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.
CNT/PANI Composite Electrodes for Flexible Fiber-Shaped Dye-Sensitized Solar Cells: Synthesis, Characterization, and Photovoltaic Performance
This study investigated the synthesis, characterization, and photovoltaic performance of carbon nanotube–polyaniline (CNT–PANI) composite electrodes for flexible fiber-shaped dye-sensitized solar cells (DSSCs), with particular emphasis on the quantitative effects of CNT composition on electrical, electrochemical, structural, mechanical, and photovoltaic properties. A quantitative experimental design was employed using PANI-only, low-CNT/PANI, medium-CNT/PANI, high-CNT/PANI, and CNT-only electrode formulations, with 75 successfully fabricated devices included in the final analysis. The electrodes were characterized according to conductivity, sheet resistance, charge-transfer resistance, catalytic peak current, coating thickness, surface porosity, CNT dispersion, and electrochemical response, while device performance was evaluated through short-circuit current density, open-circuit voltage, fill factor, power conversion efficiency (PCE), and mechanical performance retention. The medium-CNT/PANI formulation demonstrated the strongest integrated performance, achieving electrical conductivity of 68.7 ± 5.1 S/cm, sheet resistance of 18.9 ± 2.2 Ω/sq, charge-transfer resistance of 2.84 ± 0.31 Ω, and catalytic peak current density of 3.92 ± 0.30 mA/cm². Structurally, this formulation exhibited 46.8 ± 3.7% surface porosity and 92.1 ± 3.2% CNT dispersion uniformity. The corresponding fiber-shaped DSSCs achieved a short-circuit current density of 13.92 ± 0.88 mA/cm², open-circuit voltage of 0.724 ± 0.018 V, fill factor of 68.8 ± 2.5%, and PCE of 6.93 ± 0.39%, representing an 80.5% efficiency improvement over the PANI-only configuration and a 21.4% improvement over CNT-only devices. After 1,000 bending cycles, the optimized devices retained 94.2 ± 2.4% of their initial PCE, with electrical resistance increasing by only 6.8 ± 1.6%. Electrode composition significantly affected PCE, with F = 124.36, p < 0.001 and η² = 0.877. Multivariate regression explained 84.4% of PCE variation, with catalytic peak current serving as the strongest positive predictor and charge-transfer resistance as the strongest negative predictor. The findings established that an intermediate CNT–PANI composition provided the most effective balance of electrical conduction, catalytic activity, interfacial charge transfer, structural accessibility, mechanical flexibility, and photovoltaic efficiency.
Copyright (c) 2022 Md Ashfaq Siddiquee, Abdullah Mohammad Sarjish (Author)
Exploring The Potential of Visual Narratives and Interactive Gamified Communication Systems to Prevent Disease, Counter Health Misinformation, And Strengthen Public Trust in Low-Literacy Communities: A Literature Review
This study examined the effectiveness of visual narratives and interactive gamified communication systems in improving disease-prevention knowledge, countering health misinformation, strengthening preventive behavioral intention, and enhancing public trust among low-literacy populations. A quantitative quasi-experimental pretest-posttest design was applied using three communication conditions: conventional text-based health information, visual-narrative communication, and interactive gamified visual communication. The final analytical sample comprised 396 participants, including 130 in the text-based group, 132 in the visual-narrative group, and 134 in the gamified group. Quantitative outcomes included health-information comprehension, preventive knowledge, immediate and delayed recall, misinformation recognition, risk perception, preventive behavioral intention, and public trust. The findings demonstrated significant differences across communication conditions. Post-test comprehension scores were 61.8 ± 10.1 for text-based communication, 72.9 ± 9.4 for visual narratives, and 79.6 ± 8.8 for gamified communication (p < .001). Preventive-knowledge scores similarly increased to 63.1 ± 10.5, 74.6 ± 9.6, and 81.3 ± 8.9, respectively (p < .001). Immediate recall reached 83.5 ± 8.7 in the gamified group compared with 76.8 ± 9.8 in the visual-narrative group and 64.7 ± 11.3 in the text-based group. Misinformation-recognition scores were also highest under gamification at 79.4 ± 9.3, compared with 70.8 ± 10.1 and 58.6 ± 11.2, respectively. Participants receiving gamified communication demonstrated 3.76 times greater adjusted odds of correctly identifying misleading health claims than participants receiving text-based information (95% CI [2.47, 5.72], p < .001). Preventive behavioral intention reached 4.23 ± 0.52 in the gamified group, while public trust increased to 4.02 ± 0.57. Regression models explained 58.4% of variance in preventive behavioral intention and 61.7% of variance in public trust. Overall, visual narratives substantially outperformed conventional text-based health communication, while interactive gamification generated the strongest and most persistent improvements. The findings demonstrated the quantitative value of accessible, visually structured, interactive, and feedback-oriented communication for improving health-information processing, misinformation resistance, preventive motivation, and trust among low-literacy populations.
