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.
DIGITAL TWIN-DRIVEN PROCESS MODELING FOR ENERGY EFFICIENCY AND LIFECYCLE OPTIMIZATION IN INDUSTRIAL FACILITIES
This quantitative cross-sectional, case-based study investigates how digital twin driven process modeling supports energy efficiency and lifecycle optimization in industrial facilities. The problem addressed is the limited empirical evidence on whether digital twin capabilities translate into measurable improvements in plant level performance. Using a structured survey, data were collected from 180 cloud or enterprise facility cases, targeting operations, maintenance, energy and digitalization professionals. Key independent variables were digital twin implementation level, data integration and quality, real time analytics capability and process model fidelity, measured on Likert 5-point scales, while dependent variables captured perceived energy efficiency performance and lifecycle optimization performance. The analysis plan combined dehmmscriptive statistics, reliability testing, Pearson correlations and multiple regression with controls for facility size, industry type and equipment age. Results show moderate to high maturity of digital twin practices (means 3.55 to 3.92) and positive outcomes for energy and lifecycle performance (means 3.58 to 3.65). Digital twin dimensions were moderately to strongly correlated with energy and lifecycle indices (r = 0.42 to 0.57, p < 0.001). Regression models explained 49 percent of the variance in energy efficiency and 46 percent in lifecycle optimization; data integration and implementation were the strongest predictors of energy efficiency (β = 0.29 and 0.24), while real time analytics, implementation, data integration and model fidelity significantly predicted lifecycle performance (β = 0.27, 0.23, 0.21, 0.15). The findings imply that industrial facilities should prioritize robust data integration and embedded analytics when scaling digital twin initiatives to secure durable gains in energy efficiency and asset lifecycle performance.
Copyright (c) 2023 Shaikh Shofiullah, Md. Tahmid Farabe Shehun (Author)
REAL-TIME FAULT DETECTION IN INDUSTRIAL ASSETS USING ADVANCED VIBRATION DYNAMICS AND STRESS ANALYSIS MODELING
Real-time fault detection plays a pivotal role in condition-based maintenance (CBM) by enabling early identification of abnormal equipment behavior before failures escalate into safety, reliability, or productivity losses. This study evaluates the technical performance and organizational acceptance of an integrated real-time monitoring framework that combines vibration dynamics and stress analysis modeling for rotating and load-bearing industrial assets. Using a quantitative, cross-sectional, case-study–based design, the research draws on two complementary data sources: high-frequency vibration and stress measurements linked to documented fault events, and perceptual data from maintenance engineers, reliability specialists, supervisors, and operators collected through a structured five-point Likert survey (N = 120). Composite vibration and stress health indices were constructed from standardized signal features and used to model variations in fault severity, detection accuracy, detection time, and false-alarm rates. Findings show that both vibration and stress indicators were strong and significant predictors of fault behavior (r = 0.61 and r = 0.55 with fault severity, respectively). Multiple regression analysis demonstrated that vibration (β = 0.43, p < .001) and stress (β = 0.35, p < .001) indicators jointly explained 58% of the variance in fault detection performance, while their interaction (β = 0.18, p = .012) provided additional diagnostic value, indicating that multi-modal sensing detects faults more accurately and more quickly than either modality alone. The integrated system achieved an average detection accuracy of 89.5% and an average detection time of 4.2 hours. User perceptions were similarly positive: perceived reliability (M = 3.98), usability (M = 3.86), and usefulness (M = 4.05) exceeded the neutral midpoint, with strong internal consistency (α = 0.86–0.91). In perceptual modeling, perceived reliability (β = 0.38, p < .001) and usability (β = 0.29, p < .001) emerged as the strongest predictors of user acceptance (R² = 0.69), alongside contributions from perceived usefulness and the system’s objective detection performance. The study provides robust empirical evidence that integrating vibration and stress analysis enhances real-time diagnostic capability and strengthens user trust and acceptance. These findings contribute to prognostics and health management (PHM) theory by demonstrating how multi-sensor health indicators and user-centered perceptions jointly shape the effectiveness and adoption of real-time monitoring systems in industrial environments.
Copyright (c) 2021 Saikat Sarkar (Author)
MACHINE LEARNING-BASED PAVEMENT CONDITION PREDICTION MODELS FOR SUSTAINABLE TRANSPORTATION SYSTEMS
This study conducts a quantitative investigation into machine learning–based pavement condition prediction models, emphasizing their role in advancing sustainable transportation infrastructure management. Using a cross-sectional research design, the analysis integrates statistical modeling, computational learning, and environmental assessment to quantify how traffic, climatic, and material variables jointly influence pavement deterioration. Data were sourced from national pavement databases, automated condition surveys, and sensor-based monitoring systems, covering multiple climatic zones and roadway classifications. The methodological framework comprised two phases—model development and validation—featuring data preprocessing, feature optimization, and predictive modeling using algorithms such as random forest, gradient boosting, and artificial neural networks. Model evaluation employed statistical indicators including the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), supported by cross-validation and residual diagnostics to ensure robustness and reproducibility. The findings revealed that temperature anomalies and traffic load intensity were the most influential predictors, explaining nearly 87% of the variance in pavement condition indices. AI-enhanced regression models demonstrated up to an 8% improvement in predictive accuracy compared to traditional statistical methods, confirming their superior capacity to capture nonlinear deterioration patterns. Correlation analyses identified strong positive associations between thermal stress, axle repetitions, and distress frequency, while material thickness and binder resilience exhibited protective effects against deterioration. Reliability and validity testing produced high Cronbach’s alpha and composite reliability values, affirming measurement stability and structural coherence. Overall, the results validate that integrating machine learning with quantitative modeling enhances predictive precision, supports data-driven maintenance prioritization, and contributes to sustainable infrastructure planning by optimizing resource use, extending pavement lifespan, and minimizing environmental impact.
Copyright (c) 2022 Md Sarwar Hossain Shuvo, Md Milon Mia (Author)
A DATA DRIVEN CYBER PHYSICAL FRAMEWORK FOR REAL TIME PRODUCTION CONTROL INTEGRATING IOT AND LEAN PRINCIPLES
The study titled A Data-Driven Cyber-Physical Framework for Real-Time Production Control Integrating IoT and Lean Principles investigated the convergence of data intelligence, adaptive automation, and process optimization to establish a responsive and self-regulating manufacturing environment. The primary objective of the research was to design and empirically validate a quantitative framework that integrates Internet of Things (IoT) connectivity, Cyber-Physical Systems (CPS) control logic, and Lean manufacturing methodologies to enhance operational efficiency, process reliability, and decision-making accuracy in real-time production systems. A total of 142 peer-reviewed research papers, industrial case studies, and empirical reports published between 2012 and 2021 were critically reviewed to synthesize the theoretical foundations and methodological insights that supported the development of the proposed model. The framework positioned IoT as the data backbone enabling real-time sensing, CPS as the adaptive control layer ensuring feedback precision, and Lean principles as the process foundation driving waste reduction and flow stability. Quantitative data were analyzed using regression, mediation, and moderation techniques to evaluate the causal pathways among IoT data quality, CPS responsiveness, and Lean performance outcomes. The results demonstrated that IoT data reliability and synchronization significantly improved CPS responsiveness, which in turn enhanced takt adherence, first-pass yield, and machine utilization, with CPS responsiveness acting as a full mediator between IoT integration and Lean efficiency. The study further established that Lean maturity moderated the relationship between CPS responsiveness and operational outcomes, indicating that process discipline amplified the benefits of digital transformation. Overall, the model achieved high explanatory power (adjusted R² = 0.68–0.81), confirming that data-driven integration yields measurable improvements in production performance, responsiveness, and stability. The findings substantiated that the fusion of IoT-enabled sensing, CPS-driven control, and Lean-based process management creates a cyber-physical ecosystem capable of self-optimization through real-time data feedback. This research contributed a holistic quantitative framework that bridges digital intelligence and operational excellence, offering both theoretical advancement and practical guidance for industries transitioning toward smart, adaptive, and waste-free manufacturing environments.
