American Journal of Advanced Technology and Engineering Solutions
The American Journal of Advanced Technology and Engineering Solutions (AJATES) Online ISSN (3067-0470) is a leading international, peer-reviewed, open-access journal dedicated to publishing high-quality research and advancements in technology and engineering. AJATES serves as a platform for researchers, academicians, industry professionals, and innovators to present cutting-edge solutions, novel methodologies, and interdisciplinary studies that address contemporary challenges in engineering and technology. Published monthly, AJATES focuses on fostering innovation and collaboration across diverse disciplines, including mechanical engineering, electrical engineering, computer science, materials science, robotics, and sustainable technologies. By bridging the gap between theory and practice, the journal contributes to the global discourse on technological advancements and their practical applications.
A Trust-Centered AI and Security Modeling Approach for Early Cancer Diagnosis, Population-Level Health Analysis, and Secure Deployment in U.S. Healthcare Infrastructure
This study addresses the persistent gap between high-performing cancer AI prototypes and real-world adoption by proposing and testing a trust-centered AI plus security-modeling blueprint for early cancer diagnosis and population-level health analysis within U.S. healthcare infrastructure. The purpose was to quantify how often published “enterprise-ready” capabilities actually co-occur with deploy ability pillars such as interpretability, robustness, security, and equity. Using a quantitative, cross-sectional, case-based review design, each included paper was treated as a case reflecting cloud and enterprise healthcare deployment contexts (for example, multi-site systems, integrated EHR and imaging stacks, or networked inference services). The sample comprised 45 cases (N = 45). Key variables were five Likert-scored readiness dimensions (1–5): clinical validation rigor, interpretability and communication support, robustness and generalization evidence, security and privacy modeling, and fairness and equity evidence, plus composite indicators such as trust-mechanism presence and a rubric-scaled Trust-Centered Deployment Readiness (TDR). The analysis plan applied descriptive statistics (counts, percentages, means), cross-tabs between trust-mechanism grouping and validation readiness, and a composite readiness summary. Headline findings show that radiology and pathology cases dominated (31/45, 68.9%), interpretability appeared in 28/45 (62.2%) but comprehensive interpretability was limited (12/45, 26.7%; mean M = 3.1/5), external validation or multi-site evaluation occurred in only 16/45 (35.6%), and explicit security or privacy-by-design elements were present in 14/45 (31.1%) with the lowest readiness mean (M = 2.4/5). Trust-mechanism studies (19/45, 42.2%) showed higher validation readiness (M = 3.6/5) and more external validation (11/19, 57.9%) than performance-only studies (5/26, 19.2%). Overall, only 8/45 (17.8%) met high composite readiness (≥0.75), while 21/45 (46.7%) were moderate and 16/45 (35.6%) low. Implications indicate that healthcare AI procurement and governance should prioritize a complete evidence package that couples external validation, calibrated trust cues, security controls across the lifecycle, and subgroup equity reporting, rather than selecting models based on accuracy alone.
Copyright (c) 2020 Md Fokhrul Alam, Md Fardaus Alam, Md Ashraful Alam (Author)
Advanced Computing–Based Modeling of Steel Connection Behavior and Stability Performance using ETABS And STAAD Pro
Advanced computational modeling has become an essential tool for evaluating the structural performance of steel frame systems and their connection behavior under complex loading conditions. This study examined the structural behavior and stability performance of steel connections using advanced computing-based modeling through ETABS and STAAD Pro. A quantitative comparative simulation design was adopted to analyze the response of multistory steel frame structures under standardized modeling conditions. A total of 24 three-dimensional steel structural models were developed and analyzed using identical geometric properties, material characteristics, and boundary conditions in both software platforms. The analytical dataset consisted of 960 structural observations derived from multiple load combinations including gravity, wind, and seismic loading scenarios. Key structural performance indicators evaluated in the study included maximum displacement, interstory drift, column axial force, beam shear force, bending moment distribution, and structural stability indices. The findings demonstrated that both ETABS and STAAD Pro produced generally consistent structural response patterns across the simulated models. The average maximum displacement predicted by ETABS was 40.56 mm, compared with 37.12 mm generated by STAAD Pro, indicating slightly greater lateral deformation predicted by ETABS. Similarly, the average interstory drift ratio was 0.0023 in ETABS and 0.0021 in STAAD Pro, confirming similar deformation trends between the two platforms. In contrast, STAAD Pro simulations produced higher internal force values in structural members, with average column axial forces of 1287.66 kN compared with 1218.43 kN in ETABS, and average beam bending moments of 366.88 kN·m compared with 349.72 kN·m in ETABS. Subgroup analysis further showed that structural models with semi-rigid connections produced greater deformation responses, with an average displacement of 42.63 mm compared with 35.18 mm in rigid connection models. Statistical testing confirmed that several structural indicators exhibited significant differences between the two software platforms, although effect size analysis indicated that these differences were generally small to moderate in magnitude. Overall, the results demonstrated that both ETABS and STAAD Pro provide reliable analytical capabilities for modeling steel structural behavior while exhibiting modest numerical variation in certain structural performance indicators.
Copyright (c) 2023 Md. Mainuddin Faisal, Palash Chandra Das (Author)
Advanced Computing Frameworks for Real-Time SAP S/4HANA Retail Business Intelligence: Optimizing Data Processing, Latency, and System Reliability
This study addresses the persistent problem that SAP S/4HANA retail business intelligence environments often struggle to achieve simultaneously low latency, high data freshness, and dependable reliability at enterprise scale, particularly when cloud and hybrid deployments introduce additional integration and operational complexity. The purpose was to quantify which advanced computing framework combinations are most consistently associated with improved real-time BI outcomes in retail decision workflows. Using a quantitative cross-sectional, case-based design, the study coded a sample of 60 eligible enterprise and cloud-oriented cases from the reviewed corpus (N = 60). Key variables included framework adoption categories (for example CDC or incremental refresh, in-memory or HTAP, streaming, distributed processing, cloud-native orchestration), outcome constructs measured via a 5-point Likert evidence scale (Latency Improvement Evidence LIE, Processing Efficiency Evidence PEE, Reliability and Continuity Evidence RCE), and workflow clusters (inventory, promotions, fulfillment, anomaly detection). The analysis plan applied frequency distributions and descriptive statistics, then conducted evidence-based hypothesis aggregation using a predefined support rule (at least 60% directional support and mean evidence score at or above 3.50). Findings show strong concentration of CDC or incremental refresh integration at 73.3% (44/60), in-memory or HTAP execution at 68.3% (41/60), streaming at 60.0% (36/60), cloud-native orchestration at 51.7% (31/60), and distributed processing at 48.3% (29/60). Overall evidence strength was high for latency and processing (LIE M = 3.84, SD = 0.71; PEE M = 3.76, SD = 0.69) with moderate-strong reliability (RCE M = 3.62, SD = 0.74). Headline results supported all three hypotheses: hybrid multi-layer stacks outperformed single-layer designs for latency (77.8% supportive, 28/36; hybrid LIE M = 4.08 vs 3.41), CDC and event-driven strategies outperformed batch refresh for freshness and reporting delay (CDC subgroup LIE M = 3.97 vs 3.22), and observability plus automated failover improved reliability versus monitoring-only patterns (high RCE 74.1% vs 42.1%; RCE M = 3.98 vs 3.28). Implications suggest practitioners should prioritize CDC-driven ingestion and incremental maintenance, adopt hybrid stacks that combine in-memory, streaming, and distributed compute, and operationalize reliability through observability and automated recovery, while tracking end-to-end latency as a decomposed pipeline metric aligned to core retail workflows where inventory (30.0%) and promotion monitoring (26.7%) dominate use cases.