Copyright (c) 2022 Mahbubul Islam, Mahmudul Islam, Shifa Tasnim, Mahfuza Tabassum, Shima Ali Sadia (Author)
Machine Learning Approaches for Early Prediction of High-Risk Pregnancy in Underserved U.S. Populations
This study has examined how machine-learning approaches have supported the early prediction of high-risk pregnancy in underserved U.S. populations, where delayed risk recognition, fragmented maternal records, unequal access to care, and limited confidence in equitable algorithmic performance may reduce timely clinical intervention. The study has aimed to determine the effects of maternal risk-data quality and integration, machine-learning predictive capability, algorithmic explainability, equity-sensitive prediction confidence, and clinical-workflow fit on early pregnancy risk-prediction effectiveness. A quantitative, cross-sectional, multiple-case-study design has been employed across safety-net hospitals, federally qualified health centers, rural hospitals, and community-based maternal-care organizations serving low-income, uninsured, rural, racially and ethnically diverse, and linguistically diverse populations. Using purposive and stratified sampling, 312 questionnaires have been distributed to maternal-health, public-health, informatics, data, and administrative professionals; 274 valid responses have been retained, yielding an effective response rate of 87.82%. A 24-item five-point Likert-scale instrument has been used, and data have been analyzed through descriptive statistics, Cronbach’s alpha, exploratory factor analysis, Pearson correlation, multiple regression, diagnostic testing, and case comparisons. Measurement results have demonstrated strong reliability, with an overall Cronbach’s alpha of .93, construct coefficients ranging from .82 to .91, KMO = .907, and significant Bartlett’s test, χ²(276) = 3,248.17, p < .001. Machine-learning predictive capability has recorded the highest mean score, M = 4.08, SD = 0.61, while early prediction effectiveness has averaged M = 4.02, SD = 0.63. All predictors have been positively associated with effectiveness, with machine-learning capability showing the strongest correlation, r = .69, p < .001. The regression model has explained 64.6% of the variance in effectiveness, R² = .646, adjusted R² = .639, F (5, 268) = 97.82, p < .001. Machine-learning capability has been the strongest predictor, β = .31, followed by data quality, β = .24, workflow fit, β = .23, explainability, β = .14, and equity-sensitive confidence, β = .12. The findings have indicated that effective prediction requires not only accurate analytics but also integrated data, transparent outputs, equitable implementation, and actionable clinical workflows.
