American Journal of Interdisciplinary Studies
type: channel
description: 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.
publisher: American Scholarly Publishing Group Inc
language: en
publication name: American Journal of Interdisciplinary Studies
items: DIGITAL TWIN-DRIVEN PROCESS MODELING FOR ENERGY EFFICIENCY AND LIFECYCLE OPTIMIZATION IN INDUSTRIAL FACILITIES
items: REAL-TIME FAULT DETECTION IN INDUSTRIAL ASSETS USING ADVANCED VIBRATION DYNAMICS AND STRESS ANALYSIS MODELING
items: MACHINE LEARNING-BASED PAVEMENT CONDITION PREDICTION MODELS FOR SUSTAINABLE TRANSPORTATION SYSTEMS
items: A DATA DRIVEN CYBER PHYSICAL FRAMEWORK FOR REAL TIME PRODUCTION CONTROL INTEGRATING IOT AND LEAN PRINCIPLES
items: AI-ENABLED NEUROBIOLOGICAL DIAGNOSTIC MODELS FOR EARLY DETECTION OF PTSD AND TRAUMA DISORDERS
items: DATA PRIVACY-AWARE MACHINE LEARNING AND FEDERATED LEARNING: A FRAMEWORK FOR DATA SECURITY
items: CLOUD-INTEGRATED DIGITAL TWIN ARCHITECTURES FOR REAL-TIME MONITORING, RISK ASSESSMENT, AND SAFETY OPTIMIZATION IN U.S. ENERGY INFRASTRUCTURE
items: SUSTAINABLE MATERIALS CHARACTERIZATION FOR LOW-CARBON CONSTRUCTION AND INFRASTRUCTURE DURABILITY
items: AI-ENHANCED MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR LEAN MANUFACTURING EFFICIENCY AND ENERGY-CONSCIOUS PRODUCTION SYSTEMS
items: ADVANCES AND LIMITATIONS OF FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION APPROACHES FOR STRUCTURAL INTEGRITY ASSESSMENT: A SYSTEMATIC REVIEW
items: QUANTUM-INSPIRED AI METAHEURISTIC FRAMEWORK FOR MULTI-OBJECTIVE OPTIMIZATION IN INDUSTRIAL PRODUCTION SCHEDULING
items: FEDERATED LEARNING ARCHITECTURES FOR PREDICTIVE QUALITY CONTROL IN DISTRIBUTED MANUFACTURING SYSTEMS
items: PREDICTIVE MODELING OF BRIDGE LOAD CAPACITY USING MACHINE LEARNING AND REAL-TIME SENSOR DATA
items: ADAPTING PLC/SCADA SYSTEMS TO MITIGATE INDUSTRIAL IOT CYBERSECURITY RISKS IN GLOBAL MANUFACTURING
items: A QUANTITATIVE STUDY ON AI-DRIVEN EMPLOYEE PERFORMANCE ANALYTICS IN MULTINATIONAL ORGANIZATIONS
items: SYSTEMATIC REVIEW OF INDUSTRIAL ENGINEERING APPROACHES TO APPAREL SUPPLY CHAIN RESILIENCE IN THE U.S. CONTEXT
items: BALANCING PRIVACY AND SECURITY IN THE DIGITAL AGE: A GLOBAL PERSPECTIVE
items: THE INFLUENCE OF STATISTICAL MODELS FOR FRAUD DETECTION IN PROCUREMENT AND INTERNATIONAL TRADE SYSTEMS
items: MODELING GEOTECHNICAL SOIL LOSS AND EROSION DYNAMICS FOR CLIMATE-RESILIENT COASTAL ADAPTATION
items: STATISTICAL ANALYSIS OF GEOTECHNICAL SOIL LOSS AND EROSION PATTERNS FOR CLIMATE ADAPTATION IN COASTAL ZONES
items: GRAPH NEURAL NETWORKS (GNNS) FOR MODELING CYBER ATTACK PATTERNS AND PREDICTING SYSTEM VULNERABILITIES IN CRITICAL INFRASTRUCTURE
items: USING MACHINE LEARNING TO ASSESS WORKPLACE ACCIDENT COMPENSATION CASES VIA HEALTH RECORDS
items: AUTOMATED ESSAY SCORING AND FEEDBACK SYSTEMS FOR ESL LEARNERS: A META-REVIEW OF PEDAGOGICAL IMPACT
items: RISK FACTORS CONTRIBUTING TO LOW BACK PAIN AMONG HEALTHCARE PROFESSIONALS IN RAJSHAHI CITY CORPORATION, BANGLADESH
items: META-ANALYTICAL REVIEW OF CLOUD DATA INFRASTRUCTURE ADOPTION IN THE POST-COVID ECONOMY: ECONOMIC IMPLICATIONS OF AWS WITHIN TC8 INFORMATION SYSTEMS FRAMEWORKS
items: DIGITAL EQUITY AND NONPROFIT MARKETING STRATEGY: BRIDGING THE TECHNOLOGY GAP THROUGH AI-POWERED SOLUTIONS FOR UNDERSERVED COMMUNITY ORGANIZATIONS
items: A POISSON REGRESSION APPROACH TO MODELING TRAFFIC ACCIDENT FREQUENCY IN URBAN AREAS
items: INNOVATIVE TEXTILE RECYCLING AND UPCYCLING TECHNOLOGIES FOR CIRCULAR FASHION: REDUCING LANDFILL WASTE AND ENHANCING ENVIRONMENTAL SUSTAINABILITY
items: AI-DRIVEN INSIGHTS FOR PRODUCT MARKETING: ENHANCING CUSTOMER EXPERIENCE AND REFINING MARKET SEGMENTATION
items: HUMAN-MACHINE INTERFACES IN INDUSTRIAL SYSTEMS: ENHANCING SAFETY AND THROUGHPUT IN SEMI-AUTOMATED FACILITIES