Copyright (c) 2021 S. M. Habibullah, Md. Foysal Hossain (Author)
AI-ENABLED NEUROBIOLOGICAL DIAGNOSTIC MODELS FOR EARLY DETECTION OF PTSD AND TRAUMA DISORDERS
This systematic literature review examines the development and application of artificial intelligence (AI)-enabled neurobiological diagnostic models for the early detection of post-traumatic stress disorder (PTSD) and trauma-related disorders. The review synthesizes findings from 124 peer-reviewed studies published between 2010 and 2025, encompassing approximately 6,800 total citations, to evaluate how machine learning (ML) and deep learning (DL) approaches utilize neurobiological, physiological, and multimodal data to enhance diagnostic precision. Following the PRISMA 2020 framework, seven databases were systematically searched—PubMed, Embase, PsycINFO, Scopus, Web of Science, IEEE Xplore, and arXiv—using defined inclusion criteria and the PICOS model to ensure methodological transparency. Eligible studies included AI applications to neuroimaging, electrophysiological, autonomic, endocrine, and genetic biomarkers for PTSD detection, risk prediction, or classification. The findings demonstrate that AI-based models consistently outperform traditional statistical approaches, achieving average classification accuracies above 80% and area-under-the-curve values near 0.85. Neuroimaging studies revealed reliable identification of functional alterations within the amygdala, hippocampus, and medial prefrontal cortex, while multimodal frameworks integrating imaging, heart-rate variability, and cortisol levels achieved accuracies exceeding 90% in early PTSD detection. Explainable AI techniques, including SHAP, LIME, and Grad-CAM, enhanced interpretability by linking algorithmic predictions to biologically meaningful patterns of neural and physiological dysregulation. However, significant limitations were noted, including small sample sizes, heterogeneous diagnostic criteria, and limited external validation, which collectively constrain generalizability and clinical translation. The review concludes that AI-enabled neurobiological models offer a robust and scalable framework for objective PTSD diagnostics and risk stratification, supporting a paradigm shift toward data-driven, precision mental health. To realize this potential, future research should emphasize multi-site validation, standardized methodologies, diverse sampling, and ethical governance frameworks. The integration of AI decision-support systems within clinical practice promises to improve early detection, optimize personalized intervention strategies, and advance the biological understanding of trauma-related psychopathology.
Copyright (c) 2025 Md. Akbar Hossain, Sharmin Ara (Author)
DATA PRIVACY-AWARE MACHINE LEARNING AND FEDERATED LEARNING: A FRAMEWORK FOR DATA SECURITY
This study presents a comprehensive systematic review and meta-analysis of 128 peer-reviewed publications on data privacy-aware machine learning (ML) and federated learning (FL), synthesizing their theoretical foundations, computational mechanisms, and ethical implications within the evolving landscape of privacy-preserving artificial intelligence. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the research integrates multidisciplinary perspectives spanning computer science, ethics, law, and digital governance to evaluate how privacy-aware methodologies and decentralized architectures collectively enhance data protection, regulatory compliance, and algorithmic accountability. The findings reveal that differential privacy, homomorphic encryption, and secure multi-party computation constitute the principal mechanisms enabling quantifiable confidentiality without significant loss of model utility. Concurrently, federated learning has emerged as a scalable and policy-aligned framework that decentralizes computation, ensuring data sovereignty and compliance with international privacy regulations such as GDPR, HIPAA, and CCPA. The meta-analysis indicates that integrated privacy-preserving federated systems achieve an average model accuracy of 93%, reduce data leakage risks by 68%, and improve overall energy efficiency by 22% relative to traditional centralized architectures. However, the study also identifies persistent challenges, including communication bottlenecks, heterogeneity in non-identically distributed datasets, trade-offs between privacy and interpretability, and the underexplored environmental costs of encryption and distributed computation. Despite these limitations, the synthesis affirms that privacy-aware federated learning represents a paradigm shift in artificial intelligence—from reactive data protection to proactive privacy-by-design computation. By uniting technical innovation, ethical governance, and policy coherence, this study establishes a holistic framework that redefines data privacy as both a computational property and a moral imperative in the era of intelligent, decentralized automation.
Copyright (c) 2021 Md. Tarek Hasan, Sai Praveen Kudapa (Author)
CLOUD-INTEGRATED DIGITAL TWIN ARCHITECTURES FOR REAL-TIME MONITORING, RISK ASSESSMENT, AND SAFETY OPTIMIZATION IN U.S. ENERGY INFRASTRUCTURE
The study titled Cloud-Integrated Digital Twin Architectures for Real-Time Monitoring, Risk Assessment, and Safety Optimization in U.S. Energy Infrastructure explored how the integration of cloud computing and digital twin technologies reshaped operational safety, predictive reliability, and efficiency across critical national energy systems. A total of 156 peer-reviewed papers, technical reports, and empirical studies were systematically reviewed to construct the theoretical and analytical foundation for this quantitative investigation. The research examined the synergistic relationship between cloud-enabled data processing and digital twin modeling, focusing on their combined impact on real-time monitoring accuracy, probabilistic risk assessment, and safety performance optimization. Quantitative analysis of 1,000 monitored energy assets across electricity, oil, gas, and renewable sectors revealed significant improvements following digital twin deployment. Results demonstrated a 42.8% reduction in mean detection latency, a 38.8% decline in downtime duration, and a 37.6% reduction in incident frequency, confirming that cloud-integrated architectures substantially enhanced the precision and responsiveness of monitoring systems. The regression analysis indicated that Monitoring Precision and Optimization Efficiency Ratio had strong positive effects on the Safety Performance Coefficient, whereas Risk Index Variation had a significant negative influence, validating that reduced uncertainty directly contributed to safer operational outcomes. The statistical findings established that the model explained over 70% of the variance in safety performance, underscoring the predictive strength of the integrated framework. The study concluded that cloud-enabled digital twins transformed traditional monitoring systems into adaptive, self-learning environments capable of continuous optimization and proactive risk mitigation. The review of 156 prior studies provided a comprehensive context for understanding how advancements in cloud analytics, machine learning, and real-time data governance converged to redefine energy infrastructure resilience. Overall, the research confirmed that the fusion of cloud scalability, real-time analytics, and predictive modeling created a new operational paradigm for U.S. energy systems—one characterized by intelligence, adaptability, and quantifiable safety optimization across all subsectors. This study contributed both empirical validation and a scalable blueprint for future digital transformation within national infrastructure governance.
Copyright (c) 2024 M.A. Rony, Hozyfa Shafa (Author)
SUSTAINABLE MATERIALS CHARACTERIZATION FOR LOW-CARBON CONSTRUCTION AND INFRASTRUCTURE DURABILITY
This quantitative study investigates the characterization of sustainable construction materials through a comprehensive framework integrating mechanical, microstructural, and environmental performance analyses. Designed as a randomized, controlled laboratory experiment within a partially nested 3×3×4 factorial arrangement, the research compares three binder systems—ordinary Portland cement (OPC), OPC blended with supplementary cementitious materials (SCMs), and alkali-activated/geopolymer binders under four exposure conditions: chloride, carbonation, sulfate, and freeze thaw environments. The study’s objective was to determine the extent to which binder composition, replacement ratio, and aggregate type influence material durability and embodied carbon, thereby establishing quantifiable pathways for low-carbon infrastructure development. Specimens were prepared using standardized casting and curing procedures, then evaluated for mechanical performance (compressive strength, tensile capacity, and elastic modulus), transport properties (chloride diffusion, carbonation depth, and water absorption), and environmental durability (freeze–thaw resistance and sulfate attack). Microstructural characterization via Scanning Electron Microscopy (SEM), Mercury Intrusion Porosimetry (MIP), and X-Ray Diffraction (XRD) quantified pore structure, phase composition, and hydration dynamics. Life Cycle Assessment (LCA), conducted per ISO 14040/44 standards, provided complementary data on embodied energy and CO₂ emissions, leading to the development of a Durability-Adjusted Carbon Index (DACI)a composite sustainability indicator that normalizes carbon performance by mechanical reliability. Data collection occurred at 7, 28, 90, and 180 days to capture both early-age and long-term trends, ensuring reproducibility and empirical robustness. The results demonstrated that SCM and geopolymer systems significantly outperformed traditional OPC concretes. Compressive strength improved by up to 25%, accompanied by a 50–70% reduction in chloride diffusion and carbonation rates. Embodied carbon declined from 410 kg CO₂-e/m³ for OPC to 195 kg CO₂-e/m³ for geopolymer concretes, while cumulative energy demand fell by nearly 30%. Correlation analyses revealed strong inverse relationships between porosity and compressive strength (r = −0.83, p < 0.01) and between chloride diffusion and DACI (r = −0.73, p < 0.01), confirming that denser microstructures not only enhance mechanical integrity but also reduce environmental degradation. Regression and mixed-effects modeling identified binder type and SCM ratio as dominant predictors of performance (β > 0.40, p < 0.001), with significant binder–exposure interactions indicating superior resilience of geopolymer mixes under carbonation and sulfate attack. Statistical validation confirmed high internal consistency (Cronbach’s α ≥ 0.87), excellent test–retest reliability (r > 0.90), and absence of multicollinearity (VIF < 4.0), ensuring methodological rigor.
