American Journal of Interdisciplinary Studies
The American Journal of Interdisciplinary Studies (AJIS) (ISSN 3067-5146) is a distinguished, peer-reviewed, open-access international journal dedicated to fostering a global platform for scholars, academicians, researchers, and professionals from diverse disciplines. The journal serves as a bridge for interdisciplinary research, encouraging the exchange of knowledge and innovative ideas that transcend traditional academic boundaries. By publishing high-quality, original research articles and critical reviews, AJIS contributes significantly to various domains, including but not limited to Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering. Recognizing the ever-evolving landscape of research and scholarship, AJIS maintains rigorous editorial and peer-review standards to ensure that every publication upholds the highest levels of academic integrity, originality, and scholarly contribution. The journal is committed to promoting intellectual discourse, evidence-based research, and theoretical advancements that have practical implications across multiple sectors.
AI-Assisted Credit Evaluation Models for Improving Risk Assessment Accuracy in U.S. Banking Systems
This study investigated a persistent problem in U.S. banking credit decisioning: traditional scorecards and manual underwriting can produce inconsistent judgments and avoidable misclassification, especially when borrower profiles are complex and decision speed is high. The purpose was to quantify how AI-assisted credit evaluation, deployed within enterprise banking environments, improves perceived risk assessment accuracy and which enabling conditions most strongly drive those gains. Using a quantitative, cross-sectional, case-based design, data were collected via a structured 5-point Likert survey from n = 214 eligible banking professionals (usable response rate 71.3%) across underwriting (38.8%), credit analysis (27.1%), risk management (22.0%), and model risk/compliance (12.1%), representing enterprise-grade, cloud-supported decision workflows in the case banks. Key variables included Risk Assessment Accuracy Improvement (dependent) and five predictors: AI Model Capability, Data Quality and Availability, Explainability/Transparency, Governance and Compliance Alignment, and Monitoring and Drift Management. The analysis plan applied reliability testing (Cronbach’s α), descriptive statistics, Pearson correlations, and multiple regression. Measurement reliability was strong (α = .81–.90; DV α = .90). Descriptively, respondents agreed that AI improved accuracy (DV M = 3.97, SD = 0.63), with high ratings for data quality (M = 4.05) and governance (M = 3.94), while monitoring was lower (M = 3.72). Accuracy improvement correlated significantly with all predictors (r = .39–.56, p < .001), strongest for data quality (r = .56) and governance (r = .51). In regression, the model explained substantial variance (R² = .46; Adj. R² = .44; F(5,208) = 35.4, p < .001), with Data Quality (β = .29, p < .001), Governance (β = .22, p = .002), and AI Capability (β = .18, p = .006) as significant drivers; explainability was marginal (β = .11, p = .071) and monitoring was not significant after controls (β = .09, p = .104). Practically, the strongest perceived operational gain was improved underwriter consistency (M = 4.06), alongside reduced false approvals (M = 3.84), implying that banks realize the largest accuracy benefits when enterprise AI is paired with disciplined data pipelines and governance controls rather than model sophistication alone.
Copyright (c) 2026 Mahfuj Ahmed Ruzel (Author)
AI-Enabled Intrusion Detection in Enterprise Networks: A Systematic Review of Methods, Datasets, and Evaluation Metrics (2018–2026)
This study addresses the problem that AI-enabled intrusion detection systems (IDS) often report strong benchmark performance yet struggle to deliver consistently actionable, trusted alerts in real enterprise and hybrid cloud environments where traffic is heterogeneous, class imbalance is extreme, and false-positive workload can overwhelm security operations centers. The purpose was to quantify which enterprise-relevant factors most strongly predict perceived AI-IDS effectiveness and operational suitability, while linking evaluation evidence to workload impact. Using a quantitative, cross-sectional, case-based design, data were collected from enterprise and cloud-facing security stakeholders (N = 162 valid responses) involved in IDS monitoring and triage. Key variables were measured on 5-point Likert scales, including Dataset Representativeness (DREP), Evaluation Rigor (ERIG), Model Robustness (MROB), Deployment Readiness (DREADY), Explainability and Trust (TRUST), and the dependent outcome Perceived AI-IDS Effectiveness (EFFECT); construct reliability was acceptable to strong (Cronbach’s α = 0.78–0.88). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression with diagnostics (VIF = 1.28–2.05). Headline findings showed moderately high perceived effectiveness (EFFECT M = 3.73, SD = 0.66) and trust (TRUST M = 3.69, SD = 0.68), with EFFECT positively correlated with TRUST (r = .52, p < .001), ERIG (r = .46, p < .001), DREP (r = .42, p < .001), MROB (r = .39, p < .001), and DREADY (r = .34, p < .001). The regression model explained substantial variance in effectiveness (R² = .51; adjusted R² = .49; F(5,156) = 32.45, p < .001), with TRUST the strongest predictor (β = .33, p < .001), followed by ERIG (β = .22, p = .002), DREP (β = .18, p = .008), MROB (β = .15, p = .019), and DREADY (β = .11, p = .041). Operational implications were quantified using a False-Positive Burden Index: at 420 alerts/day, FPR = 0.07, and 6.5 minutes triage time, false positives consumed 191.1 minutes/day (3.19 hours/day), while reducing FPR to 0.04 lowered burden to 109.2 minutes/day (1.82 hours/day), a 42.8% reduction. Overall, the results imply that enterprises gain the most adoption-ready value when AI-IDS is explainable, evaluated rigorously with enterprise-representative data, and tuned to reduce workload through threshold governance and imbalance-aware metrics.
Copyright (c) 2026 Md Zahedul Islam (Author)
Business Intelligence for Service Optimization in U.S. Education and Customer-Facing Enterprises: A Prisma Systematic Review of KPI Frameworks
This study examined how Business Intelligence (BI) capability was empirically associated with service optimization outcomes through the quality of Key Performance Indicator (KPI) frameworks in U.S. education and customer-facing enterprises using a PRISMA-guided quantitative systematic review design. A total of 92 peer-reviewed quantitative studies met the inclusion criteria and were coded as evidence records for structured synthesis. BI capability was most frequently operationalized through information quality (66.3% of studies), system quality (58.7%), and BI usage intensity (51.1%), while analytics maturity appeared in 42.4% of studies. KPI frameworks were distributed across strategic scorecard-based models (31.5%), service-quality KPI systems (27.2%), operations KPI systems (22.8%), and commercial outcome KPI systems (18.5%). Baseline regression synthesis indicated that BI capability demonstrated positive associations with all harmonized service outcome dimensions. The strongest mean standardized effects were observed for satisfaction (β̄ = 0.33) and efficiency (β̄ = 0.31), followed by retention or loyalty (β̄ = 0.29) and responsiveness (β̄ = 0.28). Equity or access outcomes showed weaker but still positive associations (β̄ = 0.22) and appeared predominantly in education studies. When KPI framework quality was included in extended models, the average BI capability coefficient declined from β̄ = 0.30 to β̄ = 0.22, while KPI framework quality demonstrated an independent positive association (β̄ = 0.27) and increased explanatory power by an average ΔR² of 0.09. Models incorporating governance maturity and organizational enablers produced further explanatory gains, with cumulative ΔR² increases reaching 0.16 in the most comprehensive specifications. Sector-based synthesis showed that education studies emphasized retention, progression, and equity outcomes, whereas customer-facing enterprise studies emphasized satisfaction and loyalty outcomes. Reliability reporting was concentrated in SEM and PLS-SEM studies, with mean Cronbach’s alpha values ranging from 0.82 to 0.88 across major construct categories. Overall, the findings demonstrated that BI capability was most strongly associated with service optimization when analytics outputs were embedded within coherent, well-governed KPI frameworks that translated data into standardized and actionable performance indicators.