DIGITAL TWIN-DRIVEN PROCESS MODELING FOR ENERGY EFFICIENCY AND LIFECYCLE OPTIMIZATION IN INDUSTRIAL FACILITIES
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital Twin Driven Process Modeling
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Lifecycle Optimization
  topic: keywords
subject: Industrial Facilities
  topic: keywords
subject: Real Time Analytics
  topic: keywords
creator: Shaikh Shofiullah
creator: Md. Tahmid Farabe Shehun
rights: Copyright (c) 2023 Shaikh Shofiullah, Md. Tahmid Farabe Shehun (Author)
date: 2023-09-27
publication date: 2023-09-27
starting page: 65
ending page: 95
doi: 10.63125/e4q64869

REAL-TIME FAULT DETECTION IN INDUSTRIAL ASSETS USING ADVANCED VIBRATION DYNAMICS AND STRESS ANALYSIS MODELING
type: item
description: 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,…
subject: Articles
  topic: section
subject: Real-Time Fault Detection
  topic: keywords
subject: Vibration Dynamics
  topic: keywords
subject: Stress Analysis
  topic: keywords
subject: Condition-Based Maintenance
  topic: keywords
subject: Cloud Enterprise Monitoring
  topic: keywords
creator: Saikat Sarkar
rights: Copyright (c) 2021 Saikat Sarkar (Author)
date: 2021-12-24
publication date: 2021-12-24
starting page: 39
ending page: 68
doi: 10.63125/0h163429

MACHINE LEARNING-BASED PAVEMENT CONDITION PREDICTION MODELS FOR SUSTAINABLE TRANSPORTATION SYSTEMS
type: item
description: 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 modeli…
subject: Articles
  topic: section
subject: Pavement Prediction Models
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
subject: Climate Variability
  topic: keywords
subject: Sustainable Transportation Systems
  topic: keywords
creator: Md Sarwar Hossain Shuvo
creator: Md Milon Mia
rights: Copyright (c) 2022 Md Sarwar Hossain Shuvo, Md Milon Mia (Author)
date: 2022-04-24
publication date: 2022-04-24
starting page: 31
ending page: 64
doi: 10.63125/1jsmkg92