Copyright (c) 2022 Zakia Afroz, Khairum Nahar Pinky (Author)
Advanced Computing–Enabled Secure Financial Information Systems for Real-Time Fraud Detection in U.S. Digital Payments: A Quantitative Analysis
This study addressed the persistent problem that U.S. digital payment ecosystems require fraud detection decisions in real time, yet many organizations struggle to align scalable advanced computing with secure financial information system controls in ways that measurably improve fraud detection effectiveness while remaining auditable and operationally usable. The purpose was to quantify how advanced computing enablement (ACE) and secure control strength (SCS) predict real-time fraud detection effectiveness (RTFDE), and to examine whether alert trust and actionability (ATA) strengthens practical effectiveness within enterprise and cloud-based operational cases. Using a quantitative, cross-sectional, case-based design, data were collected from a multi-role sample of enterprise digital-payment stakeholders (N = 210), including fraud and risk operations (38.1%), IT and data engineering (34.3%), and security and compliance (27.6%), with mean experience of 6.8 years (SD = 3.9). Key variables were ACE (8 items), SCS (9 items), RTFDE (8 items), ATA (6 items), feature adoption and maturity (FAM), and operational error hotspots (OEH), all measured on 5-point Likert scales with strong internal consistency (ACE α = .88; SCS α = .91; RTFDE α = .89; ATA α = .86). The analysis plan applied descriptive statistics, Pearson correlations, and hierarchical multiple regression with diagnostic checks (VIF 1.31–2.08). Headline findings showed moderately high capability ratings (ACE M = 3.74, SD = 0.61; SCS M = 3.88, SD = 0.55; RTFDE M = 3.69, SD = 0.63) and strong positive associations with effectiveness (ACE–RTFDE r = .62, p < .001; SCS–RTFDE r = .58, p < .001). Regression results indicated ACE alone explained 32% of variance in RTFDE (R² = .32; β = .57, p < .001); adding SCS increased explanatory power to 40% (ΔR² = .08; ACE β = .41, p < .001; SCS β = .29, p < .001); adding ATA raised total explained variance to 44% (ΔR² = .04; ACE β = .32, p < .001; SCS β = .21, p = .002; ATA β = .24, p < .001). Operationally, the largest constraints clustered in data quality and feature completeness (HS = 3.92), alert routing and prioritization (HS = 3.71), and manual review capacity (HS = 3.63), implying that improving feature integrity and workflow throughput can yield gains even where baseline security controls are mature. Implications suggest that payment organizations should jointly invest in real-time analytics maturity and security traceability, while optimizing alert usability to reduce false-positive burden and strengthen defensible decision trails.
Copyright (c) 2022 Md Mehedi Hasan, Md. Fardous (Author)
Fraud-Detection Algorithms for Identifying Anomalous Transactions in Retail Banking Networks
This study addresses a practical problem in retail banking where fraud-detection algorithms can appear effective yet still produce low-trust alerts, heavy false-positive workload, and weak readiness for shifting fraud patterns, which reduces real operational value. Using a quantitative, cross-sectional, case-study–based design grounded in an enterprise retail banking network, the purpose was to test how six fraud-detection capability dimensions (Data Quality and Feature Readiness, Real-Time Processing, Model Robustness, Explainability, Integration and Scalability, and Monitoring and Updating) predict the primary outcome Anomalous Transaction Identification Performance and three trust-centered outcomes: Alert Quality Index, False-Positive Burden Score, and Drift Readiness Score . The sample comprised N = 200 professionals embedded in fraud operations, risk/compliance, and analytics/IT roles within the case environment. The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression for hypothesis testing. Headline results show capability maturity was highest for Data Quality (M = 3.98, SD = 0.63) and Integration (M = 3.85, SD = 0.69), while the primary outcome ATIP was moderately high (M = 3.81, SD = 0.66); trust outcomes indicated moderate alert quality (AQI M = 3.76, SD = 0.68) but noticeable false-positive burden (FPBS M = 3.12, SD = 0.81) and comparatively weaker drift readiness (DRS M = 3.33, SD = 0.79). Reliability was strong across constructs (α = .81–.88). Correlations with ATIP were strongest for Data Quality (r = .62), Explainability (r = .58), and Monitoring and Updating (r = .55), all p < .001. The regression model explained substantial variance in ATIP (R² = 0.54; F(6,193) = 37.6; p < 0.001), with significant predictors including Data Quality (β = 0.28, p < 0.001), Explainability (β = 0.22, p = 0.002), Monitoring and Updating (β = 0.19, p = 0.006), and Real-Time Processing (β = 0.12, p = 0.041), while Robustness and Integration were not significant after controls. These findings imply that banks seeking measurable improvement should prioritize data readiness, explanation-centered alert design, and monitoring and update governance to raise operational trust, reduce unnecessary investigative load, and sustain performance under drift within enterprise fraud-detection deployments.
Copyright (c) 2021 Mahfuj Ahmed Ruzel, Mostafa Kamal (Author)
A Smart Contract Framework for Automated Settlement and Compliance in Renewable Energy and Distributed Energy Resources
This study addresses the persistent problem that renewable-energy and distributed energy resource (DER) transactions generate high-volume, interval-based micro-transactions that strain conventional centralized billing, reconciliation, and compliance reporting, creating delays, disputes, and weak audit trails. The purpose was to evaluate a smart contract framework that integrates automated settlement and compliance evidence generation within cloud-hosted, enterprise-grade DER transaction workflows using a quantitative, cross-sectional, case-based design. A purposive case sample of n = 214 informed stakeholders was drawn from enterprise settlement and compliance settings, including settlement and billing personnel (31.3%), compliance and audit professionals (24.8%), DER operations and aggregation roles (22.4%), and platform/ICT specialists (21.5%). Key variables were modeled as composite 5-point Likert constructs: Smart Contract Quality (SCQ), Data Integrity and Oracle Reliability (DIO), Interoperability (INT), Security and Privacy Controls (SP), Regulatory Alignment (RA), Automated Settlement Effectiveness (ASE), Compliance Automation Performance (CAP), and Operational or Market Performance (OMP). The analysis plan applied descriptive statistics, internal consistency reliability (Cronbach’s alpha), Pearson correlations, and multiple regression hypothesis testing. Findings showed high capability and outcome perceptions, including SCQ M = 4.12 (SD = 0.61), DIO M = 4.05 (SD = 0.67), SP M = 4.18 (SD = 0.58), ASE M = 4.15 (SD = 0.60), and CAP M = 4.22 (SD = 0.55), with strong reliabilities (α = 0.82–0.91 across constructs). Correlations supported the framework pathways, notably SCQ–ASE r = 0.63, DIO–ASE r = 0.58, SP–CAP r = 0.66, RA–CAP r = 0.61, and CAP–OMP r = 0.68 (all p < 0.001). Regression results confirmed that settlement effectiveness was predicted by SCQ (β = 0.34, p < 0.001), DIO (β = 0.29, p < 0.001), and INT (β = 0.21, p = 0.002) with R² = 0.496, while compliance performance was driven by SP (β = 0.38, p < 0.001) and RA (β = 0.31, p < 0.001) plus organizational readiness (β = 0.19, p = 0.002) with R² = 0.538; together ASE (β = 0.29) and CAP (β = 0.41) explained OMP (R² = 0.572). Audit evidence readiness was also strong (ARS M = 4.25) and artifact coverage exceeded 82.7%–88.3% for trace logs, access logs, dispute logs, and provenance links, indicating compliance-grade outputs at scale. Implications suggest that enterprise implementations should prioritize contract correctness assurance, oracle and meter-data governance, secure per missioning with privacy-aware transparency, and explicit regulatory alignment to convert automation into measurable operational and audit benefits.
Copyright (c) 2023 Tasnim Kabir, Zaheda Khatun (Author)
An Empirical Evaluation of Anomaly Detection Techniques in Smart Grid Systems Using Real-Time Operational Data
This study addresses the problem that smart grid operators increasingly rely on enterprise and cloud enabled monitoring platforms, yet anomaly detection outputs are often difficult to trust due to data quality variability, limited alarm explainability, weak workflow integration, and false alarm overload. The purpose was to quantify which technical and organizational factors most strongly predict perceived detection effectiveness and adoption readiness for anomaly detection techniques in a real smart grid case. A quantitative cross sectional, case-based design was applied using a structured 5-point Likert survey administered to N = 182 stakeholders drawn from enterprise smart grid environments spanning AMI and smart metering, SCADA and EMS or DMS, PMU or WAMS, and supporting IT and cybersecurity functions (operations 28.6%, metering 20.3%, data and IT 20.9%, protection 15.9%, cybersecurity 14.3%). Key variables included Data Quality Adequacy (DQ), Robustness and Adaptability (RB), Explainability of Alarms (EX), System Integration Capability (SI), Perceived Detection Effectiveness (PDE), Adoption Readiness and Trust (ART), and False Alarm Burden and Operational Impact Index (FABOI). The analysis plan used descriptive statistics, scale reliability (Cronbach alpha .81 to .88), Pearson correlations, and multiple regression models. Headline findings showed moderately high perceptions of DQ (M = 3.92, SD = 0.61) and PDE (M = 3.81, SD = 0.62), while false alarm burden was nontrivial (FABOI M = 3.41, SD = 0.73). PDE correlated with DQ (r = .52) and RB (r = .48), while ART correlated most strongly with EX (r = .54) and PDE (r = .58), and decreased with FABOI (r = −.49), all p < .01. Regression confirmed PDE was predicted by DQ (β = .31, p < .001) and RB (β = .27, p < .001), R² = .39, whereas ART was driven by EX (β = .33, p < .001), PDE (β = .29, p < .001), SI (β = .21, p = .002) and reduced by FABOI (β = −.24, p < .001), R² = .47. Implications suggest utilities should prioritize data governance and robustness for effectiveness, but invest in explainable alarms, workflow integration, and alarm burden management to increase operational trust and sustained adoption.
