American Journal of Scholarly Research and Innovation
The American Journal of Scholarly Research and Innovation (AJSRI) ONLINE ISSN (3067-2163) is a leading Peer-Reviewed, Open Access International Journal dedicated to fostering innovation and excellence across a wide array of academic disciplines. The journal provides a platform for scholars, researchers, and professionals to disseminate groundbreaking research that addresses real-world challenges through interdisciplinary collaboration. As a monthly publication, AJSRI releases 12 issues annually, maintaining a consistent schedule that ensures the timely distribution of high-quality, impactful research. The journal encompasses diverse fields, including Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering, making it a truly interdisciplinary platform. AJSRI collaborates with international and national conferences to publish extended versions of selected papers, furthering the visibility and impact of researchers’ work. The journal is committed to publishing original, high-quality research that strengthens academic knowledge and drives innovation.
GPU-Accelerated Physics-Informed Digital Twins for Real-Time State Estimation and Fault Localization in Distribution Grids
This study addresses a persistent operational problem in distribution-grid management: utilities need fast, trustworthy state estimation and fault localization to support restoration and switching decisions, yet conventional analytics are often too slow for real-time use and can be difficult to trust under changing grid conditions. The purpose of the study was to evaluate whether GPU-accelerated, physics-informed digital twins can measurably improve real-time state estimation, fault localization effectiveness, and operational decision support in cloud and enterprise deployment contexts. Using a quantitative, cross-sectional, case-based design, data were collected from a sample of N = 210 respondents drawn from cloud and enterprise distribution-grid digital-twin cases. The key independent variables were GPU Acceleration Capability, Physics-Informed Modeling Strength, Digital Twin Fidelity, and Data Integration Quality; the dependent variables were Real-Time State Estimation Performance, Fault Localization Effectiveness, and Operational Decision Support Value, with an additional trust indicator for physics-consistency confidence. The analysis plan employed reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression modeling to estimate direct effects and the enabling pathway from acceleration and physics constraints to operational value. Reliability results indicated strong internal consistency across constructs (α = .83 to .91). Correlation results showed that GPU capability was positively associated with state estimation (r = .56, p < .001) and that physics-informed strength had an even stronger association with state estimation (r = .61, p < .001); state estimation was strongly related to fault localization (r = .63, p < .001). Regression findings confirmed that state estimation performance was significantly predicted by GPU capability (β = .29, p < .001) and physics-informed strength (β = .37, p < .001), explaining 54% of variance (R² = .54). Fault localization effectiveness was significantly predicted by state estimation (β = .41, p < .001), digital twin fidelity (β = .28, p < .001), and data integration quality (β = .19, p = .002), explaining 62% of variance (R² = .62). Operational decision support value was driven primarily by fault localization (β = .46, p < .001) and state estimation (β = .21, p = .004), with GPU providing incremental contribution (β = .14, p = .030), explaining 58% of variance (R² = .58). Trust results further indicated high physics-consistency confidence (M = 4.12, SD = 0.48). Overall, the study implies that utilities should prioritize physics-consistent modeling and GPU-ready architectures, while investing in data integration quality and twin fidelity to convert computational speed into reliable, actionable grid decisions.
Copyright (c) 2024 Zaheda Khatun, Md Hamidur Rahman (Author)
Machine Learning–Based Financial Forecasting and Valuation Models for Investment Decision-Making and Capital Allocation Efficiency
This study addresses a recurring enterprise problem: capital allocation decisions often rely on forecasts and valuation estimates that are inconsistent across tools and teams, leading to delayed approvals, mispriced projects, and inefficient resource deployment. The purpose of the study was to quantify how machine learning based forecasting and machine learning based valuation contribute to higher investment decision-making quality and improved capital allocation efficiency within cloud and enterprise decision contexts. A quantitative cross-sectional, case-based design was used, drawing on cloud and enterprise cases where ML dashboards and analytics were actively used in screening, budgeting, and portfolio governance. Data were collected using a structured 5-point Likert survey from N = 162 participants involved in the forecasting–valuation–allocation pipeline (analysts/associates 46.3%, managers/senior managers 32.1%, committee or strategic roles 21.6%). The key variables were ML Forecasting Effectiveness (FCAST), ML Valuation Effectiveness (VAL), Investment Decision-Making Quality (DMQ), and Capital Allocation Efficiency (CAE), with role and experience treated as controls; two additional indices were examined to clarify adoption conditions, Model Trust and Adoption Readiness (MTAI) and Forecasting–Valuation Alignment (FVAD). The analysis plan included reliability testing, descriptive statistics, Pearson correlations, multiple regression, and bootstrap mediation to test whether DMQ transmits the effects of FCAST and VAL to CAE. Reliability was strong (Cronbach’s alpha: FCAST = .88; VAL = .86; DMQ = .84; CAE = .87). Mean scores were above neutral (FCAST M = 3.94, SD = 0.61; VAL M = 3.88, SD = 0.64; DMQ M = 3.76, SD = 0.58; CAE M = 3.71, SD = 0.62), indicating generally favorable perceptions of ML support. Correlations were positive and significant (FCAST–DMQ r = .56; VAL–DMQ r = .52; DMQ–CAE r = .62; all p < .001). In regression, FCAST and VAL jointly predicted DMQ (R² = .41; FCAST β = .39, p < .001; VAL β = .31, p < .001), while CAE was explained by DMQ with additional direct effects (R² = .53; DMQ β = .45, p < .001; FCAST β = .18, p = .007; VAL β = .12, p = .049). Mediation results showed meaningful indirect effects via DMQ for forecasting (indirect = .18, 95% CI [ .10, .28]) and valuation (indirect = .14, 95% CI [ .07, .23]). Implementation implications are clear: enterprises should govern ML forecasting and valuation as an integrated decision system, strengthen model trust and adoption readiness (MTAI M = 3.82, SD = 0.55; 68.5% high readiness), and actively manage forecast–valuation alignment because misalignment is associated with lower allocation efficiency (FVAD–CAE r = −.34, p < .001).
Copyright (c) 2026 Shamsunnahar Chadni (Author)
AI-Driven Credit Scoring and Default Probability Modeling for Basel III Risk-Weighted Asset Optimization in Banking
This study addresses a problem: AI-driven credit scoring can strengthen probability of default (PD) estimation, but uneven data quality, governance controls, and explainability reduce model trust and constrain Basel III risk-weighted asset (RWA) optimization. The purpose was to quantify the pathway from AI capability to PD modeling effectiveness and from PD effectiveness to Basel III RWA optimization in a cloud-enabled enterprise case bank. A quantitative cross-sectional, case-based survey was administered; 300 questionnaires were distributed and 212 valid responses were analyzed (70.7% effective response rate). Key predictors were AI-driven credit scoring capability, data quality readiness, model governance maturity, and explainability readiness; PD modeling effectiveness served as the mechanism, and Basel III RWA optimization effectiveness was the outcome. Reliability was strong across constructs (Cronbach’s α = 0.84–0.90). The analysis plan combined statistics, Pearson correlations, and multiple regression with moderation testing. Mean ratings were positive (AI capability M = 3.82; PD effectiveness M = 3.91; RWA optimization M = 3.76 on a 1–5 scale). Correlations supported the framework, including an association between PD effectiveness and RWA optimization (r = 0.66, p < .001). In regression Model 1, PD effectiveness was predicted by AI capability (β = 0.34, p < .001), data quality (β = 0.21, p = .002), governance maturity (β = 0.25, p < .001), and explainability readiness (β = 0.14, p = .018), with R² = 0.57. In Model 2, PD effectiveness predicted RWA optimization (β = 0.51, p < .001) and governance retained a direct effect (β = 0.19, p = .004; R² = 0.49). Moderation results showed a significant AI × governance interaction (β = 0.11, p = .031; ΔR² = 0.02), indicating that stronger governance amplifies the benefits of AI capability for PD outcomes. Implications are that banks seeking Basel III capital efficiency via AI should invest not only in scoring capability, but also in data readiness, governance discipline, and explainability practices so that PD gains translate into defensible RWA optimization.