A DATA DRIVEN CYBER PHYSICAL FRAMEWORK FOR REAL TIME PRODUCTION CONTROL INTEGRATING IOT AND LEAN PRINCIPLES
type: item
description: 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 improveme…
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: Cyber-Physical Systems
  topic: keywords
subject: Lean Manufacturing
  topic: keywords
subject: Real-Time Control
  topic: keywords
subject: Data-Driven Framework
  topic: keywords
creator: S. M. Habibullah
creator: Md. Foysal Hossain
rights: Copyright (c) 2021 S. M. Habibullah, Md. Foysal Hossain (Author)
date: 2021-09-24
publication date: 2021-09-24
starting page: 35
ending page: 70
doi: 10.63125/20nhqs87

AI-ENABLED NEUROBIOLOGICAL DIAGNOSTIC MODELS FOR EARLY DETECTION OF PTSD AND TRAUMA DISORDERS
type: item
description: 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 f…
subject: Articles
  topic: section
subject: Post-Traumatic Stress Disorder (PTSD)
  topic: keywords
subject: Neurobiomarkers
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Early Detection
  topic: keywords
subject: Multimodal Imaging
  topic: keywords
creator: Md. Akbar Hossain
creator: Sharmin Ara
rights: Copyright (c) 2025 Md. Akbar Hossain, Sharmin Ara (Author)
date: 2025-09-24
publication date: 2025-09-24
starting page: 01
ending page: 39
doi: 10.63125/64hftc92

DATA PRIVACY-AWARE MACHINE LEARNING AND FEDERATED LEARNING: A FRAMEWORK FOR DATA SECURITY
type: item
description: 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, a…
subject: Articles
  topic: section
subject: Privacy-aware machine learning
  topic: keywords
subject: Federated learning
  topic: keywords
subject: Differential privacy
  topic: keywords
subject: Homomorphic encryption
  topic: keywords
subject: Secure multi-party computation
  topic: keywords
subject: Sustainable intelligent systems
  topic: keywords
creator: Md. Tarek Hasan
creator: Sai Praveen Kudapa
rights: Copyright (c) 2021 Md. Tarek Hasan, Sai Praveen Kudapa (Author)
date: 2021-09-24
publication date: 2021-09-24
starting page: 01
ending page: 34
doi: 10.63125/vj1hem03

CLOUD-INTEGRATED DIGITAL TWIN ARCHITECTURES FOR REAL-TIME MONITORING, RISK ASSESSMENT, AND SAFETY OPTIMIZATION IN U.S. ENERGY INFRASTRUCTURE
type: item
description: 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 …
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Cloud Integration
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Safety Optimization
  topic: keywords
creator: M.A. Rony
creator: Hozyfa Shafa
rights: Copyright (c) 2024 M.A. Rony, Hozyfa Shafa (Author)
date: 2024-11-28
publication date: 2024-11-28
starting page: 96
ending page: 133
doi: 10.63125/y9m5pz24

SUSTAINABLE MATERIALS CHARACTERIZATION FOR LOW-CARBON CONSTRUCTION AND INFRASTRUCTURE DURABILITY
type: item
description: 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 2…
subject: Articles
  topic: section
subject: Sustainable materials
  topic: keywords
subject: Low-carbon construction
  topic: keywords
subject: Durability
  topic: keywords
subject: Material characterization
  topic: keywords
subject: Life cycle assessment
  topic: keywords
creator: Md Sarwar Hossain
rights: Copyright (c) 2021 Md Sarwar Hossain (Author)
date: 2021-03-28
publication date: 2021-03-28
starting page: 01
ending page: 34
doi: 10.63125/wq1wdr64

AI-ENHANCED MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR LEAN MANUFACTURING EFFICIENCY AND ENERGY-CONSCIOUS PRODUCTION SYSTEMS
type: item
description: 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…
subject: Articles
  topic: section
subject: Lean Manufacturing
  topic: keywords
subject: Energy-Conscious Production
  topic: keywords
subject: Multi-Objective Optimization
  topic: keywords
subject: OEE
  topic: keywords
subject: Energy Intensity
  topic: keywords
creator: Sudipto Roy
rights: Copyright (c) 2023 Sudipto Roy (Author)
date: 2023-09-15
publication date: 2023-09-15
starting page: 34
ending page: 64
doi: 10.63125/s43p0363