Technology Organization Environment (TOE)
Copyright (c) 2025 S M Shakil, Alamgir Hossain, Md Mijanur Rahman (Author)
The Impact of Machine Learning on Cyber Risk Quantification in Financial Services: A Qualitative Evaluation of Threat Scoring Frameworks
This study quantitatively evaluated the impact of machine learning–enhanced threat scoring frameworks on cyber risk quantification within a regulated financial services environment. Using a cross-sectional comparative design, 18,742 security event records were analyzed, including 1,964 confirmed malicious events (10.48%) and 16,778 benign events (89.52%). Multiple model families were benchmarked under standardized preprocessing and time-aware validation protocols to assess predictive discrimination, calibration quality, and monetized risk alignment. Results demonstrated substantial improvements associated with ML-based frameworks. The ML-enhanced models achieved a mean area under the ROC curve (AUC) of 0.912 compared to 0.781 for baseline scoring systems, with higher precision (0.842 vs. 0.694) and recall (0.817 vs. 0.628). Calibration error was significantly reduced from 0.067 in conventional models to 0.028 in ML-based models, indicating stronger probability alignment. Regression analyses further showed that ML-derived threat scores exhibited a stronger association with log-transformed financial loss outcomes (β = 0.64, p < .001) compared to baseline scores (β = 0.38, p < .001). The ML model explained 42.6% of the variance in loss magnitude (Adjusted R² = 0.426), representing a statistically significant improvement over the baseline model (Adjusted R² = 0.248). High-risk decile stratification under ML scoring produced a mean financial loss of $126,840 compared to $74,390 under conventional scoring, demonstrating enhanced concentration of severe loss events. Sensitivity analyses confirmed stability across alternative sampling and imbalance handling conditions. Collectively, the findings demonstrated that ML-enhanced threat scoring frameworks provided statistically and practically significant improvements in predictive performance and financial alignment, supporting more accurate and economically meaningful cyber risk quantification in financial services environments.
Copyright (c) 2025 Ishtiaque Ahmed, Rajib Sarkar (Author)
Quantitative Risk Modeling of VPN Misconfigurations and Firewall Rule Drift in Hybrid Cloud Networks
This study addresses the persistent security problem that hybrid cloud networks often accumulate VPN misconfigurations and firewall rule drift, which jointly weaken trust-boundary enforcement and increase exposure to unintended reachability and lateral movement. The purpose was to quantify how VPN misconfiguration and firewall rule drift predict hybrid cloud risk exposure, and to examine whether Protection Motivation Theory–based governance (protection motivation) is associated with lower exposure. Using a quantitative cross-sectional, case-based design, data were collected from cloud and enterprise hybrid-network operational cases with N = 132 valid practitioner responses spanning network engineering, security operations, cloud administration, and governance roles. Key variables were VPN Misconfiguration (VMS), Firewall Rule Drift (FDS), Risk Exposure (RE), and Protection Motivation (PMS) measured via multi-item 5-point Likert constructs with strong reliability (α = .88 VMS, α = .91 FDS, α = .87 RE, α = .85 PMS). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression with multicollinearity checks (VIFs within acceptable ranges). Baseline levels were above neutral for VMS (M = 3.62, SD = 0.71), FDS (M = 3.74, SD = 0.66), and RE (M = 3.58, SD = 0.69), with moderate PMS (M = 3.41, SD = 0.62). Headline findings showed strong positive associations between exposure and both VMS (r = .61, p < .001) and FDS (r = .68, p < .001), while PMS was negatively associated with exposure (r = −.42, p < .001). Regression indicated substantial explanatory power (R² = .58, Adj. R² = .56, F = 58.7, p < .001) with significant effects for VMS (β = .33, p < .001) and FDS (β = .46, p < .001) and a protective effect for PMS (β = −.21, p = .001). Segment analysis localized higher composite exposure to the remote-access VPN zone (CREI M = 3.81) and the on-prem to cloud interconnect boundary (CREI M = 3.73). Implications indicate that drift reduction should be prioritized alongside VPN configuration verification and automation-based validation to lower boundary-driven exposure and improve auditability in hybrid-cloud security governance.
Copyright (c) 2022 Md. Fardous, MD Zahedul Islam (Author)
Reliability-Centered Maintenance of Electrical Power and Control Systems Using Manufacturing-Based Asset Management and Quality Models
This study tackles a practical reliability problem in enterprise electrical power and control systems: many reliability-centered maintenance (RCM) programs remain uneven in execution, so plants experience repeat failures, nuisance trips, and slow restoration despite having documented maintenance intentions. The purpose was to quantify whether governance and process discipline predict stronger RCM execution and, in turn, better reliability performance by testing a capability chain where Asset Management Discipline (X1) and Quality-Model Discipline (X2) predict RCM Execution Maturity (M), which predicts Reliability Outcomes (Y). A quantitative, cross-sectional, case-based design was applied in an enterprise case setting using a structured Likert-scale instrument and a multi-role sample of N = 162 respondents (42.6% electrical maintenance, 24.1% automation/control, 18.5% reliability/engineering, 14.8% operations/supervision). Key variables were X1, X2, M, and Y; construct reliability was strong (α = 0.88 for X1, α = 0.86 for X2, α = 0.90 for M, α = 0.84 for Y). Analysis followed (1) descriptive profiling (X1 M = 3.74, X2 M = 3.58, M = 3.49, Y M = 3.61), (2) Pearson correlations, and (3) multiple regression for hypothesis testing. Correlations supported the proposed linkages (X1–M r = 0.62; X2–M r = 0.55; M–Y r = 0.66; all p < .001). The headline finding was a well-fitting regression model predicting reliability outcomes, explaining 54% of the variance (R² = 0.54; adjusted R² = 0.53; F(3,158) = 62.10, p < .001), with the strongest effect from RCM Execution Maturity (β = 0.43, p < .001), followed by Asset Management Discipline (β = 0.28, p < .001) and Quality-Model Discipline (β = 0.19, p = .004). Practical triangulation reinforced these results: compared with the low-maturity tertile, the high-maturity tertile reported lower downtime frequency (2.41 vs 3.26) and repeat failure frequency (2.58 vs 3.33), and higher restoration speed (3.94 vs 3.10). Implications are that reliability gains are most likely when plants prioritize execution maturity as a managed capability and reinforce it with governance traceability and audit-driven standard work, rather than relying on RCM analysis alone.
Copyright (c) 2022 K M Tanvir Anjum Anick, Tasnim Kabir (Author)
Using Machine Learning to Identify Suicide Risk and Inform Early Therapeutic Interventions in Vulnerable Populations
This study examined the predictive relationships between key study constructs and the dependent variable using a quantitative cross-sectional design supported by multivariate regression analysis. The primary objective was to determine the extent to which the independent constructs significantly explained variance in the outcome and to evaluate hypothesis support based on regression coefficients, confidence intervals, and statistical decision rules. Data were collected from a final usable sample of 412 respondents, with demographic analysis indicating a moderately balanced gender distribution (55.3% male, 42.7% female) and strong representation of respondents holding at least a bachelor’s degree (76.7%). Most participants were employed full-time (65.0%), and the sample was predominantly urban (63.3%). Descriptive construct results indicated moderate-to-high levels across all constructs, with means ranging from 3.48 to 3.92 and standard deviations ranging from 0.66 to 0.77, confirming adequate variability for predictive modeling. Reliability testing demonstrated acceptable to strong internal consistency across constructs, with Cronbach’s alpha coefficients ranging from 0.74 to 0.88, supporting measurement stability and suitability for regression analysis. The regression model was statistically significant (F(5, 406) = 82.14, p < .001) and explained a substantial proportion of variance in the dependent variable (R² = 0.494; adjusted R² = 0.488). Four predictors demonstrated statistically significant positive effects on the outcome: Construct 1 (β = 0.321, p < .001), Construct 2 (β = 0.238, p < .001), Construct 3 (β = 0.089, p = .032), and Construct 5 (β = 0.276, p < .001). Construct 4 did not reach statistical significance (β = 0.034, p = .424), indicating no unique direct effect in the multivariate model. Multicollinearity was not problematic, with all variance inflation factor values remaining below 2.0, supporting stable coefficient interpretation. Overall, hypothesis testing indicated that four out of five hypotheses (80%) were supported, confirming that the theoretical framework was largely consistent with the observed quantitative evidence. The findings provided statistically grounded insight into the strongest determinants of the dependent variable and supported the use of multivariate modeling for identifying high-impact predictors for future research and applied intervention planning.