Copyright (c) 2026 Sazzadul Islam (Author)
A Hybrid Lean-Six Sigma Model with Automated Kaizen for Real-Time Quality Improvement
This study addresses the problem that many enterprise quality improvement programs generate dashboards and alerts yet still fail to convert real-time deviations into owned corrective actions and verified closure, which sustains defect recurrence and process instability. The purpose was to test whether a Hybrid Lean Six Sigma (LSS) capability base, strengthened by Automated Kaizen execution workflows, predicts Real-Time Quality Improvement (RQI) in a quantitative, cross-sectional, case-based model. The sample comprised n = 210 valid responses collected from operational roles across cloud-enabled enterprise quality cases, including operators/technicians (35.2%), QA/QC staff (21.0%), supervisors (19.5%), engineers (15.2%), and CI or Lean Six Sigma team members (9.0%). Key variables were LSS capability, Automated Kaizen effectiveness, signal-to-action actionability, Kaizen closure confidence, DMAIC-stage functioning under automation, and RQI, measured on 5-point Likert scales and aggregated as composite indices. The analysis plan applied reliability testing (Cronbach’s alpha), descriptive statistics, Pearson correlations, and multiple regression models to estimate both baseline effects and incremental explanatory power. Measurement reliability was strong (α = .85–.90), including LSS α = .88, Automated Kaizen α = .90, and RQI α = .87. Descriptively, respondents reported high LSS capability (M = 3.94) and moderate-to-high Automated Kaizen (M = 3.78) and RQI (M = 3.83). Correlations supported the hypothesized relationships: LSS correlated with RQI (r = .62, p < .001) and with Automated Kaizen (r = .58, p < .001), while Automated Kaizen correlated more strongly with RQI (r = .67, p < .001). Regression results showed LSS predicting RQI (β = .62; R² = .38); when Automated Kaizen was added, it became the stronger predictor (β = .49) while LSS remained significant but reduced (β = .33), increasing explained variance to R² = .55, consistent with Automated Kaizen serving as a practical execution pathway. Mechanism evidence highlighted that signal-to-action actionability strongly related to RQI (r = .69; β = .41, p < .001) and automation’s highest perceived leverage appeared in the DMAIC Measure (M = 3.96) and Control (M = 3.91) stages. Implications indicate that enterprises seeking real-time quality gains should prioritize alert relevance, workflow routing integrity, clear ownership, and control-plan updates so that digital signals reliably trigger, track, verify, and standardize Kaizen actions at scale.
Copyright (c) 2023 Shofiul Azam Tarapder, Md. Al Amin Khan (Author)
A Quantitative Data-Driven Evaluation of Cost Efficiency in Cloud and Distributed Computing for Machine Learning Pipelines
This study presents a quantitative evaluation of cost efficiency, performance behavior, and resource utilization in machine learning pipelines executed under cloud-based and distributed computing environments. Using a consolidated dataset that integrated pipeline execution logs, fine-grained telemetry, and infrastructure billing records, the analysis examined 120 replicated pipeline runs spanning training-dominant and inference-dominant workloads. Each run was decomposed into five pipeline stages—ETL, preprocessing, training, evaluation, and serving—enabling both run-level and stage-level assessment of cost and runtime behavior. Descriptive results indicated that cloud executions exhibited higher median total cost per run (USD 42.80, IQR 31.20–66.50) compared to distributed executions (USD 38.10, IQR 29.40–52.30), alongside greater cost dispersion driven by autoscaling, orchestration overhead, and network egress. Distributed runs demonstrated higher average compute utilization (median 74.8% vs. 61.2%) and lower idle time shares (11.6% vs. 18.9%), contributing to more stable cost behavior. Multivariate regression models explained a substantial proportion of cost variability (R² = 0.68 for total cost; R² = 0.54 for normalized cost efficiency). Job completion time (β = 0.018, p < 0.001) and network egress (β = 0.031, p = 0.001) emerged as the strongest positive predictors of total cost, while higher average compute utilization was associated with lower cost after controlling for runtime (β = −0.007, p < 0.001). In the cost-efficiency model, higher training throughput (β = 0.00042, p < 0.001) and higher utilization intensity improved efficiency, whereas orchestration overhead (β = −0.091, p = 0.015), higher inference concurrency (β = −0.033, p = 0.026), and increased tail latency reduced efficiency. Hypothesis testing confirmed statistically significant differences favoring distributed environments for normalized cost efficiency (Cohen’s d = 0.44) and training completion time, while cloud environments achieved lower inference tail latency (p95 difference ≈ −65 ms). Overall, the findings demonstrate that cost efficiency in machine learning pipelines is driven less by median throughput differences than by utilization stability, orchestration behavior, and network-driven variability. By empirically linking telemetry-derived predictors to monetary outcomes, this study provides robust quantitative evidence for infrastructure-aware cost optimization in modern machine learning systems.
Copyright (c) 2023 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
COMPUTATIONAL MODELING AND SIMULATION TECHNIQUES FOR MANAGING RAIL–URBAN INTERFACE CONSTRAINTS IN METROPOLITAN TRANSPORTATION SYSTEMS
Rail–urban interface constraints in metropolitan transportation systems reduce reliability and safety because dense stations, corridor conflict points, and community exposure can turn small disruptions into network-wide delays and crowding. This study examined whether computational modeling and simulation capability (CMSC) improves constraint-management effectiveness (CME) in an enterprise-scale metro rail case were cloud and enterprise analytics support scenario testing and decisions. Using a quantitative, cross-sectional, case-based design, a 5-point Likert survey was administered to N = 312 professionals from operations/control (26.3%), planning/timetabling (20.5%), station management (17.9%), engineering/maintenance (19.9%), and safety/risk (15.4%); 41.0% were direct model users and 37.8% indirect users. Key variables were rail–urban interface constraint severity (RICS), CMSC, decision integration (DI), and CME. Data screening showed mean missingness of 1.8% and Harman single-factor variance of 32.6%. Reliability was strong (α: RICS .88, CMSC .91, DI .87, CME .90). The most severe constraints were station crowding/circulation (M = 4.21, SD = 0.62) and peak dwell-time variability (M = 4.08, SD = 0.67). CMSC was moderate-high (M = 3.78, SD = 0.64), with scenario analysis strongest (M = 3.92) and validation weakest (M = 3.49). CMSC and DI correlated positively with CME (r = .62 and .58; p < .001), while RICS correlated negatively (r = −.41; p < .001). Regression was significant (R² = .51): CMSC (β = .38) and DI (β = .29) increased CME, and RICS reduced it (β = −.17); CMSC benefits were stronger at higher DI (ΔR² = .03). Implications emphasize station-area actions, stronger validation governance, and institutionalized use of simulation outputs in routine decisions.
Copyright (c) 2024 Masud Rana, Hammad Sadiq (Author)
THE IMPACT OF DEEP LEARNING AND SPEAKER DIARIZATION ON ACCURACY OF DATA-DRIVEN VOICE-TO-TEXT TRANSCRIPTION IN NOISY ENVIRONMENTS
This quantitative, cross-sectional, case-based study investigated why cloud and enterprise voice-to-text deployments still produce variable transcription accuracy in noisy, multi-speaker settings and whether Deep Learning ASR Capability (DL) and Speaker Diarization Quality (SD) function as complementary drivers of perceived Transcription Accuracy in Noise (TA). Using a one-time Likert-scale survey (1 = strongly disagree to 5 = strongly agree), the study retained N = 156 usable responses from users and reviewers embedded in operational enterprise and cloud transcription cases. The research problem centers on the persistent gap between real-world acoustic difficulty and dependable transcript quality for analytics, compliance, and decision support. In the model, DL and SD were the core independent variables, TA was the dependent variable, and Noise Severity and Overlap Frequency were included as contextual controls to isolate technical effects from environmental difficulty. Descriptively, the case context was genuinely adverse, with Noise Severity M = 3.94 (SD = 0.71) and Overlap Frequency M = 3.52 (SD = 0.82), while outcomes remained only moderate (TA M = 3.46, SD = 0.64); DL was rated moderately high (M = 3.62, SD = 0.59) and SD moderate (M = 3.38, SD = 0.67). Measurement quality supported inferential testing, with strong internal consistency (DL α = 0.88, SD α = 0.85, TA α = 0.90). The analysis plan applied composite scoring, reliability testing, Pearson correlations, and multiple regression. Correlation results showed strong positive relationships between DL and TA (r = 0.61, p < .001) and SD and TA (r = 0.55, p < .001), alongside negative associations between TA and noise (r = −0.31, p < .001) and TA and overlap (r = −0.28, p < .01). Regression findings confirmed joint predictive power: the model was significant (F(4,151) = 39.18, p < .001) and explained 51% of TA variance (R² = 0.51; Adjusted R² = 0.49); DL was the strongest positive predictor (B = 0.47, β = 0.43, p < .001) and SD added an independent positive contribution (B = 0.34, β = 0.31, p < .001), while noise (B = −0.11, p = .046) and overlap (B = −0.10, p = .021) reduced accuracy. A robustness check using TA groups further reinforced the pattern: 29.5% low TA (≤ 3.0), 52.6% moderate TA, and 17.9% high TA (> 3.75), with monotonic increases in DL and SD means across groups (DL 3.18 → 3.63 → 4.12; SD 2.97 → 3.36 → 3.98). Overall, the findings imply that enterprise teams should optimize ASR and diarization together, prioritize overlap-aware diarization improvements, and treat noise and overlap profiling as first-class deployment controls to improve transcript trust and downstream usability.