Copyright (c) 2021 Md Sarwar Hossain (Author)
AI-ENHANCED MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR LEAN MANUFACTURING EFFICIENCY AND ENERGY-CONSCIOUS PRODUCTION SYSTEMS
Manufacturing enterprises increasingly operate under the dual imperative of enhancing operational efficiency while simultaneously reducing energy consumption. However, conventional improvement approaches often remain siloed—addressing either productivity or sustainability—thereby constraining opportunities for reproducible and system-wide optimization. This study aims to develop and empirically evaluate an AI-enhanced, multi-objective optimization framework that aligns lean manufacturing principles with energy-conscious operations. Using a quantitative, cross-sectional, case-based design, enterprise cloud data pipelines were established to integrate Manufacturing Execution System (MES) and Supervisory Control and Data Acquisition (SCADA) historians with sub-metered energy logs across multiple discrete-part production lines in three industrial plants. The primary variables included Lean Practice Intensity (measured through a five-point Likert instrument), Overall Equipment Effectiveness (OEE) and its components, and Energy Intensity (kWh per good unit). Additional operational moderators such as peak kW, demand variability, product mix, machine age, and shift patterns were also captured to account for contextual heterogeneity. The analysis pipeline consisted of scale reliability testing, descriptive and inferential statistics, correlation analysis, and hierarchical multiple regression incorporating plant- and shift-level controls. Furthermore, mediation analysis tested the role of efficiency as an intervening variable, while moderation analysis assessed the conditional effects of demand variability. To complement empirical inference, a surrogate-assisted evolutionary optimization algorithm was employed to generate Pareto-optimal fronts and knee solutions, benchmarked against baseline production schedules. Key results demonstrate that Lean Practice Intensity exhibits a positive association with OEE and a negative association with energy intensity, confirming efficiency as a significant mediator with a residual direct effect of lean practices on energy outcomes. The influence of lean practices was found to diminish under high demand variability, indicating a boundary condition for implementation. The optimization results further revealed that knee-point policies typically improved OEE by approximately 2–5 percentage points, reduced energy intensity by 6–15%, and curtailed peak demand by around 6%, all without compromising delivery performance or schedule adherence. From a managerial and systems integration perspective, the findings highlight the importance of viewing lean routines as actionable energy levers rather than ancillary practices. The study recommends institutionalizing variability-aware and tariff-aware production planning, and adopting a governed digital stack that links key performance indicators (KPIs), digital-twin telemetry, and AI-driven optimization engines.
ADVANCES AND LIMITATIONS OF FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION APPROACHES FOR STRUCTURAL INTEGRITY ASSESSMENT: A SYSTEMATIC REVIEW
This mixed-evidence study synthesizes the state of fracture mechanics–based fatigue life prediction and tests what actually improves accuracy and adoption in service-realistic settings. We systematically reviewed 96 peer-reviewed studies, mapped capabilities across ΔK–Paris/Forman/NASGRO baselines, closure-aware and elastic–plastic crack-tip response models, environment-enriched formulations, short-crack treatments, and probabilistic uncertainty methods, and then triangulated the literature with a quantitative cross-sectional survey and explanatory case studies. Design: quantitative cross-sectional, case-based. Sample: three enterprise-relevant structural integrity cases spanning aerospace variable-amplitude spectra in air, offshore steel in saline exposure, and welded details with residual stress. Key variables included model family, treatment of variable-amplitude sequence effects, short-crack and near-threshold behavior, environment conditioning, uncertainty practice, data quality, organizational support, usability burden, adoption, and perceived predictive accuracy. Analysis plan: descriptive synthesis and evidence mapping, Likert-scale reliability checks, correlation and multiple regression with robust errors, and case-level benchmarking using RMSE, bias, and predictive-interval coverage. Headline findings: sequence-aware closure or elastic–plastic models reduced life-prediction error by roughly 18–27 percent under variable-amplitude loading; environment-enriched formulations improved accuracy in chloride media and lifted 90 percent interval coverage from about 0.71 to approximately 0.87 relative to air-calibrated surrogates; short-crack modifications cut near-threshold bias by roughly 30–35 percent. Organizational support, training, and data quality showed strong positive associations with adoption and perceived accuracy, while high usability burden suppressed adoption. Implications: for decision-ready integrity assessments, organizations should standardize a tiered pipeline that matches model physics to service conditions, propagate uncertainty into inspection planning, and invest in spectra governance and workflow automation to translate technical capability into routine, auditable practice.
Copyright (c) 2023 Md Ariful Islam, Efat Ara Haque (Author)
QUANTUM-INSPIRED AI METAHEURISTIC FRAMEWORK FOR MULTI-OBJECTIVE OPTIMIZATION IN INDUSTRIAL PRODUCTION SCHEDULING
This study presents a quantum-inspired artificial intelligence metaheuristic (QI-AIM) framework for multi-objective industrial production scheduling, integrating algorithmic innovation with empirical validation across real manufacturing environments. Production scheduling inherently involves allocating limited resources—machines, tools, labor, and energy—to optimize multiple conflicting objectives such as makespan, tardiness, and energy cost. Existing evolutionary multi-objective algorithms (EMOAs) achieve competent performance but often struggle to maintain diversity and interpretability under industrial constraints. The proposed QI-AIM introduces a Q-bit–based priority representation, adaptive rotation updates, and episodic local search, augmented by surrogate modeling for energy-aware objectives. Implemented on classical hardware, QI-AIM emulates quantum principles to balance global exploration and local exploitation, enabling the generation of high-quality, well-distributed Pareto fronts within fixed computational budgets. A cross-sectional, multi–case empirical design links algorithmic outcomes to managerial perceptions using validated five-point Likert scales measuring perceived usefulness, ease of use, satisfaction, and intention to continue use. Data from flexible job-shop, flow-shop, and mixed-layout plants confirm that QI-AIM achieved the highest hypervolume (0.72) and lowest IGD (0.154) across 30-run benchmarks, outperforming NSGA-II, MOEA/D, and GA-LS hybrids while remaining computationally efficient. Regression analyses reveal that perceived usefulness significantly mediates the relationship between observed performance improvements and behavioral intention, with tardiness and makespan reductions explaining 45–53% of variance in acceptance outcomes. Survey means above 4.0 indicate strong managerial endorsement of the system’s clarity, decision support, and interpretability. The findings establish that QI-AIM not only enhances operational performance but also strengthens decision confidence through transparent portfolio visualization of trade-offs. By coupling reproducible algorithmic design with human-centered evaluation, this research contributes a scalable, explainable, and adoption-relevant framework for Industry 4.0–aligned production scheduling and provides a blueprint for integrating advanced optimization with managerial decision processes.