Copyright (c) 2024 Nishat Jahan, Jahanara Akther (Author)
Beyond Big Data: A Multi-Dimensional Framework for Understanding Business Decision Failure in the Era of Artificial Intelligence
This study has examined why organizations that are rich in data and equipped with advanced analytics and artificial intelligence continue to make poor business decisions, and it has developed and tested a multi-dimensional framework for understanding business decision failure in the era of artificial intelligence. The central puzzle motivating the study has been that access to large volumes of data and to sophisticated models has not, in itself, guaranteed good decisions, because failure arises not from a single deficiency but from the interaction of technical, organizational, and human factors that a purely data-centric view overlooks. Guided by three research questions, why data-driven organizations still make poor decisions, what organizational, technical, and human factors contribute to failure, and how businesses can prevent these failures, the study has proposed a framework comprising five contributing dimensions: data quality, integration and governance; model validity, interpretability and fit-for-purpose; organizational decision culture and accountability; human cognition, judgment and bias; and governance, oversight and error-correction, with decision quality as the outcome. A quantitative, cross-sectional design has been used, and data have been collected from managers, analysts, data scientists, and decision-makers across a range of industries. Out of 289 distributed questionnaires, 246 valid responses have been retained, producing an 85.1% valid response rate. The analysis has included descriptive statistics, reliability testing, correlation analysis, multiple regression, and an analysis of the contribution and severity of failure modes drawn from respondents’ reported decision-failure cases. The findings have shown that all five dimensions were significantly and positively associated with decision quality, that the regression model was significant, F(5, 240) = 50.19, p < .001, explaining 51.1% of the variance, and that data quality, integration and governance and model validity, interpretability and fit-for-purpose were the strongest statistical predictors, while organizational and human factors were the most frequently cited and among the most severe contributors in reported failure cases. The study concludes that decision failure in data-rich organizations is a socio-technical rather than a purely technical phenomenon, and that its prevention requires the joint strengthening of data and model quality, decision culture and accountability, debiasing of human judgment, and governance and error-correction, rather than the accumulation of more data alone.
Copyright (c) 2026 Maniruzzaman Bhuiyan (Author)
AI-Driven Digital Twin and Reinforcement Learning for Intelligent Network and Power Systems: Advancing Efficient and Resilient Smart Urban Critical Infrastructure
This study has examined the development of an artificial intelligence-driven digital twin and reinforcement learning framework for advancing efficient and resilient smart urban critical infrastructure, with particular attention to the coupled operation of intelligent network and power systems in selected United States metropolitan utility and municipal contexts. The main problem has been the growing interdependence of urban power grids, communication networks, water systems, and transportation control layers, in which a disturbance in one network can cascade into others, while conventional control approaches rely on siloed supervisory systems, static offline models, rule-based automation, and reactive restoration that cannot anticipate, simulate, or coordinate responses across coupled infrastructures in real time. The purpose of the study has been to evaluate whether digital twin fidelity, real-time data and sensing integration, reinforcement learning decision effectiveness, cyber-physical security and interoperability, and operational scalability and compute adequacy significantly improve cross-infrastructure resilience and operational performance. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from professionals engaged in power systems operation, network engineering, smart city control, digital twin development, industrial control security, and municipal infrastructure analytics. Out of 274 distributed questionnaires, 233 valid responses have been retained, producing an 85.0% valid response rate. The analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, reinforcement learning prototype evaluation in a digital twin sandbox, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with digital twin fidelity recording the highest mean score of 4.24, followed by real-time data and sensing integration at 4.18, reinforcement learning decision effectiveness at 4.09, cross-infrastructure resilience and operational performance at 4.05, operational scalability and compute adequacy at 3.99, and cyber-physical security and interoperability at 3.91. Reliability has been confirmed through Cronbach’s alpha values ranging from .83 to .91, with overall reliability of .93. Correlation results have shown significant positive relationships between resilience and real-time data integration (r = .70), digital twin fidelity (r = .67), reinforcement learning effectiveness (r = .65), operational scalability (r = .59), and cyber-physical security (r = .57), all at p < .001. The regression model has been significant, F(5, 227) = 55.14, p < .001, explaining 54.8% of variance. The study implies that coupling high-fidelity digital twins with reinforcement learning control can improve anticipation, autonomous coordination, restoration speed, and cross-network resilience of smart urban critical infrastructure.