Copyright (c) 2021 Amena Begum Sumi, Md. Nazmul Haque (Author)
Secure Multi-Institutional Data Integration Models for Strengthening Clinical Research Collaboration in the U.S. Health Sector
This study examined Secure Multi-Institutional Data Integration (SMIDI) maturity as a measurable organizational capability and evaluated its association with collaboration performance in U.S. clinical research environments using a quantitative, institution-level design. A simulated sample of 96 healthcare and research institutions was analyzed to assess how variation in integration maturity, governance quality, security and privacy control maturity, and analytical infrastructure readiness corresponded with observable collaboration outcomes. SMIDI maturity was operationalized through composite indicators capturing interoperability intensity, access governance strength, auditability, privacy enforcement, and evidence automation, while collaboration performance was measured using a multidimensional index encompassing throughput, efficiency, network engagement, and output quality. Descriptive results indicated moderate-to-high dispersion across institutions, with SMIDI maturity indicators such as interface coverage averaging 46.8 interfaces (SD = 20.9), mapping completeness averaging 82.6% (SD = 9.8), and automated evidence rates averaging 69.8% (SD = 14.6). Collaboration performance measures showed substantial variability, including an average of 19.8 multi-site studies per year (SD = 10.6), a mean dataset build time of 37.6 days (SD = 16.9), and replication consistency averaging 86.2% (SD = 8.9). Reliability testing of reflective subscales produced Cronbach’s alpha values ranging from 0.79 to 0.89, supporting acceptable internal consistency for constructs used in regression models. Multivariate regression analysis demonstrated that SMIDI maturity was positively associated with collaboration performance in the baseline model (B = 0.42, SE = 0.09, p < .001), explaining 34% of outcome variance. After adjusting for governance quality and security/privacy control maturity, the SMIDI effect remained significant (B = 0.31, SE = 0.08, p < .001), and model fit improved (Adjusted R² = 0.46). Inclusion of analytical infrastructure readiness reduced the SMIDI coefficient to 0.19 (SE = 0.07, p = .004) while analytics readiness showed a strong positive association with performance (B = 0.37, SE = 0.09, p < .001), indicating partial mediation. Robustness analyses using alternative outcome definitions and index constructions yielded consistent results. Overall, the findings supported SMIDI maturity as a statistically meaningful determinant of collaboration performance and highlighted the mediating role of analytical infrastructure readiness in multi-institution clinical research settings.
Copyright (c) 2023 Jinnat Ara, Molla Al Rakib Hasan (Author)
Artificial Intelligence-Based Decision Support Systems and Managerial Performance
This quantitative, cross-sectional, case-based study addresses the persistent problem that organizations invest in cloud-deployed, enterprise AI-based decision support systems (AI-DSS) yet often lack clear, measurable evidence about which adoption and perception factors actually translate into better managerial performance; accordingly, the purpose was to test a Technology Acceptance Model (TAM) aligned explanatory model linking managers’ perceptions and use of AI-DSS to decision-linked performance outcomes in an enterprise case context. The sample comprised 120 managers across core enterprise functions (Operations 30.0%, Finance 23.3%, Sales/Marketing 18.4%, HR/Admin 15.0%, IT/Analytics 13.3%) with mixed seniority (Supervisors 33.3%, Mid-level 48.3%, Senior 18.4%), and most had received AI-DSS training (61.7%). Key variables included Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Trust in AI-DSS (TRUST), Information Quality (INFOQ), and AI-DSS Use/Adoption Maturity (USE) as predictors, with Managerial Performance (MP) as the outcome. The analysis plan applied descriptive statistics, scale reliability, Pearson correlations, and multiple regression, followed by robustness diagnostics (multicollinearity, residual independence, influence, and stability tests). Results showed generally favorable perceptions and outcomes (PU M=4.02, SD=0.62; PEOU M=3.69, SD=0.66; TRUST M=3.74, SD=0.73; INFOQ M=3.91, SD=0.60; USE M=3.78, SD=0.71; MP M=3.88, SD=0.58). Correlations with MP were positive and statistically strong (PU r=0.62, INFOQ r=0.58, TRUST r=0.55, USE r=0.49, PEOU r=0.41, all reported as significant at p<.001). The regression model explained 53% of variance in managerial performance (R²=0.53, F=38.7, p<0.001), with PU (β=0.29, p<0.001), INFOQ (β=0.24, p=0.002), TRUST (β=0.21, p=0.004), and USE (β=0.17, p=0.011) emerging as significant unique predictors, while PEOU was not significant in the full model (β=0.08, p=0.143). Consistent with these effects, higher adoption maturity aligned with higher performance (Low maturity: n=26, MP 3.52; Moderate: n=62, MP 3.86; High: n=32, MP 4.12). Managers reported the most concrete decision benefits in speed (M=3.95, SD=0.72) and accuracy (M=3.89, SD=0.69), with comparatively weaker gains in reduced rework (M=3.61, SD=0.78). Robustness checks supported model credibility (VIF 1.3–2.4, Durbin–Watson 1.95, Cook’s D max 0.21, and stable coefficient signs after excluding extreme USE cases). Implications are that enterprise leaders should prioritize improving AI-DSS usefulness, information quality, and trust-building mechanisms, while accelerating embedded, routine use through governance and workflow integration, because usability alone may be insufficient to drive net performance gains once value and credibility factors are accounted for.
AI-Based Decision Support Systems (AI-DSS)
Copyright (c) 2024 Tonay Pal, Md Maruf Islam, Al Amin (Author)
Design and Simulation of Sustainable Calibration Systems for Future Industrial Engineering Applications
This study examined the design and simulation implications of sustainable calibration systems by quantitatively analyzing the role of digital calibration information flow within industrial engineering contexts. Using archival operational data from 1,620 calibration events, the analysis evaluated how certificate digitization, interoperability, data integrity controls, and automation intensity influenced key performance outcomes. Descriptive results showed an average certificate verification time of 1.84 hours, with rework due to missing data occurring in 7.8% of calibration events and record mismatch rates averaging 2.9%. Multiple regression analyses demonstrated that digital information flow variables explained a substantial proportion of variance in operational performance, with adjusted R² values ranging from 0.46 to 0.58 across models. Higher levels of automation intensity and certificate digitization were associated with significant reductions in verification time (β = −0.45, p < .001), rework frequency (β = −0.36, p < .001), and downtime exposure (β = −0.42, p < .001). Audit retrieval efficiency improved markedly in highly interoperable systems, with mean retrieval duration reduced to 6.2 minutes. Reliability testing confirmed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.83 to 0.91. While direct relationships between digital maturity and energy intensity proxies were weaker, indirect sustainability benefits emerged through reduced non-value-added processing time, which averaged a 27.5% reduction in highly automated workflows. The findings demonstrated that digital information flow functioned as a quantitative driver of efficiency, quality, and service reliability in calibration systems. These results supported a system-level perspective in which sustainable calibration design and simulation must integrate digital information dynamics alongside physical calibration processes to enable resilient and resource-efficient industrial engineering applications.
Copyright (c) 2024 Efat Ara Haque (Author)
Strengthening Regulatory Compliance and Financial Governance in International Banking Through Blockchain-Enabled Audit Trails and Secure Ledger Systems
This study examined the extent to which blockchain-enabled audit trails and secure ledger systems were associated with regulatory compliance performance and financial governance efficiency in international banking environments using a quantitative explanatory design. The analysis was conducted on a cross-jurisdictional sample of 240 operational units and respondents drawn from compliance, internal audit, risk management, and finance functions within internationally active banks. Regulatory compliance performance was measured using indicators including reporting latency, reporting accuracy proxies, supervisory query resolution time, and compliance exception frequency, while financial governance efficiency was assessed through audit cycle duration, remediation intensity, reconciliation variance, and evidence retrieval time. Descriptive results showed a mean reporting latency of 4.82 days, a reporting accuracy proxy mean of 0.87, and an average compliance exception frequency of 3.42 per reporting period. Governance indicators revealed a mean audit cycle duration of 28.6 days and an average evidence retrieval time of 7.44 hours, with substantial variation across ledger architectures. Comparative descriptive analysis indicated that blockchain-enabled ledger environments demonstrated higher traceability completeness (mean = 0.91 versus 0.78), higher validation accuracy (mean = 0.96 versus 0.88), and lower audit exception frequency (mean = 2.61 versus 3.96) than conventional ledger environments. Reliability analysis confirmed strong internal consistency across all multi-item constructs, with Cronbach’s alpha coefficients ranging from 0.86 to 0.91 and an overall instrument alpha of 0.93. Multivariate regression analysis showed that blockchain-enabled audit trail presence was positively associated with regulatory compliance performance (β = 0.18, p < 0.001) and financial governance efficiency (β = 0.14, p = 0.004). Secure ledger attribute strength exhibited the strongest associations with compliance (β = 0.21, p < 0.001) and governance outcomes (β = 0.17, p = 0.006). The compliance model explained 46% of the variance in performance outcomes, while the governance efficiency model explained 38%. Overall, the findings provided quantitative evidence that secure, integrated ledger infrastructures functioned as governance-enabling mechanisms by improving auditability, data integrity, and regulatory reporting performance in complex international banking operations.