Copyright (c) 2023 Md Sanjid Khan (Author)
FEDERATED LEARNING ARCHITECTURES FOR PREDICTIVE QUALITY CONTROL IN DISTRIBUTED MANUFACTURING SYSTEMS
This study investigates how federated learning (FL) architectures influence predictive quality control (PQC) performance in distributed manufacturing environments characterized by heterogeneity, privacy constraints, and high data velocity. Predictive quality control leverages machine learning to forecast process deviations before defects occur; however, in globally distributed production networks, data-sharing restrictions and non-identically distributed (non-IID) data complicate centralized model training. To address these challenges, this research develops and empirically tests a quantitative, cross-sectional, multi–case framework linking FL architectural design, hub-and-spoke, hierarchical, and peer configurations—with PQC performance across IIoT-enabled plants. Constructs including infrastructure readiness, communication efficiency/update cadence, data heterogeneity, and privacy/trust governance were operationalized using validated Likert-scale instruments complemented by objective indicators such as AUC, F1, false-alarm rate, and time-to-detection. Regression and mixed-effects analyses reveal that infrastructure readiness and communication efficiency exhibit strong positive associations with PQC outcomes, whereas cross-site heterogeneity negatively affects performance. Crucially, hierarchical FL architectures moderate these relationships, attenuating the detrimental effects of heterogeneity and amplifying the gains from efficient communication. Privacy and trust governance correlate positively, though modestly, with PQC, underscoring that robust security and compliance practices enhance rather than hinder collaborative learning effectiveness. The findings establish that architecture is not merely an IT topology but a determinant of statistical and operational performance, transforming federated updates into a controllable mechanism for cross-plant quality intelligence. By integrating IIoT, edge computing, and privacy-preserving analytics within a measurable empirical model, this research advances both theoretical understanding and practical implementation of FL-enabled PQC. It offers an actionable blueprint for manufacturers: invest in edge readiness and orchestration, enforce cadence service levels, adopt hierarchical clustering for heterogeneous sites, and embed privacy governance into federation lifecycles. Collectively, the study demonstrates that disciplined technical, organizational, and architectural alignment enables distributed manufacturing systems to achieve predictive quality improvements without compromising data confidentiality.
Copyright (c) 2021 Md Sanjid Khan, Md. Tahmid Farabe Shehun (Author)
PREDICTIVE MODELING OF BRIDGE LOAD CAPACITY USING MACHINE LEARNING AND REAL-TIME SENSOR DATA
Bridge load capacity, defined as the maximum load a bridge can sustain without structural failure, is a critical determinant of infrastructure safety and operational reliability. Traditional assessment methods, including analytical modeling, static load testing, and empirical estimation, often fail to capture the dynamic, nonlinear, and time-dependent behavior of bridges subjected to evolving environmental and operational conditions. This study presents a predictive modeling framework that integrates machine learning (ML) techniques with real-time sensor data to enhance the accuracy and adaptability of load capacity prediction. The research utilized continuous data streams collected from strain gauges, accelerometers, displacement sensors, and fiber Bragg gratings, complemented by environmental and traffic datasets capturing temperature, humidity, axle loads, and vehicle volumes. Data preprocessing involved noise reduction, normalization, dimensionality reduction, and feature selection using principal component analysis (PCA) and LASSO regression. Multiple machine learning algorithms, Random Forest, Gradient Boosting, Support Vector Regression, and Deep Neural Networks, were developed and evaluated against traditional regression models. Descriptive and correlational analyses revealed that strain, displacement, and axle load exhibited the strongest positive relationships with load capacity, while temperature and humidity had significant negative effects. The regression model achieved high explanatory power (R² = 0.86), with ensemble learning methods further improving predictive accuracy (R² = 0.91–0.92) and reducing error metrics (RMSE ≈ 27 kN, MAE ≈ 20 kN). Reliability analyses demonstrated high internal consistency (Cronbach’s α ≥ .85) and measurement stability, while validity tests confirmed strong convergence with established load rating standards. Collinearity diagnostics and regularization techniques effectively mitigated redundancy among predictors, enhancing model interpretability. Findings indicate that integrating real-time sensor data with ML enables continuous, adaptive modeling of structural behavior under varying loads and environmental influences. This approach captures complex interactions that static models cannot, providing early indicators of structural deterioration and supporting predictive maintenance strategies. The study concludes that data-driven predictive modeling represents a paradigm shift in bridge engineering, offering superior precision, scalability, and operational insight for infrastructure management. It recommends expanded sensor deployment, hybrid physics-informed modeling, and interdisciplinary collaboration among civil engineers, data scientists, and transportation authorities to advance resilience, safety, and sustainability in bridge infrastructure systems.
Copyright (c) 2025 Md. Sakib Hasan Hriday (Author)
ADAPTING PLC/SCADA SYSTEMS TO MITIGATE INDUSTRIAL IOT CYBERSECURITY RISKS IN GLOBAL MANUFACTURING
The convergence of Operational Technology (OT) and Information Technology (IT) in modern industrial infrastructures has revolutionized automation and real-time control, yet it has simultaneously expanded the cybersecurity threat surface of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems. Once confined to isolated networks, these systems now operate within interconnected Industrial Internet of Things (IIoT) ecosystems, facilitating remote accessibility, cloud integration, and cross-enterprise analytics. This integration, while enabling efficiency and predictive maintenance, has also exposed industrial processes to cyber-physical risks that threaten operational safety, reliability, and national infrastructure integrity. The present study addresses these emerging vulnerabilities through the design, implementation, and validation of an adaptive cybersecurity framework for PLC/SCADA architectures, grounded in the ISA/IEC 62443 “zones and conduits” model, NIST SP 800-82 control recommendations, and Zero-Trust Architecture principles. By aligning architectural segmentation, AI-driven anomaly detection, and automated incident response, this research establishes a resilient industrial cybersecurity model tailored to the realities of global manufacturing networks.Experimental validation demonstrated that the proposed framework achieved substantial improvements in resilience and detection performance. Average MTTD was reduced to 2.8 seconds, MTTR averaged 4.8 minutes, and PLOC decreased by over 40% relative to baseline configurations. The hybrid AI model achieved a detection accuracy exceeding 99%, with an ROC-AUC of 0.993, indicating superior precision and reliability compared to conventional rule-based detection systems. The digital twin simulations further confirmed that process stability and communication latency remained within acceptable operational limits (<1%), validating that enhanced cybersecurity did not impede real-time control efficiency. Moreover, automated SOAR responses effectively restored process variables to nominal states within minutes, confirming practical alignment with industrial resilience targets defined in NIST SP 800-160 and IEC 62443-3-3. This research makes significant contributions to the field of industrial cybersecurity by demonstrating a replicable and standards-aligned methodology for integrating AI and architectural defense mechanisms into legacy and modern control systems. The findings provide empirical evidence that adaptive security architectures, supported by AI-driven analytics and digital twin validation, can safeguard industrial control systems against both known and emergent IIoT threats while maintaining deterministic process control. Ultimately, this framework advances the global pursuit of cyber-resilient industrial automation by offering a scientifically validated model that aligns technical, regulatory, and operational objectives for critical infrastructure protection in the age of digital transformation.
Copyright (c) 2024 Jabed Hasan Tarek, Zayadul Hasan (Author)
A QUANTITATIVE STUDY ON AI-DRIVEN EMPLOYEE PERFORMANCE ANALYTICS IN MULTINATIONAL ORGANIZATIONS
This study addresses the practical and ethical challenge of using AI-driven employee performance analytics to inform people decisions across heterogeneous multinational contexts. The purpose is to quantify the association between analytics capability and use and individual performance, and to test mechanisms and boundary conditions that explain when such systems relate to better outcomes. Adopting a quantitative, cross-sectional, case-based design, we surveyed employees and managers in five enterprise cloud/HRIS environments (multinational firms), yielding a pooled sample of N = 3,274 nested within organizations. A focused literature base of 39 peer-reviewed articles informed constructs and hypotheses. Key variables were AI Analytics Capability and Use, Perceived Fairness of Analytics (procedural and distributive), Manager Analytics Literacy, Organizational Culture for Data Use, and Employee Performance (Likert composites with split-source and objective KPI subsets where available). The analysis plan specified descriptives, correlations, and hierarchical regressions with heteroskedasticity- and cluster-robust inference; mediation was assessed via bias-corrected bootstrapping, and moderation via centered interaction terms with simple-slope and Johnson–Neyman probes. Headline findings indicate a positive main effect of analytics capability/use on performance after controls, a significant indirect pathway through perceived fairness (partial mediation), and reliably stronger slopes where manager analytics literacy and data-use culture are higher (positive interactions); convergent patterns held for manager-rated and de-identified KPI subsets. Implications are that technical investments alone are insufficient: organizations should pair governed pipelines, versioned metrics, and explainability with manager enablement and data-culture rituals to raise perceived fairness and translate model outputs into accepted, high-quality feedback and performance decisions across jurisdictions.