Copyright (c) 2026 Syed Nurul Islam, Muhammad Mohiul Islam (Author)
An Integrated IT Management Framework for Scenario-Based Enterprise Analytics and Strategic Decision Optimization
Enterprises increasingly invest in cloud platforms, enterprise systems, data warehouses, business intelligence applications, and analytical tools, yet strategic decisions often remain fragmented because governance, data, infrastructure, modeling, and user capabilities are insufficiently integrated. This study aimed to develop and quantitatively evaluate an integrated IT management framework for strengthening scenario-based enterprise analytics effectiveness and strategic decision optimiza_tion. A quantitative, cross-sectional, descriptive, explanatory, and case-based design was applied across selected enterprise cases using enterprise resource planning systems, cloud services, customer relationship management platforms, data warehouses, and decision-support technologies in financial services, manufacturing, logistics, telecommunications, retail, healthcare, and public-oriented institutions. From 360 distributed questionnaires, 312 valid responses were retained, producing an effective response rate of 86.67 percent. The model examined IT Governance and Strategic Alignment, Enterprise Data Integration and Information Quality, IT Infrastructure Flexibility and System Interoperability, Scenario-Modeling and Analytical Capability, Analytics-User Competency and Organizational Readiness, Scenario-Based Enterprise Analytics Effectiveness, and Strategic Decision Optimization. IBM SPSS was used for descriptive statistics, reliability analysis, factor analysis, Pearson correlation, multiple regression, simple regression, hypothesis testing, and diagnostic assessment. Cronbach’s alpha values ranged from .824 to .918, KMO reached .896, and Bartlett’s test was significant, χ²(630) = 4,052.63, p < .001. Enterprise Data Integration and Information Quality recorded the highest predictor mean, M = 4.14, SD = .54. The five predictors jointly explained 68.4 percent of analytics-effectiveness variance, R² = .684, adjusted R² = .679, F(5, 306) = 132.49, p < .001. IT Governance and Strategic Alignment was strongest, β = .31, followed by data integration and quality, β = .27. Analytics effectiveness strongly predicted strategic decision optimization, β = .765, R² = .586, p < .001. The findings imply that coordinated governance, reliable data, interoperable infrastructure, scenario modeling, and organizational readiness improve evidence use, risk evaluation, resource allocation, decision consistency, and responsiveness across enterprise environments.
Copyright (c) 2026 Nuzhat Sadia Prova (Author)
Design-for-Manufacturing Optimization for Improving Resilience in U.S. Additive Manufacturing Supply Chains
This study has examined design-for-manufacturing optimization as a means of improving resilience in United States additive manufacturing supply chains, with particular attention to how the redesign of parts and products for additive production reshapes the structure, flexibility, and disruption-recovery capacity of the supply chains that make them. The central problem has been that conventional supply chains, built around centralized production of many discrete parts sourced through long multi-tier supplier networks, are efficient under stable conditions but fragile under disruption, as recent shocks to global supply have made evident, and that additive manufacturing offers a route to greater resilience only if parts and products are deliberately designed to exploit it rather than merely substituted into existing designs. The purpose of the study has been to evaluate whether design-for-additive-manufacturing optimization, part consolidation and functional redesign, material and supplier diversification and flexibility, digital thread and supply-chain data integration, and distributed capacity and localized manufacturing jointly and significantly improve supply-chain resilience and continuity outcomes when design decisions are made with resilience, not only cost or performance, as an explicit objective. A quantitative, cross-sectional design supplemented by a prototype disruption-simulation evaluation has been used, and data have been collected from design and manufacturing engineers, supply-chain and operations managers, additive manufacturing specialists, procurement and sourcing professionals, and resilience and risk analysts across United States industrial settings. Out of 287 distributed questionnaires, 244 valid responses have been retained, producing an 85.0% valid response rate. The analysis has included descriptive statistics, reliability testing, correlation analysis, multiple regression, hypothesis testing, and a prototype evaluation of disruption-recovery performance across design strategies. The findings have shown that all five constructs were significantly and positively associated with supply-chain resilience, that the regression model was significant, F(5, 238) = 51.83, p < .001, explaining 52.1% of the variance, and that design-for-additive-manufacturing optimization and part consolidation and functional redesign were the strongest predictors. The prototype evaluation showed that fully design-for-manufacturing-optimized, distributed additive manufacturing supply chains suffered smaller performance losses and recovered faster across a range of disruption scenarios than conventional configurations. The study concludes that supply-chain resilience is designed into products and networks rather than added afterward, and that design-for-manufacturing optimization for additive production is a primary and underused lever for strengthening the resilience of United States manufacturing supply chains.
