were six protocol domains: Access Control and Authorization, Encryption and Secure Storage, Audit Trails and Monitoring, Retention and Secure Disposal, Policy Enforcement and Training, and Incident Response and Recoverability; the outcome was Safeguarding Effectiveness (confidentiality, integrity, availability, and audit defensibility). The analysis plan used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression, supported by Spearman correlations and hierarchical regression. Results showed moderate-to-high protocol adoption (M = 3.74, SD = 0.62) and high safeguarding effectiveness (M = 3.79, SD = 0.58), with reliable constructs (α = 0.81 to 0.90). All protocol domains correlated positively with safeguarding (r = .46 to .62, all p < .001), with Policy Enforcement and Training strongest (r = .62, p < .001). The regression model was significant (F(6,203) = 46.21, p < .001) and explained 58% of safeguarding variance (R² = .58; Adjusted R² = .57); Policy Enforcement and Training was the strongest unique predictor (β = .28, p < .001), while Retention and Secure Disposal was weaker and not significant at 0.05 (β = .09, p = .078). Robustness checks remained supportive (Spearman ρ = .44 to .60, all p < .001; ΔR² = .52, p < .001). Implications suggest prioritizing enforceable training and policy reinforcement, least-privilege access governance, tamper-evident monitoring, and tested recovery procedures, while improving retention and defensible disposal to close control maturity gaps across hybrid cloud and on-prem environments.
Copyright (c) 2025 Faysal Khan, Tahmina Akter Bhuya Mita (Author)
DIGITAL TWIN–ENABLED OPTIMIZATION OF ELECTRICAL, INSTRUMENTATION, AND CONTROL ARCHITECTURES IN SMART MANUFACTURING AND UTILITY-SCALE SYSTEMS
This quantitative, multi-site study examined whether digital twin–enabled optimization was associated with measurable improvements in electrical, instrumentation, and control (EIC) architecture performance across smart manufacturing and utility-scale systems. The study used a longitudinal, multilevel panel design with clustered observations at asset, system, and site levels. The final analytic sample included 18 cases (10 manufacturing plants and 8 utility-scale segments), 96 systems, and 1,842 assets observed over 180 days, yielding 17,280 system-days after data coverage screening. Composite indices representing observability, synchronization quality, integration readiness, and data integrity demonstrated acceptable internal consistency (α = 0.78–0.88). Multilevel regression results indicated that optimization exposure was associated with lower downtime and improved continuity. Downtime decreased by 0.74 hours per system-month (95% CI −1.05 to −0.43, p < 0.001) and availability increased by 1.9 percentage points (95% CI 1.1 to 2.7, p < 0.001). Efficiency outcomes improved, with normalized loss intensity decreasing by 3.6% (95% CI −5.2 to −2.0, p < 0.001) and voltage deviation indices decreasing by 4.1% (95% CI −6.8 to −1.4, p = 0.003). Integrity outcomes improved, as interruption rates decreased (IRR = 0.86, 95% CI 0.79 to 0.94, p = 0.001) and misoperation-related events declined (IRR = 0.81, 95% CI 0.70 to 0.94, p = 0.006). Data quality improved, with completeness increasing by 1.5 percentage points (95% CI 0.9 to 2.1, p < 0.001) and alignment issue rates decreasing by 0.8 percentage points (95% CI −1.2 to −0.4, p < 0.001). Domain moderation indicated stronger downtime reductions in manufacturing and stronger integrity gains in utilities. Mediation evidence suggested that observability and synchronization quality explained a meaningful portion of reliability and integrity improvements. These findings establish digital twin–enabled optimization as a critical lever for architectural resilience, providing a data-driven framework for scaling EIC systems in high-criticality environments.