Copyright (c) 2023 Abdulla Mamun, Alifa Majumder Nijhum (Author)
AI-DRIVEN PREDICTIVE ANALYTICS FRAMEWORK FOR ELECTRONIC FUNDS TRANSFER, LOAN ORIGINATION, AND AML COMPLIANCE IN DIGITAL BANKING
This study addresses the problem that electronic funds transfer (EFT) monitoring, loan origination decisioning, and anti-money laundering (AML) compliance are often governed as separate control silos in digital banking, which limits risk visibility and reduces audit ready decision defensibility. The purpose was to validate an AI driven predictive analytics framework and quantify how Predictive Analytics Capability (PAC) influences EFT monitoring effectiveness, loan origination decision quality, AML monitoring effectiveness, and overall digital banking risk control performance (DBRCP). A quantitative cross sectional, case-based survey was administered across a cloud enabled digital banking environment, yielding 268 responses from EFT operations (31.7%), lending or underwriting (27.6%), AML or compliance (24.3%), and risk, analytics, or IT (16.4%). PAC (20 items) operationalized capability maturity across data integration, data quality, model development and validation, model governance and documentation, and user competence; outcome constructs were measured as Likert 1 to 5 composites. The analysis plan combined descriptive profiling, internal consistency testing, Pearson correlations, and hypothesis driven regression models. Reliability was adequate (Cronbach’s alpha: PAC 0.91, EFT_EFF 0.88, LOAN_QUAL 0.90, AML_EFF 0.89, DBRCP 0.92). Descriptively, respondents rated PAC at M = 3.84 (SD = 0.56), with governance and documentation the lowest dimension (M = 3.68), while EFT_EFF (M = 3.79), LOAN_QUAL (M = 3.73), AML_EFF (M = 3.76), and DBRCP (M = 3.76) were all above the scale midpoint. PAC correlated positively and significantly with EFT_EFF (r = 0.56), LOAN_QUAL (r = 0.52), AML_EFF (r = 0.59), and DBRCP (r = 0.63) at p < .001. Regression results showed that PAC predicted EFT_EFF (beta = 0.48, R2 = 0.31), LOAN_QUAL (beta = 0.44, R2 = 0.27), and AML_EFF (beta = 0.51, R2 = 0.35), all p < .001, indicating the strongest capability to outcome contribution in AML. In the integrated model, EFT_EFF (beta = 0.26), LOAN_QUAL (beta = 0.21), and AML_EFF (beta = 0.37) jointly explained DBRCP (R2 = 0.58), underscoring that coordinated improvements across payments, credit, and compliance drive risk control. Implications are that banks should invest in PAC foundations, particularly governance and documentation, to translate predictive models into consistent operational decisions and demonstrable compliance outcomes.
Copyright (c) 2025 Md Nahid Hossain (Author)
PRIVACY-PRESERVING SECURITY MODEL FOR EARLY CANCER DIAGNOSIS, POPULATION-LEVEL EPIDEMIOLOGY, AND SECURE INTEGRATION INTO U.S. HEALTHCARE SYSTEMS
The increasing use of data-intensive analytics for early cancer diagnosis and population-level epidemiology has heightened concerns regarding patient privacy, data security, and analytical reliability within healthcare systems. Early detection and cancer surveillance depend on large-scale integration of clinical, imaging, and epidemiological data across institutional boundaries, creating complex environments with elevated risks of disclosure, re-identification, and system vulnerability. This study investigates the effectiveness of a privacy-preserving security model for supporting early cancer diagnosis and population-level epidemiological analysis within regulated healthcare data environments in the United States. Using a quantitative, cross-sectional design, the study analyzed 120 oncology-related analytic units, including healthcare systems, cancer registries, and public health platforms, to assess the implementation of privacy controls, security architectures, and analytical performance outcomes. Results indicate a mean Privacy Control Index score of 68.4 (SD = 11.7), reflecting moderate to high adoption of privacy-preserving mechanisms with notable variability across institutions. Security architectures were more consistently implemented, with a mean Security Control Score of 72.9 (SD = 9.6), and were found to be a significant positive predictor of analytical performance. Analytical outputs remained stable under privacy constraints, with an overall performance stability score of 0.84 (SD = 0.06). The relationship between privacy strength and analytical performance was weak and non-linear (r = −0.21, p < .05), indicating that stronger privacy controls did not substantially degrade analytic utility. At the population level, secure aggregation mechanisms achieved high consistency, with a mean Aggregation Consistency Index of 88.1 (SD = 6.4), supporting reliable epidemiological analysis while limiting disclosure risk. Overall, the findings provide empirical evidence that privacy-preserving security models can be effectively integrated into cancer analytics systems, demonstrating that privacy, security, and analytical performance can function as complementary components within modern healthcare data infrastructures.
Healthcare Data Security And Interoperability
Copyright (c) 2022 Md Fokhrul Alam, Md Ashraful Alam, Md Fardaus Alam (Author)
ASSESSING ORGANIZATIONAL AI READINESS IN CRITICAL INFRASTRUCTURE: AN INTEGRATED MATURITY FRAMEWORK FOR HEALTHCARE SYSTEMS AND SUPPLY CHAIN MANAGEMENT
Critical infrastructure sectors face unprecedented challenges requiring artificial intelligence (AI) integration to enhance operational resilience and national security. However, AI adoption remains limited due to inadequate organizational readiness assessment frameworks. This research develops and validates a comprehensive AI readiness maturity model adapted from the Capability Maturity Model Integration (CMMI) framework for healthcare systems and supply chain management. Drawing on Technology-Organization-Environment (TOE) theory, Resource-Based View (RBV), and Dynamic Capabilities Theory, we propose an integrated assessment framework encompassing six dimensions: Technical Infrastructure, Data Capabilities, Organizational Capabilities, Strategic Readiness, Governance and Ethics, and Ecosystem Integration. Through multi-method validation involving survey data from 312 organizations, 45 semi-structured interviews, and four longitudinal case studies, we demonstrate strong psychometric properties and significant positive relationships between AI readiness maturity and infrastructure resilience outcomes. Results reveal that only 23% of healthcare organizations and 18% of supply chain organizations achieve optimized maturity levels, with significant gaps in data capabilities and governance structures. Our findings provide actionable frameworks for practitioners, inform policy development for critical infrastructure protection, and establish theoretical foundations for future research on AI-enabled infrastructure resilience.