Copyright (c) 2023 Mostafa Kamal, Tahmina Akter Bhuya Mita (Author)
Artificial Intelligence Based Predictive Analytics for SKU Performance and Revenue Optimization in Competitive Markets
This study addresses the problem that many cloud-enabled enterprises invest in AI predictive analytics but still experience inconsistent SKU portfolio performance and avoidable revenue leakage because analytics capability, data integration, governance, and user adoption are uneven across functions. The purpose was to quantify how strongly AI Predictive Analytics Capability (AIPAC) influences SKU Performance (SKUPerf) and Revenue Optimization (RevOpt) in enterprise settings. Using a quantitative, cross-sectional, case-based design, data were collected via a structured 5-point Likert questionnaire from N = 210 professionals drawn from cloud and enterprise operational cases (forecasting, pricing and promotion, inventory and replenishment, and analytics roles). Key variables were AIPAC (overall construct and five capability dimensions: forecasting support, pricing and promotion decision support, inventory and replenishment decision support, data integration quality, and governance plus user adoption), SKUPerf, and RevOpt. The analysis plan included internal consistency reliability (Cronbach’s alpha), descriptive statistics, Pearson correlation, and OLS regression models predicting (1) SKUPerf from AIPAC, and (2) RevOpt from AIPAC and SKUPerf, plus a dimension-level regression to identify the most influential capability components. Reliability met accepted thresholds with AIPAC α = .91, SKUPerf α = .88, and RevOpt α = .90. Descriptively, perceived capability was high (AIPAC M = 4.02, SD = 0.61) while outcomes were moderate to high (SKUPerf M = 3.92, SD = 0.62; RevOpt M = 3.87, SD = 0.65). Correlation results showed strong positive relationships among the constructs, including AIPAC and SKUPerf (r = .62, p < .001), AIPAC and RevOpt (r = .58, p < .001), and SKUPerf and RevOpt (r = .66, p < .001). Regression findings confirmed that AIPAC significantly predicted SKU performance (β = .59, t = 10.21, p < .001; R² = .38; F(1,208) = 127.60, p < .001). In the dimension model, forecasting support (β = .24, p = .002), inventory and replenishment support (β = .19, p = .011), data integration quality (β = .16, p = .018), and governance plus user adoption (β = .27, p < .001) were significant, increasing explained variance to R² = .46. Revenue optimization was jointly explained by AIPAC and SKUPerf (R² = .52; F(2,207) = 112.40, p < .001), with SKUPerf the strongest predictor (β = .49, t = 8.02, p < .001) while AIPAC retained a direct effect (β = .29, t = 4.71, p < .001). These results imply that enterprises can improve SKU outcomes and revenue by strengthening predictive analytics capability end to end, prioritizing governance and adoption, disciplined forecasting, integrated data pipelines, and replenishment decision support so AI insights translate into measurable commercial gains in cloud analytics environments.
Copyright (c) 2026 Md Khaled Hossain (Author)
Role of Data Science Models in Enhancing Revenue Assurance and Compliance in U.S. Financial Enterprises
This study addresses revenue leakage and compliance exposure in U.S. financial enterprises where cloud enabled, high volume transaction lifecycles make manual checks and periodic audits insufficient for timely detection, reconciliation, and audit ready evidence. The purpose was to quantify whether Data Science Model Capability (DSMC) strengthens Revenue Assurance Performance (RAP) and Compliance Performance (CP) in an enterprise case setting. A quantitative cross sectional, case-based design surveyed N = 162 professionals across revenue assurance, compliance, risk, internal audit, finance operations, and data analytics roles within a cloud and enterprise systems environment. Key variables were DSMC (10 items), RAP (8 items), and CP (8 items) measured on a 5-point Likert scale. The analysis plan combined descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and linear regression models predicting RAP and CP from DSMC. Results showed favorable baseline capability and outcomes: DSMC M = 3.84 (SD = 0.61), RAP M = 3.76 (SD = 0.58), and CP M = 3.89 (SD = 0.55), with strong scale reliability (α = 0.88, 0.85, 0.87 respectively). DSMC was strongly associated with RAP (r = 0.62, p < .001) and CP (r = 0.58, p < .001); regression confirmed predictive effects for RAP (β = 0.59, t = 9.41, p < .001, R² = 0.38) and CP (β = 0.55, t = 8.61, p < .001, R² = 0.33). Risk concentration was highest at pricing and fee computation with manual overrides (mean risk = 3.97/5), and high control automation groups outperformed low automation groups (RAP 4.01 vs 3.42; CP 4.12 vs 3.56). Implications indicate that financially material assurance gains come from workflow embedded analytics, expanded automation coverage, and stronger governance and explainability to improve audit defensibility and near real time reporting. Item trends showed exception identification scored highest (M = 4.02) while audit explainability lagged (M = 3.51). Governance readiness averaged 3.63 (SD = 0.69) and related to CP (r = 0.61, p < .001).
Copyright (c) 2026 Rifat Chowdhury (Author)
APPLICATION OF HIGH-DURABILITY ENGINEERING MATERIALS FOR ENHANCING LONG-TERM PERFORMANCE OF RAIL AND TRANSPORTATION INFRASTRUCTURE
This study addresses the problem that rail and transportation infrastructure continues to experience premature deterioration, recurring defects, and higher lifecycle costs because high durability material strategies are not implemented consistently and their effectiveness depends heavily on execution rigor. The purpose of the study was to examine whether stronger Durability Material Implementation (DMI) is associated with improved Long-Term Performance (LTP) outcomes in rail and transportation projects. A quantitative, cross sectional, case-based design was used, drawing on a sample of 162 industry professionals representing multiple rail and transportation project cases across organizations and roles including design, construction, quality management, maintenance, and asset operations. The key independent variable was DMI, operationalized as a composite construct with four dimensions: Mechanical Wear and Fatigue Resistance (DMI1), Corrosion and Chemical Resistance (DMI2), Environmental and Thermal Resilience (DMI3), and QA/QC and Compliance Rigor (DMI4). The dependent variable was Long Term Performance (LTP). The analysis plan included descriptive statistics to profile implementation and performance levels, reliability testing to confirm internal consistency of constructs (Cronbach’s alpha ranged from 0.81 to 0.90), Pearson correlation to test bivariate associations, and multiple regression to estimate the predictive contribution of each DMI dimension to LTP. Findings showed high overall ratings for DMI (M = 3.88, SD = 0.54) and LTP (M = 3.95, SD = 0.57), with the strongest implementation emphasis on wear and fatigue resistance (M = 4.02, SD = 0.61). DMI demonstrated a strong positive association with performance, with the DMI composite correlating with LTP at r = 0.71 (p < .001), and dimension level correlations ranging from r = 0.49 to 0.65 (all p < .001). Regression results indicated that the model explained 58.1% of variance in LTP (R² = 0.581, F = 56.24, p < .001). QA/QC and compliance rigor emerged as the strongest predictor (β = 0.36, p < .001), followed by wear and fatigue resistance (β = 0.24, p < .001), corrosion and chemical resistance (β = 0.18, p = .002), and a smaller but significant environmental and thermal effect (β = 0.10, p = .049). The implications are that organizations should pair advanced materials with enforceable QA/QC governance, verification routines, and workforce capability building, because execution rigor and mechanical durability pathways produce the largest performance gains and can guide lifecycle-oriented procurement, design standards, and maintenance planning.
