American Journal of Scholarly Research and Innovation
The American Journal of Scholarly Research and Innovation (AJSRI) ONLINE ISSN (3067-2163) is a leading Peer-Reviewed, Open Access International Journal dedicated to fostering innovation and excellence across a wide array of academic disciplines. The journal provides a platform for scholars, researchers, and professionals to disseminate groundbreaking research that addresses real-world challenges through interdisciplinary collaboration. As a monthly publication, AJSRI releases 12 issues annually, maintaining a consistent schedule that ensures the timely distribution of high-quality, impactful research. The journal encompasses diverse fields, including Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering, making it a truly interdisciplinary platform. AJSRI collaborates with international and national conferences to publish extended versions of selected papers, furthering the visibility and impact of researchers’ work. The journal is committed to publishing original, high-quality research that strengthens academic knowledge and drives innovation.
Physics-Informed Machine Learning for Predicting Thermal Distortion in Metal Additive Manufacturing
Thermal distortion remains a critical challenge in metal additive manufacturing because repeated localized heating, rapid cooling, thermal expansion, plastic deformation, and residual-stress accumulation can produce warping, dimensional inaccuracies, component rejection, and costly post-processing. This study aimed to develop and quantitatively evaluate an integrated physics-informed machine-learning framework for improving thermal-distortion prediction across metal additive-manufacturing enterprise and research cases. A quantitative, cross-sectional, multiple-case-based design was employed using a structured five-point Likert-scale questionnaire. The cases included industrial manufacturing organizations, universities, research institutions, engineering laboratories, technical centers, and additive-manufacturing technology or software providers working with laser powder bed fusion, directed energy deposition, wire arc additive manufacturing, electron-beam processes, thermal simulation, process monitoring, and dimensional-quality evaluation. Of 350 questionnaires distributed, 318 were returned and 300 valid responses were retained, producing an 85.71 percent valid response rate. The key variables were Physics-Law Integration, Thermal-Process Data Quality and Integration, Thermomechanical Coupling Representation, Model Validation and Generalizability, Computational Efficiency and Real-Time Capability, and Thermal Distortion Prediction Effectiveness. Data were analyzed using descriptive statistics, Cronbach’s alpha, exploratory factor analysis, Pearson correlation, multiple regression, analysis of variance, and regression diagnostics in SPSS. The instrument demonstrated strong reliability, with construct alpha coefficients ranging from .83 to .90 and an overall alpha of .92. The KMO value was .913, Bartlett’s test was significant, χ²(435) = 4,286.37, p < .001, and the six-factor structure explained 69.80 percent of total variance. Thermal-Process Data Quality and Integration achieved the highest mean of 4.14, while Thermal Distortion Prediction Effectiveness recorded 4.09. The five predictors jointly explained 66.60 percent of the variance in prediction effectiveness, R² = .666, adjusted R² = .660, F(5, 294) = 117.25, p < .001. Thermomechanical Coupling Representation was the strongest predictor, β = .30, followed by data quality, β = .26, physics-law integration, β = .21, validation and generalizability, β = .17, and computational capability, β = .11. The findings imply that reliable distortion prediction requires coordinated physical constraints, integrated process data, coupled thermal-mechanical modeling, rigorous validation, and efficient computation rather than reliance on isolated algorithms.