Copyright (c) 2025 Tonay Pal, Md Maruf Islam (Author)
REINFORCEMENT LEARNING APPROACHES TO OPTIMIZE IT SERVICE MANAGEMENT UNDER DATA SECURITY CONSTRAINTS
This quantitative study evaluated reinforcement learning (RL) approaches to optimize IT Service Management (ITSM) decision-making under explicit data security constraints using a retrospective observational design and historical operational logs. The analytic dataset was extracted from a 12-month observation window and contained 128,450 raw incident and service-request tickets. After deterministic preprocessing, including timestamp normalization, deduplication, linkage resolution, and censoring under pre-specified rules, 112,680 tickets were retained for analysis, representing 46.3% incidents and 53.7% service requests across 10 service lines and 5 regions. Mandatory ITSM fields were complete for 96.8% of retained records, and 78.4% of tickets were linked to usable telemetry context for state construction. Descriptive statistics indicated distributional asymmetry typical of service systems: the median time-to-acknowledge was 0.42 hours and the median time-to-resolve was 14.6 hours, with substantial tail behavior (P90 resolution time 63.4 hours; P95 resolution time 97.6 hours). Governance indicators were consistently recorded, with audit-log completeness averaging 98.9%. Correlational analysis showed that time-to-resolve was positively associated with congestion and workflow friction, including relationships with backlog intensity (0.46), ticket aging (0.52), reassignment count (0.41), and escalation occurrence (0.35), motivating multivariable modeling with collinearity screening. Adjusted regression models controlling for ticket severity, category, region, service line, workload context, and major-incident regime indicated that the RL policy was associated with improved performance relative to baseline handling, including reduced time-to-acknowledge (β = −0.08 hours; p < .001) and improved resolution performance (log-linear β = −0.12; p < .001), alongside improved percentile-oriented SLA attainment (P90 threshold OR = 1.28, p < .001; P95 threshold OR = 1.19, p = .001). Process-quality outcomes also improved, including reduced reopen occurrence (OR = 0.86; p < .001) and reduced reassignment occurrence (OR = 0.89; p < .001). Constraint-related models showed no statistically significant increases in privileged-action events (IRR = 0.99; p = .61), exception approvals (IRR = 0.98; p = .44), or restricted-field access (IRR = 1.01; p = .69), and audit completeness remained statistically unchanged, supporting admissibility of security constraints while service outcomes improved.
Copyright (c) 2023 Md Mohaiminul Hasan, Md Muzahidul Islam (Author)
NEUROBIOTECHNOLOGY-DRIVEN REGENERATIVE THERAPY FRAMEWORKS FOR POST-TRAUMATIC NEURAL RECOVERY
This study addresses the problem that neurobiotechnology-driven regenerative therapies for post-traumatic neural injury often yield inconsistent recovery across organizations because framework implementation strength and its operational drivers are rarely measured and compared systematically. The purpose was to quantify Neurobiotechnology-Driven Regenerative Therapy Framework Strength (NDRTF) and test its association with Post-Traumatic Neural Recovery Outcomes (PNRO), and to identify which framework dimensions most strongly predict outcomes. Using a quantitative, cross-sectional, case-study-based design, a five-point Likert questionnaire was administered in three enterprise-scale case settings with technology-enabled monitoring and protocol tracking (Case A, B, C). The sample comprised 180 professionals (60 per case) spanning clinicians/therapists (52.8%), biomedical or neurotech staff (30.0%), and program coordinators (17.2%). Measurement quality was verified via reliability testing (α = 0.88 for the 25-item NDRTF scale; α = 0.86 for the 10-item PNRO scale). The analysis plan combined descriptive profiling, Pearson correlations, multiple regression, and case-specific regressions. Descriptively, overall NDRTF was moderate to high (M = 3.62, SD = 0.54) and PNRO was similar (M = 3.58, SD = 0.57), with Case A highest (NDRTF M = 3.74; PNRO M = 3.70) and Case C lowest (NDRTF M = 3.52; PNRO M = 3.49). Overall framework strength correlated strongly with recovery (r = 0.71, p < .001). In the multivariate model, the five NDRTF dimensions explained 59% of PNRO variance (R² = 0.59; F(5,174) = 49.30, p < .001), with Monitoring and Feedback Quality (β = 0.29, p < .001), Multidisciplinary Coordination (β = 0.23, p = .001), and Bio-Neurotech Integration (β = 0.19, p = .004) as the strongest predictors. Case models showed R² = 0.64 (A), 0.57 (B), and 0.49 (C). Implications include prioritizing monitoring feedback loops, cross-team coordination, and integrated biological plus neurotechnology delivery, supported by secure cloud data governance to sustain fidelity and scale outcomes.
Copyright (c) 2022 Md. Akbar Hossain, Sharmin Ara (Author)
DATA-DRIVEN FRAMEWORKS FOR IMPROVING DECISION-MAKING IN U.S. PUBLIC AND PRIVATE ORGANIZATIONS
This study examined the statistical relationships between data-driven framework maturity and decision-making effectiveness across U.S. public and private organizations using a quantitative, explanatory, cross-sectional comparative design. Analysis was conducted at the organizational level for 140 organizations, including 68 public-sector and 72 private-sector organizations. Data-driven framework maturity was operationalized through five dimensions—governance maturity, data quality management, architecture and integration depth, analytics capability, and decision integration—while decision-making effectiveness was assessed using five outcome families: decision timeliness, decision consistency, accuracy/error sensitivity, compliance-adjusted outcomes, and performance attainment. Descriptive results showed higher mean digital maturity in private organizations (M = 3.62, SD = 0.68) than public organizations (M = 3.28, SD = 0.74), while regulatory intensity was higher in public organizations (M = 4.01, SD = 0.63) than private organizations (M = 3.42, SD = 0.77). Correlation analysis indicated positive associations among maturity dimensions, with the strongest inter-correlation between analytics capability and decision integration (r = 0.71). Reliability results were acceptable to strong across constructs, with Cronbach’s alpha ranging from 0.80 to 0.90 and composite reliability from 0.83 to 0.91; confirmatory factor analysis supported the measurement model (CFI = 0.94, TLI = 0.93, RMSEA = 0.061, SRMR = 0.049). Collinearity diagnostics were within acceptable limits (VIF range: 1.20–2.56). Regression results showed that maturity dimensions improved explanatory power beyond controls, with adjusted R² reaching 0.41 for performance attainment and 0.36 for decision timeliness. Decision integration was positively associated with decision timeliness (β = 0.31, 95% CI [0.14, 0.46]), accuracy/error sensitivity (β = 0.28, 95% CI [0.11, 0.43]), and performance attainment (β = 0.30, 95% CI [0.13, 0.45]). Analytics capability was also positively related to performance attainment (β = 0.26, 95% CI [0.10, 0.40]), while governance maturity aligned most strongly with compliance-adjusted outcomes (β = 0.29, 95% CI [0.12, 0.44]).
Copyright (c) 2022 Md. Wahid Zaman Raj, Sai Praveen Kudapa (Author)
AI APPLICATIONS IN EMERGING TECH SECTORS: A REVIEW OF AI USE CASES ACROSS HEALTHCARE, RETAIL, AND CYBERSECURITY
Organizations increasingly deploy AI use cases to improve decisions, yet many implementations underperform because data, people, and governance readiness are uneven and the pathway from readiness to outcomes is rarely quantified. This study tested a readiness to adoption intensity to outcomes model using a cross-sectional, case-based survey (Likert 1 to 5) across three enterprise case settings in real operational contexts (healthcare, retail, cybersecurity). From 500 invitations, 342 responses were received (68.4%), and 318 usable cases were retained (63.6%), split across healthcare (n=108), retail (n=110), and cybersecurity (n=100). Key variables were data readiness, human capability, governance readiness, AI adoption intensity, and performance outcomes, with organization size, role group, and years of AI exposure as controls. Analyses used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression (standardized β, R²), plus mediation interpretation via the combined regression pattern. Construct means were moderate-to-positive (data readiness 3.62, human capability 3.55, governance readiness 3.48, adoption intensity 3.58, outcomes 3.67), and cybersecurity reported the highest governance (3.60) and outcomes (3.73). Reliability was strong (α=0.82 to 0.90). Adoption intensity correlated with outcomes (r=0.62, p<.01) and with data readiness (r=0.54), human capability (r=0.49), and governance readiness (r=0.46), all p<.01. In regression, readiness explained substantial variance in adoption (R²=0.48), led by data readiness (β=0.33, p<.001), followed by human capability (β=0.24, p<.001) and governance readiness (β=0.19, p=.002). Outcomes were explained (R²=0.52) by adoption intensity (β=0.45, p<.001) and governance readiness (β=0.21, p=.004), with a smaller direct data effect (β=0.12, p=.041) and a non-significant direct human capability effect once adoption was included (p=.180), indicating that skills primarily improve outcomes by increasing routine AI use. Implications are that organizations should prioritize data integration and quality, invest in workforce capability to sustain adoption, and strengthen governance to translate AI deployments into measurable gains.