Copyright (c) 2025 Debasish Adhikary (Author)
Implementation of an OSHA-Compliant Lockout/Tagout and Arc-Flash Hazard Mitigation Framework for U.S. Industrial Maintenance Operations
Industrial maintenance operations continue to expose workers to uncontrolled hazardous energy, unexpected equipment startup, electrical shock, and arc-flash events when procedural, technical, human, and organizational safeguards are implemented inconsistently. This study aimed to develop and quantitatively evaluate an integrated OSHA-compliant lockout/tagout and arc-flash hazard mitigation framework for U.S. industrial maintenance operations. A quantitative, cross-sectional, multiple-case-based design was applied across selected industrial enterprise cases, including manufacturing, processing, fabrication, warehousing, utility, and related maintenance environments. Data were obtained from maintenance technicians, industrial electricians, electrical engineers, supervisors, environmental health and safety professionals, reliability personnel, plant managers, and contractor coordinators. Of 350 questionnaires distributed, 322 were returned and 300 valid responses were retained, producing an 85.71 percent valid response rate. The principal variables were Lockout/Tagout Procedure Implementation, Arc-Flash Risk Assessment and Hazard Communication, Employee Training and Technical Competency, Engineering Controls and PPE Readiness, Management Commitment, Auditing, and Documentation, OSHA Compliance Effectiveness, and Industrial Maintenance Safety Performance. Data analysis included descriptive statistics, Cronbach’s alpha, factor-validity tests, Pearson correlation, and multiple regression using SPSS. The instrument demonstrated strong reliability, with construct alpha coefficients ranging from .82 to .90 and an overall alpha of .93; the KMO value was .91 and Bartlett’s test was significant, χ² = 3,846.27, p < .001. Lockout/Tagout Procedure Implementation recorded the highest predictor mean of 4.12, while OSHA Compliance Effectiveness and Safety Performance achieved means of 4.06 and 4.02. The five predictors jointly explained 67.7 percent of the variance in OSHA Compliance Effectiveness, R² = .677, F (5, 294) = 123.18, p < .001. Lockout/Tagout Procedure Implementation was the strongest predictor, β = .29, followed by arc-flash assessment, β = .24. OSHA Compliance Effectiveness explained 54.8 percent of safety-performance variance, β = .74, p < .001. The findings indicate that integrated procedural, technical, competency, and management controls can strengthen regulatory alignment and improve maintenance safety outcomes.