AI-Enabled Financial Information Systems for Credit Risk Forecasting to Support Small Business Growth
This quantitative study examined how AI-enabled financial information systems (AI-FIS) influenced credit risk forecasting performance, credit allocation outcomes, and small business growth indicators in SME lending. Using a cross-sectional dataset of 420 SME borrower records, the analysis measured AI-FIS adoption intensity through integrated data sources, underwriting automation, model update frequency, real-time monitoring capability, and explain ability module availability. Descriptive results showed that institutions integrated an average of 4.21 data sources (SD = 1.37), automated 62.40% (SD = 18.55) of underwriting decisions, and updated forecasting models 3.10 times per year (SD = 1.25). Real-time monitoring capability averaged 3.88/5 (SD = 0.92), and explain ability modules were present in 73% of institutions. Credit allocation outcomes showed moderate approval probability (M = 3.41/5, SD = 0.86) and strong pricing consistency (M = 3.92/5, SD = 0.74). SME outcomes indicated high survival (89%) and positive average revenue growth (M = 3.62/5, SD = 0.83), with working capital improvement (M = 3.58/5, SD = 0.82). Regression results indicated that AI-FIS adoption intensity significantly predicted forecasting performance (β = 0.54, p < .001), explaining 41% of the variance (R² = 0.41). Forecasting performance significantly predicted approval probability (β = 0.38, p < .001; R² = 0.29), pricing consistency (β = 0.42, p < .001; R² = 0.33), loan size alignment (β = 0.31, p < .001; R² = 0.25), and monitoring intensity (β = 0.35, p < .001; R² = 0.27). Credit allocation outcomes significantly predicted SME growth. Approval probability and loan size alignment predicted revenue growth (β = 0.29 and β = 0.24, p < .001; R² = 0.26) and employee growth (β = 0.21 and β = 0.19, p < .001; R² = 0.19). Pricing consistency predicted working capital improvement (β = 0.27, p < .001; R² = 0.31). Logistic regression showed monitoring intensity increased survival odds (OR = 1.48, p < .001), while approval probability increased survival odds (OR = 1.36, p = .003). Mediation analysis confirmed forecasting performance mediated the adoption-to-allocation relationship (p < .01), and loan approval partially mediated forecasting-to-growth outcomes (p < .05). Overall, findings supported a measurable mechanism in which AI-FIS adoption improved forecasting quality, strengthened credit allocation efficiency, and was associated with higher SME growth and survival outcomes.
Copyright (c) 2026 Md Mehedi Hasan (Author)
Artificial Intelligence Assisted Power Flow Control, Fault Classification, and Adaptive Protection in Utility-Scale Electrical Power Grids
The increasing penetration of renewable energy resources, power electronics-based devices, and distributed generation has significantly increased the operational complexity of utility-scale electrical power grids. Conventional power flow control, fault detection, and protection schemes that rely on static models and fixed thresholds are often insufficient for managing the nonlinear, dynamic, and data-intensive behavior of modern power systems. This study examines the application of Artificial Intelligence (AI) techniques to enhance power flow control, fault classification, and adaptive protection in utility-scale grids. Machine learning and deep learning models are utilized to support real-time grid monitoring, predictive power flow optimization, and rapid fault identification under diverse operating conditions. The proposed AI-assisted framework leverages historical and real-time measurements obtained from phasor measurement units, intelligent electronic devices, and supervisory control and data acquisition systems to improve situational awareness and decision-making. In addition, adaptive protection strategies are designed to dynamically adjust relay settings in response to changes in network topology, load profiles, and fault characteristics, thereby improving system reliability, selectivity, and resilience. Simulation-based results indicate that AI-driven approaches achieve higher fault classification accuracy, faster response times, and greater robustness under uncertainty compared to conventional protection methods. The findings demonstrate the effectiveness of AI-assisted control and protection solutions in supporting secure and efficient operation of future utility-scale electrical power grids.
Copyright (c) 2026 Md Shahinur Islam (Author)
Who Needs Humans? A Study on How Virtual Influencer Autonomy Builds Synthetic Parasocial Bonds Stronger Than Anticipated
Virtual influencers are increasingly used in social media marketing, yet their ability to drive consumer behavior remains theoretically ambiguous. Drawing on parasocial relationship theory and human–AI interaction research, this study examines how perceived virtual influencer likeness and perceived agent autonomy shape purchase intention through synthetic parasocial relationships (S-PSR). We further account for ambivalent affective responses, coolness and eeriness, and test the moderating roles of product type and authenticity skepticism. Survey data from 372 U.S. Instagram users exposed to virtual and human influencer endorsements across technology products and experiential services were analyzed using PLS-SEM. Results show that S-PSR is the strongest predictor of purchase intention. Perceived agent autonomy significantly strengthens S-PSR, while perceived virtual likeness simultaneously increases coolness and eeriness, revealing an ambivalent pathway. The impact of S-PSR on purchase intention is stronger for technology products and weaker under high authenticity skepticism. Overall, the findings demonstrate that virtual influencers are most persuasive when perceived as autonomous, cool, rather than eerie, and contextually aligned with technology-oriented products.
Copyright (c) 2026 Jarif Ul Alam, Ajaan R. Khan (Author)
Development of Model Influence on Consumer Behavior in U.S. e-commerce and Digital Marketing
This study addresses the practical and theoretical problem that, although recommender systems are now embedded across U.S. e-commerce and digital marketing touchpoints, organizations still lack clear quantitative evidence on which perceived recommender attributes most strongly drive consumer behavioral outcomes and how privacy concerns constrain those effects. The purpose was to test a perception-driven influence model using a quantitative, cross-sectional, case-based design anchored in enterprise-scale, cloud-deployed e-commerce recommendation environments (multiple recommendation “surfaces” such as home-page personalization, product-page similar items, cart cross-sells, and email or push recommendations). Data were collected from N = 312 eligible consumers with recent recommender exposure, where 71.5% reported interacting with recommendation carousels at least weekly; key stimulus and organism variables included Personalization Quality (PQ), Perceived Relevance (PR), Transparency/Explainability (TRNSP), Trust (TR), Privacy Concern (PVC), and two study-specific indices, Recommendation Exposure and Interaction Intensity (REI²) and Algorithm Aversion–Appreciation (AAAT), while response variables were Purchase Intention (PI), Satisfaction (SAT), and Loyalty/Repurchase Intention (LOY). The analysis plan applied 5-point Likert scale measurement, reliability testing (Cronbach’s α range .81–.90 across constructs), descriptive statistics, Pearson correlations, and multiple regression models with multicollinearity diagnostics (VIF approximately 1.28–2.34). Descriptives indicated above-midpoint perceptions for PR (M = 4.01, SD = 0.66) and PQ (M = 3.88, SD = 0.72) with moderate TRNSP (M = 3.46, SD = 0.81) and PVC (M = 3.21, SD = 0.84), alongside strong PI (M = 3.97, SD = 0.70) and SAT (M = 3.90, SD = 0.68). Correlations showed trust as a central mechanism (TR with PI r = .61, p < .001; TR with SAT r = .55, p < .001), while privacy concern reduced trust (PVC with TR r = −.34, p < .001). In the main PI regression, the model explained substantial variance (R² = .54, p < .001), with the strongest predictors being PR (β = .31, p < .001) and TR (β = .29, p < .001); PQ (β = .18, p = .003), TRNSP (β = .12, p = .019), REI² (β = .15, p = .006), and AAAT (β = .11, p = .022) added significant positive effects, while PVC showed a smaller negative effect (β = −.09, p = .041). Exposure intensity also produced clear practical differences: high REI² users reported higher PI (M = 4.18) than low REI² users (M = 3.62), indicating an interpretable engagement-linked uplift. These findings imply that enterprise e-commerce teams should prioritize perceived relevance and trust-building transparency (explanations and control cues) while implementing privacy-assurance and preference-editing features to reduce trust erosion and improve purchase and loyalty outcomes.
