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.
A Predictive Model Linking Food-Borne Heavy-Metal Exposure to Antimicrobial Resistance Co-Selection
This study has examined a predictive model linking food-borne heavy-metal exposure to antimicrobial resistance co-selection in a selected food-system case context. The problem has been that heavy-metal contamination and antimicrobial resistance are often studied separately, although contaminated food environments may create non-antibiotic selective pressure that supports resistant bacteria. The purpose of the study has been to test whether food-borne heavy-metal exposure, food contamination pathways, food safety monitoring weakness, and regulatory control have predicted antimicrobial resistance co-selection. The study has used a quantitative, cross-sectional, case-based research design with 220 valid respondents from food safety, public health, laboratory, agriculture, environmental monitoring, academic, and food industry-related sectors. Data have been collected through a structured questionnaire using Likert’s five-point scale and analyzed through descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. The key variables have included food-borne heavy-metal exposure, food contamination pathways, food safety monitoring weakness, regulatory control, and antimicrobial resistance co-selection. The findings have shown that food contamination pathways have recorded the highest mean score of 3.94 with SD = 0.58, followed by food-borne heavy-metal exposure at 3.89 with SD = 0.62 and antimicrobial resistance co-selection at 3.87 with SD = 0.64. Food safety monitoring has recorded a moderate mean of 3.41, while regulatory control has recorded 3.36. Reliability has been strong, with Cronbach’s alpha values ranging from 0.81 to 0.88 and overall reliability at 0.91. Correlation results have shown significant relationships between antimicrobial resistance co-selection and heavy-metal exposure (r = 0.64), contamination pathways (r = 0.61), monitoring weakness (r = 0.49), and regulatory control (r = -0.43), all at p < 0.001. Regression results have shown that the model has explained 57.4% of the variance in antimicrobial resistance co-selection, with R² = 0.574 and adjusted R² = 0.566. The study has implied that stronger food safety monitoring, contamination control, and regulatory enforcement can reduce co-selection risk in food systems.
Copyright (c) 2024 Bushra Mumtaz (Author)
Lifecycle Carbon Reduction and Net Zero Pathway Modeling Using LEED, EDGE, and SBTI Frameworks in Developing Economy Industrial Buildings
This study examined lifecycle carbon reduction and net zero pathway modeling using LEED, EDGE, and SBTi frameworks in developing economy industrial buildings. The problem addressed in this research is that many industrial buildings in developing economies remain highly dependent on fragmented sustainability practices, limited lifecycle carbon accounting, weak green finance readiness, and insufficient integration between building certification, resource efficiency, and science-based emissions targets. The purpose of the study was to assess how LEED-based sustainability practices, EDGE-based resource efficiency, SBTi-aligned target setting, and integrated framework adoption influence lifecycle carbon reduction and net zero pathway readiness. A quantitative, cross-sectional, case-based research design was adopted, using industrial enterprise building cases such as manufacturing plants, warehouses, processing facilities, logistics buildings, and industrial parks. The sample included 180 valid respondents, consisting of engineers, facility managers, sustainability consultants, project managers, energy auditors, construction managers, and industrial owners or developers. The key variables were LEED-based sustainability practices, EDGE-based resource efficiency, SBTi-aligned target setting, lifecycle carbon reduction, LEED-EDGE-SBTi integration, and net zero pathway readiness. Data were analyzed through descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression modeling. The findings showed strong reliability across all constructs, with alpha values ranging from 0.82 to 0.89. EDGE-based resource efficiency recorded the highest mean score of 4.18, followed by lifecycle carbon reduction at 4.11, LEED-based sustainability practices at 4.05, and net zero pathway readiness at 3.98. Correlation results showed that lifecycle carbon reduction was strongly associated with net zero readiness, r = 0.71, p < 0.01. Regression analysis showed that LEED, EDGE, and SBTi jointly explained 58.4% of lifecycle carbon reduction variance, while lifecycle carbon reduction and framework integration explained 62.7% of net zero readiness variance. The study implies that integrated sustainability frameworks can strengthen industrial decarbonization, improve resource efficiency, and support measurable net zero transition planning.
Copyright (c) 2025 Mukut Kanti Barua (Author)
The Role of IOT-Driven Energy Analytics in Improving Operational Efficiency and Sustainability in Modern Industrial Facilities
This study examined the problem of persistent energy waste, high operational costs, fragmented monitoring, and weak integration between energy management and sustainability performance in modern industrial facilities. The purpose was to assess how IoT-driven energy analytics improves operational efficiency and sustainability by enabling real-time energy visibility, predictive analytics, automated alerts, dashboard reporting, and data-driven energy decision-making. A quantitative, cross sectional, case-based research design was applied using structured questionnaire data from 196 valid respondents drawn from industrial enterprise cases involving operations, engineering, maintenance, energy management, sustainability and compliance, and facility management departments. The key variables were IoT-driven energy analytics, real-time energy monitoring, predictive energy analytics, operational efficiency, sustainability performance, energy optimization, cost reduction, resource efficiency, IoT energy analytics maturity, and energy efficiency sustainability alignment. The analysis plan included descriptive statistics, reliability testing using Cronbach’s alpha, Pearson correlation analysis, regression modeling, IoT Energy Analytics Maturity Index assessment, Energy Efficiency Sustainability Alignment Score analysis, and hypothesis testing. The findings showed high agreement across the major constructs, with IoT-driven energy analytics recording M = 4.12, SD = 0.54, real-time energy monitoring M = 4.18, SD = 0.51, predictive energy analytics M = 3.96, SD = 0.59, operational efficiency M = 4.09, SD = 0.56, and sustainability performance M = 4.03, SD = 0.58. Reliability was strong, with Cronbach’s alpha values ranging from 0.81 to 0.88. Correlation results confirmed significant positive relationships between IoT-driven energy analytics and operational efficiency, r = 0.68, p < 0.01, and sustainability performance, r = 0.64, p < 0.01. Regression results showed that IoT-driven energy analytics significantly predicted operational efficiency, β = 0.46, p < 0.001, R² = 0.47, and sustainability performance, β = 0.42, p < 0.001, R² = 0.42. The findings imply that industrial facilities should treat IoT energy analytics as a strategic capability for reducing energy waste, improving resource efficiency, strengthening sustainability reporting, and aligning productivity with environmental responsibility.
Copyright (c) 2022 Md Asif Ali Sheak Arju, Risha Alam (Author)
Predictive Financial Volatility Analytics Using Machine Learning Pipelines in ERP-Integrated Enterprises
This study examined predictive financial volatility analytics using machine learning pipelines within ERP-integrated enterprises to evaluate forecasting accuracy, enterprise financial intelligence, operational monitoring capability, and predictive risk management performance across multiple industrial sectors. The study adopted a quantitative longitudinal research design using ERP-generated enterprise financial datasets collected from 126 organizations operating within banking, manufacturing, retail, healthcare, telecommunications, logistics, and energy sectors. A total of 1,248,560 transactional records covering the operational period from 2019 to 2024 were extracted from accounting systems, procurement platforms, inventory management databases, customer payment systems, operational expenditure records, and enterprise financial reporting infrastructures. Machine learning forecasting models including linear regression, decision trees, random forests, support vector machines, and recurrent neural networks were comparatively evaluated using predictive accuracy indicators such as root mean square error, mean absolute error, precision, recall, cross-validation consistency, and computational efficiency measures. The findings demonstrated that ERP-generated operational variables significantly influenced predictive financial volatility estimation across digitally integrated enterprises. Procurement cost variability produced the strongest predictive effect with a standardized beta coefficient of 0.462, followed by operational expenditure growth at 0.451 and revenue volatility at 0.428. Liquidity ratios and inventory turnover efficiency demonstrated inverse relationships with financial instability, indicating that stronger operational efficiency reduced predictive volatility outcomes. Comparative predictive analysis revealed that recurrent neural network architectures achieved the highest forecasting accuracy at 96.14%, followed by random forest models at 93.86%, while traditional regression models generated comparatively lower predictive accuracy at 79.82%. Banking and energy enterprises demonstrated the strongest predictive forecasting performance with forecasting accuracy levels of 95.72% and 94.84%, respectively, due to stronger ERP integration maturity and centralized financial synchronization systems. Statistical significance testing confirmed that all major predictive variables remained analytically significant at p < 0.05, while effect size analysis demonstrated substantial practical influence across enterprise forecasting outcomes. The study concluded that ERP-integrated machine learning pipelines significantly improved predictive financial volatility estimation, operational transparency, enterprise monitoring capability, anomaly detection performance, and quantitative decision-support efficiency across modern digitally integrated organizational environments.