Copyright (c) 2023 Rakibul Hasan, Alifa Majumder Nijhum (Author)
RELIABILITY-CENTERED MAINTENANCE OPTIMIZATION USING MULTI-OBJECTIVE AI ALGORITHMS IN REFINERY EQUIPMENT
This quantitative study on Reliability-Centered Maintenance Optimization Using Multi-Objective AI Algorithms in Refinery Equipment had examined how reliability-centered maintenance (RCM) logic could be translated into measurable decision variables and optimized under refinery constraints using multi-objective AI. A structured review of 62 peer-reviewed papers had informed objective selection, constraint definition, and algorithm benchmarking practices. The empirical analysis had used a retrospective refinery case dataset comprising 420 assets observed over 60 months (190 rotating, 170 static, 60 instrumentation/control) with 17,980,000 operating exposure hours. The dataset had included 612 functional failure events, 9,284 work orders (59.0% preventive, 41.0% corrective), and 4,116 inspection observations. Downtime severity had been right-skewed, with median downtime per event of 6.2 hours (IQR 2.1–18.4) and a 95th percentile of 92.0 hours, with static equipment exhibiting longer restoration burdens. Composite indices for operating severity, monitoring coverage, execution burden, degradation evidence, and risk exposure had demonstrated acceptable internal consistency (0.77–0.85). Collinearity screening reduced model predictors and improved stability, lowering maximum VIF from 9.4 to 2.8 in rotating equipment models. Regression results showed that operating severity increased functional failure hazard (HR 1.29, 95% CI 1.18–1.41, p < 0.001) and degradation evidence increased hazard (HR 1.33, 95% CI 1.22–1.46, p < 0.001), while monitoring coverage reduced hazard (HR 0.83, 95% CI 0.75–0.92, p = 0.001). Preventive intensity reduced corrective recurrence (IRR 0.79, 95% CI 0.70–0.90, p < 0.001) but increased direct cost (+5.4%, p = 0.001). Turnaround proximity reduced unavailability (RR 0.86, p = 0.001). Safety-critical assets showed higher inspection adherence (96.1% vs 88.7%; OR 2.72, p < 0.001). The findings had clarified the trade-off structure motivating Pareto-efficient maintenance portfolios that remained interpretable and constraint-feasible in refinery operations.
Copyright (c) 2023 Saikat Sarkar, Aditya Dhanekula (Author)
GLOBAL SOURCING, CYBERSECURITY VULNERABILITIES, AND U.S. RETAIL MARKET OUTCOMES: A REVIEW OF PRICING IMPACTS AND CONSUMER TRENDS
This quantitative review synthesizes evidence on how global sourcing and emerging cybersecurity vulnerabilities jointly influence U.S. retail pricing structures and consumer market outcomes. Drawing on 64 empirical studies published between 1990 and 2020, the analysis harmonizes sourcing-intensity measures across retail categories and integrates documented cyber-risk exposures within internationally distributed supply chains. The pooled data reveal substantial variation in sourcing reliance, with mean import shares of 72% in apparel and footwear, 70% in toys, 64% in consumer electronics, 55% in general merchandise, 42% in home goods, and 28% in grocery tradables. Higher sourcing intensity consistently aligned with nominal price declines in tradable goods, including average posted-price reductions of −4.8% in apparel, −4.1% in toys, and −3.5% in electronics, while grocery tradables experienced a modest −0.8% decrease. Transaction-price effects were even larger, reaching −6.2% in apparel and −5.4% in toys, indicating strong promotional pass-through. Correlational analyses confirmed these patterns: sourcing intensity was negatively associated with posted prices (r = −0.62) and price-change magnitudes (r = −0.58), and positively associated with imported-variety expansion (r = 0.66) and quality-adjusted affordability (r = 0.49). Random-effects meta-regressions further demonstrated significant price-reducing effects of sourcing intensity on both posted (β = −0.032, p < .001) and transaction prices (β = −0.041, p < .001). Exchange-rate cost shocks generated additional price decreases (β = −0.021), whereas tariff shocks produced modest increases (β = 0.012). Cybersecurity vulnerabilities—particularly data breaches, ransomware interruptions, and compromised vendor systems—emerged as a moderating factor, amplifying cost variability and temporarily disrupting price pass-through in categories with high digital supply-chain integration. Despite these risks, non-price outcomes remained substantial: sourcing intensity predicted imported-variety growth (β = 0.058) and stronger substitution patterns (β = 0.026). Overall, the evidence shows that global sourcing lowers U.S. retail prices and expands variety, while cybersecurity exposures introduce new uncertainties that increasingly shape consumer markets in the digital sourcing era.
Copyright (c) 2021 Md. Hasan Imam, Shaikat Biswas (Author)
QUANTITATIVE ASSESSMENT OF AI-ENABLED CONSTRUCTION PLANNING TOOLS FOR REDUCING DELAYS IN U.S. INFRASTRUCTURE PROJECTS
This study addresses schedule overruns in U.S. infrastructure projects and the limited quantitative evidence on whether AI-enabled construction planning tools reduce delays. The purpose is to quantify relationships between AI-based planning adoption and schedule performance using project-level data. A quantitative cross-sectional, case-based design used a Likert five-point survey of practitioners covering 198 infrastructure cases across public agencies and enterprise contractors and consultants. Key variables included AI-enabled planning tool adoption, planning quality, coordination effectiveness, project size, complexity, contract type, and a Schedule Delay Index (SDI) from planned and actual durations. Reliability was high for all multi-item scales (α = 0.84-0.91). The analysis plan combined descriptive statistics, Pearson correlations, and multiple regression with moderation tests. Projects showed moderate AI adoption (M = 3.47, SD = 0.78) and an average 11% schedule overrun (SDI M = 0.11, SD = 0.09). AI adoption correlated negatively with SDI (r = −0.41, p < .001) and remained a significant predictor of lower delay after controlling for size, complexity, and contract type; a one-point increase in adoption was associated with a 2.8 percentage point reduction in SDI. Adding planning quality and coordination effectiveness increased explained variance in SDI from 25% to 41% and partially mediated the AI-delay relationship, with effects strongest on highly complex projects. The headline finding is that AI-enabled planning tools contribute meaningfully to delay reduction when embedded in robust planning and coordination practices. The study implies that infrastructure owners should treat AI-enhanced planning as a strategic capability for improving delivery reliability across the sample.
IMPACT OF HIGH-PERFORMANCE COMPUTING IN THE DEVELOPMENT OF RESILIENT CYBER DEFENSE ARCHITECTURES
This study addresses the growing problem that many cloud and enterprise environments are investing in artificial intelligence and advanced computing for cyber defense without clear evidence of how these capabilities translate into resilient performance under attack. The purpose is to empirically examine how AI capability and advanced computing capacity contribute to cyber defense resilience in real organizational settings. A quantitative, cross sectional, case-based survey was conducted with organizations that operate AI enabled security solutions on cloud, high performance, and edge or fog infrastructures, yielding 220 usable responses from 310 distributed questionnaires (70.9 percent) from cybersecurity and IT professionals. Core variables included AI capability, advanced computing capacity, cybersecurity readiness, and a composite cyber defense resilience index. Data were analyzed using reliability and factor analysis, descriptive statistics, Pearson correlations, and multiple regression. Results show moderately high AI capability (M = 3.72, SD = 0.71) and advanced computing capacity (M = 3.85, SD = 0.66), but only moderate resilience (M = 3.61, SD = 0.69). AI capability (β = 0.39, p < .001) and advanced computing capacity (β = 0.27, p < .001) significantly predict resilience in a model that explains 61 percent of variance (R² = 0.61), with strong correlations between AI and resilience (r = .62) and computing and resilience (r = .55). These findings indicate that resilient cyber defense in cloud and enterprise contexts depends on the integrated development of AI analytics, scalable computing infrastructures, and cybersecurity readiness, offering actionable guidance for CISOs and security architects.