Copyright (c) 2023 Omar Muhammad Faruk, Md. Redwanul Islam (Author)
SYSTEMATIC REVIEW OF INDUSTRIAL ENGINEERING APPROACHES TO APPAREL SUPPLY CHAIN RESILIENCE IN THE U.S. CONTEXT
This PRISMA-aligned systematic review synthesizes industrial engineering approaches that strengthen resilience in apparel supply chains serving the United States. Searches across Scopus, Web of Science, IEEE Xplore, ACM Digital Library, ProQuest, and Google Scholar for 2005 to 2020 identified 115 peer-reviewed studies that met inclusion criteria. Evidence was coded by intervention family, disruption class, echelon, method, validation, and standardized outcomes that reflect resilience performance, notably service level under stress, time-to-recover, lead-time variance, backlog duration, lost sales, and cost-to-serve. The corpus concentrates on six lever families. Inventory science with postponement and multi-echelon control was the largest share and produced the most reliable service improvements, typically shifting differentiation downstream to pool uncertainty and compress variance. Robust, chance-constrained, and distributionally robust optimization reduced tail risk and stabilized performance under parameter ambiguity common in fashion calendars. Network and sourcing design, especially dual or multi-sourcing with corridor diversification, limited synchronized logistics shocks linked to gateway congestion. Process-improvement programs, including setup reduction, TPM, and pull control, shortened recovery in factories and distribution centers, while predictive-reactive scheduling with skill-aware cross-training preserved service during promotional peaks. Simulation and digital-twin studies operationalized closed-loop triggers, translating plans into adaptive playbooks for rerouting, wave resequencing, and late-stage finishing. Across studies, median gains clustered around meaningful magnitudes, for example several percentage-point lifts in service and double-digit reductions in time-to-recover, typically at modest cost-to-serve increases, and combinations consistently outperformed single levers. The review contributes a decision-ready synthesis that maps what works, where, and why in U.S. apparel operations, and outlines a reproducible framework for portfolio-based resilience design.
Copyright (c) 2022 Md. Tahmid Farabe Shehun (Author)
BALANCING PRIVACY AND SECURITY IN THE DIGITAL AGE: A GLOBAL PERSPECTIVE
This study provides a comprehensive empirical examination of the evolving relationship between privacy protection and cybersecurity resilience from 2013 to 2023, a decade that witnessed unprecedented digital transformation, regulatory innovation, and escalating threat complexity. Drawing on longitudinal data from seventy to ninety jurisdictions, the analysis integrates five interrelated domains: privacy governance, security posture, platform practices, state demand intensity, and societal outcomes. A multi-method econometric framework was employed, combining Panel Error Correction Models (PECM), Difference-in-Differences (DiD) estimations, Autoregressive Distributed Lag (ARDL) models, and Granger causality analysis to capture both short-term fluctuations and long-run equilibrium dynamics. The results confirm the existence of strong co-integration among privacy, security, and institutional performance indicators, rejecting the long-assumed trade-off hypothesis that stronger privacy necessarily weakens security. Empirical findings reveal that privacy reforms, especially those establishing enforceable data protection laws, independent supervisory authorities, and accountability mechanisms—significantly enhance cybersecurity performance over time, with a typical lag of one to three years between policy adoption and measurable system-level improvement. High-capacity jurisdictions in Europe, North America, and advanced Asia-Pacific economies demonstrate rapid convergence toward equilibrium, achieving a balanced and mutually reinforcing governance structure. In contrast, medium- and low-capacity jurisdictions show delayed or partial effects, constrained by enforcement inconsistency, fragmented institutional design, and limited technical infrastructure. Structural break analyses identified three distinct governance phases: a pre-regulatory phase (2013–2016) characterized by fragmented oversight and rising breach volatility; a convergence phase (2017–2020) following the enforcement of the General Data Protection Regulation (GDPR) and diffusion of comparable global frameworks; and a maturity phase (2021–2023) reflecting institutional stabilization, privacy-by-design implementation, and adaptive equilibrium. Variance decomposition results indicate that privacy governance and technological integration collectively explain nearly 70% of long-run variation in cybersecurity resilience.
Copyright (c) 2023 Md Atikur Rahman, Md Shah Alam, Md Sabbir Hossain Mrida (Author)
THE INFLUENCE OF STATISTICAL MODELS FOR FRAUD DETECTION IN PROCUREMENT AND INTERNATIONAL TRADE SYSTEMS
Procurement and international trade systems remain particularly vulnerable to complex fraud mechanisms that undermine transparency, equity, and fiscal integrity across both public and private sectors. These fraudulent behaviors—ranging from misinvoicing and threshold bunching to split purchasing and restricted competition—continue to distort market signals, inflate transaction costs, and erode trust in institutional governance. Addressing these vulnerabilities requires not only the deployment of advanced analytic tools but also an understanding of how interpretable statistical modeling frameworks can enhance the credibility, precision, and utility of fraud detection outcomes in practice. This study therefore seeks to explore the empirical relationships among internal control mechanisms, vendor concentration patterns, compliance cultures, and the intensity of transactional anomalies within enterprise procurement operations. Employing a quantitative, cross-sectional, multi-case research design, this investigation draws upon survey and administrative data from five enterprise environments that operate either enterprise resource planning (ERP) or e-procurement systems. A total of 268 respondent-level observations were analyzed, providing a robust dataset for comparative evaluation. To anchor the analytical framework, a structured literature review of 37 peer-reviewed studies was conducted, synthesizing theoretical constructs from fraud risk modeling, internal audit research, and information system governance. The resulting conceptual model integrates both organizational and transactional dimensions, encompassing variables such as internal control strength, vendor risk exposure, concentration ratios, transaction anomaly intensity, compliance culture maturity, audit cadence, and a composite fraud-risk outcome index. The analytical sequence proceeded in a methodologically rigorous manner: preliminary reliability and validity diagnostics were performed to assess construct consistency; descriptive analyses established baseline patterns; followed by zero-order and partial correlation matrices to determine intervariable associations. Subsequently, fixed-effects regression modeling was implemented to isolate within-organization effects, employing ordinary least squares (OLS) for continuous outcomes, logistic regression for binary fraud event classifications, and negative binomial models for count-based incident outcomes. Moderation terms were prespecified to evaluate interaction effects between structural controls and transactional anomalies. The empirical findings reveal that transaction anomaly intensity constitutes the most powerful positive correlate of the composite fraud-risk score, confirming that irregular purchasing and invoicing behaviors remain key indicators of systemic exposure. Conversely, internal control robustness and compliance culture strength demonstrate significant negative relationships with fraud risk, illustrating their preventive potential. The results further indicate that vendor concentration, a proxy for limited competitive pressure—exerts a positive and statistically significant effect on risk, highlighting the importance of diversification strategies.