Copyright (c) 2024 Samia Hossain Swarnali (Author)
Machine Learning–Based Fraud Detection and Conventional Audit Approaches in Government Deposit Processing
This study investigates the problem of fraud vulnerability in government deposit processing, where duplicate entries, delayed reconciliation, unauthorized adjustments, false receipts, abnormal deposit timing, and inconsistent depositor records can weaken public financial accountability and reporting reliability. The purpose of the study was to examine how machine learning based fraud detection and conventional audit approaches influence fraud detection effectiveness, audit efficiency, reporting reliability, and financial control strength in public sector deposit environments. A quantitative, cross sectional, case-based research design was adopted, using structured survey data from 200 professionals representing government finance, accounting, internal audit, external audit, compliance, treasury operations, IT audit, and enterprise financial system cases. The key variables included machine learning based fraud detection, conventional audit approaches, fraud detection effectiveness, audit efficiency, reporting reliability, financial control strength, deposit fraud risk signals, and comparative trust between machine learning and traditional audit methods. The analysis plan involved descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, regression modeling, hypothesis testing, fraud risk signal mapping, and comparative trust indexing. The findings showed strong support for machine learning based fraud detection, with a mean score of 4.21 and SD of 0.61, compared with conventional audit approaches at M = 3.89 and SD = 0.68. Fraud detection effectiveness recorded M = 4.12, audit efficiency M = 4.05, reporting reliability M = 4.08, and financial control strength M = 4.16. Reliability values were acceptable, ranging from 0.82 to 0.90. Correlation results showed that machine learning based fraud detection had a strong positive relationship with fraud detection effectiveness, r = 0.71, p < 0.01, while conventional audit approaches also showed a significant relationship, r = 0.58, p < 0.01. Regression results confirmed that machine learning and conventional audit together explained 47% of fraud detection effectiveness, with machine learning showing the stronger effect, β = 0.52, p < 0.001. The findings imply that government agencies should integrate machine learning screening with audit verification to improve accountability, detection speed, evidence reliability, and financial control.
Copyright (c) 2023 S M Arif Al Sany, H M Mahir Uddin (Author)
A Quantitative Assessment of Cloud-Based Enterprise GIS Platforms for Scalable Asset Management in Public Water Utilities
This study presented a quantitative assessment of cloud-based enterprise Geographic Information System (GIS) platforms for scalable asset management in public water utilities, with a focus on evaluating system performance, scalability, and operational efficiency in comparison to traditional on-premises systems. A cross-sectional analytical design was adopted, incorporating system benchmarking and statistical evaluation using datasets derived from multiple GIS platforms and infrastructure environments. The analysis included key performance indicators such as response time, throughput, system uptime, concurrent user capacity, and data processing efficiency. Descriptive statistics revealed that the average response time across all systems was 1.82 seconds, while throughput averaged 320 transactions per second, with cloud-based platforms demonstrating significantly better performance. Comparative results showed that cloud-based systems reduced response time by approximately 34% and increased throughput by 51% compared to traditional systems, while maintaining higher uptime levels (99.1% vs. 98.0%). Inferential statistical analysis confirmed these differences as significant, with ANOVA results indicating strong variation across deployment models (p < 0.001) and effect sizes ranging from moderate to large (Cohen’s d between 0.54 and 0.95). Regression analysis further demonstrated a strong positive relationship between cloud deployment and system efficiency (β = 0.68, p < 0.001). Subgroup analysis revealed that performance improvements were more pronounced in large-scale systems handling data volumes exceeding 4 terabytes, where throughput increased to 420 transactions per second and response time decreased to 1.30 seconds in cloud environments. Correlation results also indicated that system integration levels were strongly associated with improved performance (r = 0.72, p < 0.001). Overall, the findings confirmed that cloud-based enterprise GIS platforms provided superior scalability, efficiency, and reliability, particularly in high-demand and data-intensive operational settings. The study contributed empirical evidence supporting the adoption of cloud-based GIS solutions for modern infrastructure management, highlighting their capacity to enhance performance and support complex asset management requirements in public water utilities.
Copyright (c) 2024 Kazi Mohammad Khalid Ahsan (Author)
Enterprise-Scale Risk Decision Intelligence Systems Using Large Language Models (LLMs) For Automated Governance, Compliance, and Policy Enforcement
This study investigated enterprise-scale risk decision intelligence systems using Large Language Models (LLMs) for automated governance, compliance, and policy enforcement within digitally integrated enterprise environments. The research examined the influence of LLM capability integration, governance automation intensity, compliance intelligence capability, explainable artificial intelligence implementation, cybersecurity readiness, and data governance maturity on governance performance, compliance accuracy, operational resilience, organizational transparency, decision-making efficiency, and institutional accountability. A quantitative cross-sectional research design was adopted, and data were collected from 400 governance professionals, compliance officers, cybersecurity analysts, enterprise risk managers, and operational executives employed in medium-sized and large enterprises across finance, healthcare, information technology, telecommunications, logistics, manufacturing, and insurance sectors. Statistical analysis was conducted using SPSS and SmartPLS software packages through descriptive statistics, reliability testing, correlation analysis, multiple regression analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. The findings demonstrated that governance performance achieved the highest mean score (M = 4.31, SD = 0.63), followed by compliance accuracy (M = 4.27, SD = 0.66) and governance automation intensity (M = 4.26, SD = 0.65), indicating strong organizational adoption of AI-enabled governance infrastructures. Regression and Structural Equation Modeling findings revealed statistically significant positive relationships between governance variables and organizational performance outcomes. Cybersecurity readiness demonstrated the strongest predictive influence on operational resilience (β = 0.44, p < 0.001), while LLM capability integration significantly influenced governance performance (β = 0.41, p < 0.001). Compliance intelligence capability also positively predicted decision-making efficiency (β = 0.40, p < 0.001), whereas explainable AI implementation significantly strengthened organizational transparency (β = 0.35, p < 0.01). Reliability analysis further confirmed strong internal consistency across all constructs, with Cronbach’s alpha values ranging from 0.87 to 0.93. The findings demonstrated that enterprise-scale LLM governance systems significantly improved governance coordination, compliance reliability, institutional accountability, operational resilience, and predictive governance intelligence across digitally interconnected enterprise ecosystems.
Copyright (c) 2025 B. M. Taslimul Haque, Md. Arifur Rahman (Author)
A Meta-Analysis of Deep Reinforcement Learning for Dynamic Project Scheduling in Engineering Systems
This study conducted a quantitative meta-analysis to evaluate the effectiveness of deep reinforcement learning (DRL) for dynamic project scheduling in engineering systems. The analysis synthesized data from 52 empirical studies across multiple domains, including manufacturing (38.5%), construction (23.1%), logistics (17.3%), and infrastructure systems (11.5%). The findings demonstrated that DRL-based scheduling models significantly outperformed traditional deterministic, heuristic, and classical reinforcement learning approaches across key performance indicators. The aggregated results indicated an average makespan reduction of 18.7%, resource utilization improvement of 14.2%, cost efficiency gain of 11.6%, tardiness reduction of 15.3%, and throughput improvement of 12.8%. Statistical analysis confirmed that these improvements were significant, with 84.6% of studies reporting p-values below 0.05. Effect size evaluation showed moderate to large effects, with makespan reduction achieving a standardized mean difference of 0.91 and resource utilization 0.84, indicating strong practical significance. Subgroup analysis revealed that hybrid DRL models achieved the highest overall improvement (21.5%), followed by Actor-Critic (16.8%), Deep Q-Network (17.0%), and Policy Gradient approaches (14.0%). Domain-specific results indicated more consistent improvements in manufacturing systems, while construction and infrastructure projects showed higher variability due to increased uncertainty. Heterogeneity analysis produced an I² value of 61.3%, reflecting moderate to high variability across studies, while meta-regression indicated that dataset size, domain, and algorithm type explained 47.8% of the variance in outcomes. Visual analysis supported these findings, showing consistent positive effect distributions and minimal publication bias. Overall, the study provided robust quantitative evidence that DRL-based scheduling models enhance efficiency, adaptability, and performance in complex engineering environments, particularly under dynamic and uncertain conditions.