Copyright (c) 2021 Zamal Haider Shish, Mst. Shahrin Sultana (Author)
IMPACT OF BIG DATA AND PREDICTIVE ANALYTICS ON FINANCIAL FORECASTING ACCURACY AND DECISION-MAKING IN GLOBAL CAPITAL MARKETS
This quantitative study examined how big data intensity and predictive-analytics capability influenced financial forecasting accuracy and decision-making quality in global capital markets. The study was grounded in an extensive review of 112 peer-reviewed empirical and methodological papers spanning econometric forecasting, alternative data applications, machine learning in asset pricing, and decision-value testing across international markets. Using a cross-sectional time-series panel of market–asset–time observations, the analysis integrated developed and emerging markets and covered equities, fixed income, foreign exchange, and derivatives. Big data intensity was operationalized as a composite index reflecting data volume, variety, frequency, latency, dimensionality, and coverage breadth, while predictive-analytics capability was measured as a ranked model-class indicator ranging from classical econometrics to hybrid econometric–machine-learning systems. Forecasting accuracy served as the primary outcome and was evaluated through rolling out-of-sample errors; decision-making quality was assessed through standardized portfolio, trading, and risk-performance indicators. Descriptive results showed substantial dispersion across markets, with mean big data intensity at 0.56 (SD = 0.19) and predictive-analytics capability at 3.12 (SD = 1.09) on a five-point scale. Developed markets exhibited higher data intensity (0.63) and sophistication (3.54) than emerging markets (0.48 and 2.66, respectively). Correlation analysis indicated that big data intensity and predictive-analytics capability were each inversely associated with forecasting error (r = −0.46 and r = −0.52), and forecasting error was inversely related to decision-making quality (r = −0.48). Fixed-effects panel regressions confirmed significant direct effects: big data intensity reduced forecasting error (β = −0.021, p < .001) and predictive-analytics capability reduced forecasting error (β = −0.018, p < .001). The interaction term was also negative (β = −0.007, p < .01), indicating complementarity such that analytics benefits strengthened under higher data intensity. In the decision model, forecasting error significantly predicted decision quality (β = −28.6, p < .001), demonstrating that statistical accuracy gains translated into improved economic outcomes. Overall, the study provided integrated evidence that richer data environments and advanced predictive analytics jointly enhanced forecasting precision and decision performance across global capital markets.
Copyright (c) 2024 Md. Mosheur Rahman, Md Arman Hossain (Author)
ARTIFICIAL INTELLIGENCE APPLICATIONS FOR PREDICTING RENEWABLE-ENERGY DEMAND UNDER CLIMATE VARIABILITY
This quantitative, cross-sectional, case-study–based research investigates how artificial intelligence (AI) applications can improve renewable-energy demand forecasting under climate variability while being adopted in real operational settings. The study addresses the problem that utilities and renewable-intensive enterprises still underuse climate-variability indicators in AI models, limiting the accuracy and decision value of demand forecasts. Using a structured Likert five-point questionnaire and secondary operational and climate data from renewable-energy systems, 280 questionnaires were distributed and 214 valid responses were obtained (76.4% usable rate). Key constructs included organizational analytics capability, data quality and integration, climate-variability integration in AI models, AI model transparency, perceived forecast accuracy, trust in AI outputs, and intention to use AI-based forecasts. Reliability analysis showed Cronbach’s alpha values between 0.82 and 0.91, and multiple regression and correlation analyses were used alongside benchmarking of traditional regression and AI models. Compared with a traditional multiple regression model (MAPE 7.8%, RMSE 18.4 MW) and an AI model without explicit climate indicators (MAPE 6.1%, RMSE 15.2 MW), the climate-enhanced AI model achieved substantially lower error (MAPE 4.3%, RMSE 11.6 MW). Survey results indicated moderately high perceived forecast accuracy (mean 3.92) and trust (3.88), with intention to use AI forecasts averaging 4.03. Data quality and integration and climate-variability integration were the strongest predictors of perceived accuracy, while perceived accuracy and trust primarily drove intention to use. The findings imply that climate-aware AI forecasting delivers measurable accuracy gains but requires robust data pipelines and analytics capability to be trusted and embedded in renewable-energy planning and operations.
Copyright (c) 2024 Dr. Zulqarnain, Zayadul Hasan (Author)
INTEGRATION OF COMMUNICATIONS-BASED TRAIN CONTROL (CBTC) INTO CIVIL ENGINEERING DESIGN FOR SAFER AND CYBER-SECURE RAIL SYSTEMS
This study addresses the practical problem that Communications-Based Train Control (CBTC) technologies are often engineered separately from civil infrastructure, leaving unclear to what extent integrated design actually improves safety and cyber-secure performance in operating rail enterprises. The purpose of the research is to quantify how CBTC-civil engineering integration relates to perceived safety performance, cybersecurity resilience, and overall system performance in real CBTC-enabled corridors. A quantitative, cross-sectional, case-study based survey design was adopted, using a structured five-point Likert questionnaire administered to 220 experienced professionals in CBTC-equipped rail infrastructure managers, operators, consultants, and suppliers, yielding a 71.0% usable response rate. Key latent variables were CBTC-civil integration quality, safety performance, cybersecurity resilience capability, and overall system performance, all measured with reliable scales (Cronbach’s alpha 0.87-0.91). The analysis plan combined descriptive statistics, reliability and validity checks, Pearson correlations, and multiple regression models. Results show that integration quality is strongly associated with safety (r = 0.62) and cybersecurity resilience (r = 0.57), explaining 39% and 33% of their variance respectively, while integration, safety, and cybersecurity together explain 65% of the variance in overall performance. Safety and cybersecurity emerge as the strongest direct predictors of performance, with CBTC-civil integration exerting an additional indirect effect through these constructs, indicating that integrated civil-signaling design is an upstream driver of safer and more cyber-resilient CBTC enterprises. The findings imply that rail authorities and CISOs should institutionalize CBTC-civil co-design, security zoning, and safety-security co-engineering from the earliest alignment and station-planning stages to realize the full performance benefits of CBTC deployment.
Communications-Based Train Control (CBTC)
Copyright (c) 2023 Syed Zaki Uddin, Md Sarwar Hossain Shuvo (Author)
ARTIFICIAL INTELLIGENCE-DRIVEN DIGITAL TRANSFORMATION MODELS FOR ENHANCING ORGANIZATIONAL COMMUNICATION AND DECISION-MAKING EFFICIENCY
This quantitative study investigated how artificial intelligence (AI) capability and digital transformation (DT) maturity influenced organizational communication quality and decision-making efficiency, with communication quality tested as a mediator and DT maturity as a moderator. The literature review synthesized evidence from 68 prior quantitative papers to refine construct definitions, measurement logic, and empirical pathways. A cross-sectional survey was conducted with 412 respondents from AI-adopting organizations across multiple sectors. Descriptive results indicated moderate-to-high levels of AI capability (M = 3.71, SD = 0.64) and DT maturity (M = 3.62, SD = 0.61). Communication quality recorded the highest mean (M = 3.84, SD = 0.59), followed by decision-making efficiency (M = 3.68, SD = 0.62), and distributional diagnostics supported parametric modeling. Measurement quality was strong (Cronbach’s α = .86–.93; CR = .88–.94; AVE = .60–.70), and CFA fit was acceptable (CFI = .95, TLI = .94, RMSEA = .05, SRMR = .04). Correlations among principal constructs were positive and significant, with no multicollinearity risk (VIFs < 2.10). Structural modeling confirmed all hypothesized direct effects: AI capability positively predicted communication quality (β = .58, p < .001) and decision-making efficiency (β = .33, p < .001), and communication quality positively predicted decision-making efficiency (β = .49, p < .001). Mediation testing showed a significant indirect effect of AI capability on decision efficiency via communication quality (β_indirect = .28, p < .001), indicating partial mediation. Moderation analysis demonstrated that DT maturity strengthened the AI-to-decision efficiency relationship (β_interaction = .14, p = .001). Overall, the findings supported an integrated mediated–moderated model explaining how AI-driven digital transformation enhances communication and decision efficiency in organizational settings.