ADVANCES AND LIMITATIONS OF FRACTURE MECHANICS–BASED FATIGUE LIFE PREDICTION APPROACHES FOR STRUCTURAL INTEGRITY ASSESSMENT: A SYSTEMATIC REVIEW
type: item
description: 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 …
subject: Articles
  topic: section
subject: Fatigue Crack Growth
  topic: keywords
subject: Fracture Mechanics
  topic: keywords
subject: Variable-Amplitude Loading
  topic: keywords
subject: Crack Closure
  topic: keywords
subject: Short-Crack Behavior
  topic: keywords
creator: Md Ariful Islam
creator: Efat Ara Haque
rights: Copyright (c) 2023 Md Ariful Islam, Efat Ara Haque (Author)
date: 2022-09-15
publication date: 2022-09-15
starting page: 68
ending page: 98
doi: 10.63125/fg8ae957

QUANTUM-INSPIRED AI METAHEURISTIC FRAMEWORK FOR MULTI-OBJECTIVE OPTIMIZATION IN INDUSTRIAL PRODUCTION SCHEDULING
type: item
description: 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…
subject: Articles
  topic: section
subject: Quantum-Inspired Metaheuristic
  topic: keywords
subject: Multi-Objective Scheduling
  topic: keywords
subject: Industrial Production
  topic: keywords
subject: Hypervolume
  topic: keywords
subject: IGD
  topic: keywords
creator: Md Sanjid Khan
rights: Copyright (c) 2023 Md Sanjid Khan (Author)
date: 2023-09-05
publication date: 2023-09-05
starting page: 01
ending page: 33
doi: 10.63125/2mba8p24

FEDERATED LEARNING ARCHITECTURES FOR PREDICTIVE QUALITY CONTROL IN DISTRIBUTED MANUFACTURING SYSTEMS
type: item
description: 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 emp…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Predictive Quality Control
  topic: keywords
subject: Distributed Manufacturing
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Data Heterogeneity
  topic: keywords
creator: Md Sanjid Khan
creator: Md. Tahmid Farabe Shehun
rights: Copyright (c) 2021 Md Sanjid Khan, Md. Tahmid Farabe Shehun (Author)
date: 2021-07-28
publication date: 2021-07-28
starting page: 01
ending page: 31
doi: 10.63125/222nwg58

PREDICTIVE MODELING OF BRIDGE LOAD CAPACITY USING MACHINE LEARNING AND REAL-TIME SENSOR DATA
type: item
description: 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 …
subject: Articles
  topic: section
subject: Predictive Modeling
  topic: keywords
subject: Bridge Load Capacity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Real-Time Sensor Data
  topic: keywords
subject: Structural Health Monitoring
  topic: keywords
creator: Md. Sakib Hasan Hriday
rights: Copyright (c) 2025 Md. Sakib Hasan Hriday (Author)
date: 2025-04-29
publication date: 2025-04-29
starting page: 314
ending page: 353
doi: 10.63125/r9c9pt87

ADAPTING PLC/SCADA SYSTEMS TO MITIGATE INDUSTRIAL IOT CYBERSECURITY RISKS IN GLOBAL MANUFACTURING
type: item
description: 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…
subject: Articles
  topic: section
subject: Industrial IoT (IIoT)
  topic: keywords
subject: PLC
  topic: keywords
subject: SCADA
  topic: keywords
subject: ICS Security
  topic: keywords
creator: Jabed Hasan Tarek
creator: Zayadul Hasan
rights: Copyright (c) 2024 Jabed Hasan Tarek, Zayadul Hasan (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 67
ending page: 95
doi: 10.63125/0v4cms60