Copyright (c) 2025 Tahmina Akter Rainy, Aditya Dhanekula (Author)
AI-Assisted Underwriting Models for Improving Risk Assessment Accuracy in U.S. Insurance Markets
AI-assisted underwriting has gained prominence as insurers seek to improve risk assessment accuracy within increasingly complex U.S. insurance markets. This quantitative study evaluated the performance of AI-assisted underwriting models relative to conventional underwriting models using policy-level data from U.S. personal automobile and residential property insurance portfolios. The analytical sample comprised 48,620 underwriting observations across 18 U.S. states, with personal automobile insurance representing 64.7% of policies and residential property insurance accounting for 35.3%. Risk assessment accuracy was operationalized as a multi-dimensional construct encompassing discrimination, calibration alignment, loss sensitivity, and stability. Descriptive results showed that AI-assisted models achieved higher average discrimination (mean = 0.748, SD = 0.058) compared with conventional models (mean = 0.692, SD = 0.041), alongside improved loss sensitivity (0.721 versus 0.667). Calibration alignment increased from a mean of 0.914 under conventional models to 0.941 under AI-assisted models, while stability declined slightly from 0.884 to 0.861, indicating greater segment-level variability. Regression analysis confirmed statistically significant effects of AI-assisted models on discrimination (β = 0.056, p < 0.001), calibration alignment (β = 0.031, p < 0.001), and loss sensitivity (β = 0.049, p < 0.001), with adjusted R² values ranging from 0.32 to 0.41 across accuracy dimensions. Enriched data inputs produced additional gains in discrimination (β = 0.043, p < 0.001) and loss sensitivity (β = 0.038, p < 0.001), independent of model family. Reliability analysis demonstrated strong internal consistency for composite accuracy constructs, with Cronbach’s alpha values between 0.816 and 0.889. Overall, the findings provided quantitative evidence that AI-assisted underwriting models improved multiple dimensions of risk assessment accuracy in U.S. insurance markets, while introducing measurable trade-offs in performance stability across states and underwriting tiers.
Copyright (c) 2022 Tahmina Akter Bhuya Mita, Rebeka Sultana (Author)
Remote Sensing Based Integrity Assessment of Infrastructure Corridors Using Spectral Anomaly Detection and Material Degradation Signatures
This study developed and empirically evaluated a quantitative remote sensing–based framework for integrity assessment of infrastructure corridors by integrating spectral anomaly detection, material degradation signature engineering, multisensor fusion, and time-series change analytics. Corridor integrity was operationalized as a continuous, segment-level outcome derived from multispectral, thermal, and synthetic aperture radar observations constrained to corridor footprints. The analytical dataset consisted of 480 corridor segments, retained after quality screening from an initial extraction of 520 segments, and stratified across land-cover adjacency, terrain class, and exposure conditions. Spatially blocked partitions were applied to reduce spatial dependence bias, producing 336 development segments and 144 testing segments with comparable contextual and condition distributions. Descriptive analysis showed moderate right-skew in spectral anomaly intensity (mean = 0.42, SD = 0.21) and wider dispersion in degradation signatures (mean = 0.55, SD = 0.27), while fused integrity scores exhibited reduced variance (mean = 0.48, SD = 0.18), indicating stabilization through multisensor integration. Reliability analysis confirmed strong internal consistency for composite constructs, with Cronbach’s alpha values ranging from 0.79 to 0.88 across exposure strata. Multiple regression modeling demonstrated substantial explanatory power, with the final model achieving R² = 0.62 and remaining statistically significant (F = 153.4, p < 0.001). Material degradation signatures showed the strongest standardized effect on corridor integrity (β = 0.42, p < 0.001), followed by spectral anomaly intensity (β = 0.31, p < 0.001) and temporal change constructs (β = 0.19, p < 0.001), while contextual controls exhibited smaller but significant contributions. Diagnostic checks indicated low multicollinearity (maximum VIF = 2.14) and minimal residual autocorrelation after spatial blocking (Durbin–Watson = 1.96). Hypothesis testing results remained stable under conservative multiple-testing adjustment, confirming robustness of inferential conclusions. Overall, the findings demonstrated that physically interpretable, multisensor-derived indicators can be transformed into reliable and statistically defensible measures of corridor integrity, supporting quantitative monitoring across heterogeneous and spatially extensive infrastructure systems.
Copyright (c) 2022 Ratul Debnath, Subrato Sarker (Author)
Quantitative Benchmarking of ERP Analytics Architectures: Evaluating Cloud vs On-Premises ERP Using Cost-Performance Metrics
This study addresses the problem that organizations choose between cloud and on-premises ERP analytics without workload-normalized evidence, materially increasing cost and performance risk. The purpose was to benchmark ERP analytics architectures and test how architecture type relates to analytics effectiveness. Using a quantitative, cross-sectional, case-based design, objective cost and performance indicators were extracted over a fixed four-week window and paired with a 5-point Likert survey from cloud and on-prem cases (n = 152 valid responses; cloud-exposed n = 79, on-prem-exposed n = 73). Key variables included architecture type (cloud = 1), total monthly analytics-related cost and cost per active user, latency (median and 95th percentile), throughput under 50-user concurrency, availability, incident rate and recovery time, and perceptual constructs: System Quality (SQ), Information Quality (IQ), Service Quality (ServQ), User Satisfaction (US), and Analytics Effectiveness (AE). The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha: SQ = .89, IQ = .86, ServQ = .84, US = .88, AE = .90), Pearson correlations, and multiple regression with controls (usage frequency, experience, role). Results show that cloud delivered lower cost ($48,200 vs $61,750 per month; $214 vs $289 per user) and stronger performance (median latency 2.3 s vs 3.7 s; 95th percentile 6.9 s vs 10.8 s; throughput 1,420 vs 1,050 queries/hour; availability 99.91% vs 99.62%; incidents 3 vs 6 per month; mean time to recovery 38 vs 64 minutes). Perceptions aligned, with higher AE for cloud (M = 4.14, SD = 0.60) than on-prem (M = 3.49, SD = 0.73). AE correlated most strongly with US (r = .71) and measured performance (r = .55), and regression explained substantial variance (Adjusted R² = .49), with performance (β = .34, p < .001), architecture (β = .21, p = .002), and cost efficiency (β = .17, p = .009) as significant predictors. Implications are that ERP analytics selection should use repeatable workload definitions, percentile-based SLAs, and TCO accounting that includes support and downtime, because performance stability and cost efficiency jointly drive decision value.
Copyright (c) 2020 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
Explainable AI Models for Transparent Grammar Instruction and Automated Language Assessment
This study addresses the problem that cloud-hosted AI grammar feedback and automated scoring tools are often experienced as opaque, which can weaken transparency, trust, and perceived fairness and ultimately reduce learning value and adoption in real institutional, enterprise-managed deployments. The purpose was to quantify how explainability features shape user outcomes and to test whether explanation clarity, actionability, and consistency predict perceived transparency, trust, fairness, perceived learning effectiveness, and acceptance or intention to use within a quantitative, cross-sectional, case-based design using a five-point Likert instrument and hypothesis testing through associations and prediction models. The sample comprised N = 210 end users from a single case setting with meaningful system exposure (2–4 weeks: 29.5%; 5–8 weeks: 44.8%; 9+ weeks: 25.7%), providing a realistic cloud or enterprise usage context for perceptions of explainable feedback and scoring. Key variables were operationalized as Explanation Clarity, Explanation Actionability, Explanation Consistency, Perceived Transparency, Trust in AI Outputs, Perceived Fairness, Perceived Learning Effectiveness, and Acceptance or Intention. The analysis plan applied descriptive statistics to profile construct levels, internal consistency reliability testing, Pearson correlations to evaluate hypothesized relationships, and multiple regression to estimate unique predictor effects while controlling overlap among constructs. Headline findings showed consistently positive perceptions above the neutral midpoint, including Clarity (M = 3.98, SD = 0.62), Actionability (M = 3.87, SD = 0.66), Transparency (M = 3.81, SD = 0.64), Trust (M = 3.76, SD = 0.68), Fairness (M = 3.69, SD = 0.73), Learning Effectiveness (M = 3.85, SD = 0.65), and Acceptance (M = 3.90, SD = 0.63). Reliability was strong across constructs (α range .83 to .90). Correlations supported the mechanism that clearer explanations strengthen transparency and that transparency supports trust, for example Clarity–Transparency r = .62 and Transparency–Trust r = .63 (p < .001), while Actionability–Learning Effectiveness r = .58 and Trust–Acceptance r = .59 (p < .001). In regression, the learning model achieved R² = .56 with Actionability as the strongest predictor (β = .36, p < .001), followed by Transparency (β = .21, p = .002) and Clarity (β = .17, p = .009); the acceptance model achieved R² = .59, led by Trust (β = .29, p < .001) and Fairness (β = .22, p = .001), with Transparency and Actionability also contributing. These findings imply that cloud and enterprise deployments should prioritize explanation designs that are not only understandable but concretely actionable, while governance and communication features that enhance transparency and fairness are central to calibrated trust and sustained adoption.