Copyright (c) 2025 Alifa Majumder Nijhum (Author)
HACCP AND ISO FRAMEWORKS FOR ENHANCING BIOSECURITY IN GLOBAL FOOD DISTRIBUTION CHAINS
This quantitative study investigated how HACCP and ISO 22000–aligned food safety management systems enhanced biosecurity performance across global food distribution chains. The analysis used a multi-country, multi-node dataset comprising 120 firms, 286 facilities, 412 international routes, 1,034 suppliers, and 6,480 shipments. Firms were distributed across HACCP-only (30.0%), ISO-only (25.8%), integrated HACCP–ISO (29.2%), and low-maturity baseline chains (15.0%). High-risk commodities represented 58.5% of shipments, and cold-chain dependency averaged 0.64, indicating extensive exposure to temperature-sensitive biosecurity risks. Preventive capability was operationalized through HACCP maturity, ISO maturity, and integration depth indices, all of which demonstrated strong reliability (α = 0.88–0.91) and coherent factor structure. Descriptively, integrated chains showed the strongest preventive profiles (HACCP maturity M = 4.12; ISO maturity M = 4.05; integration depth M = 4.21) and the most favorable outcomes, including microbial compliance of 94.3% and only 1.8 CCP deviations per 100 shipments, compared with 86.1% compliance and 5.7 deviations in baseline chains. Multilevel regressions indicated that HACCP maturity predicted higher microbial compliance (β = 0.24, p < .001) and fewer CCP deviations (β = −0.31, p < .001), while ISO maturity independently improved compliance (β = 0.19, p < .001) and reduced recalls or border rejections (β = −0.23, p < .001). Integration depth added further explanatory power across all outcomes, especially CCP deviations (β = −0.18, p < .001) and supplier nonconformity dispersion (β = −0.21, p < .001). Final models explained substantially more variance than baseline risk controls alone (R² rising from 0.12–0.25 to 0.30–0.44). Overall, higher HACCP and ISO maturity, reinforced by deep integration, aligned with stronger, more stable chain-wide biosecurity in multinational distribution corridors.
Copyright (c) 2023 Pankaz Roy Sarkar, Md. Kamrul Khan (Author)
AI-DRIVEN THREAT DETECTION AND RESPONSE FRAMEWORK FOR CLOUD INFRASTRUCTURE SECURITY
This quantitative study developed and evaluated an AI-Driven Threat Detection and Response Framework for cloud infrastructure security using a controlled multi-service cloud testbed. The review phase synthesized evidence from over 30 peer-reviewed studies addressing cloud threat surfaces, telemetry foundations, AI detection models, automated response orchestration, drift robustness, and closed-loop security control. The experiment analyzed 120,000 fixed-length telemetry windows (114,000 benign; 6,000 malicious) and 360 injected incident episodes stratified by workload volatility, identity complexity, and attack stealth. Multi-modal telemetry from IAM logs, control-plane audit trails, network flow logs, runtime metrics, and application traces was transformed into single-source, late-fusion, and early-fusion feature sets. Detection comparisons showed high overall performance (precision M = 0.91, recall M = 0.88, PR-AUC M = 0.93) with low alert noise (false alarm rate M = 0.021) and rapid detection (MTTD M = 2.8 minutes). Mixed-effects regressions indicated that deep sequence (β = 0.041, p < .001), deep graph (β = 0.038, p < .001), and hybrid ensemble models (β = 0.052, p < .001) significantly improved PR-AUC relative to supervised baselines, and early multi-modal fusion yielded the largest gain (β = 0.047, p < .001). Drift-triggered recalibration reduced detection delay (β = −0.62 minutes, p < .001) and false alarms (β = −0.006, p < .01), stabilizing performance across drift phases where PR-AUC shifted from 0.95 (pre-drift) to 0.89 (drift) and recovered to 0.94 (post-drift). Calibrated threat scores reduced false containment via significant mediation (indirect β = −0.012). Risk-weighted response decreased MTTR by 1.21 minutes (p < .001), while sequential response produced the highest containment success (β = 0.058, p < .001) with lower service-impact cost. Detection and response models explained 62% and 61% of variance in PR-AUC and containment success, supporting a quantitative closed-loop cloud defense framework.
Copyright (c) 2025 Md Harun-Or-Rashid Mollah (Author)
THERMAL & ELECTRICAL PERFORMANCE ENHANCEMENT OF POWER DISTRIBUTION TRANSFORMERS IN SMART GRIDS
This study addresses the persistent problem of thermal stress, electrical losses, and premature failures in power distribution transformers operating in smart grid environments, where dynamic loading and high reliability expectations intensify asset risk. The purpose is to quantify how smart grid oriented operational, monitoring, and maintenance practices enhance transformer thermal and electrical performance and reliability. Using a quantitative, cross-sectional, case-based survey design, data were collected from 210 engineers, operators, maintenance supervisors, and asset managers across eight smart grid enabled utility and industrial enterprises, using a five-point Likert scale instrument with construct reliabilities between 0.86 and 0.91. Key independent variables were smart monitoring and automation, thermal management and cooling practices, load management and demand response, and predictive maintenance and diagnostics, while dependent variables captured perceived thermal performance, electrical performance and efficiency, and reliability and lifespan outcomes. Data were analyzed using descriptive statistics, Pearson correlations, and multiple regression. Headline findings show all practice constructs are positively and significantly related to performance, with correlations between 0.53 and 0.71 and regression models explaining 53–60 percent of variance in outcomes; predictive maintenance (β = 0.38, p < .001) and smart monitoring (β = 0.30–0.31, p < .001) emerge as the strongest predictors of reliability and thermal performance, while thermal management most strongly predicts electrical efficiency (β = 0.34, p < .001). Organizations with more than five years of smart grid deployment report significantly higher practice levels and reliability scores (mean reliability 4.05 versus 3.71, p = .001). The findings imply that integrated investment in smart monitoring, disciplined thermal and load management, and predictive maintenance is critical for extending transformer life and improving smart grid resilience.
Copyright (c) 2023 Md Hamidur Rahman (Author)
SMART CONTINUOUS IMPROVEMENT WITH ARTIFICIAL INTELLIGENCE, BIG DATA, AND LEAN TOOLS FOR ZERO DEFECT MANUFACTURING SYSTEMS
This study addresses the practical problem that many Industry 4.0 oriented manufacturers invest separately in artificial intelligence, big data analytics, and lean tools without clear empirical evidence of how their integrated deployment supports smart continuous improvement and zero defect manufacturing outcomes. The purpose is to test a model linking AI capability, big data analytics capability, lean tools deployment, smart continuous improvement performance, and zero defect results using a quantitative cross-sectional, case-study based survey of 214 professionals from ten enterprise manufacturing plants (response rate 71.3 percent). Key variables are measured on five-point Likert scales and analyzed using descriptive statistics, reliability analysis, Pearson correlations, and multiple regression. All scales show strong internal consistency (Cronbach’s alpha 0.88 to 0.92). Lean tools deployment has the highest mean (3.94), followed by smart continuous improvement (3.78) and zero defect outcomes (3.65), while AI and big data capabilities are moderately developed (3.46 and 3.59). Smart continuous improvement is strongly predicted by AI, big data, and lean (R² = 0.582), with lean as the dominant driver (β = 0.36). Smart continuous improvement in turn explains 41.6 percent of the variance in zero defect performance (β = 0.65), and the full model combining all capabilities plus smart continuous improvement explains 53.8 percent of zero defect variance, indicating partial mediation. These results imply that lean and continuous improvement should be treated as the backbone of zero defect programs, with AI and big data designed explicitly to enhance data driven problem solving and defect prevention routines rather than operating as isolated digital initiatives.