Copyright (c) 2022 Hammad Sadiq (Author)
DEEP LEARNING–ENABLED LIDAR AND MULTISPECTRAL SIGNATURE FUSION FOR FLOOD HAZARD MAPPING AND LAND-SURFACE VULNERABILITY PREDICTION
This study developed and validated a quantitative flood hazard and land-surface vulnerability modeling framework that integrated LiDAR-derived terrain information with multispectral surface-condition signatures using deep learning and benchmark statistical approaches. Flood vulnerability was operationalized as a continuous, probability-like spatial outcome, and model performance was evaluated under a decision-utility framework that emphasized discrimination, calibration, and ranking quality in a rare-event setting. The analysis was conducted using a retrospective observational design applied to a finalized dataset of approximately 1.25 million raster cells, of which 62,500 (5.0%) were labeled as flood-positive based on event-linked inundation references. The dataset was partitioned into training (70%), validation (15%), and holdout test (15%) subsets using spatially disciplined splits to reduce leakage from spatial autocorrelation. Descriptive results indicated that flood-positive observations were concentrated in low-elevation, low-slope, high-convergence terrain and were more frequently associated with elevated imperviousness and wetness signatures. Reliability analysis showed acceptable internal consistency for composite predictor constructs, with Cronbach’s alpha values ranging from 0.75 to 0.86 across terrain and multispectral feature families. Baseline regression modeling confirmed statistically significant associations for all major construct families, with the strongest effects observed for convergence and wetness proxies and negative associations for elevation and slope structure. Comparative model evaluation demonstrated that non-linear approaches outperformed the regression benchmark. The fused LiDAR–multispectral convolutional neural network achieved the highest discrimination on the holdout test set, with an AUC-ROC of 0.94 and an AUC-PR of 0.63, compared with 0.84 and 0.41, respectively, for the regression baseline. Calibration quality also improved, as reflected by a lower Brier score (0.084 versus 0.118) and reduced expected calibration error (0.025 versus 0.041). Ranking utility gains were substantial, with precision at the top 1% of ranked locations increasing from 0.34 for regression to 0.52 for the fused model, and top-decile capture improving from 0.52 to 0.74 while reducing false alerts from 620 to 470 per 10,000 evaluated cells. Robustness testing across regional, catchment, and terrain-class holdouts confirmed that performance gains persisted under spatial generalization. Overall, the findings demonstrated that integrating LiDAR microtopography and multispectral surface-condition information through disciplined data fusion and spatially credible validation produced statistically robust and decision-useful flood vulnerability maps.
Copyright (c) 2026 Ratul Debnath (Author)
A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE BASED PREDICTIVE SAFETY MODELS FOR REDUCING WORKPLACE INJURIES IN MANUFACTURING AND CONSTRUCTION
This study presented a systematic review of artificial intelligence–based predictive safety models aimed at reducing workplace injuries in manufacturing and construction, with emphasis on quantitative comparability across outcomes, data modalities, validation designs, and performance metrics. A total of 312 observational units and respondent-linked records from manufacturing and construction contexts were synthesized to evaluate injury occurrence, high-severity injury outcomes, and leading-indicator–based risk prediction. Manufacturing accounted for 51.9% of the analyzed records, while construction represented 48.1%. Descriptive results showed moderate-to-high levels of perceived AI usefulness (mean = 3.92, SD = 0.64) and leading-indicator maturity (mean = 3.74, SD = 0.69), with construction exhibiting higher median near-miss activity (median = 2 events per unit window) than manufacturing (median = 1). Logistic regression analyses indicated that data quality readiness was significantly associated with reduced injury occurrence (odds ratio = 0.78, p = 0.002) and reduced high-severity injury occurrence (odds ratio = 0.73, p = 0.006). Safety culture also demonstrated a protective association with injury occurrence (odds ratio = 0.82, p = 0.013). Sector-stratified analyses showed stronger readiness effects in construction (odds ratio = 0.72, p = 0.001) than in manufacturing (odds ratio = 0.83, p = 0.041). Leading-indicator maturity was associated with lower general injury odds (odds ratio = 0.85, p = 0.028) but did not reach significance for high-severity injuries. Validation design and metric selection were found to substantially influence reported performance, with temporal and site-held-out testing yielding more conservative and credible estimates than random splits. Overall, the findings underscored that predictive safety effectiveness depended primarily on data readiness, measurement quality, and validation rigor rather than algorithm complexity alone.
Copyright (c) 2026 Jahangir Shekh (Author)
QUANTITATIVE ASSESSMENT OF CORROSION-FATIGUE IN NUCLEAR POWER PLANT COMPONENTS USING SIMULATION AND EXPERIMENTAL DATA
Corrosion-fatigue (CF) is widely recognized as one of the most complex and deleterious degradation mechanisms threatening the structural integrity and long-term operation (LTO) of primary circuit components in Nuclear Power Plants (NPPs). As global nuclear fleets age and transition toward Subsequent License Renewal (SLR) periods of 80 years, the limitations of traditional, empirical fatigue models—which often rely on conservative, air-based design curves—have become increasingly apparent. This research presents a comprehensive quantitative assessment framework that bridges the gap between theoretical materials science and practical structural health monitoring by integrating high-fidelity numerical simulations with advanced experimental datasets. The study focuses on quantifying the synergistic effects of cyclic mechanical loading and the aggressive electrochemical environment of high-temperature, high-pressure reactor coolant, specifically targeting the vulnerabilities of 316L stainless steel and Alloy 690.The methodology utilizes a dual-pillar approach: first, conducting rigorous fatigue tests in a high-pressure recirculating autoclave loop to establish empirical crack initiation and growth rates ( ) under simulated reactor water chemistry. These experiments employ Direct Current Potential Drop (DCPD) sensors for sub-millimeter precision in real-time crack monitoring. Second, these physical results are synthesized into a Multiphysics Finite Element Analysis (FEA) framework that incorporates the Slip-Dissolution/Oxidation Model to simulate the localized electrochemical conditions at the crack tip. A key finding of this research is the identification of a critical "threshold" in the loading waveform, where tensile rise times exceeding 45 seconds were found to accelerate environmental damage by a factor of 15 compared to fast-transient cycles.Furthermore, the study quantifies the impact of Manganese Sulfide (MnS) inclusions, demonstrating that 85% of cracks initiate at micro-pits with a critical depth-to-width ratio of 0.8. By applying Monte Carlo simulations, the research establishes a probabilistic distribution for the Remaining Useful Life (RUL) of critical components, identifying that fluctuations in local strain rates account for 45% of total predictive variance. The ultimate output of this study is a calibrated "Digital Twin" framework that achieved a 98% accuracy in real-time crack growth prediction. These results provide a technically defensible foundation for risk-informed maintenance, suggesting that proactive, data-driven monitoring can safely extend the operational life of nuclear assets while maintaining the highest margins of structural safety and regulatory compliance.
Copyright (c) 2026 Efat Ara Haque (Author)
ROLE OF FINTECH ACCOUNTING AUTOMATION IN MINIMIZING MANUAL ERRORS AND SUPPORTING DIGITAL MARKETING DECISION-MAKING IN FINANCIAL OPERATIONS
Manual processing, spreadsheet workarounds, and nonstandard exception handling continue to generate posting mistakes, rework cycles, and reconciliation breaks in modern finance operations, even when organizations deploy cloud and enterprise platforms. This study therefore examined whether FinTech enabled accounting automation reduces manual errors and which technical and human factors most strongly explain that reduction. Using a quantitative, cross sectional, case-based design, data were collected via a structured survey from 210 finance and accounting professionals working in cloud integrated enterprise environments across functions such as accounts payable and receivable, general ledger, reporting, and control review, representing multiple enterprise implementation cases. The model treated Manual Error Reduction (MER) as the dependent variable and specified Automation Intensity (AI), Validation Strength (VS), Automated Control Monitoring (ACM), and Human System Fit and Compliance (HSF) as key predictors, with experience and role level included as controls. The analysis plan included descriptive statistics to profile construct levels, reliability assessment using Cronbach alpha, Pearson correlation tests to examine bivariate relationships, and multiple regression with multicollinearity diagnostics (VIF) to estimate unique predictor effects. Descriptive results indicated above midpoint adoption and capability levels (AI M=3.94, SD=0.61; VS M=3.88, SD=0.66; ACM M=3.71, SD=0.70; HSF M=3.96, SD=0.63) alongside high perceived manual error reduction (MER M=3.82, SD=0.64), with strong internal consistency (alpha 0.82 to 0.90). Correlations supported meaningful positive associations with MER (AI r=0.62, VS r=0.55, ACM r=0.49, HSF r=0.66; all p<.001). The regression model explained 54.0 percent of the variance in MER (R=0.735, R2=0.540, Adjusted R2=0.526; F(6,203)=39.72, p<.001) and showed that HSF was the strongest predictor (beta=0.35, p<.001), followed by AI (beta=0.29, p<.001), VS (beta=0.18, p=.002), and ACM (beta=0.12, p=.024); experience was also significant (beta=0.10, p=.031) while role level was marginal (p=.054), and VIF values (1.34 to 2.11) indicated acceptable multicollinearity. Overall, the findings imply that error reduction is a socio technical outcome: organizations should expand automation coverage and embedded validations, strengthen continuous control monitoring, and prioritize training and governance that improve compliant exception handling and reduce manual overrides, using operational KPIs such as correction rates, exception aging, and straight through processing to sustain benefits.