Copyright (c) 2023 Fahimul Habib (Author)
ADVANCING TRAUMA-INFORMED PSYCHOTHERAPY AND CRISIS INTERVENTION FOR ADULT MENTAL HEALTH IN COMMUNITY-BASED CARE: INTEGRATING NEURO-LINGUISTIC PROGRAMMING
This quantitative study examined the effectiveness of an integrated trauma-informed psychotherapy and crisis intervention approach, incorporating structured communication-based techniques consistent with Neuro-Linguistic Programming–oriented delivery, within a community-based adult mental health setting. A quasi-experimental pretest–posttest design was applied to data collected from 180 adult service users receiving routine outpatient psychotherapy and crisis stabilization services. Standardized instruments were administered at baseline and post-intervention to assess trauma-related symptom severity, acute psychological distress, emotional regulation capacity, distress tolerance, perceived control, coping efficacy, psychosocial functioning, and quality of life. Internal consistency reliability across all multi-item scales was acceptable to excellent, with Cronbach’s alpha values ranging from .81 to .94 at baseline and .83 to .94 post-intervention. Descriptive analyses indicated substantial baseline clinical burden, with 62.2% of participants scoring above clinical thresholds for trauma-related symptoms and 67.2% exhibiting elevated emotional regulation difficulties. Post-intervention results demonstrated consistent improvement across all constructs, including reductions in mean trauma symptom severity from 48.6 to 34.2 and acute psychological distress from 21.8 to 14.1, alongside increases in distress tolerance from 31.6 to 39.8 and quality-of-life scores from 46.8 to 58.4. Multivariate regression analyses showed that therapy session dosage was a significant positive predictor of improvement across outcomes, explaining up to 46% of the variance in trauma symptom change and 38% of the variance in psychosocial functioning change. Higher crises contact frequency was negatively associated with outcome improvement and positively associated with crisis recurrence. Mechanism-consistent patterns were observed, with changes in emotional regulation, perceived control, and coping efficacy significantly associated with reductions in trauma symptoms and gains in functional recovery. Subgroup analyses indicated differential improvement based on baseline trauma severity, comorbidity status, and socioeconomic indicators. Overall, the findings provide empirical support for integrated trauma-informed and crisis-oriented care models in community adult mental health services, demonstrating multidimensional recovery patterns across psychological, emotional, functional, and quality-of-life domains under routine clinical conditions.
Copyright (c) 2025 Amena Begum Sumi (Author)
EHS Analytics for Improving Hazard Communication, Training Effectiveness, and Incident Reporting in Industrial Workplaces
This study addresses a persistent problem in industrial enterprises: EHS programs generate substantial data, yet many workplaces struggle to translate those signals into consistently clear hazard communication, effective safety training, and high-quality incident reporting. The purpose was to test whether stronger EHS analytics capability predicts these upstream EHS process outcomes within an enterprise case workplace that uses routine EHS logs, dashboards, and trend reviews. This responds to growing interest in leading indicators and data-driven safety governance. A quantitative, cross-sectional, case-based survey was administered to employees across operational roles. After data-quality screening, 210 valid responses were retained from 228 submissions, with frontline operators representing 52.4%, supervisors 27.6%, and EHS or support staff 20.0%; mean experience was 6.8 years (SD = 4.9). The independent variable was EHS Analytics Capability (EHSAC, 8 items; M = 3.62, SD = 0.67; α = 0.88) and the dependent variables were Hazard Communication Quality (HCQ, M = 3.71, SD = 0.63; α = 0.86), Training Effectiveness (TE, M = 3.68, SD = 0.61; α = 0.89), and Incident Reporting Quality (IRQ, M = 3.55, SD = 0.70; α = 0.87). The analysis plan combined descriptive statistics, reliability testing, Pearson correlations, and ordinary least squares regressions, followed by robustness models controlling for role and experience. EHSAC showed moderate-to-strong correlations with HCQ (r = 0.56), TE (r = 0.52), and IRQ (r = 0.49), all p < .001. Regression results provided convergent evidence, with EHSAC significantly predicting HCQ (β = 0.56, t = 10.42, R² = 0.34), TE (β = 0.52, t = 9.32, R² = 0.29), and IRQ (β = 0.49, t = 8.36, R² = 0.24), and remaining significant with controls (β = 0.51, 0.47, 0.43; adjusted R² = 0.38, 0.32, 0.27). Item diagnostics indicated that timely feedback after reporting (M = 3.21) and post-training reinforcement (M = 3.39) were key gaps. Findings imply that investing in analytics capability and strengthening feedback loops can measurably improve communication, learn transfer, and report quality in enterprise EHS systems.
Copyright (c) 2023 Jahangir Shekh, Muhammad Mohiul Islam (Author)
A Hybrid Genetic Algorithm–Neural Network Framework for Optimizing Thermal and Mechanical Properties of 3D-Printed Components: An Empirical Study
Additive manufacturing, particularly fused deposition modeling (FDM), enables the fabrication of complex polymer components; however, achieving simultaneous optimization of mechanical and thermal performance remains challenging due to the nonlinear and interdependent nature of process parameters. This study presents an empirical hybrid optimization framework integrating an Artificial Neural Network (ANN) with a Genetic Algorithm (GA) to optimize the mechanical and thermal properties of FDM-fabricated components. A quantitative experimental design was employed, generating an empirical dataset from 120 printed specimens produced across 40 experimental runs with systematic variation of layer thickness (0.12–0.28 mm), raster angle (0–90°), infill density (25–100%), print speed (35–75 mm/s), and extrusion temperature (195–235 °C). Mechanical testing results showed tensile strength values ranging from 43.7 ± 2.9 MPa to 52.8 ± 3.1 MPa across build orientations, flexural strength from 76.8 ± 4.7 MPa to 88.2 ± 4.5 MPa, and elastic modulus from 2.05 ± 0.07 GPa to 2.42 ± 0.08 GPa. Thermal measurements indicated thermal conductivity values between 0.26 ± 0.02 W/m·K and 0.31 ± 0.02 W/m·K, with warpage ranging from 0.42 ± 0.06 mm to 0.61 ± 0.07 mm. The ANN achieved high predictive accuracy, with R² values of 0.94 for mechanical properties and 0.92 for thermal properties. GA-based optimization identified parameter configurations that improved tensile strength by 8.4 MPa, flexural strength by 11.2 MPa, elastic modulus by 0.29 GPa, and thermal conductivity by 0.06 W/m·K, while reducing warpage by 0.17 mm and dimensional change by 0.28% relative to baseline conditions. Empirical validation confirmed close agreement between predicted and measured results, demonstrating the effectiveness of the hybrid GA–ANN framework for data-driven optimization of FDM process parameters.