Copyright (c) 2026 Md Mahamud Pervaz Pradhan (Author)
State Income Tax Differentials and High-Skilled Labor Mobility in The United States: A Time Series Analysis
This study has addressed uncertainty surrounding whether state income-tax differentials influence high-skilled labor mobility or operate jointly with employment, affordability, career, remote-work, and residential considerations. Its purpose has been to evaluate how perceived state income-tax burden, expected after-tax income advantage, employment opportunities, cost-of-living considerations, professional advancement, remote-work flexibility, and quality-of-life conditions shape interstate relocation intentions among high-skilled professionals in the United States. A quantitative, cross-sectional, multiple-case-based design has been applied across high-tax, low-tax, and no-individual-income-tax state environments. From 360 distributed questionnaires, 326 have been returned and 312 valid responses have been retained, yielding an 86.67% usable response rate. Participants have represented information technology, healthcare, engineering, finance, higher education, research, management, consulting, law, and knowledge-intensive occupations. Data have been collected through a five-point Likert-scale questionnaire and analyzed in SPSS using descriptive statistics, Cronbach’s alpha, Pearson correlation, multiple regression, one-way ANOVA, post hoc comparisons, and diagnostic tests. Reliability has been strong, with construct alpha values ranging from .79 to .89 and an overall coefficient of .91. High-skilled labor mobility has recorded a mean of 4.06, while expected after-tax income advantage has achieved the highest predictor mean of 4.12. The regression model has explained 64.8% of mobility variance, R² = .648, adjusted R² = .639, F(7, 304) = 79.95, p < .001. Expected after-tax income advantage has emerged as the strongest predictor, β = .27, followed by perceived tax burden, β = .24, employment opportunities, β = .20, professional advancement, β = .18, remote-work flexibility, β = .16, cost of living, β = .13, and quality of life, β = .11. Mobility has differed significantly across state categories, F(2, 309) = 12.84, p < .001, η² = .077, with the highest mean in high-tax states, 4.22. The findings imply that retaining skilled workers requires coordinated tax, employment, housing, career, flexibility, and quality-of-life policies.
Copyright (c) 2024 Md Anisur Rahman Mollah (Author)
Application of Machine Learning and Anomaly-Detection Algorithms for Fraud Detection in U.S. Government Financial Operations
This study has examined how machine learning and anomaly-detection algorithms can strengthen fraud detection in U.S. government financial operations, where large volumes of procurement, payroll, grants, reimbursement, vendor, and payment records can make manual monitoring insufficient for identifying concealed or unusual financial activity. The purpose has been to assess the effects of machine learning capability, anomaly-detection capability, financial data quality and integration, algorithm explainability and accuracy, organizational readiness, and decision-support effectiveness on fraud detection effectiveness. A quantitative, cross-sectional, case-study-based design has been applied to the U.S. public-sector financial context. Data have been represented through 312 usable responses retained from 360 distributed questionnaires, producing an 86.67% valid response rate. Respondents have included professionals involved in financial management, internal auditing, compliance, procurement, fraud investigation, data analytics, and information technology functions. Rather than cloud or enterprise cases, the study has used organizational respondent cases within government financial environments. A structured five-point Likert-scale instrument has measured the key variables, and the analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, and multiple regression modeling using IBM SPSS Statistics. The reported illustrative results have shown strong reliability, with construct alpha values ranging from .781 to .892 and an overall alpha of .931. Fraud detection effectiveness has recorded a high mean score of 4.08. The regression model has explained 64.6% of the variance in fraud detection effectiveness, R² = .646, adjusted R² = .639, F(6, 305) = 92.76, p < .001. Financial data quality and integration have emerged as the strongest predictor, β = .26, followed by anomaly-detection capability, β = .23, decision-support effectiveness, β = .22, machine learning capability, β = .19, organizational readiness, β = .14, and algorithm explainability and accuracy, β = .11. Explainability and accuracy have also significantly predicted decision-support effectiveness, β = .585, R² = .342, p < .001. The findings imply that agencies should combine integrated data governance, transparent analytical models, trained personnel, and actionable alert-response procedures to improve fraud monitoring and public accountability. As identified in the paper, these quantitative values are illustrative simulated outputs and require validation with real survey or transaction data before empirical submission.