Copyright (c) 2026 Chapal Barua (Author)
AI-Driven Accounts Payable and Receivable Automation for Operational Risk Mitigation in U.S. SMEs: A Systematic Review (2018–2026)
This study examined the problem of operational risk exposure in U.S. small and medium-sized enterprises caused by manual, fragmented, or weakly integrated accounts payable and accounts receivable workflows. The purpose was to determine whether AI-driven AP/AR automation reduces risks related to invoice errors, duplicate payments, delayed collections, reconciliation mismatches, fraud exposure, weak audit trails, and limited cash flow visibility. Using a quantitative, cross-sectional, case-based design, the study collected survey data from 220 valid respondents drawn from cloud-based and enterprise financial automation cases in U.S. SMEs, including SME owners, finance managers, accountants, bookkeepers, controllers, operations managers, and AP/AR staff. The key variables were accounts payable automation, accounts receivable automation, AI-enabled invoice processing, AI-enabled reconciliation, AI-based fraud detection, cash flow visibility, AI automation maturity, and operational risk mitigation. Data were analyzed using descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, multiple regression, and hypotheses testing at the 0.05 significance level. The findings showed high to very high agreement that AI automation improves financial control, with operational risk mitigation recording a mean of 4.27, AI-enabled reconciliation 4.31, fraud detection 4.24, invoice processing 4.20, AP automation 4.18, AR automation 4.12, cash flow visibility 4.16, and AI automation maturity 3.98. Reliability was strong, with an overall Cronbach’s Alpha of 0.91. Correlation results confirmed significant positive relationships between all automation variables and operational risk mitigation, with reconciliation showing the strongest association, r = 0.72, p < 0.001, followed by fraud detection, r = 0.70, p < 0.001. Regression analysis showed that the model explained 65.8% of the variance in operational risk mitigation, R² = 0.658, F (7, 212) = 58.47, p < 0.001. AI-enabled reconciliation was the strongest predictor, β = 0.24, followed by fraud detection, β = 0.21. The study implies that AI-driven AP/AR automation should be treated as a strategic financial control mechanism for improving accuracy, transparency, liquidity awareness, fraud prevention, and operational resilience in U.S. SMEs.
Copyright (c) 2026 Mst Shurovi Akter (Author)
Impact of IOT-Based Energy Monitoring Systems on Operational Efficiency in Industrial Facilities: A Qualitative Evaluation
This study examined the impact of IoT-based energy monitoring systems on operational efficiency in industrial facilities using a quantitative quasi-experimental and longitudinal design. The research analyzed data collected from 12 industrial facilities and 48 production units over a 12-month period, comparing pre-implementation and post-implementation performance indicators. Key variables included energy consumption, production output, equipment utilization, and downtime frequency. The findings revealed a significant reduction in average energy consumption from 15,300 kWh to 13,200 kWh, representing a decrease of approximately 13.7%. At the same time, production output increased from 8,450 units to 9,720 units, reflecting a growth of 15.0%. Equipment utilization improved from 68.5% to 79.2%, while downtime frequency declined from 14.3 hours to 8.7 hours per month, indicating a reduction of nearly 39%. Statistical analysis confirmed that these improvements were significant, with strong effect sizes observed across all variables. Regression results showed that IoT-related factors, including data frequency and system integration, accounted for over 60% of the variance in operational efficiency outcomes. Facilities with higher system integration demonstrated greater improvements, achieving up to 18.6% energy reduction and 19.8% production increase. Time-series analysis further indicated that efficiency gains were gradual and sustained rather than immediate, reflecting stable performance improvements over the study period. The results demonstrated that IoT-based energy monitoring systems contributed to enhanced operational efficiency by improving real-time visibility, enabling faster decision-making, and optimizing resource utilization. Variations across sub-groups highlighted the influence of contextual factors such as production scale and energy intensity, with high-energy-consuming processes showing greater efficiency gains. Overall, the study provided robust quantitative evidence that IoT-based monitoring systems significantly improved industrial performance and operational stability across multiple facilities.
Copyright (c) 2022 Md Aminul Islam, Mst Shamima Akter (Author)
Proactive Server Monitoring and Threat Assessment on Uptime in Financial Trading Systems: A Qualitative Evaluation
This study examined the impact of proactive server monitoring and threat assessment on uptime performance in financial trading systems, addressing the problem that many trading platforms still experience service disruption due to weak server visibility, delayed threat detection, poor alerting, and limited response readiness. The purpose was to quantify how preventive infrastructure and cybersecurity practices support continuous availability in cloud-based and enterprise financial trading environments. A quantitative, cross-sectional, case-based design was used, with data collected from 200 professionals across banks, brokerage firms, fintech companies, investment/capital market institutions, and technology service providers. The key variables were proactive server monitoring, threat assessment, real-time alerting, incident response readiness, and uptime performance. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, multiple regression, and an Uptime Resilience Index. The findings showed high agreement for all major constructs: proactive server monitoring recorded a mean of 4.18, threat assessment 4.11, real-time alerting 4.23, incident response readiness 4.05, and uptime performance 4.20. Reliability was strong, with Cronbach’s alpha values ranging from 0.82 to 0.88 and an overall scale reliability of 0.91. Correlation results confirmed significant positive relationships between uptime performance and proactive server monitoring (r = 0.68), threat assessment (r = 0.63), real-time alerting (r = 0.71), and incident response readiness (r = 0.59), all at p < 0.01. Regression analysis showed that the model explained 62.8% of the variance in uptime performance, R² = 0.628, F (4,195) = 82.46, p < 0.001. Real-time alerting was the strongest predictor (β = 0.31), followed by proactive monitoring (β = 0.27), threat assessment (β = 0.22), and incident response readiness (β = 0.18). The Uptime Resilience Index was 4.15, indicating high resilience. The study implies that financial institutions should integrate monitoring dashboards, automated alerts, threat intelligence, vulnerability assessment, and response playbooks to reduce downtime and strengthen operational resilience.
Copyright (c) 2022 Binayan Dey, Md. Shakhawat Hossen (Author)
DinSAR AND Remote Sensing–Based Predictive Modeling of Ground Subsidence Induced by Mineral Extraction: Implications for Environmental Risk Mitigation and Land-Use Planning
This study examined how DInSAR and remote sensing based predictive modeling can improve the assessment of ground subsidence induced by mineral extraction and strengthen environmental risk mitigation and land use planning in mining affected areas. The problem addressed in this research is that although mining related subsidence creates serious threats to infrastructure, ecosystems, settlements, and planning systems, its monitoring and predictive use in decision making are often fragmented and insufficiently integrated. The purpose of the study was therefore to test whether stronger DInSAR and remote sensing capability improves predictive modeling accuracy and whether such predictive strength contributes to better environmental mitigation and planning effectiveness. The study adopted a quantitative, cross sectional, case-based design using cloud and enterprise oriented geospatial and institutional case contexts in mineral extraction environments, with a purposive sample of 162 professional respondents drawn from mining engineering, geology, GIS and remote sensing, environmental management, planning, and regulatory fields. The key variables were DInSAR and Remote Sensing Capability, Predictive Modeling Accuracy, Environmental Risk Mitigation, and Land Use Planning Effectiveness. Data were collected through a 5-point Likert scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression analysis in SPSS. The findings showed high mean scores across all constructs, including DInSAR and Remote Sensing Capability (M = 4.18, SD = 0.61), Predictive Modeling Accuracy (M = 4.09, SD = 0.66), Environmental Risk Mitigation (M = 4.14, SD = 0.58), and Land Use Planning Effectiveness (M = 4.11, SD = 0.63). Regression results revealed that DInSAR capability significantly predicted predictive modeling accuracy (β = 0.652, R² = 0.425, p < .001), predictive modeling significantly predicted environmental risk mitigation (β = 0.718, R² = 0.516, p < .001) and land use planning effectiveness (β = 0.681, R² = 0.478, p < .001), while environmental risk mitigation also significantly predicted land use planning effectiveness (β = 0.603, p < .001). The study implies that integrating satellite-based deformation monitoring with predictive analytics can provide a stronger evidence base for safer environmental governance, hazard prioritization, infrastructure protection, and sustainable land use planning in subsidence prone mining regions.