Copyright (c) 2025 Tasnim Kabir (Author)
PREDICTIVE ANALYTICS AS A STRATEGIC TOOL FOR FINANCIAL FORECASTING AND RISK GOVERNANCE IN U.S. CAPITAL MARKETS
This study addresses a persistent problem in U.S. capital-markets organizations: even with rapid adoption of predictive analytics, many enterprises still struggle to translate analytics investments into consistently accurate, decision-ready financial forecasts and stronger risk governance. The purpose was to quantify how Predictive Analytics Capability (PAC) influences Financial Forecasting Performance (FFP) and Risk Governance Effectiveness (RGE) within cloud-enabled and enterprise analytics cases. Using a quantitative cross-sectional, case-based design, data were collected from professionals working in enterprise finance and risk functions across multiple organizational cases; after removing incomplete responses (n = 18) and low-quality response patterns (n = 6), the final analytic sample was N = 240 respondents from forecasting/finance (34.6%), risk management (28.8%), compliance and governance (18.3%), analytics/quant teams (12.1%), and audit/controls (6.2%). Key variables were PAC as the independent variable, FFP and RGE as dependent variables, with role level and years of experience treated as controls. The analysis plan included descriptive statistics, reliability testing, Pearson correlations, and multiple regression with model diagnostics. Descriptive results indicated moderately high levels on a 1–5 Likert scale: PAC M = 3.87 (SD = 0.61), FFP M = 3.74 (SD = 0.66), and RGE M = 3.79 (SD = 0.63). Measurement reliability was strong (PAC α = 0.89; FFP α = 0.87; RGE α = 0.90). Correlation results supported the hypothesized relationships: PAC was strongly associated with FFP (r = 0.61, p < .001) and with RGE (r = 0.58, p < .001), while FFP was also positively related to RGE (r = 0.55, p < .001). Regression analyses reinforced the headline findings: PAC was the dominant predictor of FFP (B = 0.68, β = 0.59, p < .001) in a significant model (F(3,236) = 56.21, p < .001; R² = 0.42). For RGE, both PAC (B = 0.38, β = 0.33, p < .001) and FFP (B = 0.41, β = 0.37, p < .001) contributed unique explanatory power in a significant model (F(4,235) = 49.17, p < .001; R² = 0.46), with controls remaining non-dominant. These results imply that enterprises can strengthen risk governance not only by adopting predictive tools, but by building end-to-end analytics capability and operationalizing forecast outputs into governance workflows through validation, monitoring, and escalation practices.
Copyright (c) 2022 Md. Towhidul Islam, Alifa Majumder Nijhum, Mst. Shahrin Sultana (Author)
ADVANCED ENGINEERING MATERIALS AND PERFORMANCE-BASED DESIGN FRAMEWORKS FOR RESILIENT RAIL-CORRIDOR INFRASTRUCTURE
This study addresses a persistent problem in rail corridor infrastructure: service disruptions and accelerated asset deterioration reduce reliability, yet decision makers often lack quantitative evidence showing how advanced engineering materials and performance-based design governance jointly translate into resilience outcomes. The purpose was to test whether Advanced Engineering Materials Integration (AEMI) and Performance Based Design Maturity (PBDM) predict Rail Corridor Resilience Performance (RCRP) using a quantitative, cross sectional, case-based design across enterprise rail projects. Data were collected from 212 rail engineering and operations professionals (mean experience 9.7 years, SD 4.8), and 83.0 percent reported direct involvement in the studied corridor cases. Measures showed strong reliability: AEMI (8 items, Cronbach alpha 0.86), PBDM (8 items, alpha 0.88), and RCRP (8 items, alpha 0.84). Descriptively, respondents reported moderate to high implementation (AEMI M 3.68, SD 0.59; PBDM M 3.74, SD 0.63) and comparatively higher resilience performance (RCRP M 3.81, SD 0.56); durability improvement was the strongest AEMI facet (M 3.92, SD 0.71), while restoration time target integration was the weakest PBDM facet (M 3.41, SD 0.83). The analysis plan applied descriptive statistics, internal consistency testing, Pearson correlations, and multiple regression with controls for perceived hazard exposure severity and asset age or condition pressure, plus an interaction term. Correlations supported the hypothesized relationships (AEMI to RCRP r 0.58, PBDM to RCRP r 0.62, AEMI to PBDM r 0.55; all p < .001). In regression, AEMI (beta 0.28, p < .001) and PBDM (beta 0.39, p < .001) jointly explained 49 percent of variance in RCRP (R2 0.49, Adj. R2 0.48); adding controls improved fit to R2 0.52 and effects remained significant (AEMI beta 0.25; PBDM beta 0.36; both p < .001). The AEMI by PBDM interaction was positive but small (beta 0.10, p = .031), indicating that performance-based maturity amplifies the resilience payoff of advanced materials. These results suggest procurement and standards teams should institutionalize performance verification workflows and data logging to sustain long term gains. Implications are that corridor programs should pair materials upgrades with verifiable performance requirements, especially restoration time criteria, to maximize resilience returns and support evidence-based capital planning.