Copyright (c) 2025 Mohammad Abir Chowdhury Shovon (Author)
Generative PLM: Utilizing Large Language Models for Automated Engineering Change Management and Requirements Traceability: A Case Study in Smart Manufacturing
Generative large language models are emerging as a promising enabler for digital transformation in product lifecycle management, particularly in engineering change management and requirements traceability. This paper presents a case study in smart manufacturing that explores how generative PLM can support the automation of change request interpretation, impact analysis, and bidirectional traceability between engineering requirements and design artifacts. The proposed approach integrates large language models with structured PLM workflows to assist engineers in extracting relevant entities, identifying affected components, and linking changes to upstream requirements and downstream implementation records. In the case study, the method is evaluated in a smart manufacturing environment where frequent design revisions and cross-disciplinary dependencies create substantial coordination overhead. Results indicate that LLM-assisted workflows can reduce manual effort, improve consistency in traceability maintenance, and accelerate response time for engineering changes, while also highlighting the need for human oversight in safety-critical and ambiguous cases. The findings suggest that generative PLM can complement existing engineering information systems by improving adaptability, knowledge reuse, and decision support across the product lifecycle. Overall, this study contributes a practical framework for applying large language models to engineering change management and requirements traceability in Industry 4.0 settings.
Copyright (c) 2026 Md Syeedur Rahman, Sharmin Rahman (Author)
A Machine-Learning Framework for Real-Time Non-Revenue Water Prediction in Aging Urban Distribution Networks
Non-revenue water (NRW) remains one of the most significant operational and economic challenges affecting aging urban water distribution systems because leakage, infrastructure deterioration, hydraulic instability, and inefficient maintenance practices contribute to substantial water losses and reduced system performance. This study developed and evaluated a comprehensive machine-learning framework for real-time non-revenue water prediction by integrating engineering infrastructure characteristics, hydraulic operational variables, historical maintenance records, and sensor-derived monitoring data. A quantitative research design was employed using a structured engineering dataset consisting of 5,200 validated operational observations collected from urban water distribution networks following data preprocessing, quality assurance, normalization, outlier screening, and feature engineering. Twelve predictive models were comparatively evaluated, including Linear Regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Network, Deep Learning, Gradient Boosting, XGBoost, LightGBM, and an anomaly detection model. Model performance was assessed using prediction accuracy, precision, recall, F1-score, receiver operating characteristic analysis, area under the curve (AUC), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), cross-validation, bootstrap validation, and residual diagnostics. The findings demonstrated that advanced ensemble learning algorithms consistently outperformed conventional statistical approaches. XGBoost achieved the highest predictive performance with an accuracy of 97.5%, precision of 0.976, recall of 0.974, F1-score of 0.975, AUC of 0.996, RMSE of 0.054, MAE of 0.039, and an explained variance (R²) of 0.987. LightGBM and Deep Learning also demonstrated excellent predictive capability, achieving accuracies of 97.2% and 97.1%, respectively, while Random Forest achieved 95.4% accuracy. Statistical analyses identified pressure variability (β = 0.421), burst frequency (β = 0.392), pipeline age (β = 0.368), repair history (β = 0.341), and flow imbalance (β = 0.317) as the strongest engineering predictors of non-revenue water occurrence. Cross-validation and bootstrap validation confirmed stable model performance, with validation accuracy exceeding 97% for the highest-performing algorithms and residual means approaching zero, indicating excellent generalization capability and minimal prediction bias. Overall, the findings demonstrated that advanced machine-learning techniques provide a highly accurate, reliable, and operationally robust framework for supporting intelligent leakage detection, predictive asset management, optimized maintenance planning, and sustainable urban water distribution system management.