Copyright (c) 2022 Taufiqur Rahaman, Albert Anokye (Author)
AI-Enabled Predictive Maintenance for Fire Alarm and Smoke Management Systems: A Systematic Review of Models
This study presented a quantitative systematic review of AI-enabled predictive maintenance models applied to fire alarm and smoke management systems, with the objective of evaluating their effectiveness in improving system reliability, operational efficiency, and fault detection accuracy. The analysis synthesized data from 62 peer-reviewed studies covering diverse infrastructure environments, including hospitals, airports, commercial buildings, industrial facilities, and high-rise structures. The findings indicated that predictive maintenance models achieved a high overall mean accuracy of 91.6%, with deep learning and ensemble models outperforming traditional approaches by reaching accuracy levels of 94.2% and 93.5%, respectively. The results further demonstrated a significant reduction in false alarm rates by an average of 37.4%, alongside improvements in detection sensitivity, with recall values increasing to 90.8%. The study also revealed that dataset size and sensor integration significantly influenced model performance, as large datasets exceeding 200,000 observations achieved mean accuracy levels of 93.8%, compared to 87.2% in smaller datasets. Multi-sensor IoT-based systems demonstrated superior anomaly detection performance at 92.9%, highlighting the importance of integrated data environments. Detection latency improved by approximately 24.3%, indicating faster system responsiveness, while overall system reliability scores increased by 28.6% following predictive maintenance implementation. Statistical analysis confirmed that these improvements were significant, with large effect sizes observed in false alarm reduction and fault detection performance. Subgroup analysis showed variability across infrastructure types, with commercial buildings achieving the highest accuracy at 94.1%, while more complex environments such as industrial facilities reported lower performance at 88.9%. Hybrid models demonstrated consistent improvements across all environments, particularly in dynamic conditions. The findings also indicated strong positive correlations between dataset size, sensor integration, and predictive accuracy. Overall, the study provided comprehensive quantitative evidence that AI-enabled predictive maintenance significantly enhances the performance and reliability of fire safety systems, supporting its application as an effective and scalable solution for safety-critical infrastructure management.
Blockchain-Enabled Security Protocols Combined with AI For Securing Next-Generation Internet of Things (IOT) Networks
This study examined the effectiveness of integrating blockchain-enabled security protocols with artificial intelligence for securing next-generation Internet of Things (IoT) networks through a quantitative experimental approach. The research addressed critical security challenges associated with distributed IoT environments, including data integrity, authentication reliability, and threat detection accuracy. A total of 52,480 network observations were analyzed, comprising 31,200 normal traffic instances and 21,280 labeled cyberattack records, including distributed denial-of-service, spoofing, and data tampering scenarios. The proposed integrated framework was evaluated against traditional and standalone AI-based security models using multiple performance indicators, including accuracy, precision, recall, F1-score, latency, throughput, and data integrity metrics. The findings demonstrated that the integrated blockchain-AI model achieved a detection accuracy of 96.8%, significantly outperforming the standalone AI model at 91.4% and the traditional model at 82.3%. Precision and recall values reached 95.9% and 96.2%, respectively, resulting in an F1-score of 96.0%, indicating balanced and reliable detection performance. The framework also reduced unauthorized access incidents from 124 cases in the baseline model to 77 cases, representing a 38% improvement in authentication security. Data integrity violations decreased from 98 to 52 instances, confirming the effectiveness of blockchain in ensuring secure data transactions. Despite the additional computational processes introduced by blockchain and AI integration, latency increased only marginally from 32.6 ms to 34.7 ms, while throughput remained stable at approximately 1.78 Mbps. Statistical analysis confirmed that these improvements were significant at p < 0.05, with large effect sizes observed for key performance indicators. The results indicated that the integrated framework maintained consistent performance across varying network conditions and device densities. Overall, the study established that the combination of blockchain and artificial intelligence provides a robust, scalable, and efficient solution for enhancing IoT security, offering substantial improvements in detection capability, system reliability, and data protection.
Copyright (c) 2022 Atif Khan, MD Hasibur Rahman Murad (Author)
Impact Of GIS-Based Spatial Decision Support Systems on Urban Water Supply Network Optimization: A Qualitative Evaluation
This study examined the impact of GIS-based Spatial Decision Support Systems on the optimization of urban water supply networks using a quantitative cross-sectional case study design. Data were collected from 120 professionals, including engineers, GIS analysts, planners, and maintenance supervisors, alongside operational and spatial data from 15 urban water network zones. Descriptive and inferential statistical analyses were conducted to evaluate relationships between 3SDSS utilization and key performance indicators such as network efficiency, pressure stability, leakage detection, service coverage, and maintenance prioritization. The findings revealed strong positive correlations between SDSS usage and network optimization outcomes, with correlation coefficients ranging from 0.55 to 0.72, all statistically significant at p < 0.05. Regression analysis indicated that GIS-based SDSS variables explained 62% of the variance in network performance (R² = 0.62), demonstrating substantial predictive power. Leakage rates were significantly lower in high SDSS integration zones (17.9%) compared to low integration zones (27.1%), while pressure stability scores improved from 3.42 to 4.25 on a five-point scale. Sub-group analysis showed that GIS analysts reported higher effectiveness scores (mean = 4.32) compared to maintenance personnel (mean = 3.61), highlighting the influence of user expertise. Reliability testing confirmed strong internal consistency (Cronbach’s alpha = 0.82). The results demonstrated that GIS-based SDSS significantly enhanced decision-making accuracy and operational efficiency, particularly in complex and high-demand network environments. Overall, the study provided empirical evidence supporting the effectiveness of GIS-based spatial decision tools in improving urban water supply system performance and infrastructure management.
Copyright (c) 2022 Taufiqur Rahaman, Kazi Mohammad Khalid Ahsan (Author)
Strengthening United States Economic Competitiveness Through AI-Driven Workforce Transformation and Organizational Innovation Systems
This study examined how AI-driven workforce transformation and organizational innovation systems strengthen United States economic competitiveness. The problem addressed was that many organizations adopt AI technologies without sufficient workforce readiness, employee reskilling, leadership support, digital infrastructure, or innovation systems, limiting the economic value of AI implementation. The purpose was to assess whether AI adoption, workforce reskilling, employee AI readiness, leadership support, and digital infrastructure significantly influence organizational innovation systems and competitiveness outcomes. A quantitative, cross-sectional, case-based design was applied using structured five-point Likert-scale data from 250 respondents representing cloud-enabled and enterprise organizations across technology, finance, healthcare, manufacturing, logistics, retail, and professional services. The key variables included AI adoption, workforce reskilling and upskilling, employee AI readiness, leadership support, digital infrastructure, organizational innovation systems, and economic competitiveness. Data analysis involved descriptive statistics, Cronbach’s Alpha reliability testing, Pearson correlation, regression modeling, readiness index scoring, and mediation/pathway analysis. The findings showed high levels of AI adoption (M = 4.12, SD = 0.61), organizational innovation systems (M = 4.08, SD = 0.59), and economic competitiveness (M = 4.10, SD = 0.62). Reliability was strong, with Cronbach’s Alpha ranging from 0.80 to 0.89 and an overall instrument reliability of 0.91. Correlation analysis showed that organizational innovation systems had the strongest relationship with economic competitiveness (r = 0.72, p < 0.01). Regression results revealed that AI adoption, reskilling, leadership support, and digital infrastructure explained 58% of the variance in AI-driven workforce transformation, while the full AI innovation competitiveness model explained 64% of competitiveness variance. The mediation result confirmed that organizational innovation systems partially mediated the relationship between AI-driven workforce transformation and competitiveness, with an indirect effect of 0.21. The study implies that U.S. enterprises should manage AI as a combined technological, human-capital, and innovation strategy to improve productivity, adaptability, efficiency, and long-term organizational growth.