Copyright (c) 2025 Md Tawfiqul Islam (Author)
AN EMPIRICAL EVALUATION OF MACHINE LEARNING TECHNIQUES FOR FINANCIAL FRAUD DETECTION IN TRANSACTION-LEVEL DATA
Financial institutions increasingly rely on cloud hosted, data driven transaction monitoring, yet many fraud programs still struggle to balance fraud capture with false alert workload. This study tested how readiness determinants shape fraud detection effectiveness and compared machine learning classifiers on transaction level data in a quantitative, cross sectional, case-based design. Survey data were collected from 180 practitioners in one enterprise fraud platform case, spanning fraud and risk analysts, compliance and audit, IT and data engineering, and operations management, complemented by transaction records from the same environment. Independent variables were Data Quality, System Integration, Analytics Competency, Model Interpretability, Management Support, and Compliance Readiness; the dependent variable was Fraud Detection Effectiveness. Analysis included reliability testing, descriptive statistics, Pearson correlations, multiple regression, and an ML benchmark using precision, recall, F1, and ROC AUC with cross validation and threshold sensitivity. All constructs were reliable (Cronbach alpha .81 to .89). Fraud Detection Effectiveness was moderately high (M 3.74, SD 0.62), while System Integration was the weakest area (M 3.41, SD 0.71). Correlations were positive, strongest for Data Quality (r .62) and Model Interpretability (r .49). The regression model explained 53 percent of variance (F (6,173) 32.41, R2 .53); Data Quality (beta .36, p < .001), Model Interpretability (beta .19, p .002), Management Support (beta .17, p .004), and Analytics Competency (beta .14, p .017) were significant, while System Integration was not (beta .07, p .192). On transaction evaluation, XGBoost achieved the best balance (Precision 0.84, Recall 0.79, F1 0.81, ROC AUC 0.93) and remained stable (F1 0.80 ± 0.03). Profiling showed higher fraud rates at night (2.8 percent) and in high velocity bursts (4.6 percent). Compliance Readiness showed borderline influence (beta .09, p .052), and mid-range amounts of $120 to $500 contained 46.9 percent of fraud cases. Implications are that data governance and explainability should be treated as core controls alongside model selection, improving performance, auditability, and threshold tuning to match investigation capacity.
Copyright (c) 2023 Md Jamil Ahmmed, Mostafa Kamal (Author)
INTELLIGENT CONDITION MONITORING AND FAULT DIAGNOSIS OF ELECTRICAL POWER AND CONTROL SYSTEMS USING MACHINE LEARNING–BASED PREDICTIVE ANALYTICS
Intelligent condition monitoring and fault diagnosis of electrical power and control systems using machine learning–based predictive analytics was quantitatively investigated to evaluate diagnostic reliability, robustness across operating regimes, and explanatory value under realistic data conditions. The study analyzed a large multi-asset dataset consisting of 211,200 time-window observations derived from electrical, thermal, mechanical, insulation-related, and control-residual measurements, including 312 documented fault events. Descriptive analysis showed strong class imbalance, with healthy operation representing 88.2% of observations, degraded states 7.1%, and fault states 4.7%, and fault behavior occurring in temporally clustered events with a median duration of 18 minutes. Correlation and collinearity analyses indicated moderate within-domain feature dependence but low cross-domain redundancy, with mean variance-based collinearity indices remaining below conservative thresholds across predictor groups, supporting multivariate modeling. Reliability analysis demonstrated acceptable to strong internal consistency across indicator domains, with internal consistency coefficients ranging from 0.76 to 0.87 and temporal stability coefficients exceeding 0.86 under stable operating regimes. Multivariate regression and hypothesis testing results showed that multi-domain predictive models explained substantially more variance in fault outcomes than baseline context-only models, with adjusted fit indices improving from approximately 0.27 to 0.59 after integration of regime-aware normalization and multi-domain features. Hypothesis testing confirmed statistically significant and practically meaningful improvements in diagnostic performance, with effect sizes exceeding 0.50 for multi-domain versus baseline comparisons. Prognostic-oriented analyses further demonstrated statistically meaningful associations between degradation indicators and time-to-event proxies, indicating systematic risk escalation prior to documented intervention events. Overall, the findings provided quantitative evidence that regime-aware, multi-domain machine learning–based predictive analytics improved fault detection reliability, reduced confounding from operating variability, and supported interpretable diagnostic inference in electrical power and control systems.
Copyright (c) 2026 Tasnim Kabir (Author)
AI-DRIVEN PREDICTIVE MAINTENANCE FOR MOTOR DRIVES IN SMART MANUFACTURING A SCADA-TO-EDGE DEPLOYMENT STUDY
This quantitative study evaluated AI-driven predictive maintenance for motor drives in smart manufacturing by comparing three deployment architectures: SCADA-only, edge-only, and hybrid SCADA-to-edge fusion. A longitudinal multi-asset dataset was analyzed from 48 motor drives monitored over 16 weeks, representing 18,720 operating hours, 12,614,380 SCADA tag records, and 3,456,000 edge analysis windows. Outcomes were defined using tiered event labels, including 37 Tier-1 confirmed failures, 64 Tier-2 verified defect findings, and 142 Tier-3 operational abnormality episodes. Time-based evaluation and motor-drive clustering controls were applied, and performance was assessed under a fixed alert-budget policy using event detection, precision, false alarm density, and lead time outcomes, alongside deployment feasibility metrics. Compared with SCADA-only, edge-only deployment improved event detection (odds ratio = 1.62, p = .014) and increased precision from 0.41 to 0.53 (p = .006), while reducing false alarms from 1.48 to 1.31 per 100 operating hours (p = .041). Hybrid fusion produced the strongest predictive outcomes, increasing event detection (odds ratio = 2.08, p < .001), raising precision to 0.58 (p < .001), and lowering false alarms to 1.27 per 100 operating hours (p = .018). Median lead time increased from 18.6 hours (SCADA-only) to 31.4 hours (edge-only) and 37.9 hours (hybrid) (p < .01). Deployment tradeoffs were quantified: inference latency increased from 18 ms per window (SCADA-only) to 122 ms (edge-only) and 141 ms (hybrid), while bandwidth use was reduced by 96.1%–96.8% in edge and hybrid configurations through feature-level reporting. Overall, SCADA-to-edge fusion yielded the most stable and effective predictive maintenance performance across operating regimes with manageable system overhead.
Copyright (c) 2025 Zaheda Khatun (Author)
AI-ENABLED FINANCIAL ACCURACY MODELS FOR IMPROVING ERROR DETECTION AND REPORTING INTEGRITY IN CORPORATE ACCOUNTING SYSTEMS
This quantitative study examined the effectiveness of AI-enabled financial accuracy models in improving error detection and reporting integrity within corporate accounting systems. Using a retrospective observational design, the analysis was based on 48,620 accounting transactions extracted from integrated accounting information systems, including general ledger entries, subledger postings, and period-end adjustments. Confirmed error outcomes were identified through documented reversals, reclassifications, reconciliation resolutions, and audit adjustment linkages, yielding an observed error rate of 2.5% (1,215 transactions). Transaction-level and account-period indicators capturing transaction magnitude, posting lateness, exception frequency, reconciliation variance, account-linkage intensity, and workflow complexity were operationalized and analyzed using statistical and machine learning techniques. Descriptive findings showed that error-labeled transactions exhibited substantially higher reconciliation variance (mean USD 1,420 vs 180), longer posting delays (mean 5.6 days vs 1.8 days), and extended correction cycle times (mean 6.9 days vs 1.1 days) compared with non-error transactions. Correlation analysis demonstrated coherent positive associations among timing disruption, exception frequency, and reconciliation variance, while reporting integrity indicators showed strong alignment with audit adjustment volume (r = 0.63). Regression results indicated that posting lateness (odds ratio 1.48), exception frequency (1.36), reconciliation variance (1.29), and account-linkage intensity (1.21) were significant predictors of confirmed error occurrence after controlling for organizational and system factors. Comparative model evaluation showed that AI-based classifiers outperformed baseline rule-based exception detection, with a tree-based ensemble model achieving an AUC of 0.87, precision of 0.49, and F1-score of 0.60, compared with an AUC of 0.68 and precision of 0.22 for the rule-based approach. Overall, the findings demonstrated that AI-enabled financial accuracy models provided superior discriminatory power and more balanced detection performance by capturing multivariate process, control, and linkage-driven risk patterns that traditional rule-based mechanisms failed to identify consistently.