Copyright (c) 2023 Md Foysal Hossain, Aditya Dhanekula (Author)
DIGITAL TWIN-DRIVEN THERMODYNAMIC AND FLUID DYNAMIC SIMULATION FOR EXERGY EFFICIENCY IN INDUSTRIAL POWER SYSTEMS
This study addresses the persistent problem that many industrial power systems still exhibit low exergy efficiency even after decades of energy efficiency programs, largely because second law metrics are not embedded in day-to-day digital decision tools. The purpose is to examine how digital twin driven thermodynamic and fluid dynamic simulation, deployed through cloud and enterprise platforms, relates to exergy efficiency at plant level. Using a quantitative, cross sectional, case-based design, data were collected via structured questionnaires from 214 engineers, operators and managers in steam, combined cycle and cogeneration power plants that have implemented digital twin solutions. Key variables include digital twin adoption, simulation sophistication, data quality, organizational support and perceived exergy efficiency, measured on five-point Likert scales. Descriptive statistics and reliability analysis (Cronbach’s alpha 0.84 to 0.89) were followed by correlation and multiple regression, including interaction terms. The core model explained 51.9 percent of the variance in exergy efficiency (R² = 0.519), with simulation sophistication (β = 0.34, p < .001) and digital twin adoption (β = 0.27, p < .001) as the strongest predictors; data quality (β = 0.18, p = .002) and organizational support (β = 0.16, p = .006) were also significant. When moderation effects were added, explained variance rose to 57.3 percent (R² = 0.573), and interactions between digital twin adoption and data quality (β = 0.15, p = .004) and between adoption and organizational support (β = 0.12, p = .015) were significant, showing that high quality data and strong management backing amplify exergy gains. The findings imply that exergy-oriented improvements are maximized when plants jointly invest in high fidelity digital twin models, robust data governance and sustained organizational support for simulation-based decision making in industrial power systems.
Thermodynamic and Fluid Dynamic Simulation
Copyright (c) 2023 S. M. Habibullah, Muhammad Mohiul Islam (Author)
MACHINE LEARNING APPROACHES FOR OPTIMIZATION OF LUBRICANT PERFORMANCE AND RELIABILITY IN COMPLEX MECHANICAL AND MANUFACTURING SYSTEMS
This quantitative, cross-sectional, case-based study examines the role of machine learning (ML) approaches in enhancing lubricant condition monitoring and, in turn, optimizing lubricant performance and improving equipment reliability across complex mechanical and manufacturing systems. The research is motivated by the persistent underutilization of lubricant-related data in predictive maintenance programs, even as modern industrial assets increasingly incorporate dense sensor arrays capable of generating high-frequency tribological, thermal, and chemical measurements. To address this gap, the study surveyed 214 professionals responsible for maintenance and reliability management in enterprise-scale manufacturing plants operating high-duty rotating and sliding equipment, generating 204 complete, analyzable cases measured on a five-point Likert scale. The conceptual framework was structured around several core latent variables: ML-driven lubrication adoption, lubricant performance, system reliability, data quality, technical readiness, and organizational readiness. Descriptive statistics indicated that while respondents reported only moderate levels of ML adoption (M = 3.21, SD = 0.74), the plants demonstrated comparatively high lubricant performance (M = 3.68, SD = 0.69) and system reliability (M = 3.59, SD = 0.71), suggesting that lubrication improvements and reliability outcomes are already being pursued through traditional means, with ML technologies representing an emergent, rather than fully mature, complement to existing programs. All multi-item measurement scales showed strong internal consistency, with Cronbach’s alpha values ranging from .81 to .89, confirming the reliability of the constructs and supporting their suitability for subsequent correlation and regression analyses. Pearson correlation coefficients revealed that ML adoption was positively and moderately associated with both lubricant performance (r = .52, p < .001) and system reliability (r = .48, p < .001), providing initial empirical support for the theorized relationships. These correlations suggest that plants beginning to integrate ML-enabled lubrication analytics are already experiencing measurable operational benefits, potentially due to improved detection of lubricant degradation, earlier identification of contamination events, or more precise adjustment of lubrication intervals. Multiple regression results further clarified these relationships, demonstrating that the proposed model explained 41.8% of the variance in lubricant performance and 49.2% of the variance in system reliability. ML adoption emerged as a significant predictor across models (β up to .38, p < .001), but data quality and organizational readiness also showed strong predictive influence, highlighting the critical interdependencies between algorithmic tools, underlying data infrastructures, and human/organizational processes.
Copyright (c) 2021 Zobayer Eusufzai (Author)
BIG DATA AND ENGINEERING ANALYTICS PIPELINES FOR SMART MANUFACTURING: ENHANCING EFFICIENCY, QUALITY, AND PREDICTIVE MAINTENANCE
This study addresses the practical problem that many smart manufacturing firms deploy isolated analytics tools without coherent big data and engineering analytics pipelines, which limits gains in efficiency, quality, and predictive maintenance. The purpose is to empirically examine how the maturity of such pipelines influences three core performance dimensions in smart factories. A quantitative, cross sectional, case-based survey design was applied to 150 respondents from cloud enabled smart manufacturing enterprises, using Likert 5-point scales to measure analytics pipeline maturity, efficiency performance, quality performance, and predictive maintenance effectiveness, with firm size, industry segment, and automation level as controls. Descriptive analysis shows moderate to high maturity (PIPE mean 3.84, SD 0.62) and positive perceived outcomes for efficiency (mean 3.91), quality (3.77), and predictive maintenance (3.69), with strong reliability for all scales (Cronbach’s alpha 0.86 to 0.89). Correlation analysis indicates significant positive associations between pipeline maturity and efficiency (r 0.52), quality (r 0.47), and predictive maintenance (r 0.58, p less than .001). Multiple regression confirms that pipeline maturity is a significant predictor of efficiency (β 0.41, R² 0.34), quality (β 0.37, R² 0.29), and predictive maintenance (β 0.48, R² 0.41) after controls, while mediation tests show that predictive maintenance partially mediates the pipeline–efficiency relationship, increasing explained variance in efficiency to 0.43. These results imply that managers should treat engineering analytics pipelines as a strategic, plant level capability, prioritizing end to end data integration, high quality sensor and enterprise data, and predictive maintenance analytics to unlock scalable improvements in throughput, defect reduction, and downtime control.
Copyright (c) 2022 M.A. Rony, Ashraful Islam (Author)
POST-GDPR DIGITAL COMPLIANCE IN MULTINATIONAL ORGANIZATIONS: BRIDGING LEGAL OBLIGATIONS WITH CYBERSECURITY GOVERNANCE
This study examined how multinational corporations operationalize the requirements of the General Data Protection Regulation (GDPR) within cybersecurity governance frameworks to achieve measurable compliance performance. Using a quantitative, cross-sectional research design, the study analyzed the interrelationships among compliance maturity, control effectiveness, governance efficiency, and risk mitigation across approximately 400 multinational subsidiaries operating in at least three international jurisdictions. Data were collected through structured surveys and archival compliance records and analyzed using confirmatory factor analysis and structural equation modeling (SEM). Reliability and validity were confirmed through high Cronbach’s alpha, composite reliability, and average variance extracted (AVE) values, ensuring methodological rigor and construct accuracy. Descriptive analysis revealed that organizations generally demonstrated high compliance maturity and governance efficiency, though variation persisted in control execution and risk mitigation outcomes. Correlation and regression analyses indicated strong, positive, and statistically significant relationships among all constructs. Compliance maturity emerged as a significant predictor of both governance efficiency and control effectiveness, while governance efficiency and control effectiveness significantly influenced risk mitigation performance. Mediation analysis confirmed that governance efficiency partially mediated the link between compliance maturity and risk mitigation, establishing governance as the conduit through which compliance maturity translates into improved cybersecurity outcomes. Moderation analysis showed that cross-border operational complexity weakened the impact of control effectiveness on risk mitigation, highlighting the challenges of maintaining consistent compliance performance across diverse regulatory environments. The structural model achieved strong goodness-of-fit indices, validating the hypothesized relationships and confirming that integrated compliance and governance systems enhance cybersecurity resilience. Overall, the findings demonstrated that post-GDPR digital compliance functions as a quantifiable governance mechanism—linking legal adherence, operational control, and risk reduction into a unified accountability framework. The study recommends that multinational organizations institutionalize compliance as a continuous governance process supported by data analytics, automated monitoring, cross-functional oversight, and jurisdiction-specific adaptation to sustain measurable regulatory alignment and long-term digital trust.
Copyright (c) 2021 Md. Omar Faruq, Md Harun-Or-Rashid Mollah (Author)