Copyright (c) 2025 Hisham Mahmood, Risha Alam (Author)
Pharmaceutical Manufacturing Practices and Antimicrobial Resistance Mitigation: A Quantitative Case-Based Assessment
Antimicrobial resistance (AMR) remains one of the most pressing threats to global public health, yet scholarly and policy attention has largely concentrated on clinical prescribing behavior and patient-level misuse, leaving the upstream contribution of pharmaceutical manufacturing comparatively underexamined. This study addresses that gap by investigating how deficiencies in production discipline, quality control, waste handling, and compliance infrastructure may elevate resistance-related risks through the circulation of substandard antimicrobial products and the unsafe discharge of pharmaceutical effluents into the environment. Accordingly, the study evaluates the effects of four manufacturing dimensions, Good Manufacturing Practice (GMP), Quality Control Systems, Waste Management and Environmental Safety, and Regulatory Compliance, on Antimicrobial Resistance Mitigation across selected pharmaceutical enterprise case settings. A quantitative, cross-sectional, case-based design was adopted, with primary data collected through structured questionnaires administered to personnel drawn from production, quality assurance, quality control, regulatory compliance, and environmental health and safety functions across four manufacturing sites. Of 230 instruments distributed, 214 valid responses were retained, yielding a response rate of 93.0%. The data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis.Descriptive findings revealed uniformly high ratings across the study constructs: GMP (M = 4.18, SD = 0.64), Quality Control Systems (M = 4.11, SD = 0.69), Regulatory Compliance (M = 4.06, SD = 0.67), Waste Management and Environmental Safety (M = 3.97, SD = 0.73), and Antimicrobial Resistance Mitigation (M = 4.14, SD = 0.61). Correlation analysis indicated that GMP exhibited the strongest association with mitigation outcomes (r = .68, p < .001), followed by Regulatory Compliance (r = .66), Quality Control Systems (r = .63), and Waste Management and Environmental Safety (r = .59). Regression results corroborated these relationships, identifying GMP as the strongest predictor (β = .31, p < .001), followed by Regulatory Compliance (β = .27, p < .001), Quality Control Systems (β = .24, p = .002), and Waste Management and Environmental Safety (β = .19, p = .008). Collectively, the four predictors accounted for 47.3% of the variance in mitigation outcomes (R² = .473; F = 46.82, p < .001).The findings underscore that pharmaceutical manufacturing should be conceptualized not merely as an industrial activity but as a critical public health control system, one that plays a decisive role in preserving antimicrobial efficacy and curbing the emergence and spread of resistance.
Copyright (c) 2023 Beatrice Onyinyechi Mkpa (Author)
Computational Thermo-Mechanical Modeling for Energy-Efficient Solid-State Metal Manufacturing Processes
This study examined computational thermo-mechanical modeling for energy-efficient solid-state metal manufacturing processes using a quantitative quasi-experimental simulation design. A total of 120 validated simulation cases were analyzed, covering a wide range of process parameters including tool velocity (200–1200 rpm), applied force (5–25 kN), and varying thermal boundary conditions. The results showed that peak temperature varied significantly from 420.00 K to 980.00 K, with a mean value of 685.40 K (SD = 112.35), while stress ranged from 85.00 MPa to 410.00 MPa, averaging 236.70 MPa (SD = 68.45). Strain values exhibited a mean of 0.42 (SD = 0.15), and energy consumption ranged between 1.85 J/mm³ and 6.75 J/mm³, with a mean of 3.92 J/mm³ (SD = 1.12). Statistical analysis confirmed that tool velocity (F = 18.45, η² = 0.42), applied force (F = 15.72, η² = 0.38), and thermal conditions (F = 12.89, η² = 0.34) had significant effects on thermo-mechanical outputs (p < 0.05). Regression modeling demonstrated strong predictive capability, with an R² value of 0.81 for energy consumption and low standard error (0.42), indicating high model accuracy. Subgroup analysis revealed that friction-based processes generated higher peak temperatures (765.30 K) and energy consumption (4.35 J/mm³) compared to diffusion-based processes (612.85 K and 3.25 J/mm³). High-speed conditions further increased temperature to 825.60 K and stress to 285.90 MPa, while low-speed conditions maintained lower energy demand at 2.95 J/mm³. Optimal performance was observed at moderate parameter ranges, where energy consumption remained below 3.50 J/mm³ and stress levels were controlled below 240 MPa. These findings confirmed that thermo-mechanical coupling significantly influenced process efficiency, and precise parameter optimization was essential for achieving balanced energy-efficient manufacturing performance.
Copyright (c) 2022 Manam Ahmed, Md. Ashfaq Siddiquee (Author)
Development of an AI-Driven Predictive Decision Support Model for Multi-Domain Business Analytics and Infrastructure Optimization
This study addresses the persistent organizational problem that many firms possess growing volumes of business and infrastructure data but still lack an integrated AI-driven predictive decision support model capable of converting those data into high-quality decisions and optimization outcomes across multiple domains. Its purpose was to develop and empirically examine a unified model linking predictive analytics, business analytics capability, data integration, organizational readiness, decision quality, and infrastructure optimization within a quantitative, cross-sectional, case-based research design. Using survey data from 210 respondents drawn from cloud-enabled and enterprise-oriented operational contexts across operations, IT/data systems, finance/planning, infrastructure or asset management, and strategy or administration, the study measured six core variables: AI-Driven Predictive Analytics, Business Analytics Capability, Data Integration Capability, AI Decision Readiness, Decision-Making Quality, and Infrastructure Optimization. The analysis plan combined descriptive statistics, reliability testing, correlation analysis, and multiple regression modeling. Findings showed strong construct reliability with Cronbach’s alpha values ranging from 0.83 to 0.89, while all major variables recorded high mean scores, including AI-Driven Predictive Analytics at 4.18, Business Analytics Capability at 4.09, AI Decision Readiness at 4.05, Decision-Making Quality at 4.21, and Infrastructure Optimization at 4.12. Correlation results were positive and significant, with the strongest association found between Decision-Making Quality and Infrastructure Optimization at r = .71, p < .001. Regression results indicated that the model explained 56% of the variance in Decision-Making Quality and 62% of the variance in Infrastructure Optimization. AI-Driven Predictive Analytics significantly improved Decision-Making Quality (beta = .34, p < .001), while Decision-Making Quality was the strongest predictor of Infrastructure Optimization (beta = .38, p < .001). Overall, the study concludes that AI-driven predictive decision support becomes most effective when analytical capability, integrated data, and organizational readiness are aligned, producing measurable gains in decision quality and infrastructure performance. The implications are that organizations should treat AI decision support as an integrated capability system rather than as a standalone technical tool.