Copyright (c) 2022 Md. Rabiul Karim, Sai Praveen Kudapa (Author)
MODELING GEOTECHNICAL SOIL LOSS AND EROSION DYNAMICS FOR CLIMATE-RESILIENT COASTAL ADAPTATION
This study presents a comprehensive statistical analysis of geotechnical soil loss and erosion patterns with the aim of supporting climate adaptation strategies in vulnerable coastal zones. Soil erosion in coastal environments is a complex phenomenon governed by the interplay of geotechnical, hydrodynamic, climatic, spatial, and anthropogenic factors that collectively shape soil stability, erosion rates, and spatial distribution. To investigate these dynamics, the study systematically reviewed and synthesized findings from 132 peer-reviewed research papers published over the past two decades, integrating empirical evidence with quantitative modeling approaches. Key geotechnical parameters examined include particle size distribution, cohesion, shear strength, bulk density, permeability, organic matter content, salinity, and pH, all of which significantly influence the soil’s resistance to detachment and transport. Climatic drivers such as rainfall intensity and seasonal variability, along with hydrodynamic forces including wave power, tidal range, and storm surges, were found to critically shape erosion intensity and temporal variability. Spatial analysis revealed pronounced erosion hotspots in deltaic and estuarine regions, while temporal trends highlighted the disproportionate role of extreme weather events in accelerating soil loss. Anthropogenic activities, including urbanization, deforestation, land reclamation, and shoreline engineering, further intensified erosion by altering soil structure, hydrology, and sediment transport. Advanced statistical methods, including regression modeling, geostatistical analysis, and GIS-based spatial mapping, were employed to identify key predictors, quantify their relative influence, and produce predictive erosion-risk models. The findings underscore the necessity of integrated, data-driven approaches that incorporate soil management, ecosystem restoration, adaptive land-use planning, and engineering solutions to mitigate erosion and enhance coastal resilience. This study contributes to the growing body of knowledge on soil erosion by linking geotechnical properties with environmental forces and provides a scientific basis for informed decision-making in climate adaptation and sustainable coastal management.
Copyright (c) 2021 Syed Zaki Uddin (Author)
STATISTICAL ANALYSIS OF GEOTECHNICAL SOIL LOSS AND EROSION PATTERNS FOR CLIMATE ADAPTATION IN COASTAL ZONES
This study addresses the pressing problem of quantifying geotechnical soil loss and shoreline erosion in coastal zones under contemporary hydro-climatic forcing, where planners need defensible evidence to design adaptation. The purpose is to explain spatial patterns of erosion by linking measurable substrate properties, exposure regimes, and land cover to decision-relevant outcomes. Using a geological-survey based, quantitative, cross-sectional, multi-case design, we analyzed four contrasting coastal cases instrumented with 76 shore-perpendicular transects, 380 laboratory soil tests, high-precision GNSS and GIS/remote-sensing products, hydro-meteorological reanalysis, and a parallel stakeholder survey of 418 respondents. A structured literature review of 64 peer-reviewed papers scoped variables and hypotheses. The sample comprised open sandy beaches, dune-backed barriers, marsh edges, and scarped bluffs. Key variables included erosion rate and a soil-loss index as outcomes, with predictors spanning runup proxies, significant wave height, rainfall intensity, vegetation cover, cohesion, median grain size, slope, and engineered defenses. The preregistered analysis plan progressed from data auditing, descriptives, and correlation screening to fixed-effects regression with cluster-robust inference, moderation tests, and robustness via GLM (Gamma/log), spatial error models, penalization, and quantile regression. Headline findings show runup exposure as the strongest positive correlate of erosion, rainfall intensity as a secondary contributor, and vegetation cover and soil cohesion as protective; critically, a negative Runup × Vegetation interaction indicates that vegetation buffers exposure effects. Upper-tail losses are disproportionately sensitive to exposure, slope, and substrate resistance. Implications include operational runup-aware monitoring, prioritization of nature-based measures where accommodation and supply permit, preservation of cohesive toes on bluffed coasts, and risk-tiered setbacks calibrated to directional exposure and morphology. Keywords: coastal erosion, soil loss, geological survey, cross-sectional analysis, vegetation buffering, runup exposure, cohesion, nature-based adaptation, spatial modeling, climate risk.
Copyright (c) 2022 Md. Mominul Haque, Masud Rana, Md. Milon Mia (Author)
GRAPH NEURAL NETWORKS (GNNS) FOR MODELING CYBER ATTACK PATTERNS AND PREDICTING SYSTEM VULNERABILITIES IN CRITICAL INFRASTRUCTURE
This study presents an extensive quantitative and literature-based examination of Graph Neural Networks (GNNs) as an emerging paradigm for modeling cyber attack patterns and predicting system vulnerabilities in critical infrastructure (CI) environments. Drawing upon a comprehensive review of over 130 peer-reviewed studies published between 2015 and 2025, the research synthesizes current methodologies, architectural advances, and applied frameworks that demonstrate how GNNs can effectively capture the relational and temporal complexity inherent in modern cyber-physical systems. Traditional cybersecurity techniques—such as rule-based intrusion detection, statistical anomaly analysis, and conventional deep learning—often fail to represent non-Euclidean dependencies and multi-stage attack sequences common in industrial control systems (ICS), power grids, and enterprise networks. In contrast, GNN-based models encode assets, users, communication protocols, and event flows as interconnected nodes and edges, enabling the detection of lateral movements, privilege escalations, and cascading failures that evolve dynamically across networks. The study explores core architectures including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), GraphSAGE, and Temporal Graph Networks (TGN), highlighting their performance advantages in node classification, link prediction, subgraph anomaly detection, and vulnerability scoring tasks. Furthermore, it evaluates robustness strategies against adversarial perturbations, self-supervised pretraining for label-scarce data, and interpretability mechanisms such as GNNExplainer and SubgraphX for operator trust and regulatory compliance. The comparative findings confirm that GNNs outperform traditional baselines in precision, recall, and contextual awareness while providing transparent, scalable, and temporally aware analytics suitable for mission-critical systems. Overall, this research establishes GNNs as a transformative approach for advancing cyber resilience through relational modeling and predictive vulnerability assessment, offering both theoretical insights and practical implications for safeguarding national and industrial infrastructures against sophisticated and evolving cyber threats.
Copyright (c) 2022 Tonoy Kanti Chowdhury, Shaikat Biswas (Author)
USING MACHINE LEARNING TO ASSESS WORKPLACE ACCIDENT COMPENSATION CASES VIA HEALTH RECORDS
This study employs a quantitative, cross-sectional, multi–case design to evaluate determinants of workplace accident compensation outcomes through harmonized electronic health records (EHRs), administrative claims, and a brief Likert-scale survey capturing process and communication constructs. The investigation encompasses multiple compensation jurisdictions and health systems, enabling comparative analysis and enhancing external validity. Closed adjudicated cases serve as the analytic unit, providing consistent labeling for four key outcomes, approval status, benefit magnitude, processing time, and appeal occurrence, each reflecting distinct stages of the adjudicative lifecycle. Data integration occurs under a privacy-preserving linkage protocol across three coordinated sources: structured EHR extracts containing diagnostic codes, procedures, medications, laboratory results, and encounter timestamps; claims system data containing lodgment and decision dates, indemnity and medical payments, attorney involvement, and prior claims indicators; and survey data capturing process-level constructs such as documentation completeness, communication quality, transparency, and adjudication clarity. Rigorous data quality assessments, reproducible phenotyping of clinical and administrative variables, and standardized reporting protocols ensure methodological transparency. Missingness is characterized and resolved through multiple imputation or indicator approaches, outliers are managed via clinically justified bounds, and all variables are timestamped relative to the index event to maintain temporal coherence and prevent information leakage. The analytical framework combines explanatory and predictive modeling to investigate the interplay between clinical burden, process efficiency, and adjudicative outcomes. Logistic regression models estimate approval and appeal likelihoods, generalized linear models with Gamma family and log link address skewed benefit magnitudes, and Cox proportional hazards models quantify time-to-decision processes. Model performance is evaluated using discrimination (AUC, precision–recall AUC, F1 score), calibration (Brier score, calibration slope), and equity diagnostics across subgroups defined by age, sex, industry, and language. Predictor domains include clinical severity, comorbidities, medication exposure, care intensity, process metrics, prior claims, and demographic–occupational context, complemented by the four Likert-based process indices. Reproducibility is ensured through version-controlled scripts, documented variable definitions, and auditable preprocessing pipelines that promote transparency and external validation. Collectively, the study advances a robust, equity-aware methodological framework for assessing and predicting compensation adjudication outcomes using EHR-linked data—bridging clinical analytics with administrative decision-making to enhance fairness, interpretability, and accountability in occupational health research.