A QUANTITATIVE STUDY ON AI-DRIVEN EMPLOYEE PERFORMANCE ANALYTICS IN MULTINATIONAL ORGANIZATIONS
type: item
description: 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 jurisd…
subject: Articles
  topic: section
subject: AI-Driven Performance Analytics
  topic: keywords
subject: Organizational Justice
  topic: keywords
subject: Manager Analytics Literacy
  topic: keywords
subject: Data-Use Culture
  topic: keywords
creator: Omar Muhammad Faruk
creator: Md Redwanul Islam
rights: Copyright (c) 2023 Omar Muhammad Faruk, Md. Redwanul Islam (Author)
date: 2023-12-15
publication date: 2023-12-15
starting page: 145
ending page: 176
doi: 10.63125/vrsjp515

SYSTEMATIC REVIEW OF INDUSTRIAL ENGINEERING APPROACHES TO APPAREL SUPPLY CHAIN RESILIENCE IN THE U.S. CONTEXT
type: item
description: 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,…
subject: Articles
  topic: section
subject: Apparel Supply Chain Resilience
  topic: keywords
subject: Industrial Engineering
  topic: keywords
subject: Postponement
  topic: keywords
subject: Multi-Echelon Inventory
  topic: keywords
subject: Digital Twin
  topic: keywords
creator: Md. Tahmid Farabe Shehun
rights: Copyright (c) 2022 Md. Tahmid Farabe Shehun (Author)
date: 2022-12-25
publication date: 2022-12-25
starting page: 235
ending page: 267
doi: 10.63125/teherz38

BALANCING PRIVACY AND SECURITY IN THE DIGITAL AGE: A GLOBAL PERSPECTIVE
type: item
description: 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 enfo…
subject: Articles
  topic: section
subject: Privacy–Security Trade-off
  topic: keywords
subject: Cybersecurity Governance
  topic: keywords
subject: Data Protection Regulations
  topic: keywords
subject: Digital Trust and Compliance
  topic: keywords
subject: Global Information Policy Evolution
  topic: keywords
creator: Md Atikur Rahman
creator: Md Shah Alam
creator: Md Sabbir Hossain Mrida
rights: Copyright (c) 2023 Md Atikur Rahman, Md Shah Alam, Md Sabbir Hossain Mrida (Author)
date: 2023-07-05
publication date: 2023-07-05
starting page: 64
ending page: 90
doi: 10.63125/d2brsb39

THE INFLUENCE OF STATISTICAL MODELS FOR FRAUD DETECTION IN PROCUREMENT AND INTERNATIONAL TRADE SYSTEMS
type: item
description: 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 c…
subject: Articles
  topic: section
subject: Procurement Fraud
  topic: keywords
subject: International Trade
  topic: keywords
subject: Statistical Modeling
  topic: keywords
subject: Internal Controls
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Vendor Concentration
  topic: keywords
creator: Md. Rabiul Karim
creator: Sai Praveen Kudapa
rights: Copyright (c) 2022 Md. Rabiul Karim, Sai Praveen Kudapa (Author)
date: 2022-12-09
publication date: 2022-12-09
starting page: 203
ending page: 234
doi: 10.63125/9htnv106

MODELING GEOTECHNICAL SOIL LOSS AND EROSION DYNAMICS FOR CLIMATE-RESILIENT COASTAL ADAPTATION
type: item
description: 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 erosi…
subject: Articles
  topic: section
subject: Soil erosion
  topic: keywords
subject: Geotechnical properties
  topic: keywords
subject: Coastal zones
  topic: keywords
subject: Climate adaptation
  topic: keywords
subject: Statistical analysis
  topic: keywords
creator: Syed Zaki Uddin
rights: Copyright (c) 2021 Syed Zaki Uddin (Author)
date: 2021-12-30
publication date: 2021-12-30
starting page: 01
ending page: 38
doi: 10.63125/vsfjtt77

STATISTICAL ANALYSIS OF GEOTECHNICAL SOIL LOSS AND EROSION PATTERNS FOR CLIMATE ADAPTATION IN COASTAL ZONES
type: item
description: 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 coa…
subject: Articles
  topic: section
subject: Coastal Erosion
  topic: keywords
subject: Soil Loss
  topic: keywords
subject: Shoreline Change
  topic: keywords
subject: Geological Survey
  topic: keywords
subject: Quantitative Cross-Sectional Design
  topic: keywords
creator: Md. Mominul Haque
creator: Masud Rana
creator: Md. Milon Mia
rights: Copyright (c) 2022 Md. Mominul Haque, Masud Rana, Md. Milon Mia (Author)
date: 2022-09-07
publication date: 2022-09-07
starting page: 36
ending page: 67
doi: 10.63125/xytn3e23