Copyright (c) 2026 Md. Mujahidul Islam, Tahmina Akter Bhuya Mita (Author)
DEEP NEURAL NETWORK MODELS FOR REAL-TIME FINANCIAL FORECASTING AND MARKET INTELLIGENCE
This study addresses the problem that organizations deploy deep neural network (DNN) forecasting services in cloud and enterprise environments, yet decision teams lack quantitative evidence on which operational capabilities drive real-time forecasting effectiveness and whether forecasting gains convert into decision-ready market intelligence. The purpose was to evaluate a case-based DNN forecasting service and test how perceived capability dimensions influence Forecasting Effectiveness (FE) and Market Intelligence Effectiveness (MIE). Using a quantitative cross-sectional, case-study design, a five-point Likert survey was administered to N = 210 active users in the selected enterprise case (58.1% analysts, 21.9% traders, 20.0% risk or portfolio staff). Key capability variables were Data Quality (DQ), Feature Richness (FR), Update Responsiveness (UR), Robustness (ROB), and Explanation Quality (EQ); outcomes were FE and MIE. The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and two multiple regression models with diagnostic checks. Reliability was strong (α = .84 to .90). Descriptive results indicated high perceived maturity (DQ M = 4.12, SD = 0.54; FR M = 3.98, SD = 0.61; UR M = 3.85, SD = 0.66; ROB M = 3.90, SD = 0.63; EQ M = 3.76, SD = 0.70; FE M = 3.94, SD = 0.58; MIE M = 4.01, SD = 0.55). Associations supported the proposed pathway: capability correlated with FE (r = .68, p < .001) and MIE (r = .62, p < .001), and FE correlated with MIE (r = .71, p < .001). Regression Model 1 explained 56% of variance in FE (R² = .56), with significant effects for DQ (β = .32), ROB (β = .28), FR (β = .21), and UR (β = .14). Regression Model 2 explained 61% of variance in MIE (R² = .61), driven by FE (β = .52), EQ (β = .29), and UR (β = .12). The findings imply that cloud and enterprise programs should prioritize data integrity and robust delivery to improve forecast usefulness, and invest in explainability and low-latency refresh to maximize intelligence value and decision confidence. These results provide actionable levers for governance, service-level monitoring, and user-centered adoption of secure DNN forecasting platforms.
Copyright (c) 2026 Aditya Dhanekula, Mosa Sumaiya Khatun Munira (Author)
ROBUST AND VERIFIABLE LLMS FOR HIGH-STAKES DECISION-MAKING (HEALTHCARE, DEFENSE, FINANCE)
Robust and verifiable large language models (LLMs) are increasingly considered for high-stakes decision-support in healthcare, defense, and finance, yet empirical evidence on their reliability, security, and audit readiness remains limited. This quantitative study evaluated four LLM system configurations—baseline, retrieval-grounded, schema/rule-constrained, and tool-augmented verification—across 360 domain-specific cases and 5,760 evaluated case-instances under clean, perturbation, out-of-distribution, and adversarial conditions. Descriptive and multivariable analyses showed that tool-augmented verification achieved the highest overall task correctness at 80% on clean inputs, compared to 64% for baseline, while maintaining higher decision stability under perturbations at 81% versus 61%. Evidence support rates increased from 58% in baseline outputs to 82% in tool-augmented configurations, and schema validity exceeded 94% under constrained outputs across domains. Under adversarial testing, retrieval-grounded systems exhibited the highest policy violation rate at 18.9%, whereas schema/rule-constrained and tool-augmented systems reduced violations to 7.2% and 6.9%, respectively. However, stricter controls increased false refusals, rising from 2.3% in baseline to 7.0% in schema-constrained configurations. Mixed-effects regression results indicated that tool augmentation more than doubled the odds of task correctness relative to baseline, while schema constraints reduced policy violations by nearly 50%. Out-of-distribution conditions reduced correctness across all configurations, with the smallest degradation observed in tool-augmented systems. Overall, the findings demonstrated that robustness and verifiability in high-stakes LLM decision-support depended on layered grounding, constraint enforcement, and deterministic verification mechanisms, and that measurable tradeoffs emerged between security controls and operational utility across domains.
Copyright (c) 2026 Manish Bolli, Sai Srinivas Matta (Author)
QUANTITATIVE ASSESSMENT OF DATA PROTECTION PRACTICES IN U.S. REVENUE CYCLE MANAGEMENT
This study quantitatively assessed data protection practices in U.S. revenue cycle management (RCM) by applying an evidence-oriented measurement instrument and summarizing results as an overall RCM Data Protection Index (RCM-DPI) with five domain subscales. A cross-sectional, observational design was used, and data were obtained from 120 organizations representing provider-operated, outsourced vendor, and hybrid RCM operating models. The overall RCM-DPI averaged 67.4 (SD = 9.8; median = 68.1; range = 41.0–88.5), indicating mid-range protection maturity with meaningful variability. Subscale means were highest for workforce safeguards (74.2, SD = 8.6) and data security controls (70.8, SD = 10.1), while auditability and monitoring (62.7, SD = 12.7) and vendor oversight (60.3, SD = 13.4) showed lower central tendency and wider dispersion. The overall index correlated strongly with auditability and monitoring (r = 0.85), identity and access governance (r = 0.82), vendor oversight (r = 0.80), and data security controls (r = 0.77), and moderately with workforce safeguards (r = 0.69), supporting coherent index composition. Organizational descriptors were negatively associated with protection outcomes, including toolchain complexity (r = -0.36 overall; r = -0.41 for auditability) and outsourcing proportion (r = -0.33 overall; r = -0.44 for vendor oversight). In multivariable regression, governance cadence positively predicted the overall index (B = 3.12, p < .001), while outsourcing intensity (B = -2.76, p < .001), toolchain complexity (B = -2.21, p = .003), and clearinghouse concentration (B = -1.48, p = .034) predicted lower scores; size tier was not significant (p = .18). Model fit was R² = 0.49 (adjusted R² = 0.46). Subscale reliability ranged from 0.81 to 0.88, supporting measurement consistency.
Copyright (c) 2024 Mohammad Mushfequr Rahaman, Aditya Dhanekula (Author)
INTEGRATION OF MACHINE LEARNING MODELS AND ADVANCED COMPUTING FOR REDUCING LOGISTICS DELAYS IN PHARMACEUTICAL DISTRIBUTION
This study examined how integrating machine learning models with advanced computing–enabled prescriptive controls reduced logistics delays in pharmaceutical distribution, with particular attention to cold-chain reliability. The conceptual and measurement framework was grounded in an evidence base of 58 peer-reviewed quantitative papers that informed delay constructs, cold-chain risk measures, and predictive–prescriptive integration logic. A retrospective, multi-source observational dataset was analyzed, consisting of 4,820 shipment episodes across 26 transportation corridors and 8 third-party carriers, covering both ambient and cold-chain products. Ambient flows represented 68.4% of shipments and cold-chain flows 31.6%, enabling criticality-stratified analysis. Descriptive results indicated clear schedule deviation: mean planned lead time was 42.6 hours (SD=11.9), mean actual lead time was 49.8 hours (SD=17.4), and mean delay was 7.2 hours (SD=9.6). The delay distribution was right-skewed with pronounced tail risk, showing a P90 delay of 18.7 hours and P95 delay of 26.4 hours. Node-wise dwell decomposition showed the largest time accumulation at distribution centers (6.8 hours) and customs/regulatory nodes (5.7 hours), confirming multi-echelon delay formation. Baseline performance before integration recorded OTIF of 83.9%, lead-time variance of 92.1, coefficient of variation of 0.36, emergency shipment frequency of 7.4%, and cold-chain excursion incidence of 2.9%. Correlation and multicollinearity checks supported the retained predictor structure, with congestion and corridor volatility most strongly associated with lateness. Regression findings showed lane volatility and queue pressure as significant positive predictors of delay (β=2.41 and β=3.09, p<0.001), while carrier reliability reduced delay (β=-1.68, p<0.001). The integrated predictive–prescriptive mechanism was significantly associated with lower delay magnitude (β=-2.27, p<0.001), lower lead-time variance (β=-5.91, p<0.001), improved OTIF, and reduced severe-delay odds (OR=0.58, p<0.001). For cold-chain shipments, severe delay substantially increased excursion risk (OR=2.36, p<0.001), demonstrating coupled time–temperature vulnerability. Overall, the results confirmed that ML-driven risk forecasts embedded into prescriptive decision rules corresponded with measurable improvements in timeliness and cold-chain safety across pharmaceutical distribution networks.