A Comparative Analysis of Monitoring and Observability Tools for Machine Learning and Data Science Pipelines
This study investigates the growing problem of limited visibility, delayed fault diagnosis, and inconsistent operational control in machine learning and data science pipelines, where traditional monitoring tools often provide only surface-level metrics while failing to explain complex cross-stage failures. The purpose of the research was to comparatively evaluate monitoring and observability tools and determine how their core capabilities influence overall pipeline effectiveness in real cloud and enterprise analytical environments. Using a quantitative, cross-sectional, case-based design, the study collected data from 210 valid respondents drawn from cloud and enterprise pipeline cases involving data scientists, ML engineers, data engineers, MLOps and DevOps engineers, and technical managers. The key independent variables were monitoring capability, observability capability, integration capability, scalability, and information interpretability, while the dependent variable was overall pipeline effectiveness, measured through reliability, issue detection efficiency, operational efficiency, and user satisfaction. Data were analyzed using descriptive statistics, Cronbach’s alpha, correlation analysis, and multiple regression. The results showed that observability capability recorded the highest mean score (M = 4.12, SD = 0.68), followed by overall pipeline effectiveness (M = 4.08, SD = 0.66) and information interpretability (M = 4.05, SD = 0.69), while monitoring capability remained positive but lower (M = 3.89, SD = 0.71). Reliability was strong across all constructs, with Cronbach’s alpha ranging from 0.79 to 0.88. Correlation analysis revealed that observability capability had the strongest relationship with pipeline effectiveness (r = 0.710, p < .001), followed by information interpretability (r = 0.670, p < .001). The regression model was statistically significant, F (5, 204) = 42.63, p < .001, explaining 51.1% of the variance in pipeline effectiveness (R² = 0.511). Observability capability emerged as the strongest predictor (β = 0.31, p < .001), followed by information interpretability (β = 0.27, p < .001). The study implies that organizations should prioritize observability-rich, interpretable, and scalable tools to strengthen pipeline governance, reliability, and troubleshooting performance in modern ML operations.
Copyright (c) 2022 Aditya Dhanekula, Mohammad Robel Miah (Author)
Large-Scale Data-Driven Intelligence Frameworks for Predictive Healthcare Analytics, Early Clinical Intervention, and Sustainable Chronic Disease Management in the United States
This study addresses the problem that many U.S. healthcare organizations can build predictive models but struggle to scale them into reliable, workflow-embedded, and governable intelligence frameworks that consistently enable early clinical intervention and sustainable chronic disease management. The purpose was to quantify, across published enterprise and cloud enabled implementations, which end-to-end design elements (data integration, predictive evaluation discipline, workflow actionability, and governance) are most strongly associated with measurable operational and patient-impact signals. Using a quantitative cross-sectional, case-based synthesis, each included study was treated as a “case” drawn from enterprise health systems, multi-site networks, payer programs, and cloud or app-platform deployments (for example, standards-based interoperable apps), and coded for key variables: data readiness (EHR, claims, device and patient-generated integration), model and information quality (validation and utility reporting), workflow fit (CDS embedding and routing), governance (monitoring, privacy, equity), and sustainability indicators (utilization, mortality, continuity). The analysis plan combined descriptive statistics (frequencies and proportions), cross-case comparison matrices, and a light numeric evidence-rating procedure (Likert 1–5) to summarize strength and consistency of effects. Headline findings show that workflow-embedded decision support most consistently improved care processes: across 148 trials, 86% assessed process outcomes and significant improvements were reported for preventive services (OR = 1.42, 95% CI 1.27–1.58), ordering clinical studies (OR = 1.72, 95% CI 1.47–2.00), and prescribing therapies (OR = 1.57, 95% CI 1.35–1.82), while only 20% assessed clinical outcomes and 15% assessed costs. For chronic disease sustainability, remote monitoring and structured support in heart failure reduced admissions by 21% (95% CI 11%–31%) and all-cause mortality by 20% (95% CI 8%–31%), and diabetes self-management apps improved HbA1c by a median 0.4%. Implications are that organizations should treat predictive analytics as a governed data-to-action capability, prioritizing interoperable pipelines, decision-utility evaluation, alert-fatigue controls, subgroup equity checks, and lifecycle monitoring to achieve scalable and durable impact.
Copyright (c) 2022 Md Fardaus Alam, Md Fokhrul Alam, Md Ashraful Alam (Author)
Material Degradation and Durability Assessment of Pipelines and Sanitation Structures Under Aggressive Environmental Conditions
This study investigates the problem of accelerating material degradation and declining durability in pipelines and sanitation structures exposed to aggressive environmental conditions, where moisture intrusion, corrosive soils, chemical attack, salinity, microbial activity, and temperature fluctuation progressively weaken structural integrity and serviceability. The purpose of the study was to quantitatively assess how these environmental stressors influence degradation patterns and durability outcomes across selected infrastructure cases. A quantitative, cross-sectional, case-based research design was adopted, using structured questionnaire data collected from 186 valid respondents out of 214 distributed instruments, yielding an 86.9% valid response rate. The sample reflected clouded enterprise-style infrastructure case contexts through pipelines, sewer chambers, wastewater conduits, and sanitation tanks/manholes assessed by engineers, maintenance officers, utility managers, and sanitation specialists. The key variables were aggressive environmental conditions as the independent variable, material degradation indicators as the mediating variable, and durability and serviceability performance as the dependent variable. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. The findings showed a high presence of aggressive exposure conditions with an aggregate mean of 4.18, led by moisture intrusion at 4.36, corrosive soil exposure at 4.27, and chemical attack at 4.21. Material degradation was also severe with an aggregate mean of 4.09, with corrosion intensity recording the highest mean of 4.33, followed by cracking severity at 4.17 and wall thinning at 4.09. Durability concern was substantial with an aggregate mean of 3.88, especially increased maintenance frequency at 4.12 and reduced expected service life at 4.07. Correlation analysis revealed a strong positive relationship between aggressive environmental conditions and material degradation (r = .742, p < .001), while degradation was strongly negatively related to durability performance (r = -.711, p < .001). Regression results further showed that environmental conditions explained 55.1% of degradation variance (R² = .551), and together with degradation explained 61.7% of durability loss (R² = .617). The study implies that durability management should shift toward risk-based, condition-focused, and exposure-sensitive maintenance planning for long-term infrastructure resilience.
Copyright (c) 2022 Aditya Dhanekula, Palash Chandra Das (Author)
A Panel Data Econometric Analysis on the Impact of Digital Payment Adoption on Small Business Revenue Growth in Global Business
This study addresses the persistent problem that many small businesses adopt digital payments, yet the revenue impact remains uneven because adoption is often shallow and not operationally integrated. The purpose was to quantify how digital payment adoption and adoption maturity influence small business revenue growth and to test which adoption drivers strengthen or constrain this relationship. Using a quantitative, cross-sectional, case-based design, data were collected from N = 240 small businesses as enterprise-style operational cases embedded in cloud-enabled and platform-mediated payment ecosystems (for example, wallet, QR, POS gateway, and settlement services). The sample covered retail (38%), food/service (34%), and other transaction-intensive firms (28%), with most respondents being owners or general managers (71%) who manage payment decisions. Key variables included Digital Payment Adoption (ADOPT), Digital Payment Adoption Maturity Index (DPAMI), perceived ease of use (PEOU), trust/security perception (TR), perceived transaction cost (COST), customer preference for digital payment (CUSTPREF), and a triangulated Revenue Growth Composite (REVCOMP), with controls for firm size, firm age, sector, location, and sales-channel mix. The analysis plan applied scale reliability tests (Cronbach’s alpha), descriptive statistics, Pearson correlations, interaction and subgroup tests, and diagnostics (VIF). Measurement reliability was strong (for example, REVCOMP α = .88, TR α = .86, PEOU α = .83). Descriptives showed high adoption (ADOPT M = 3.74, SD = 0.71) and positive revenue outcomes (REVCOMP M = 3.49, SD = 0.69). Revenue growth correlated with adoption (r = .46, p < .001) and more strongly with maturity (DPAMI r = .54, p < .001). In regression, adding adoption increased model fit (Adj. R² = .27) with a significant effect (β = .31, p < .001), while adding maturity improved fit further (Adj. R² = .34) with a stronger effect (DPAMI β = .39, p < .001). Trust and usability promoted adoption (TR β = .33, p < .001; PEOU β = .28, p < .001), while cost constrained it (COST β = −.21, p = .002); the adoption–growth link strengthened when customers preferred digital payments (Adoption × CUSTPREF β = .17, p = .011). Implications suggest that SMEs should prioritize maturity practices (integration, staff readiness, reconciliation discipline), providers should reduce friction and strengthen trust signals, and policymakers should support interoperability and transparent pricing to help firms convert adoption into measurable growth.