AI-ENABLED CUSTOMER-INTERACTION MODELS FOR IMPROVING SERVICE EFFICIENCY IN U.S. HOSPITALITY AND RETAIL OPERATIONS
This study examined the relationship between AI-enabled customer-interaction models and service efficiency outcomes in U.S. hospitality and retail operations using a quantitative, observational research design. The analysis was informed by an extensive review of 82 peer-reviewed academic studies spanning service operations, information systems, retailing, hospitality management, and technology-mediated service research, which provided the conceptual and methodological foundation for variable selection, model specification, and interpretation of results. Empirical analysis was conducted using a multi-source dataset comprising 68,420 customer-service interaction episodes collected from 132 hospitality and retail sites, integrating operational interaction logs, site-level performance records, and linked customer feedback data where available. AI exposure was operationalized through the share of interactions handled within AI-mediated channels and the extent of automated containment, while capability quality was captured using response latency, knowledge coverage proxies, escalation behavior, and containment outcomes. Service efficiency was measured using time-based indicators, including average handling time, time-to-resolution, wait time, and service cycle time, alongside productivity and cost metrics. Descriptive results indicated that AI-mediated interactions accounted for approximately 51% of total recorded service volume, with containment achieved in 64% of AI-handled interactions compared to 19% in human-mediated channels. Regression findings demonstrated that higher AI exposure was associated with statistically significant reductions in average handling time (β = −1.12 minutes, p < .001), time-to-resolution (β = −4.85 minutes, p < .001), and overall service cycle time (β = −5.60 minutes, p < .001), after controlling for channel type, inquiry category, severity, peak demand, and site scale. Response latency and escalation were positively associated with longer resolution cycles, while containment was associated with lower recontact risk and higher first-contact resolution. Multilevel modeling showed that AI-related effects were primarily driven by within-site variation, although meaningful site-level differences persisted. Overall, the findings provided quantitative evidence that AI-enabled customer-interaction models functioned as operational mechanisms that improved service efficiency through faster resolution, higher containment, and reduced repeat contact when deployed within coherent omnichannel service architectures.
Copyright (c) 2026 Mohammad Towhidul Islam (Author)
QUANTITATIVE EVALUATION OF RISK MANAGEMENT AND STRUCTURAL INTEGRITY IN LARGE-SCALE GLOBAL CIVIL CONSTRUCTION PROJECTS
This study addresses a persistent problem in enterprise, case-based civil construction delivery: risk tools are common, yet structural integrity outcomes still vary substantially across interfaces, increasing exposure to nonconformities, rework, and reliability risks. The purpose was to quantify how Risk Management Effectiveness (RME) predicts Structural Integrity Performance (SIP) and to identify which risk-cycle dimensions drive integrity outcomes. A quantitative cross-sectional, case-study–based design used a five-point Likert questionnaire in a large-scale global civil construction project case (N = 210 valid responses after screening). Key variables were overall RME and four dimensions (risk identification, risk assessment and prioritization, mitigation or planning, monitoring and communication) and SIP as the dependent construct (compliance discipline, inspection and testing rigor, materials control, and defect closure). The analysis plan applied data screening, composite indexing, descriptive statistics, reliability testing, Pearson correlations, and hierarchical multiple regression controlling for role, experience, and phase. Reliability was strong (RME α = 0.91; SIP α = 0.88). Descriptively, RME was high (M = 3.87, SD = 0.54), with monitoring and communication the lowest RME dimension (M = 3.71, SD = 0.66); SIP was high (M = 3.76, SD = 0.57) but showed weaker areas in defect closure (M = 3.62, SD = 0.69) and materials traceability and QC consistency (M = 3.67, SD = 0.65). RME correlated strongly with SIP (r = 0.68, p < .001), and each dimension was positively related to SIP (r = 0.56 to 0.63, p < .001). In regression, controls explained 12% of SIP variance (R² = 0.12), while adding overall RME increased explanatory power to 49% (ΔR² = 0.37; β = 0.66, p < .001). The dimensional model reached R² = 0.53, with risk assessment (β = 0.22, p = .004), mitigation or planning (β = 0.31, p < .001), and monitoring or communication (β = 0.19, p = .011) as significant unique predictors, whereas identification was marginal (β = 0.12, p = .071). The findings imply that integrity improvements depend most on execution-focused risk governance that strengthens prioritization discipline, mitigation ownership, and monitoring and escalation, especially to speed defect closure and improve traceability across subcontract tiers.
Copyright (c) 2026 Syed Zaki Uddin (Author)
AI-ENABLED STRUCTURAL HEALTH MONITORING AND SAFETY OPTIMIZATION MODELS FOR HIGH-SPEED RAIL INFRASTRUCTURE IN SEISMIC REGIONS
This study had evaluated AI-enabled structural health monitoring (SHM) inference and safety optimization linkages for high-speed rail infrastructure in seismic regions using a corridor-scale quantitative dataset and decision-output records. The analyzed dataset had included 48 assets across 12 corridor segments observed for 365 days, producing 312,480 train-pass windows and 96 seismic event windows. Data integrity had remained high overall, with mean system uptime of 93.6%, while missingness had varied by modality, with accelerometers at 4.2% and displacement/tilt channels at 11.7%. Outcome distributions had been highly imbalanced, with damage-present windows representing 1.8% of all windows and severe outcomes representing 0.2%, confirming rare-event conditions for model evaluation. Correlation analysis had shown strong redundancy among amplitude features, where peak and RMS response had correlated at 0.84, and confounding patterns where train speed had correlated with peak acceleration at 0.63 and temperature had correlated with dominant-frequency proxies at –0.46. Reliability assessment had shown acceptable within-regime stability for key indicators, including antiregime consistency of 0.82 for peak acceleration and 0.76 for dominant-frequency proxies. Collinearity diagnostics had identified inflated overlap in the full feature set (peak acceleration VIF 6.8), which had been reduced after screening (retained amplitude indicator VIF 2.6). Regression results had indicated statistically meaningful contributions from probabilistic risk and selected engineered features to damage detection and condition scoring, with the detection model achieving PR-AUC 0.56 compared with 0.41 for a baseline feature-only specification and the condition-score model achieving adjusted R² 0.48. Operational decision alignment had been supported by correlations between risk score and speed restriction tier (0.71) and inspection priority rank (0.67), indicating coherent translation from inferred risk to safety actions under corridor constraints.
Copyright (c) 2026 Hammad Sadiq (Author)
EFFECTIVENESS OF A TASK-BASED LEARNING (TBL) MODULE IN ENHANCING GOOD MANUFACTURING PRACTICE (GMP) MASTERY IN PHARMACEUTICAL ENGINEERING PROGRAMS AT CHINESE VOCATIONAL COLLEGES
The issuance of China’s Guidelines for the Modernisation of Vocational Education by 2025, together with the Opinions on Promoting the High-Quality Development of Modern Vocational Education, underscores the urgent need to transform traditional teaching models and enhance instructional methodologies within vocational education. As a core and compulsory course for pharmaceutical engineering students in higher education institutions, Good Manufacturing Practice (GMP) plays a critical role in developing industry-ready competencies. However, existing GMP instruction remains largely reliant on conventional teaching approaches, which are often misaligned with contemporary educational reforms and evolving industrial demands. In response to this gap, the present study investigates the effectiveness of Task-Based Learning (TBL) in enhancing students’ academic performance, learning interest, and practical proficiency in GMP courses. The study considers individual student competencies and learning abilities, while also examining the challenges encountered by instructors in implementing TBL to strengthen GMP mastery in pharmaceutical engineering programs at Chinese vocational colleges. To achieve an in-depth analysis, a survey was administered to 91 undergraduate students enrolled in pharmaceutical engineering programs in China. In addition, semi-structured interviews were conducted with four instructors from two classes, each possessing a minimum of five years of teaching experience in vocational GMP courses. The findings indicate that TBL has a significant positive impact on students’ academic performance and skill development. Moreover, the results suggest that the TBL approach enhances instructional effectiveness and supports more engaging learning environments in vocational education settings. Nevertheless, the study also reveals that instructors’ pedagogical competence—particularly their ability to stimulate students’ critical thinking—plays a pivotal role in the successful implementation of TBL. Overall, this study contributes to the integration of theoretical and practical perspectives by highlighting the pedagogical value of TBL in improving GMP education within pharmaceutical engineering programs in the Chinese vocational education context.