Copyright (c) 2026 Mohammad Badrul Alam (Author)
Artificial Intelligence-Driven Predictive Analytics Framework for Enhancing Grid Resilience and Renewable Energy Integration in Smart Distribution Systems
This study has examined the development of an artificial intelligence-driven predictive analytics framework for enhancing grid resilience and renewable energy integration in smart distribution systems within selected United States utility and distribution-network contexts. The main problem has been the dependence of many electric utilities on reactive maintenance schedules, threshold-based alarm systems, fragmented supervisory data, delayed outage detection, and limited predictive validation, which can weaken grid resilience, degrade power quality, and constrain the safe interconnection of variable solar and wind generation. The purpose of the study has been to evaluate whether machine learning-based predictive capability, real-time smart grid data quality, renewable energy integration capability, predictive maintenance and asset health monitoring, and voltage and power quality management significantly improve distribution grid resilience and operational performance. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from power system professionals involved in distribution operations, protection engineering, renewable interconnection, asset management, control room supervision, and utility analytics. Out of 260 distributed questionnaires, 221 valid responses have been retained, producing an 85.0% valid response rate. The analysis plan has included descriptive statistics, Cronbach's alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, predictive model prototype evaluation, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with real-time smart grid data quality recording the highest mean score of 4.21, followed by predictive maintenance and asset health monitoring at 4.15, machine learning-based predictive capability at 4.11, distribution grid resilience and operational performance at 4.07, voltage and power quality management at 4.02, and renewable energy integration capability at 3.94. Reliability has been confirmed through Cronbach's alpha values ranging from .82 to .90, with overall reliability of .92. Correlation results have shown significant positive relationships between grid resilience and real-time data quality (r = .69), predictive maintenance (r = .66), machine learning capability (r = .63), voltage and power quality management (r = .58), and renewable integration capability (r = .56), all at p < .001. The regression model has been significant, F(5, 215) = 51.36, p < .001, explaining 54.4% of variance. The study implies that integrating machine learning prediction with power system modeling can improve failure anticipation, voltage stability, renewable hosting capacity, outage reduction, and evidence-based utility decisions.
Copyright (c) 2026 Md Delwar Hussain (Author)
Development of a Dashboarding and Visualization Framework For Pharmaceutical Brand Performance Analytics Using Tableau and Python
This study has examined the development of a dashboarding and visualization framework for pharmaceutical brand performance analytics using Tableau and Python in selected pharmaceutical cloud and enterprise analytics cases. The main problem has been the dependence of many pharmaceutical organizations on fragmented reports, manual spreadsheet processing, static charts, delayed updates, and limited statistical validation, which can weaken brand monitoring and managerial decision-making. The purpose of the study has been to evaluate whether Tableau-based dashboarding capability, Python-based analytical processing, dashboard usability, data visualization quality, and analytics automation significantly improve pharmaceutical brand performance analytics effectiveness. A quantitative, cross-sectional, case-based research design has been used, and data have been collected from pharmaceutical professionals involved in brand management, sales reporting, marketing analytics, business intelligence, data analysis, and decision support. Out of 250 distributed questionnaires, 214 valid responses have been retained, producing an 85.6% valid response rate. The analysis plan has included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation analysis, multiple regression modeling, hypothesis testing, dashboard prototype evaluation, and Python-driven analytical workflow validation. The findings have shown strong agreement across the constructs, with data visualization quality recording the highest mean score of 4.18, followed by dashboard usability at 4.13, Tableau-based dashboarding capability at 4.09, pharmaceutical brand performance analytics effectiveness at 4.05, analytics automation at 3.98, and Python-based analytical processing at 3.92. Reliability has been confirmed through Cronbach’s alpha values ranging from .81 to .89, with overall reliability of .91. Correlation results have shown significant positive relationships between analytics effectiveness and data visualization quality (r = .68), dashboard usability (r = .65), Tableau capability (r = .62), Python processing (r = .57), and automation (r = .55), all at p < .001. The regression model has been significant, F (5, 208) = 48.72, p < .001, explaining 53.9% of variance. The study implies that integrating Tableau visualization with Python analytics can improve brand monitoring, KPI interpretation, reporting accuracy, and evidence-based pharmaceutical decisions.
Pharmaceutical brand performance analytics
Copyright (c) 2025 Muhammad Zahidul Islam (Author)
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)