GRAPH NEURAL NETWORKS (GNNS) FOR MODELING CYBER ATTACK PATTERNS AND PREDICTING SYSTEM VULNERABILITIES IN CRITICAL INFRASTRUCTURE
type: item
description: 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 approac…
subject: Articles
  topic: section
subject: Graph Neural Networks
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Critical Infrastructure
  topic: keywords
subject: Vulnerability Prediction
  topic: keywords
subject: Attack Modeling
  topic: keywords
creator: Tonoy Kanti Chowdhury
creator: Shaikat Biswas
rights: Copyright (c) 2022 Tonoy Kanti Chowdhury, Shaikat Biswas (Author)
date: 2022-12-07
publication date: 2022-12-07
starting page: 157
ending page: 202
doi: 10.63125/1ykzx350

USING MACHINE LEARNING TO ASSESS WORKPLACE ACCIDENT COMPENSATION CASES VIA HEALTH RECORDS
type: item
description: 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 pro…
subject: Articles
  topic: section
subject: Workplace Accident Compensation
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Administrative Claims
  topic: keywords
subject: Quantitative Cross-Sectional
  topic: keywords
creator: Rumaesa Mandana Rob
creator: Subaya Rahman Sabrin
rights: Copyright (c) 2023 Rumaesa Mandana Rob, Subaya Rahman Sabrin (Author)
date: 2023-06-28
publication date: 2023-06-28
starting page: 29
ending page: 63
doi: 10.63125/fqs8ca04

AUTOMATED ESSAY SCORING AND FEEDBACK SYSTEMS FOR ESL LEARNERS: A META-REVIEW OF PEDAGOGICAL IMPACT
type: item
description: 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 r…
subject: Articles
  topic: section
subject: Automated essay scoring
  topic: keywords
subject: Feedback
  topic: keywords
subject: ESL
  topic: keywords
subject: Pedagogy
  topic: keywords
subject: Writing
  topic: keywords
creator: Elmoon Akhter
creator: Md Arif Uz Zaman
rights: Copyright (c) 2024 Elmoon Akhter, Md Arif Uz Zaman (Author)
date: 2024-04-28
publication date: 2024-04-28
starting page: 31
ending page: 65
doi: 10.63125/brzv3333

RISK FACTORS CONTRIBUTING TO LOW BACK PAIN AMONG HEALTHCARE PROFESSIONALS IN RAJSHAHI CITY CORPORATION, BANGLADESH
type: item
description: 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 redu…
subject: Articles
  topic: section
subject: Low back pain (LBP)
  topic: keywords
subject: Healthcare professionals
  topic: keywords
subject: Occupational risk factors
  topic: keywords
subject: Musculoskeletal disorders
  topic: keywords
creator: Mst. Mahfuza Arefin Rinki
rights: Copyright (c) 2025 Mst. Mahfuza Arefin Rinki (Author)
date: 2025-04-28
publication date: 2025-04-28
starting page: 295
ending page: 313
doi: 10.63125/esss4a13

META-ANALYTICAL REVIEW OF CLOUD DATA INFRASTRUCTURE ADOPTION IN THE POST-COVID ECONOMY: ECONOMIC IMPLICATIONS OF AWS WITHIN TC8 INFORMATION SYSTEMS FRAMEWORKS
type: item
description: 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 institu…
subject: Articles
  topic: section
subject: Cloud Data Infrastructure
  topic: keywords
subject: AWS Adoption
  topic: keywords
subject: Post-COVID Economy
  topic: keywords
subject: Information Systems Frameworks (TC8)
  topic: keywords
subject: Economic Implications
  topic: keywords
creator: Danish Mahmud
creator: Md.Kamrul Khan
rights: Copyright (c) 2022 Danish Mahmud, Md.Kamrul Khan (Author)
date: 2022-06-08
publication date: 2022-06-08
starting page: 62
ending page: 90
doi: 10.63125/1eg7b369