Advanced Engineering Materials Integration
Copyright (c) 2025 Hammad Sadiq, Md Sarwar Hossain Shuvo (Author)
QUANTITATIVE STUDY ON MACHINE LEARNING-BASED INDUSTRIAL ENGINEERING APPROACHES FOR REDUCING SYSTEM DOWNTIME IN U.S. MANUFACTURING PLANTS
This study addresses the problem that unplanned equipment downtime continues to erode productivity and maintenance budgets in smart manufacturing, yet many organizations implement machine learning solutions without clear evidence about which capability mix most strongly translates analytics into measurable downtime reduction. The purpose of the study was to quantify how machine learning enabled industrial engineering capabilities influence downtime reduction and to identify the most influential predictors that organizations should prioritize. Using a quantitative, cross sectional, case-based design, data were collected from manufacturing plant cases operating enterprise and shop floor environments that typically integrate CMMS, SCADA, and ERP workflows. The final sample included 214 usable respondent cases from U.S. plants. Key variables included predictive maintenance capability, monitoring and anomaly detection capability, data quality and availability, integration capability across maintenance and production systems, workforce readiness, and management support, with downtime reduction as the dependent outcome measured as a composite Likert 1 to 5 index capturing reductions in downtime frequency and duration and faster response and recovery. The analysis plan applied descriptive statistics, reliability testing, Pearson correlation analysis, and multiple regression to estimate unique effects while controlling for overlap among predictors. Reliability was acceptable to strong, including downtime reduction (Cronbach alpha = 0.88). Descriptive results indicated moderate to high implementation levels (grand mean M = 3.62, SD = 0.71) and moderate perceived downtime reduction (M = 3.59, SD = 0.72). Correlations showed that downtime reduction was positively associated with all predictors (r range = 0.44 to 0.61, p < .01), with the strongest relationships observed for predictive maintenance (r = 0.61) and integration capability (r = 0.58). In the multivariate model, the predictors collectively explained 56% of the variance in downtime reduction (R2 = 0.56; adjusted R2 = 0.55; F(6,207) = 44.19, p < .001). The headline findings were that predictive maintenance (beta = 0.29, p < .001) and integration capability (beta = 0.25, p < .001) were the strongest unique predictors, followed by management support (beta = 0.18, p = .001), monitoring and anomaly detection (beta = 0.14, p = .010), and data quality (beta = 0.11, p = .034), while workforce readiness was not statistically significant after controlling for the other factors (beta = 0.07, p = .157). These results imply that organizations seeking tangible downtime reduction should prioritize robust predictive maintenance routines and end to end workflow integration that converts model outputs into executed maintenance actions, reinforced by leadership governance and sustained investment in data readiness.
Copyright (c) 2024 Shoflul Azam Tarapder, Md. Al Amin Khan (Author)
INTEGRATING ERP, IOT, AND AI FOR SMART WAREHOUSE AUTOMATION AND LOGISTICS
Smart warehouse automation and logistics performance were examined in this study through the lens of closed-loop integration among enterprise resource planning (ERP), Internet of Things (IoT), and artificial intelligence (AI). A quantitative multi-site panel design was applied using 28 warehouse sites tracked across 52 weekly periods, producing 1,456 site–week observations after excluding 20 outage-flagged weeks. Data completeness was high across systems, with ERP 99.3%, WMS/WCS 98.8%, IoT 96.2%, and AI 93.5% field coverage. Integration maturity was measured on a 0–100 scale with a mean of 67.4 and a standard deviation of 11.2, and data quality averaged 0.92 (SD 0.04). Descriptive outcomes indicated inventory record accuracy of 97.1% (SD 1.6), pick errors of 3.4 per 1,000 lines (SD 1.5), order cycle time of 21.8 hours (SD 7.9), exception volume of 148.0 tickets/week (SD 71.0), trailer dwell time of 74.6 minutes (SD 38.2), and on-time shipment rate of 92.3% (SD 4.8). Fixed-effects panel regression models with time-period controls and clustered standard errors showed that higher integration maturity was associated with improved warehouse execution, including higher inventory accuracy (β = 0.031, 95% CI 0.011 to 0.051, p = 0.004), fewer pick errors (β = −0.028, 95% CI −0.045 to −0.011, p = 0.001), shorter order cycle time (β = −0.184, 95% CI −0.239 to −0.129, p < 0.001), and lower exception ticket volume (β = −1.62, 95% CI −2.15 to −1.09, p < 0.001). Logistics outcomes also improved, with higher on-time shipment (β = 0.052, 95% CI 0.026 to 0.078, p < 0.001), reduced trailer dwell (β = −0.92, 95% CI −1.41 to −0.43, p < 0.001), and reduced dispatch delay (β = −0.38, 95% CI −0.57 to −0.19, p < 0.001). Mediation tests indicated partial mediation by data quality; after mediator inclusion, the integration effect decreased for cycle time from −0.184 to −0.081 (p 0.008) and for exceptions from −1.62 to −0.74 (p 0.012), while data quality remained significant for cycle time (β = −0.236, p < 0.001) and exceptions (β = −2.11, p < 0.001), accounting for 45.2%–56.0% of total effects. Collinearity diagnostics were acceptable, with composite-model VIFs of 2.08–2.27 and a maximum condition index of 14.8. Reliability and validity results supported measurement adequacy, including integration maturity reliability (α = 0.89, CR = 0.91, AVE = 0.67) and data quality reliability (α = 0.86, CR = 0.88, AVE = 0.71), with discriminant validity supported by HTMT = 0.78. Overall, the findings indicated that measurable gains in smart warehouse automation and logistics were associated with higher ERP–IoT–AI integration maturity through improved data quality, reduced latency, and stronger performance under volatile, complex, and automated operating conditions.