Copyright (c) 2024 Tan Lingzi, Khairul Azhar Bin Jamaludin (Author)
AN ARTIFICIAL INTELLIGENCE-DRIVEN FRAMEWORK FOR AUTOMATION IN INDUSTRIAL ROBOTICS: REINFORCEMENT LEARNING-BASED ADAPTATION IN DYNAMIC MANUFACTURING ENVIRONMENTS
Industrial robots often struggle to sustain throughput, quality, and operational flexibility during disturbances because reinforcement learning (RL) adaptation is implemented without adequate sensing, data pipelines, governance controls, and workforce readiness across enterprise automation systems. This study tested an AI-driven enterprise framework linking organizational and technical enablement factors to RL-based adaptation effectiveness and, in turn, to automation performance outcomes. Using a quantitative, cross-sectional, case-based design, a 5-point Likert survey was administered across cloud and enterprise manufacturing cases; after screening, N = 210 valid responses were retained (96.2% completeness; item-level missingness < 3.0%) from engineering, production, maintenance, quality, and operations roles. Independent variables were real-time sensing and integration (X1), data quality and accessibility (X2), digital twin or simulation support (X3), governance and safety readiness (X4), and human-robot collaboration readiness (X5); RL-based adaptation effectiveness (M1) acted as the mediator and automation performance (Y) as the outcome. Analyses included descriptive statistics, reliability testing, Pearson correlations, and multiple regression with multicollinearity diagnostics. Mean scores indicated generally favorable conditions (X1 M = 3.92, SD = 0.63; M1 M = 3.77, SD = 0.64; Y M = 3.85, SD = 0.60) and internal consistency (Cronbach’s α = 0.81 to 0.89). M1 correlated with enablement factors (r = 0.39 to 0.56, all p < .001) and Y correlated with M1 (r = 0.62, p < .001). The regression model predicting Y was significant (R² = 0.54; Adjusted R² = 0.52; F(6,203) = 39.80, p < .001) with collinearity within limits (VIF 1.28 to 2.11). M1 was the strongest predictor (β = 0.41, p < .001), followed by X2 (β = 0.19, p = .002), X4 (β = 0.15, p = .007), X1 (β = 0.12, p = .039), and X5 (β = 0.10, p = .046), while X3 was not significant (p = .133). Implications are that enterprises should prioritize data quality, safety governance, and human readiness; digital twins add value when integrated with data and control processes.
Copyright (c) 2025 Shoflul Azam Tarapder (Author)
POLICY OPTIMIZATION FOR SUSTAINABLE ENERGY SECURITY: DATA-DRIVEN COMPARATIVE ANALYSIS BETWEEN THE U.S. AND SOUTH ASIA
This study investigates how policy optimization shapes sustainable energy security in a comparative context between the United States and South Asia, responding to the problem that many energy security reforms are selected through narrative judgment rather than evidence-backed prioritization. The purpose was to test a quantitative model linking Policy Optimization (PO) to Sustainable Energy Security (SES) and to identify which policy levers most strongly predict energy security outcomes across two real-world cloud and enterprise informed cases (one U.S. case and one South Asia case). Using a quantitative, cross sectional, case-based design, data were collected through structured five-point Likert instruments from 420 stakeholders drawn from energy enterprises, regulators, and institutional policy units (n = 210 per case). Key independent variables were six PO dimensions: regulatory quality and enforcement, renewable integration policy strength, grid modernization policy, energy efficiency and demand side policy, affordability and equity policy, and climate resilience policy. The dependent construct was SES, measured through reliability and resilience, affordability and access, sustainability and acceptability, and supply risk management. The analysis plan applied reliability testing, independent samples t tests for case comparison, Pearson correlation to assess association, and multiple regression to estimate predictive effects with controls. Internal consistency was strong (Cronbach’s alpha = .91 for PO and .89 for SES). Case results showed higher PO in the United States than South Asia (M = 3.74, SD = 0.61 vs M = 3.21, SD = 0.67) and higher SES (M = 3.86, SD = 0.58 vs M = 3.12, SD = 0.63). The largest SES gaps were observed in reliability and resilience (4.01 vs 3.05) and supply risk management (4.03 vs 3.13). Overall, PO was positively associated with SES (r = .62, p < .001). In the pooled regression, policy levers significantly explained SES variance (Adjusted R² = .54; F(10, 409) = 50.12; p < .001), with grid modernization emerging as the strongest predictor (β = .31), followed by regulatory quality (β = .24) and affordability and equity (β = .21). Region specific models indicated different leverage points: the United States was most driven by grid modernization (β = .35) and regulatory quality (β = .22), while South Asia was most driven by affordability and equity (β = .29), regulatory quality (β = .26), and climate resilience (β = .20). The findings imply that energy security gains can be accelerated by prioritizing enforceable governance and grid modernization in mature systems, while emphasizing affordability, governance capacity, and resilience delivery in constrained systems.
Copyright (c) 2022 FNU Zulqarnain (Author)
PREDICTIVE DATA-DRIVEN MODELS LEVERAGING HEALTHCARE BIG DATA FOR EARLY INTERVENTION AND LONG-TERM CHRONIC DISEASE MANAGEMENT TO STRENGTHEN U.S. NATIONAL HEALTH INFRASTRUCTURE
The rapid expansion of healthcare big data—derived from electronic health records (EHRs), medical imaging, genomics, wearable devices, and population-level public health systems—has created unprecedented opportunities to transform chronic disease management and early clinical intervention in the United States. Predictive data-driven models leverage advanced analytics, machine learning, and artificial intelligence to extract actionable insights from these heterogeneous and high-volume datasets, enabling proactive rather than reactive healthcare delivery. This study examines the role of predictive healthcare analytics in strengthening the U.S. national health infrastructure by supporting early disease detection, personalized treatment planning, and long-term management of chronic conditions such as diabetes, cardiovascular diseases, cancer, and respiratory disorders. The abstract emphasizes how data-driven predictive models enhance clinical decision-making, optimize resource allocation, reduce avoidable hospitalizations, and improve population health outcomes. By integrating longitudinal patient data with real-time monitoring systems, these models facilitate risk stratification, disease progression forecasting, and timely interventions across care continuums. Furthermore, the study highlights the strategic significance of scalable, interoperable, and secure health data ecosystems in supporting public health resilience, cost containment, and equitable access to care. The findings underscore that predictive healthcare models are not merely technological innovations but foundational components of a robust, sustainable, and prevention-oriented national health infrastructure. Their effective implementation can substantially advance early intervention strategies, improve chronic disease outcomes, and reinforce the overall efficiency and responsiveness of the U.S. healthcare system.
Copyright (c) 2020 Md Ashraful Alam, Md Fokhrul Alam, Md Fardaus Alam (Author)
MACHINE LEARNING–ENHANCED MOLECULAR DOCKING AND VIRTUAL SCREENING FOR DRUG REPURPOSING IN INFLAMMATORY DISEASES
Inflammatory diseases represent a broad class of acute and chronic conditions characterized by dysregulated immune responses and persistent tissue damage, posing substantial clinical and socioeconomic burdens worldwide. Drug repurposing has emerged as a time- and cost-efficient strategy to identify new therapeutic indications for existing compounds with established safety profiles. In this context, machine learning–enhanced molecular docking and virtual screening have gained increasing prominence as integrative computational approaches capable of accelerating the identification of candidate drugs targeting inflammation-associated biomolecules. This study presents a comprehensive framework that combines classical structure-based molecular docking with supervised and deep learning models to improve binding affinity prediction, ranking accuracy, and hit enrichment during virtual screening campaigns. Molecular descriptors, protein–ligand interaction fingerprints, and docking-derived scores are leveraged as input features to train predictive models that discriminate high-affinity binders from inactive compounds across multiple inflammatory targets. The proposed workflow is applied to curated drug libraries and key protein targets implicated in inflammatory signaling pathways, enabling the prioritization of repurposable compounds with favorable interaction profiles. Results demonstrate that machine learning integration significantly enhances screening performance compared to conventional docking-only strategies by reducing false positives and improving early recognition of promising candidates. This approach underscores the potential of hybrid computational methodologies to support rational drug repurposing in inflammatory diseases and provides a scalable platform for translational pharmacological research.