DIGITAL EQUITY AND NONPROFIT MARKETING STRATEGY: BRIDGING THE TECHNOLOGY GAP THROUGH AI-POWERED SOLUTIONS FOR UNDERSERVED COMMUNITY ORGANIZATIONS
type: item
description: 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 s…
subject: Articles
  topic: section
subject: Digital Equity
  topic: keywords
subject: Nonprofit Marketing
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Underserved Communities
  topic: keywords
subject: Technology Gap
  topic: keywords
creator: Reduanul Hasan
rights: Copyright (c) 2023 Reduanul Hasan (Author)
date: 2023-12-28
publication date: 2023-12-28
starting page: 117
ending page: 144
doi: 10.63125/zrsv2r56

A POISSON REGRESSION APPROACH TO MODELING TRAFFIC ACCIDENT FREQUENCY IN URBAN AREAS
type: item
description: 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-base…
subject: Articles
  topic: section
subject: Poisson Regression
  topic: keywords
subject: Urban Crash Frequency
  topic: keywords
subject: Negative Binomial
  topic: keywords
subject: Zero Inflation
  topic: keywords
subject: Offsets
  topic: keywords
subject: Overdispersion
  topic: keywords
creator: Md.Kamrul Khan
creator: Md. Tarek Hasan
rights: Copyright (c) 2022 Md.Kamrul Khan, Md. Tarek Hasan (Author)
date: 2022-12-05
publication date: 2022-12-05
starting page: 117
ending page: 156
doi: 10.63125/wqh7pd07

INNOVATIVE TEXTILE RECYCLING AND UPCYCLING TECHNOLOGIES FOR CIRCULAR FASHION: REDUCING LANDFILL WASTE AND ENHANCING ENVIRONMENTAL SUSTAINABILITY
type: item
description: 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.
subject: Articles
  topic: section
subject: Circular fashion
  topic: keywords
subject: Textile recycling
  topic: keywords
subject: Upcycling
  topic: keywords
subject: Mechanical recycling
  topic: keywords
subject: Chemical depolymerization
  topic: keywords
creator: Md Rezaul Karim
creator: Md Mesbaul Hasan
rights: Copyright (c) 2022 Md Rezaul Karim, Md Mesbaul Hasan (Author)
date: 2022-09-05
publication date: 2022-09-05
starting page: 01
ending page: 35
doi: 10.63125/kkmerg16

AI-DRIVEN INSIGHTS FOR PRODUCT MARKETING: ENHANCING CUSTOMER EXPERIENCE AND REFINING MARKET SEGMENTATION
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Product Marketing
  topic: keywords
subject: Customer Experience
  topic: keywords
subject: Market Segmentation
  topic: keywords
subject: Personalization
  topic: keywords
creator: Mohammad Shoeb Abdullah
creator: Reduanul Hasan
rights: Copyright (c) 2023 Mohammad Shoeb Abdullah, Reduanul Hasan (Author)
date: 2023-12-05
publication date: 2023-12-05
starting page: 80
ending page: 116
doi: 10.63125/pzd8m844

HUMAN-MACHINE INTERFACES IN INDUSTRIAL SYSTEMS: ENHANCING SAFETY AND THROUGHPUT IN SEMI-AUTOMATED FACILITIES
type: item
description: 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, oper…
subject: Articles
  topic: section
subject: Human–Machine Interfaces
  topic: keywords
subject: Safety
  topic: keywords
subject: Throughput
  topic: keywords
subject: Semi-Automated Facilities
  topic: keywords
subject: Industrial Systems
  topic: keywords
creator: Moin Uddin Mojumder
creator: Rezwanul Ashraf Ruddro
rights: Copyright (c) 2023 Moin Uddin Mojumder, Rezwanul Ashraf Ruddro (Author)
date: 2023-04-30
publication date: 2023-04-30
starting page: 01
ending page: 26
doi: 10.63125/s2qa0125