Copyright (c) 2025 Md Jahidul Islam, Md Newaz Shorif (Author)
HYBRID DIGITAL TWIN AND MONTE CARLO SIMULATION FOR RELIABILITY OF ELECTRIFIED MANUFACTURING LINES WITH HIGH POWER ELECTRONICS
Reliability assessment of electrified manufacturing lines with high power electronics had remained difficult because disruption behavior had been driven by electro-thermal stress, operating-regime variability, restoration logistics, and line-level propagation effects that had produced heavy-tailed downtime and throughput-loss distributions. This study had developed and evaluated a hybrid digital twin integrated with Monte Carlo simulation and discrete-event line modeling to estimate distribution-based reliability and production-impact outcomes for a converter-driven manufacturing line. The merged operational timeline had covered 12 months, had included 18 converter-driven stations, and had captured 86,400 scheduled production minutes. A total of 3,462 disruption events had been reconciled, comprising 2,691 protection trips, 412 hard stops, and 359 reduced-capacity events. Line-level cumulative downtime had totaled 8,964 minutes, corresponding to an operational availability of 89.6%, and downtime per event had been right-skewed with a mean of 2.59 minutes and a median of 1.10 minutes. Downtime concentration had been evident, as the top three stations had contributed 41.2% of total downtime. Correlation analysis had shown that the thermal stress proxy had been strongly associated with downtime totals (r = 0.68) and throughput loss (r = 0.57), while utilization intensity had been most aligned with stop counts (r = 0.62). Regression modeling based on 6,480 station-days had indicated that thermal stress increased stop incidence (IRR = 1.24, 95% CI 1.16–1.33, p < 0.001), and high-load regimes increased stop rates (IRR = 1.19, 95% CI 1.10–1.28, p < 0.001). Repair-duration modeling using 412 repairs had shown that maintenance delays inflated restoration time by 18% (p < 0.001) and hard-stop class produced longer repairs (ratio = 1.74, p < 0.001). Monte Carlo–discrete-event simulation had matched weekly downtime distributions with a Kolmogorov–Smirnov distance of 0.09 and improved tail accuracy, reducing ninety-fifth-percentile downtime absolute error from 210 minutes (independence) to 172 minutes (dependency), an 18.1% reduction.
Copyright (c) 2025 Zaheda Khatun (Author)
COMPUTATIONAL PSYCHOMETRICS AND DIGITAL BIOMARKER MODELING FOR PRECISION MENTAL HEALTH DIAGNOSTICS
This study addressed the problem that mental health screening based mainly on self-report may miss ecologically valid behavioral signals, limiting precision diagnostics in real-world cloud and enterprise deployments; therefore, the purpose was to test whether integrating computational psychometrics with engineered digital biomarkers improves prediction of a continuous Precision Diagnostic Index (PDI) in a quantitative, cross-sectional, case-based design. The sample comprised 220 cloud/enterprise cases (N = 220; 56.4% female; age M = 26.8, SD = 6.1) who completed a Likert 5-point instrument and provided sufficient passive data for biomarker computation. Key psychometric variables were Affective Distress (M = 3.62, α = .88), Cognitive Dyscontrol (M = 3.31, α = .84), Functional Impairment (M = 3.45, α = .86), and Self-Regulation Capacity (reverse-coded; M = 2.74, α = .81). Digital biomarkers were z-scored Routine Irregularity, Sleep Disruption Proxy, Mobility Constriction, and Interaction Volatility, and the outcome PDI was standardized (M = 0.12, SD = 0.94). The analysis plan used descriptives and reliability checks, Pearson correlations, and hierarchical multiple regression comparing psychometrics-only, biomarkers-only, and integrated models. Headline findings showed strong psychometric alignment with PDI (Affective Distress r = .62; Functional Impairment r = .58; Cognitive Dyscontrol r = .49; Self-Regulation r = −.46; all p < .001) and moderate biomarker associations, strongest for Routine Irregularity (r = .38, p < .001) and Sleep Disruption (r = .32, p < .001). In regression, psychometrics explained R² = .49 of PDI variance (β = .41 distress, β = .29 impairment, β = .18 dyscontrol, β = −.14 self-regulation), biomarkers explained R² = .22 (β = .24 routine irregularity, β = .19 sleep disruption), and the integrated model increased prediction to R² = .56 with ΔR² = .07 (ΔF p < .001); routine irregularity (β = .16, p = .001) and sleep disruption (β = .12, p = .018) remained significant after controls, with acceptable multicollinearity (VIF 1.18–2.36). These results imply that enterprise screening systems can retain interpretable psychometric anchors while adding a small, theory-guided subset of passive biomarkers that delivers incremental validity and supports more precise triage and better resource allocation decisions across services.