Copyright (c) 2022 Istiaq Ahmed, Nusrat Jahan (Author)
Fabrication-Driven Structural Optimization Techniques for Cost-Efficient Steel Construction Using CNC-Based Design Workflows
This study investigated how fabrication-driven structural optimization improves cost efficiency in steel construction when delivered through CNC-based, BIM-enabled design workflows, addressing the problem that many “optimized” steel designs lose their economic advantage due to traceability gaps, revision churn, and incomplete fabrication constraint alignment across multi-tool, multi-organization pipelines. The purpose was to quantify the relationships among Fabrication-Driven Structural Optimization (FDSO), CNC Workflow Traceability and Data Integrity (CWTDI), Fabrication Constraint Compliance Index (FCCI), and Cost-Efficiency outcomes (CE) using a quantitative cross-sectional, case-based design. Data were collected from a purposive sample of 162 professionals embedded in enterprise steel delivery cases that used CNC fabrication and cloud or enterprise digital collaboration tools for model-to-detail-to-machine handoffs (structural engineers 24.1%, BIM/modeling 18.5%, detailers 21.0%, shop supervisors 16.7%, CNC/QA 9.3%, project managers 10.5%; mean experience 7.8 years, SD 4.6; 72.2% frequent CNC exposure). Reliability was strong (α: FDSO 0.88; CWTDI 0.90; FCCI 0.86; CE 0.89). Descriptively, respondents reported above-mid maturity (FDSO M 3.92, SD 0.62; CWTDI M 3.74, SD 0.71; FCCI M 0.77, SD 0.12; CE M 3.85, SD 0.66 on 1–5 scales). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression with controls (role, experience, project complexity). Headline findings showed strong positive associations with CE (FDSO r 0.63; CWTDI r 0.69; FCCI r 0.58; all p < .001) and a high explanatory regression model (F(6,155)=33.9, p<.001; R²=0.57; Adj R²=0.55) where CWTDI was the strongest predictor (β=0.41, p<.001), followed by FDSO (β=0.29, p<.001) and FCCI (β=0.18, p=.006; VIFs < 2.1). Case validation aligned higher traceability with fewer major revision cycles (2.1 vs 3.6) and fewer CNC regeneration events (1.4 vs 2.6), and cost-impact decomposition attributed perceived savings mainly to rework reduction (31%), then labor-hours reduction (24%), waste reduction (19%), coordination-cycle reduction (15%), and machine-time gains (11%). Implications suggest that organizations should prioritize digital-thread governance (release gates, revision propagation, audit trails) alongside fabrication-aware standardization to realize reliable cost efficiency from CNC-integrated optimization.
Copyright (c) 2022 Md. Mainuddin Faisal, Palash Chandra Das (Author)
Advanced Computing, IT Strategy, and Network-Optimized Frameworks for Retail Business Intelligence
This study addresses a problem in retail business intelligence (BI) modernization: retailers upgrade compute and analytics, yet dashboards still suffer from stale data, inconsistent KPIs, and unreliable Realtime performance because computing, governance, and network engineering are implemented in silos. The purpose is to test, using a quantitative cross sectional, case-based synthesis, whether advanced computing capability (AC), IT strategy alignment (ITSA), and network optimized frameworks (NOF) jointly improve BI service quality and decision usefulness in retail deployments. The sample comprises literature derived cloud and BI cases, with each publication treated as a case unit representing an architecture or evaluated deployment. Key variables are AC (cloud elasticity, distributed processing, streaming ingestion, modular orchestration), ITSA (data governance and stewardship, BI operating model, KPI ownership), NOF (WAN traffic engineering, observability, resiliency, edge placement), and BI outcomes (data freshness, dashboard latency, uptime, KPI consistency, adoption intensity). The analysis plan applies a standardized 1 to 5 evidence scoring rubric per case, vote counting of hypothesis support, and an Integrated Capability Index computed as the mean of AC, ITSA, and NOF. Findings show strong support for AC with a composite score of 4.2/5 (SD 0.6; 76% support), led by cloud elasticity (M 4.4; SD 0.6; 78% support) and distributed processing (M 4.3; SD 0.7; 74% support). ITSA scores 3.9/5 (SD 0.6; 70% support), driven by governance and stewardship (M 4.2; SD 0.7). NOF shows moderate to strong support at 3.7/5 (SD 0.6; 62% support), with observability highest (M 4.0; SD 0.7). Integrated configurations outperform siloed upgrades: high integrated cases report BI_Perf of 4.4/5 versus 3.1/5 in low integrated cases. Implications are that retailers should manage BI as an end-to-end service by coordinating cloud scale, KPI governance, and network reliability with explicit latency, freshness, and uptime targets to sustain secure decision support across omnichannel operations.
Copyright (c) 2022 Tanjina Binte Sohrab, Md. Hasan Or Rashid (Author)
Anticipatory Intelligence Systems: How Data Analytics Reshape Organizational Preparedness and Action Timing
This study addresses the persistent problem that many organizations deploy advanced analytics yet still respond too late to emerging threats and opportunities because signals are not converted into governed decisions and executable actions in time. The purpose is to quantify how Anticipatory Intelligence Systems (AIS) reshape organizational preparedness and action timing in cloud and enterprise operating contexts. Using a quantitative, cross-sectional, case-based design, evidence from N = 40 documented organizational cases and empirical studies was coded on 5-point Likert rubrics to operationalize three AIS capability layers (data-to-signal, signal-to-decision, decision-to-action), an Organizational Preparedness Index (OP), and Action Timing Advantage (ATA) as proportional decision-latency reduction. The sample covered cloud and enterprise implementations across manufacturing and asset maintenance (30%), supply chain and logistics (25%), finance and risk (20%), healthcare operations (15%), and cybersecurity (10%). Key variables included preparedness markers (governance clarity, resource mobilization readiness and timing stages (alert-to-triage, decision commitment, execution initiation, lead-time advantage). The analysis plan computed descriptive statistics (means, SDs, and percent of cases scoring ≥4) and compared “high-enabler” versus “lower-enabler” subsets based on data maturity and governance strength. Headline findings show strong preparedness gains (OP = 3.92/5, SD = 0.63; 80% of cases ≥4) with the highest single element in cross-functional coordination (mean = 4.06; 82% ≥4). Action timing improved meaningfully, with ATA = 0.28 (SD = 0.12), indicating an average 28% reduction in decision latency, and 75% of cases reporting measurable timing gains; stage-wise reductions were strongest for alert-to-triage (ATA = 0.32; baseline 6.2 days to 4.2 days) and weakest for commitment-to-execution (ATA = 0.20; 5.1 to 4.1 days). Stability and resilience effects were moderate (composite = 3.66/5, SD = 0.70; 65% with ≥4 on at least one dimension). Enabler analysis indicates stronger outcomes when governance and data maturity were high (OP = 4.26; ATA = 0.34) versus lower-enabler contexts (OP = 3.64; ATA = 0.23). Implications are that cloud and enterprise leaders should treat AIS as end-to-end decision infrastructures by prioritizing semantic data integration, explicit decision rights, explainability, and pre-approved response playbooks to close the loop from sensing to execution and capture timing advantage at scale.