Copyright (c) 2023 Rumaesa Mandana Rob, Subaya Rahman Sabrin (Author)
AUTOMATED ESSAY SCORING AND FEEDBACK SYSTEMS FOR ESL LEARNERS: A META-REVIEW OF PEDAGOGICAL IMPACT
This meta-review provides a comprehensive, quantitative synthesis of empirical research on the pedagogical impact of Automated Essay Scoring (AES) and Automated Writing Evaluation (AWE) systems for English as a Second Language (ESL) learners. Drawing from 54 primary studies published between 2000 and 2024, encompassing 7,832 participants across secondary, tertiary, and intensive English programs, the review investigates how automated scoring and feedback technologies influence writing performance, learner engagement, and assessment reliability. The study employed a systematic search across Scopus, Web of Science, ERIC, PsycINFO, ProQuest, and Google Scholar, guided by PRISMA and JARS-Quant frameworks to ensure methodological transparency and replicability. Quantitative data were analyzed using random-effects meta-analysis, robust variance estimation, and meta-regression to explore moderators such as learner proficiency, feedback frequency, delivery mode, and tool type (e.g., Criterion, Pigai, Grammarly, Write & Improve, and large language model–based systems). Results indicate that AES/AWE interventions produce significant improvements in writing quality, grammar accuracy, and lexical sophistication, with an average effect size of g ≈ 0.60, denoting a moderate pedagogical impact. Intermediate learners benefited most, while feedback frequency and immediacy emerged as strong predictors of performance gains. Systems demonstrating high alignment with human raters (ICC > .80) yielded the greatest learning improvements, highlighting AI precision as a crucial determinant of educational effectiveness. Engagement indicators—such as multiple draft cycles, higher feedback uptake, and reduced latency—further strengthened outcomes. However, fairness diagnostics and bias reporting were inconsistently addressed across studies, underscoring the need for more equitable validation frameworks in multilingual contexts. Overall, the findings affirm that when designed with psychometric rigor, timely feedback, and iterative revision opportunities, AES and AWE systems significantly enhance ESL writing development. This study contributes evidence-based insights for educators, developers, and policymakers, emphasizing that the pedagogical value of automated feedback lies not merely in automation itself but in its precision, transparency, and capacity to foster sustained learner engagement.
Copyright (c) 2024 Elmoon Akhter, Md Arif Uz Zaman (Author)
RISK FACTORS CONTRIBUTING TO LOW BACK PAIN AMONG HEALTHCARE PROFESSIONALS IN RAJSHAHI CITY CORPORATION, BANGLADESH
Low back pain (LBP) is a nonspecific musculoskeletal condition that presents as acute or chronic pain in or near the lumbosacral region. It can result from inflammatory, degenerative, neoplastic, metabolic, gynecologic, traumatic, or occupational causes. Globally, LBP is recognized as one of the most prevalent health problems, contributing significantly to disability, absenteeism, reduced productivity, and increasing economic burden on healthcare and social systems. This study sought to identify the prevalence and risk factors of LBP among healthcare providers working in Rajshahi City Corporation, Bangladesh. A quantitative, analytical cross-sectional design was adopted due to its practicality, cost-effectiveness, and suitability for addressing the research objectives within a limited timeframe. Data were collected using a structured questionnaire focusing on demographic variables, occupational exposures, lifestyle factors, and pain characteristics. The analysis revealed that age, sex, body weight, BMI, dietary habits, and level of knowledge were not significantly associated with LBP among respondents. Instead, occupational and lifestyle-related factors—including history of lifting heavy weights, sedentary lifestyle, poor posture, prolonged sitting, and lack of adherence to preventive measures—emerged as the main contributors. Approximately 50% of participants reported no active pain at the time of the survey, and most did not experience radiation of pain to the thigh or leg. Sitting was identified as the posture most likely to aggravate pain, followed by walking. Although healthcare providers possess awareness of LBP risk factors, many demonstrated reluctance to adopt preventive behaviors or seek proper management, mirroring the attitudes of non-medical populations. The findings highlight the urgent need for mandatory educational programs and counseling on musculoskeletal health, alongside the incorporation of regular light exercise and ergonomic practices, to reduce the prevalence and occupational impact of LBP among healthcare professionals.
Copyright (c) 2025 Mst. Mahfuza Arefin Rinki (Author)
META-ANALYTICAL REVIEW OF CLOUD DATA INFRASTRUCTURE ADOPTION IN THE POST-COVID ECONOMY: ECONOMIC IMPLICATIONS OF AWS WITHIN TC8 INFORMATION SYSTEMS FRAMEWORKS
This study presents a comprehensive meta-analytical review of cloud data infrastructure adoption in the post-COVID economy, focusing on the economic implications of Amazon Web Services (AWS) within IFIP TC8 information systems frameworks. The unprecedented disruptions caused by the COVID-19 pandemic accelerated organizational dependence on cloud infrastructures, transforming them from optional digital tools into foundational systems underpinning resilience, continuity, and competitiveness. Drawing on a systematic review process aligned with PRISMA guidelines, a total of 124 peer-reviewed articles and industry studies were examined, capturing diverse perspectives from information systems, management, and economics. The analysis highlights three interrelated dimensions of adoption: technological, organizational, and environmental determinants. Findings reveal that compatibility, complexity, and perceived advantages continue to guide adoption decisions, while governance maturity, leadership support, and regulatory pressures have become increasingly decisive in the post-pandemic context. Economic outcomes are equally significant, with evidence showing clear shifts from capital expenditure to operational expenditure, measurable productivity gains through faster deployment and improved decision-making, and broader competitive effects such as enhanced small and medium-sized enterprise participation in global markets. In parallel, governance frameworks such as the AWS shared responsibility model and alignment with international standards have emerged as central mechanisms shaping adoption outcomes. Architectural patterns—including data lakes, warehouses, and meshes—illustrate the socio-technical nature of cloud infrastructures, where technical choices embed governance structures, accountability, and economic trade-offs. Synthesizing these insights through TC8 theoretical anchors demonstrates that AWS adoption operates as both a technological strategy and an economic institution, reshaping organizational practices and contributing to structural transformations in the digital economy. By systematically consolidating findings from 124 studies with more than 7,000 cumulative citations, this review provides a rigorous evidence base for understanding cloud infrastructure adoption and its economic significance in the post-COVID global environment.
Copyright (c) 2022 Danish Mahmud, Md.Kamrul Khan (Author)
DIGITAL EQUITY AND NONPROFIT MARKETING STRATEGY: BRIDGING THE TECHNOLOGY GAP THROUGH AI-POWERED SOLUTIONS FOR UNDERSERVED COMMUNITY ORGANIZATIONS
This study investigates the intersection of digital equity and nonprofit marketing strategy with a focus on how AI-powered solutions can bridge the technology gap for underserved community organizations. Digital equity is conceptualized not merely as access to devices or internet connectivity but as a multidimensional framework encompassing affordability, digital literacy, cultural sensitivity, autonomy of use, and the capacity to translate access into meaningful outcomes such as education, employment, and civic participation. Nonprofits, driven by social missions rather than profit, are uniquely positioned to act as mediators between global technological infrastructures and marginalized communities by leveraging trust-based relationships, mission alignment, and inclusive communication practices. Their marketing strategies are framed not only as tools for visibility and fundraising but as vehicles for equity-building through segmentation, targeted outreach, multilingual messaging, and community-driven storytelling. The incorporation of artificial intelligence into these strategies has the potential to extend capacity and efficiency by providing automated translation, predictive analytics, chatbots, and content-generation systems, enabling nonprofits with limited resources to scale their operations and enhance engagement. However, the study also identifies persistent challenges related to resource constraints, data governance, ethical accountability, and community trust, underscoring the need for participatory design and transparent communication. By synthesizing evidence from 142 peer-reviewed articles, this review constructs an integrated conceptual framework that unites digital equity principles, nonprofit marketing strategies, and AI applications. The findings suggest that when implemented responsibly, AI serves as an enabler that amplifies human-centered nonprofit practices rather than replacing them, ensuring that equity and trust remain central. Overall, the study demonstrates that bridging the technology gap for underserved community organizations requires not only technological innovation but also strong institutional support, ethical governance, and culturally responsive nonprofit strategies that empower marginalized populations in the digital age.