Copyright (c) 2024 Md. Akbar Hossain (Author)
ARTIFICIAL INTELLIGENCE–DRIVEN PREDICTIVE MICROBIOLOGY IN DAIRY AND LIVESTOCK SUPPLY CHAINS
This study had examined Artificial Intelligence Driven Predictive Microbiology in Dairy and Livestock Supply Chains using a quantitative, stage-based framework that integrated sensor-derived exposure histories, laboratory microbiology outcomes, operational process logs, and traceability context. The final analytic dataset had comprised 6,420 traceable lots (3,280 dairy and 3,140 livestock) drawn from four facilities, with facility contributions of 1,860, 1,710, 1,520, and 1,330 lots, respectively. A total of 18,760 microbiological observations had been retained across checkpoints, including 6,340 intake, 6,270 post-process, and 6,150 pre-dispatch observations. Sensor coverage had been 84.6% overall, and 16.6% of lots had contained at least one laboratory value recorded below detection thresholds. Descriptive patterns had shown that pre-dispatch segments had exhibited higher exposure variability than post-process segments, with mean temperature instability increasing from 1.4 ± 1.1 post-process to 3.1 ± 2.0 pre-dispatch and handling intensity increasing from 1.4 ± 2.1 to 4.6 ± 5.2 events per lot. Correlation analysis had shown positive associations between microbial counts and temperature instability (r = 0.34) and cumulative warm exposure (r = 0.29), with stage-specific correlations for instability of 0.26 at intake, 0.18 post-process, and 0.41 pre-dispatch. Reliability testing had indicated strong internal consistency for the cold-chain stability indicator (α = 0.88) and traceability completeness (α = 0.84), with moderate reliability for sanitation timing (α = 0.76). Collinearity diagnostics had identified redundancy within temperature features, including elevated variance inflation for maximum excursion (VIF = 6.9) and cumulative warm exposure (VIF = 5.6), supporting predictor consolidation. Mixed-effects regression had shown significant stage-dependent effects, including temperature instability coefficients of β = 0.18 (intake), β = 0.07 (post-process), and β = 0.24 (pre-dispatch). For exceedance prediction, facility-held-out benchmarking had improved from PR-AUC 0.24 to 0.39 and recall from 0.58 to 0.74, while shelf-life prediction error had declined from 2.8 to 1.9 days median absolute error.
MACHINE-LEARNING MODELS FOR PREDICTING BLOOD PRESSURE AND CARDIAC FUNCTION USING WEARABLE SENSOR DATA
Cuffless monitoring can degrade because motion noise, inconsistent wear, and context alter wearable signals, weakening prediction of blood pressure (BP) and cardiac function. This study tested an integrated wearable plus Likert survey framework for predicting systolic BP (SBP), diastolic BP (DBP), and RMSSD in a quantitative, cross-sectional, case-study-based design. The case sample included 180 observations with data completeness 0.86; outcomes averaged SBP 128.4 mmHg and DBP 79.6 mmHg, with RMSSD 32.5 ms. Key variables comprised wearable predictors (pulse arrival time (PAT), PPG upstroke time, pulse width, activity intensity) and five Likert constructs (stress, sleep quality, activity routine, adherence, usability), with acceptable reliability (Cronbach alpha 0.78 to 0.88). The analysis plan applied descriptive statistics, Pearson correlations, multivariable regression (wearable-only then wearable plus Likert), and 5-fold cross-validated machine-learning benchmarking using MAE, RMSE, and R². Headline findings showed PAT correlated with SBP (r = -0.41, p < .001) and DBP (r = -0.29, p < .001), while stress correlated positively with SBP (r = 0.30, p < .001) and negatively with RMSSD (r = -0.33, p < .001). Wearable-only regression explained R² = 0.46 (SBP), 0.33 (DBP), and 0.38 (RMSSD), improving to 0.55, 0.40, and 0.47 after adding Likert predictors. In benchmarking, XGBoost delivered the best SBP model (MAE 5.0 mmHg, RMSE 7.3 mmHg, R² 0.70), improving on wearable-only performance (MAE 5.6; R² 0.66), with gains for DBP (MAE 3.7; R² 0.57) and RMSSD (MAE 4.8; R² 0.58). These results support enterprise-style monitoring pipelines that combine sensor features with survey measures and quality screening, while causal claims require longitudinal validation.