Copyright (c) 2022 Rukaiya Khatun Moury, Md. Morshedul Islam (Author)
Enhancing U.S. Manufacturing Competitiveness through Lean Manufacturing: A Framework for Supply Chain Resilience and Risk Mitigation
This study addresses a persistent problem in U.S.-oriented manufacturing supply chains: firms pursue lean to stabilize cost and flow, yet disruptions expose gaps in preparedness, response, recovery, and risk mitigation, creating uncertainty about which lean bundles most reliably strengthen supply chain resilience. The purpose was to quantify and synthesize how lean practice clusters relate to resilience capabilities and risk-mitigation outcomes across enterprise case evidence using a quantitative, cross-sectional, case-based research design. Using a structured evidence-synthesis dataset of 42 enterprise manufacturing cases reported in peer-reviewed studies, lean was operationalized as practice clusters (LP1–LP9) such as standardized work and visual management, continuous improvement, quality at the source, TPM, pull routines, supplier development, and visibility dashboards; resilience was measured as four capability variables (Readiness RC1, Response RC2, Recovery RC3, Adaptation RC4) and an overall Resilience Capability Score (RCS); risk mitigation was captured via an overall Risk Mitigation Index (RMI) across risk categories. The analysis plan applied a five-point Likert evidence-strength scoring for each construct link and aggregated means, standard deviations, and study counts, complemented by cross-case comparisons by manufacturing segment. Headline findings show strong overall support for a positive lean–resilience relationship (RCS M = 4.1, SD = 0.6, n = 42), with the strongest capability evidence in Response (RC2 M = 4.3, SD = 0.6, n = 41) and Recovery (RC3 M = 4.2, SD = 0.6, n = 40), while Adaptation was comparatively lower (RC4 M = 3.8, SD = 0.8, n = 33) . Lean practice evidence was highest for standardized work and visual management (LP1 M = 4.4, SD = 0.6, n = 42) and continuous improvement (LP2 M = 4.2, SD = 0.7, n = 40), and overall risk mitigation was strong (RMI M = 3.9, SD = 0.7, n = 42). Cross-case patterns indicate higher resilience in discrete, coordination-intensive segments such as automotive (RCS M = 4.4, SD = 0.5) than process industries (RCS M = 3.9, SD = 0.7), and supplier-facing lean yielded stronger supplier-risk mitigation (M ≈ 4.1) than internal-only lean (M ≈ 3.6). Implications are that managers should treat lean as a capability-building system, prioritizing visibility, disciplined escalation, and joint problem-solving across supplier interfaces, and should pair flow-focused tools with selective buffers and segment-appropriate continuity strategies where external logistics constraints dominate.
Copyright (c) 2025 Kazi Saiful Islam (Author)
Empirical Validation of a Hybrid Deep Learning Architecture for Real-Time Fault Detection and Cyber-Threat Classification in SCADA-Based Smart Grid Environments
This study addresses a critical operational gap in smart grid infrastructure management: while hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have been theoretically proposed for simultaneous fault detection and cyber-threat classification in SCADA-based environments, no empirically validated quantitative framework exists to determine which hybrid architecture capability dimensions most strongly predict operational reliability, threat response effectiveness, and detection latency reduction in real-world deployment contexts. The purpose was to empirically validate a hybrid CNN-LSTM deep learning framework that links core architectural capabilities to smart grid operational outcomes within an enterprise-scale, case-based setting. Using a quantitative, cross-sectional, case-study design, data were collected from a purposive sample of N = 218 smart grid security and operations professionals including SCADA engineers, network security analysts, data scientists, grid operations managers, and protection relay engineers working in operational environments where AI-driven monitoring and anomaly detection platforms are actively deployed. Key independent variables were CNN Feature Extraction Capability (C), LSTM Temporal Sequence Modeling Capability (L), and Hybrid Architecture Integration Depth (H), alongside two domain-specific indices: the Hybrid Architecture Performance Index (HAPI) and the Real-Time Threat Detection Alignment (RTDA); key dependent variables were Fault Detection Accuracy (Y1), Cyber-Threat Classification Effectiveness (Y2), and Operational Continuity Performance (Y3). The analysis applied descriptive statistics, reliability and validity testing (Cronbach's alpha, EFA with KMO and Bartlett's test), Pearson correlations, and multiple regression models. Headline findings demonstrate strong measurement quality (alpha = .83-.89; KMO = .91; Bartlett's chi-squared = 2318.7, p < .001) and moderately high capability levels (CNN Capability M = 3.97, SD = 0.61; LSTM Capability M = 4.11, SD = 0.56; Hybrid Integration M = 3.84, SD = 0.67). All core relationships were positive and significant (p < .001), including Fault Detection Accuracy with HAPI (r = .66) and LSTM Capability (r = .63), Cyber-Threat Classification with RTDA (r = .68), and Operational Continuity with Hybrid Integration (r = .64). Regression results indicate substantial explained variance for Fault Detection Accuracy (R2 = .63; F(5,212) = 72.4, p < .001), with HAPI as the strongest predictor (beta = .37, p < .001), followed by LSTM Capability (beta = .25, p = .001).
Digital-Twin-Based Quantitative Frameworks for Modeling, Monitoring, and Optimization of Electrical Power Infrastructure
This study addresses a critical problem in electrical power infrastructure operations: many organizations deploy digital-twin technologies, yet lack a validated, quantitative way to determine which digital-twin capabilities actually drive measurable improvements in reliability, response effectiveness, and operational efficiency. The purpose was to test a digital-twin-based quantitative framework that links core capabilities to infrastructure outcomes within an enterprise-scale, case-based setting. Using a quantitative, cross-sectional, case-study design, data were collected from a purposive sample of N = 212 power-infrastructure professionals working in enterprise operational contexts where monitoring and decision-support platforms are used. Key independent variables were Digital Twin Modeling Capability (M), Monitoring Capability (N), and Optimization Capability (O), alongside two domain indices: Digital Twin Fidelity and Synchronization Readiness Index (DT-FSRI) and Event Detection and Response Alignment (EDRA); key dependent variables were Infrastructure Reliability/Continuity (Y₁), Response Effectiveness (Y₂), and Operational Efficiency (Y₃). The analysis plan applied descriptive statistics, reliability and validity testing (Cronbach’s alpha, EFA with KMO and Bartlett’s test), Pearson correlations, and multiple regression models. Headline findings show strong measurement quality (α = .82–.88; KMO = .89; Bartlett’s χ² = 2146.3, p < .001) and moderately high capability levels (Modeling M = 3.94, SD = 0.63; Monitoring M = 4.07, SD = 0.58; Optimization M = 3.76, SD = 0.69). All core relationships were positive and significant (p < .001), including Reliability with DT-FSRI (r = .63) and Monitoring (r = .61), Response Effectiveness with EDRA (r = .65), and Efficiency with Optimization (r = .62). Regression results indicate substantial explained variance for Reliability (R² = .60; F(5,206) = 62.7, p < .001), with DT-FSRI as the strongest predictor (β = .34, p < .001), followed by Monitoring (β = .23, p = .002). Implications suggest that utilities should prioritize synchronization discipline and event-to-response alignment before scaling advanced optimization, because these coherence mechanisms most strongly predict operational performance.
Event Detection and Response Alignment (EDRA)
Copyright (c) 2022 Md. Shahinur Islam, Md. Sultan Mahamud (Author)
A Systematic Review of AI-Driven Credit Risk Assessment Models in Commercial Banking (2018–2026)
This systematic review examines the evolution, methodological advancements, and governance implications of AI-driven credit risk assessment models in commercial banking from 2018 to 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review synthesizes evidence from 27 peer-reviewed studies, representing a diverse body of research exploring machine learning and deep learning applications in probability of default estimation, loss and exposure modeling, early-warning systems, portfolio monitoring, and automated credit decisioning. The findings show that AI models consistently outperform traditional statistical approaches in predictive accuracy and behavioral sensitivity, particularly when leveraging ensemble architectures and temporal or transactional features. However, despite these technical advantages, the review identifies substantial constraints related to data quality, model generalizability, explainability, fairness, drift vulnerability, and regulatory acceptability. Many studies highlight persistent challenges in achieving transparency and stability under stress scenarios, indicating that current AI systems often struggle to meet prudential and consumer-protection expectations. The review also notes a widening gap between research innovation and real-world deployment, as operational requirements such as continuous monitoring, documentation, and interoperability create significant barriers to adoption. Overall, this study provides a comprehensive evidence base demonstrating both the promise and limitations of AI credit risk models, emphasizing the need for more interpretable model architectures, standardized validation frameworks, privacy-preserving data ecosystems, and cross-institutional benchmarking. These insights contribute to shaping the next generation of trustworthy, governable, and regulator-aligned AI systems for commercial banking.
Copyright (c) 2026 Rajib Sarkar (Author)