Copyright (c) 2021 Md. Rabiul Karim, Samia Akter (Author)
THE IMPACT OF BIM AND DIGITAL TWIN TECHNOLOGIES ON RISK REDUCTION IN CIVIL INFRASTRUCTURE PROJECTS
This study investigated how Building Information Modeling (BIM) and Digital Twin (DT) technologies influenced risk reduction in civil infrastructure projects using a cross-sectional quantitative design. The dataset comprised 168 civil infrastructure projects across highways (36.9%), bridges (24.4%), rail corridors (19.6%), tunnels (10.7%), and water infrastructure (8.3%), delivered through design–bid–build (39.9%), design–build (32.1%), and PPP/alliance procurement (28.0). Digital implementation showed mid-to-high BIM practice (BIM adoption intensity M = 3.74, SD = 0.71; BIM maturity M = 3.58, SD = 0.68) and more dispersed DT practice (DT utilization M = 3.12, SD = 0.89; DT predictive capacity M = 3.26, SD = 0.84), while integration maturity remained moderate (M = 3.41, SD = 0.79). Risk reduction outcomes demonstrated substantial variability (cost risk reduction M = 64.8, SD = 12.5; schedule M = 61.3, SD = 13.9; safety M = 55.7, SD = 15.6; quality M = 59.9, SD = 14.2; operational M = 67.2, SD = 11.8; composite index M = 61.8, SD = 10.7). Correlations were positive across domains, with integration maturity showing the strongest association with the composite risk index (r = 0.61). Reliability and validity were adequate for all constructs (Cronbach’s alpha 0.81–0.89, composite reliability 0.86–0.92, AVE 0.53–0.64), and collinearity remained acceptable (VIF 1.20–2.27). Hierarchical regression indicated that BIM predictors explained the largest incremental variance in cost and schedule risk reduction (ΔR² = 0.18–0.20), DT predictors contributed most to safety and operational risk reduction (ΔR² = 0.15–0.22), and integration maturity added broad multi-risk explanatory power, especially for the composite index (ΔR² = 0.14, β = 0.41, p < .001). Moderation tests showed complexity weakened BIM scheduling effects, whereas data reliability and team digital capability strengthened DT and integration impacts. Overall, BIM stabilized delivery risks, DTs strengthened safety and operational reliability, and BIM–DT integration yielded the most comprehensive multidimensional risk reduction.
Copyright (c) 2023 Md Milon Mia, Md. Mominul Haque (Author)
IRRADIATION-ENHANCED CREEP–FATIGUE INTERACTION IN HIGH-TEMPERATURE AUSTENITIC STEEL: CURRENT UNDERSTANDING AND CHALLENGES
This study presents a comprehensive quantitative assessment of irradiation-enhanced creep–fatigue interaction in high-temperature austenitic stainless steels (304/304L, 316/316L/316H/316LN, and 347H), materials that anchor nuclear, petrochemical, and advanced power infrastructures. Creep–fatigue interaction the concurrent action of time-dependent creep and cyclic plasticity becomes critically accelerated under neutron irradiation, which alters microstructural pathways through hardening, dislocation channeling, and radiation-induced segregation. To quantify these coupled effects, a cross-sectional, case-based dataset of 168 tests across 12 campaigns was curated, harmonizing variables for dose (dpa), test temperature, strain range, tensile-hold duration, environment, and metallurgy. Multiple linear and mixed-effects regressions were pre-specified to estimate adjusted associations between these predictors and log life (ln Nf), explicitly testing irradiation–temperature (dose×T) and irradiation–dwell (dose×hold) interactions. Energy-augmented models incorporating cycle-resolved hysteresis and creep work were used to link statistical effects to physical damage mechanisms, while an expert Likert survey (n = 18) independently captured practitioner consensus on variable salience and data quality constraints. Results demonstrate that dose, temperature, strain range, and tensile hold each impose statistically significant, independent life penalties. Two interactions dominate: higher temperature steepens the life reduction per unit dose (negative dose×T), and irradiation amplifies dwell sensitivity (negative dose×hold). Metallurgical stabilization offers a modest but significant attenuation of irradiation-related life debit. Energy-augmented extensions confirm that increased creep power during tensile holds correlates with reduced life, aligning regression coefficients with mechanistic expectations. Cross-validation, penalized stability checks, and environment-stratified analyses verify the robustness of these findings, with stronger dwell penalties observed in oxidizing atmospheres. Expert ratings converge with quantitative results, underscoring the high salience of irradiation dose, temperature coupling, and dwell-time effects. The study provides a transparent, reproducible framework linking microstructural mechanisms, empirical data, and statistical modeling to quantify irradiation-modified creep–fatigue behavior. The findings support the development of interaction-aware assessment tools for life prediction, inspection scheduling, and code-based design in the life extension of nuclear and high-temperature energy systems.
Copyright (c) 2022 Md Ariful Islam (Author)
DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
This study investigates how data-driven capabilities and sustainable resource-allocation policies jointly influence supply chain resilience (SCRes) under disruption and uncertainty. Adopting a quantitative, cross-sectional, multiple case-study design, the research integrates survey-based measurement, archival operational data, and stochastic simulation–optimization to link organizational capabilities—visibility, collaboration, flexibility, supplier diversification, redundancy, risk orientation, and allocation efficiency—to key resilience outcomes, including service recovery, time-to-recovery (TTR), backorder intensity, cost variance, and emissions. Data were collected from 190 professionals across four international firms in discrete manufacturing, FMCG, healthcare logistics, and electronics sectors, each providing both perceptual and objective data spanning 12–24 months of operations. Hierarchical multiple regression models, supported by mediation and moderation analyses, revealed that collaboration, digital visibility, and allocation efficiency were the strongest predictors of resilience performance, while flexibility and diversification contributed moderate incremental effects. Allocation efficiency partially mediated the impact of visibility and collaboration on outcomes, demonstrating that information and coordination enhance resilience primarily through improved resource allocation. Moreover, capability effects intensified under uncertainty—visibility’s and collaboration’s benefits were significantly amplified by demand volatility and lead-time variability. A composite Resilience Performance Index (RPI) was developed to align statistical and operational metrics, linking survey constructs to observed KPIs. Monte Carlo simulation experiments using empirically calibrated disruption parameters validated these statistical insights: sustainability-aware optimized policies improved mean service levels by 3–6 percentage points, reduced TTR by 15–27%, lowered backorder intensity by up to 24%, and cut emissions intensity by 6–11% compared to status quo. These findings confirm that resilience and sustainability can be jointly enhanced through data-driven allocation strategies. The study contributes an integrated, replicable framework that bridges measurement, inference, and decision experimentation—offering both theoretical clarity on capability mechanisms and practical guidance for managers seeking to design resilient, carbon-conscious supply chains under stochastic conditions.
Copyright (c) 2024 Sudipto Roy, Md. Hasan Imam (Author)
INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW
This systematic review investigates the performance, optimization, and governance practices of business intelligence (BI) systems with a focus on SQL-driven dashboards, streaming pipelines, cloud scalability, and organizational integration. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 142 studies published between 2005 and 2024 were systematically identified, screened, and synthesized. The review highlights several key findings. First, SQL continues to serve as the foundational language in BI, maintaining its dominance through adaptability across distributed and hybrid environments. Second, dashboards have evolved beyond their original descriptive function, now acting as governance instruments that embed compliance, accountability, and transparency into organizational workflows. Third, sectoral and regional variations demonstrate that BI adoption and optimization are shaped by contextual factors, with financial and healthcare sectors prioritizing compliance and risk management, while retail and e-commerce emphasize customer analytics and speed. Fourth, performance optimization remains a critical concern, with techniques such as query pushdown, incremental refresh, caching, and streaming pipelines shown to reduce latency and improve scalability in real-time analytics. Fifth, monitoring, testing, and maintainability practices are increasingly emphasized, aligning BI with DevOps principles and continuous integration frameworks. Additionally, cloud and hybrid scalability emerge as both technical and economic challenges, requiring elastic infrastructures balanced against cost and governance demands. Finally, the review underscores the need for conceptual integration of technical, organizational, and governance dimensions, as BI success depends on harmonizing these three axes. Collectively, the findings confirm earlier scholarship while extending it with new empirical and conceptual insights, illustrating that BI has matured into a strategic infrastructure supporting decision-making, regulatory compliance, and organizational resilience. This review provides actionable recommendations for practitioners and highlights research gaps that call for deeper exploration of cross-sectoral comparisons, governance integration models, and economic trade-offs in BI scalability.
Copyright (c) 2023 Momena Akter, Md Nur Hasan Mamun (Author)