IMPACT OF IOT AND BLOCKCHAIN INTEGRATION ON REAL-TIME SUPPLY CHAIN TRANSPARENCY
This study investigated how Internet of Things (IoT) adoption intensity, blockchain deployment intensity, and their combined IoT–blockchain integration capability shaped real-time supply chain transparency and related operational performance. Drawing on a focused review of 78 peer-reviewed empirical papers, the study defined real-time transparency as a multidimensional latent capability reflected in timeliness of visibility, traceability completeness across tiers, integrity and trustworthiness of shared records, and accessibility/auditability for stakeholders. A cross-sectional explanatory design was applied using firm-level survey data from transparency-critical sectors including food and agro-processing, pharmaceuticals and healthcare distribution, manufacturing/apparel export chains, and e-commerce logistics. After screening, 312 valid responses were retained. Constructs were measured with validated five-point Likert scales and analyzed using PLS-SEM to accommodate second-order integration capability modeling, mediation, and interaction testing. Descriptive statistics indicated moderate-to-high technology intensity (IoT M = 3.71, SD = 0.68; blockchain M = 3.54, SD = 0.74) but comparatively lower integration capability (M = 3.29, SD = 0.63). Transparency baselines were highest for timeliness (M = 3.83, SD = 0.59) and traceability completeness (M = 3.76, SD = 0.61), with wider dispersion in integrity/trust and accessibility/auditability. The measurement model showed strong reliability and validity (Cronbach’s alpha = .86–.92; composite reliability = .90–.94; AVE = .62–.69; HTMT ratios below conservative cutoffs), and collinearity diagnostics remained acceptable (VIF range = 1.33–2.11). Structural results supported all hypotheses. IoT intensity significantly predicted timeliness and granularity aspects of transparency (β = 0.31, p < .001), while blockchain intensity significantly predicted integrity/trust aspects (β = 0.27, p < .001). Integration capability exerted the strongest direct effect on overall transparency (β = 0.42, p < .001) and increased explained variance from R² = 0.46 to R² = 0.59 (ΔR² = 0.13). A significant interaction effect confirmed synergy between IoT and blockchain intensities (β = 0.14, p = .003). Trust and information quality partially mediated the integration–transparency relationship (total indirect β = 0.18), and transparency significantly predicted operational performance (β = 0.39, p < .001), confirming partial mediation from integration to performance through transparency.
Copyright (c) 2024 Md. Rabiul Karim, Khairul Alam Talukder (Author)
THE ROLE OF ERP-INTEGRATED DECISION SUPPORT SYSTEMS IN ENHANCING EFFICIENCY AND COORDINATION IN HEALTHCARE LOGISTICS: A QUANTITATIVE STUDY
This study addresses a logistics problem: ERP platforms integrate transactions, yet limited decision support can keep ordering and exception handling inconsistent, weakening coordination and efficiency. The purpose was to test whether ERP integrated Decision Support System capability (ERP-DSS capability) improves logistics coordination and logistics efficiency, and whether coordination contributes to efficiency beyond ERP-DSS capability. Using a quantitative cross-sectional, case-based design in an ERP case, survey data were collected from 220 ERP users (mean experience 5.37 years). Key variables were ERP-DSS capability, logistics coordination, and logistics efficiency; means were slightly above midpoint (M = 3.16, 3.16, and 3.18) and reliability was strong (α = .84 to .87). Analyses used descriptive statistics, Pearson correlations, and multiple linear regression with experience and ERP training as controls. ERP-DSS capability related positively to coordination (r = .55, p < .001) and efficiency (r = .46, p < .001), and coordination related to efficiency (r = .44, p < .001). In regression, ERP-DSS capability predicted coordination (b = 0.58, β = .55, R² = .316) and efficiency (b = 0.47, β = .47, R² = .219). When both predictors entered, ERP-DSS capability (b = 0.33, β = .32) and coordination (b = 0.25, β = .26) remained significant and fit increased (R² = .265), indicating a coordination pathway alongside a direct ERP-DSS effect. Implications are that organizations should prioritize ERP-DSS features that enhance visibility, analytics, alerts, and decision rules and reinforce cross-unit workflows in enterprise and cloud ERP deployments.