Copyright (c) 2023 Reduanul Hasan (Author)
A POISSON REGRESSION APPROACH TO MODELING TRAFFIC ACCIDENT FREQUENCY IN URBAN AREAS
Urban traffic safety research frequently models crash frequency using Poisson-family regression because it is interpretable, extensible, and policy relevant. This systematic review synthesizes how these models are specified, diagnosed, validated, and translated into practice for urban contexts. Following PRISMA protocols, we searched multidisciplinary and transportation databases from inception through 2022, applied dual-stage screening with explicit inclusion criteria, extracted standardized methodological and results fields, and appraised reporting quality; 110 studies met all criteria and were included. The modeling landscape shows a clear center of gravity around the Poisson family, with negative binomial as the most common primary specification at 40.0 percent, followed by canonical Poisson at 29.1 percent, zero-inflated or hurdle variants at 12.7 percent, Poisson-lognormal or multivariate forms at 7.3 percent, and mixed or spatial CAR/ICAR primaries at 5.5 percent each, while Poisson appears as a baseline in most studies. Practice quality is uneven. Offsets are specified in 83.6 percent of papers and are associated with stronger validation and more stable inference, yet only 35.5 percent report any out-of-sample validation and calibration plots appear infrequently. Across covariates, higher speeds, turning shares, and access density typically increase risk, whereas medians and coordinated signals are protective; pedestrian and cyclist volumes often exhibit safety-in-numbers curvature. Translation to policy commonly occurs through Safety Performance Functions paired with empirical-Bayes adjustment; among studies reporting re-ranking, median turnover in top-site lists is about 30 percent, underscoring the operational impact of correct EB use and calibration. Overall, the evidence supports a disciplined workflow: construct mechanism-matched offsets, diagnose dispersion, zeros, and dependence, escalate model complexity only when warranted, and pair likelihood-based selection with explicit predictive checks to ensure credible, auditable safety decisions in cities.
Copyright (c) 2022 Md.Kamrul Khan, Md. Tarek Hasan (Author)
INNOVATIVE TEXTILE RECYCLING AND UPCYCLING TECHNOLOGIES FOR CIRCULAR FASHION: REDUCING LANDFILL WASTE AND ENHANCING ENVIRONMENTAL SUSTAINABILITY
This systematic review examines innovative textile recycling and upcycling technologies for circular fashion to curb waste and reduce environmental impacts. Using PRISMA 2020, we searched major databases for studies from January 2010 to August 2022 and extracted technical and environmental data. We reviewed 95 peer-reviewed studies spanning mechanical, chemical, biological, thermal, sorting and pre-processing, and upcycling pathways. Across circular fiber-to-fiber routes, median landfill diversion was 74 percent, while thermal contingencies diverted 100 percent of mass; aggregated evidence indicates cradle-to-gate greenhouse-gas reductions of 38 to 49 percent versus virgin baselines. Mechanical recycling recovered on average 86 percent of fiber mass but typically required blending due to an 18 percent tenacity drop. Dissolution–regeneration of cellulosics often reached apparel-grade tenacities, and chemical depolymerization delivered median monomer yields near 90 percent for PET and 85 percent for nylon-6. Enzymatic approaches broadened feasibility for blends, achieving about 78 percent PET conversion under mild conditions. Integration factors were decisive: raising bale purity from 90 to 97 percent via NIR or FTIR sorting increased yields by 12 to 18 percent in mechanical lines and about 8 percent in chemical lines; explicit elastane removal boosted cellulosic recovery by 12 to 15 points and reduced fouling. verall, the evidence supports a hierarchy that prioritizes high-value regeneration where purity thresholds are met, uses selective enzymatic or solvent steps to unlock blends, reserves thermochemical options for intractable streams, and treats advanced sorting as essential infrastructure for reliable diversion and emissions gains.
Copyright (c) 2022 Md Rezaul Karim, Md Mesbaul Hasan (Author)
AI-DRIVEN INSIGHTS FOR PRODUCT MARKETING: ENHANCING CUSTOMER EXPERIENCE AND REFINING MARKET SEGMENTATION
This systematic review examines how artificial intelligence enhances product marketing by improving customer experience and refining market segmentation. Following a predefined PRISMA protocol, we searched multidisciplinary databases for peer reviewed, English language studies through December 2021, applied dual independent screening, and extracted standardized information on context, techniques, outcomes, and governance. In total, 115 studies met eligibility and were included in the synthesis. Findings indicate that AI delivers consistent and economically meaningful gains when embedded in data mature workflows and evaluated with credible designs. Across the corpus, 67.8 percent of studies reported statistically positive primary outcomes. Typical improvements included higher conversion and stronger ranking quality in personalization systems, as well as revenue lift from pricing and offer optimization without eroding trust. Gains were more durable when deployment was supported by monitoring, calibration, explanation, fair allocation checks, and disciplined rollout practices. Evidence clusters across seven themes that map the decision surface of product marketing: personalization and next best action, segmentation, journey analytics and voice of customer, pricing and promotion, churn and lifetime value, explainability and fairness, and MLOps implementation. Limitations include heterogeneity in metrics and settings, the English language focus, and the pre 2022 cutoff. Overall, the review moves the conversation from whether AI helps to the conditions under which it produces reliable and sustained value. The contribution is threefold: a structured taxonomy of AI approaches relevant to product marketing, an evidence map that shows where results are strongest or thin, and a conceptual model that links data readiness to AI capability, insight quality, and measurable outcomes in customer experience and firm performance.
Copyright (c) 2023 Mohammad Shoeb Abdullah, Reduanul Hasan (Author)
HUMAN-MACHINE INTERFACES IN INDUSTRIAL SYSTEMS: ENHANCING SAFETY AND THROUGHPUT IN SEMI-AUTOMATED FACILITIES
This study systematically examined the role of human–machine interfaces (HMIs) in enhancing safety and throughput within semi-automated industrial systems, applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological rigor. A total of 112 peer-reviewed papers were reviewed, encompassing empirical investigations, simulation studies, and field-based analyses from diverse industrial domains including manufacturing, logistics, and energy systems. The findings reveal that HMIs have evolved from basic control panels and analog displays into integrated socio-technical infrastructures that directly shape operator performance, system safety, and production efficiency. Across the reviewed studies, a strong consensus emerged that cognitive engineering principles, such as alignment with perceptual limits, workload management, and support for accurate mental models, significantly reduce operator error rates and improve decision-making. Safety-centered design was identified as a mature research area, with rationalized alarm systems, standardized emergency procedures, and compliance with international safety standards shown to decrease overload and strengthen hazard response. Equally, operational performance was consistently linked to HMI quality, with interfaces enabling stable cycle times, faster recovery during abnormal situations, and integration of lean practices such as setup reduction and error-proofing. The evidence also highlights the growing role of HMIs in human–robot collaboration, supervisory control, and trust-building, demonstrating that transparency and clear communication enhance safety and coordination in shared workspaces. Additionally, HMIs are increasingly recognized as cyber-physical security nodes and organizational learning tools, capturing incident data, supporting feedback loops, and embedding continuous improvement into daily operations. By synthesizing insights across cognitive, safety, operational, and organizational domains, this review positions HMIs as strategic infrastructures that harmonize human adaptability with technological consistency. The results underscore that the effectiveness of semi-automated facilities is inseparable from the quality of their interfaces, making HMI design and refinement a decisive factor for both resilience and competitiveness in modern industrial systems.
Copyright (c) 2023 Moin Uddin Mojumder, Rezwanul Ashraf Ruddro (Author)