Copyright (c) 2025 Md Abdul Ahad Maraj (Author)
IMPACT OF DIGITAL CONSTRUCTION MANAGEMENT PLATFORMS ON PROJECT PERFORMANCE POST-COVID-19
The COVID-19 pandemic accelerated the adoption of digital construction management platforms as organizations sought to mitigate disruptions, enhance coordination, and maintain project continuity under unprecedented constraints. This study examines the impact of digital construction management platforms on project performance in the post-COVID-19 construction environment, with particular emphasis on cost efficiency, schedule adherence, quality outcomes, and stakeholder collaboration. Drawing on recent empirical studies and industry reports, the abstract highlights how platforms integrating cloud-based collaboration, Building Information Modeling (BIM), real-time data analytics, and remote monitoring tools have transformed traditional project management practices. The findings suggest that post-COVID-19 construction projects leveraging digital platforms demonstrate improved decision-making speed, enhanced transparency, reduced rework, and greater resilience to supply chain and workforce disruptions. Moreover, the increased reliance on digital workflows has contributed to improved risk management and more effective communication among geographically dispersed project teams. However, the analysis also acknowledges persistent challenges related to data integration, cybersecurity, workforce digital skills, and organizational resistance to change. Overall, the study underscores the strategic role of digital construction management platforms in strengthening project performance and organizational adaptability in a post-pandemic construction landscape, positioning digitalization as a critical enabler of sustainable and resilient project delivery models.
Copyright (c) 2020 Muhammad Mohiul Islam (Author)
LSTM AND GRU-BASED FORECASTING MODELS FOR PREDICTING HEALTH FLUCTUATIONS USING WEARABLE SENSOR STREAMS
This quantitative, cross-sectional, case-study–based research addresses the problem that short-horizon health fluctuations in wearable streams are difficult to operationalize and forecast, limiting actionable monitoring. The purpose was to define fluctuation outcomes, identify wearable indicators linked to perceived variability, and compare LSTM versus GRU forecasting pipelines under matched preprocessing and evaluation. The bounded case sample comprised 120 monitored participant cases retained after quality screening (mean wear-time 12.8 hours/day, SD 2.1; overall missingness 9.6%, SD 6.4; 84.2% high adherence at or above 10 hours/day). The primary subjective variable, Health Fluctuation Severity, was a 6-item 5-point Likert composite with acceptable reliability (Cronbach’s alpha .86; mean 3.18, SD 0.74), and the primary objective variable was an Objective Fluctuation Index (mean 0.61, SD 0.19). The analysis plan combined descriptive statistics, reliability checks, Pearson correlations, and regression with data-quality controls, alongside forecasting evaluation using MAE and RMSE and a paired t-test on participant-level errors. Headline findings showed convergence between subjective and objective measures (r .52, p < .001) and significant associations with resting heart-rate shift (r .41), sleep disruption (r .46), activity instability (r .29), and HRV proxy (r −.38) (all p ≤ .001). GRU outperformed LSTM on the test set (MAE 0.072 vs 0.081; RMSE 0.094 vs 0.106; t(119) 4.62, p < .001) and increased explanatory power when combined with wearable features (R squared 0.41; delta R squared 0.07, p = .004). The results imply that baseline-centered indices plus GRU forecasting can support validated fluctuation monitoring in real-world programs when adherence and missingness are managed.
Copyright (c) 2021 Jinnat Ara, Md. Kamrul Khan (Author)
IS THE METAVERSE THE NEXT FRONTIER FOR CORPORATE GROWTH AND INNOVATION? EXPLORING THE POTENTIAL OF THE ENTERPRISE METAVERSE
This study addresses the problem that evidence exists on whether enterprise metaverse adoption improves corporate innovation and growth and which enablers create value. The purpose was to test a readiness to adoption to outcomes framework in a cloud enterprise case using a quantitative, cross-sectional, case-based survey. Purposive and convenience sampling produced N = 210 responses from staff. Predictors were Technology Readiness, Governance and Security Preparedness, Facilitating Conditions, Strategic Alignment, Adoption Intensity, and Collaboration Quality; outcomes were Corporate Innovation and Corporate Growth. Analysis used statistics, Cronbach alpha, Pearson correlations, and regression. Readiness and adoption were above the midpoint (Technology Readiness M = 3.74, SD = 0.68), and reliability was strong (alpha = 0.81 to 0.90). Collaboration Quality showed the strongest association with Innovation (r = 0.66, p < .001), while Strategic Alignment related to Growth (r = 0.60, p < .001). The innovation regression explained 58% of variance (F(6,203) = 46.70, p < .001; R2 = 0.58) with Collaboration Quality as the dominant predictor (beta = 0.38, p < .001) and Adoption Intensity also significant (beta = 0.24, p = .002). The growth regression explained 52% (F(6,203) = 36.90, p < .001; R2 = 0.52) and was led by Strategic Alignment (beta = 0.31, p < .001) with additional effects from Adoption Intensity (beta = 0.22, p = .004). Implications are that metaverse value depends less on technology presence and more on aligned strategy, governed scaling, sustained use, and collaboration design for performance gains across functions.
Copyright (c) 2025 Md Harun-Or-Rashid Mollah (Author)
ASSESSING THE ROLE OF STATISTICAL MODELING TECHNIQUES IN FRAUD DETECTION ACROSS PROCUREMENT AND INTERNATIONAL TRADE SYSTEMS
This study addresses the problem that fraud detection in procurement and international trade workflows often depends on rule-based checks and fragmented records, weakening detection and investigation prioritization. The purpose was to quantify how statistical modeling techniques relate to fraud detection effectiveness in enterprise case environments. Using a quantitative cross sectional, case-based design, 220 usable survey responses were retained after screening (12 removed from 232; item missingness 1.6%), covering procurement (n=108, 49.1%), international trade (n=92, 41.8%), and hybrid exposure (n=20, 9.1%). Key variables were descriptive analytics use (DAU), correlation-based screening (CBS), regression modeling practice (RMP), data readiness (DR), process control context (PCC), and fraud detection effectiveness (FDE), measured as 1–5 Likert composites with strong internal consistency (α=.81–.88). The analysis plan applied descriptive statistics, reliability testing, Pearson correlations, and multiple regression predicting FDE from DAU, CBS, RMP, DR, and PCC with controls. Respondents reported moderate to high adoption (DAU M=3.92, CBS M=3.71, RMP M=3.54) and relatively high effectiveness (FDE M=3.84). FDE correlated with DAU (r=.52), CBS (r=.45), and RMP (r=.49), all p<.001. In regression, the model was significant (R²=.48; F (7,212) =27.61, p<.001) and showed that DAU (β=.24, p<.001), CBS (β=.15, p=.017), RMP (β=.21, p=.001), DR (β=.18, p=.003), and PCC (β=.17, p=.004) each contributed uniquely to FDE. Subgroup models indicated procurement emphasized descriptive monitoring and controls (R²=.51) while trade emphasized regression scoring and data readiness (R²=.44). Implications are that fraud programs should implement layered, interpretable analytics and invest in data readiness and process controls to translate modeling capability into detection gains.
Copyright (c) 2022 Rifat Chowdhury, Khairul Alam Talukder (Author)