Copyright (c) 2023 Rifat Chowdhury, Rebeka Sultana (Author)
DATA-DRIVEN COMPLIANCE FRAMEWORKS FOR ANTI-MONEY LAUNDERING (AML) AND TAX RISK MANAGEMENT IN FINANCIAL INSTITUTIONS
This study addresses the problem that financial institutions often deploy fragmented governance, analytics, and automation controls across AML and tax functions, which can weaken evidence traceability and reduce compliance effectiveness. The purpose was to test whether a data-driven compliance capability framework predicts AML compliance effectiveness and tax risk management effectiveness within an enterprise financial institution case, using a quantitative, cross-sectional, case-based design. Purposive sampling targeted role-relevant staff across compliance, risk, audit, tax/finance control, operations, and IT or data governance support; 210 questionnaires were distributed, and 184 valid responses were retained (response rate 87.6%). Key variables included four independent dimensions, Data Governance and Data Quality (DGQ), Analytics Capability (AC), Automation and Integration Maturity (AIM), and Organizational Readiness (OR), plus two dependent outcomes, AML effectiveness (AML-EFF) and tax risk management effectiveness (TRM-EFF). The analysis plan applied descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression. Findings show moderately high overall maturity (DDCC index M = 3.74, SD = 0.61), with AML-EFF (M = 3.72, SD = 0.66) slightly higher than TRM-EFF (M = 3.63, SD = 0.69). Reliability was strong (α = 0.82–0.90). Correlations were positive and significant (e.g., AC with AML-EFF r = 0.64; DGQ with TRM-EFF r = 0.58; OR with TRM-EFF r = 0.60; all p < .001). Regression explained substantial variance (AML model R² = 0.53; tax model variance explained = 49%), with AC the strongest AML predictor (β = 0.33, p < .001) and OR the strongest tax predictor (β = 0.34, p < .001); AIM was significant for AML (β = 0.12, p = .048) but not for tax at 0.05 (β = 0.10, p = .061). Implications indicate institutions should prioritize governed data and readiness as foundations across both domains, intensify analytics capability for AML performance, and treat automation as value-adding only when paired with strong governance and execution discipline.
Copyright (c) 2025 Md Arman Hossain (Author)
MODELING CLEAN-ENERGY GOVERNANCE THROUGH DATA-INTENSIVE COMPUTING AND SMART FORECASTING SYSTEMS
This study examined the quantitative relationships among digital infrastructure, forecasting accuracy, governance performance, and renewable-energy outcomes across 218 jurisdictions. The analysis integrated 7 governance indicators, 7 digital-infrastructure measures, 6 forecasting metrics, and 5 renewable-outcome variables to construct a comprehensive socio-technical evaluation model. Descriptive results showed substantial variation, with renewable penetration ranging from 12.5% to 68.7%, forecasting error between 4.3% and 17.8%, and governance scores spanning 2.1 to 4.9 on a five-point scale. Correlation coefficients demonstrated moderate to strong associations, with digital infrastructure correlated at r = .62 with governance performance and forecasting accuracy at r = .48. Reliability coefficients ranged from .86 to .91, confirming internal consistency across all multi-item scales. Factor loadings between .68 and .89 supported the validity of the measurement structure, and model-fit indices (RMSEA = .047; CFI = .956; SRMR = .041) confirmed strong structural alignment. Regression analysis revealed that digital-infrastructure capability exerted the strongest influence on governance performance (β = .41, p < .001), followed by forecasting accuracy (β = .28, p < .001), while data-intensive computing showed a nonsignificant direct effect (β = .09, p = .084). Governance performance significantly predicted renewable penetration (β = .46), grid reliability (β = .39), and efficiency outcomes (β = .41), indicating that governance maturity served as a central institutional determinant. Digital infrastructure also predicted renewable outcomes with coefficients ranging from .28 to .36 across models. Indirect-effect patterns demonstrated that both forecasting accuracy and digital capability influenced renewable-energy results partly through governance performance. Model explanatory power was substantial, with R² values between .44 and .57 across predictive models. Overall, the study provided evidence that clean-energy governance effectiveness depended on a combination of digital readiness, predictive-system quality, and institutional performance, highlighting their integrated contribution to renewable-energy advancement.
Copyright (c) 2021 FNU Zulqarnain, Subrato Sarker (Author)
QUANTITATIVE ASSESSMENT OF HYDRAULIC MODELING TOOLS IN OPTIMIZING FIRE SPRINKLER SYSTEM EFFICIENCY
This study addresses the persistent problem that, although automatic fire sprinkler systems are widely recognized as highly effective, there is limited quantitative evidence on how hydraulic modeling tools used at the design stage are associated with perceived sprinkler system efficiency in real projects. The purpose of the research is to quantify these relationships by linking practitioners’ perceptions of tool performance to perceived system outcomes. A quantitative cross-sectional, case-based survey design was employed, using a structured five-point Likert questionnaire administered to 180 practitioners, including fire protection engineers (42.2 percent), sprinkler design technicians (31.7 percent), consultants (15.6 percent), and facility or safety managers (10.5 percent) working on commercial, industrial, and institutional sprinkler systems. Key variables included perceived hydraulic accuracy (ACC), usability (USE), user expertise (EXP), input data quality (DATA), layout quality (LOUT), and perceived sprinkler system efficiency (EFF). All multi-item scales demonstrated strong reliability (Cronbach’s alpha 0.87-0.93). Descriptive results showed generally positive evaluations (means 3.61-3.92), and correlation analysis indicated strong positive associations between ACC, LOUT, USE, DATA, EXP and EFF (r = 0.58-0.71, p < .001). Multiple regression analysis confirmed that these factors jointly explained 62 percent of the variance in perceived efficiency, with ACC (β = 0.31, p < .001) and LOUT (β = 0.29, p < .001) emerging as the strongest predictors, followed by USE (β = 0.21, p = .002), DATA (β = 0.18, p = .006), and EXP (β = 0.12, p = .031). The findings imply that organizations seeking efficient, code compliant sprinkler systems should prioritize accurate and transparent modeling engines, invest in user training, and enforce robust data and layout quality practices within hydraulic design workflows.
Copyright (c) 2024 Md. Milon Mia, Md. Mominul Haque (Author)
MODELLING THE IMPACT OF TEMPERATURE COEFFICIENTS ON PV SYSTEM PERFORMANCE IN HOT AND HUMID CLIMATES
This quantitative study examined how temperature coefficients shaped photovoltaic (PV) system performance in hot and humid climates through 12-month, minute-resolution monitoring and coefficient-aware modeling across three representative installations: a coastal roof-flush site, an urban roof-standoff site, and an inland rack-mounted site, including crystalline-silicon and thin-film technologies. Descriptive findings indicated persistent thermal stress, with plane-of-array irradiance averaging 715–768 W/m², ambient temperatures averaging 30.4–33.2°C, and module temperatures averaging 44.8–49.6°C. Relative humidity remained high (73.6–81.9%) and wind speeds were modest (1.6–2.4 m/s), producing sustained module heating above ambient. Under these conditions, normalized DC power averaged 0.84–0.89 p.u., confirming baseline thermal derating during productive hours. Correlation results showed that module temperature was strongly and negatively associated with voltage and power outputs, including open-circuit voltage (r = −0.71), normalized DC power (r = −0.62), and efficiency (r = −0.63), while associations with short-circuit current were weakly positive or near zero (r = 0.06), supporting voltage-dominated thermal losses. Site-level multivariate regressions controlling for irradiance, wind, humidity, and ambient temperature confirmed an independent temperature penalty on normalized DC power, with coefficients of −0.0043, −0.0037, and −0.0030 p.u./°C (all p < .001) and strong model fits (adjusted R² = 0.84–0.88; RMSE = 0.058–0.069 p.u.). Mixed-effects pooling showed crystalline silicon to be more temperature-sensitive than thin-film, consistent with more negative effective power slopes in silicon arrays. Using field-effective coefficients reduced prediction error compared with datasheet coefficients (RMSE 0.064 vs 0.091) and lowered annual yield estimates by about 4.7%. Overall, the results demonstrated that climate-calibrated temperature coefficients and accurate module-temperature modeling were essential for realistic PV yield prediction in hot and humid environments.
Copyright (c) 2022 MD Hamidur Rahman (Author)
AI-DRIVEN PREDICTIVE ANALYTICS MODELS FOR ENHANCING GROUP INSURANCE PORTFOLIO PERFORMANCE AND RISK FORECASTING
This study evaluated whether AI-driven predictive analytics models enhanced group insurance portfolio performance and improved risk forecasting under renewal-cycle volatility and heavy-tailed claims. The empirical dataset covered four renewal years and included 412 sponsors and 186,540 members in Year 1, expanding to 463 sponsors and 209,940 members by Year 4, with total exposure rising from 2,143,210 to 2,409,760 member-months. Claim incidence remained stable at 0.27–0.29, yet utilization intensity increased as mean claim frequency per claimant rose from 2.6 to 2.9 and mean severity increased from 2,960 to 3,360 USD, while the 95th percentile severity exceeded 21,000 USD in Year 4. High-cost members comprised only 3.4–3.9% of lives but generated 41.8–44.2% of total costs, confirming tail dominance. Sponsor performance showed baseline instability, with mean loss ratios increasing from 0.83 to 0.90 and loss-ratio standard deviation widening from 0.19 to 0.23; sponsors exceeding stop-loss attachment increased from 7.1% to 8.9%. Benchmark actuarial models achieved moderate discrimination (0.66–0.74 across tasks) and higher errors for tail-sensitive outcomes. AI models—particularly gradient boosting and hybrid actuarial–AI specifications—produced consistent uplift: discrimination improved for high-cost events from 0.74 to 0.87 and for sponsor loss-ratio forecasting from 0.66 to 0.81. Sponsor loss-ratio error declined from 0.084 to 0.057, tail-exceedance misclassification fell from 0.078 to 0.049, stop-loss attachment error decreased from 0.071 to 0.045, reserve bias reduced from 2.9% to 1.7%, and sponsor loss-ratio volatility compressed from 0.23 to 0.18. These gains persisted under sponsor-stratified out-of-sample testing, renewal-forward validation, industry-shift checks, and feature perturbation tests. Overall, AI-driven predictive analytics materially strengthened group portfolio forecasting and stability by improving tail risk identification and sponsor-level loss ratio prediction.
Copyright (c) 2025 Md. Mosheur Rahman (Author)