Copyright (c) 2025 Nasrin Akter Shopno (Author)
Development of a CMMS-Integrated Predictive Maintenance Framework for Industrial PLC and Drive Systems
Industrial organizations depend on programmable logic controllers, drives, sensors, and computerized maintenance management systems, yet these technologies often remain fragmented, limiting the conversion of condition data into maintenance action. This study aimed to develop and evaluate a CMMS-integrated predictive maintenance framework for PLC and drive systems. A quantitative, cross-sectional, multiple-case-based design was applied across 12 enterprise cases representing textile, food, pharmaceutical, cement, steel, power, chemical, automotive, packaging, water-treatment, and electronics operations. Of 360 questionnaires distributed, 330 were returned and 312 valid responses were retained, producing an 86.67 percent valid response rate. Respondents included maintenance, automation, electrical, reliability, instrumentation, production, CMMS, and management professionals. The key variables were CMMS Integration Capability, Condition-Monitoring Data Quality, Predictive Analytics Capability, Maintenance Workforce Competency, Organizational Readiness, Predictive Maintenance Effectiveness, and PLC and Drive System Maintenance Performance. Data were analyzed using descriptive statistics, Cronbach’s alpha, factor-based validity assessment, Pearson correlation, multiple regression, and diagnostics in SPSS. The instrument demonstrated overall reliability, α = .93, with construct alphas ranging from .82 to .90, while KMO = .918 and Bartlett’s test was significant, χ² (861) = 7,426.31, p < .001. Condition-Monitoring Data Quality achieved the highest predictor mean of 4.16. The five predictors explained 64.2 percent of the variance in Predictive Maintenance Effectiveness, R² = .642, F (5, 306) = 109.75, p < .001. Data quality was the strongest predictor, β = .29, followed by predictive analytics, β = .27, CMMS integration, β = .21, workforce competency, β = .17, and organizational readiness, β = .14. Predictive Maintenance Effectiveness explained 57.1 percent of maintenance-performance variance, β = .76, F (1, 310) = 412.60, p < .001. The findings imply that reliable data, integrated systems, analytical capability, skilled personnel, and organizational support must be developed together to reduce downtime, repeated faults, repair duration, and production interruption while improving availability, reliability, and continuity.
Copyright (c) 2025 Md Mahamud Pervaz Pradhan (Author)
Digital-Twin And IOT-Based Condition Monitoring for Rotating Machinery and Process Equipment in Petrochemical and Industrial Environments
Digital Twin and Internet of Things (IoT)-based condition monitoring have emerged as essential technologies for improving the reliability, maintenance effectiveness, and operational performance of rotating machinery and process equipment within petrochemical and industrial environments. This study quantitatively examined the influence of Digital Twin capability, IoT monitoring infrastructure, equipment condition monitoring effectiveness, and predictive maintenance performance on operational performance using an integrated conceptual framework grounded in Digital Twin Theory, Cyber-Physical Systems Theory, Condition-Based Maintenance Theory, Reliability-Centered Maintenance Theory, Predictive Analytics Theory, and Systems Theory. A quantitative cross-sectional correlational research design was employed. Data were collected from industrial professionals working in petrochemical, oil and gas, chemical manufacturing, and heavy industrial organizations using a structured five-point Likert-scale questionnaire. A total of 420 questionnaires were distributed, 389 responses were received, and 368 valid questionnaires were retained for statistical analysis, yielding an effective response rate of 87.62%. Data analysis was conducted using SPSS and AMOS through descriptive statistics, reliability analysis, Confirmatory Factor Analysis, Pearson correlation, multiple regression analysis, and Structural Equation Modeling. The measurement model demonstrated excellent psychometric properties, with Cronbach's alpha values ranging from 0.918 to 0.941, Composite Reliability values ranging from 0.931 to 0.949, and Average Variance Extracted values between 0.702 and 0.758. The structural model also demonstrated satisfactory goodness-of-fit, including χ²/df = 2.184, CFI = 0.972, TLI = 0.968, GFI = 0.948, RMSEA = 0.057, and SRMR = 0.041. Multiple regression analysis indicated that equipment condition monitoring effectiveness was the strongest predictor of operational performance (β = 0.374, p < 0.001), followed by predictive maintenance performance (β = 0.289, p < 0.001), Digital Twin capability (β = 0.248, p < 0.001), and IoT monitoring capability (β = 0.192, p < 0.001). The integrated model explained 79.6% of the variance in operational performance (R² = 0.796), while the predictive relevance value reached 0.581 and overall predictive accuracy was 91.8%.
Copyright (c) 2026 Mukut Kanti Barua (Author)
A Review of the Impact of Covid-19 On Maternal and Child Health Service Utilization in Bangladesh
The COVID-19 pandemic created unprecedented challenges for healthcare systems worldwide, significantly affecting the delivery and utilization of essential maternal and child health services. This study reviewed the impact of COVID-19 on maternal and child health service utilization in Bangladesh through a comprehensive quantitative synthesis of published empirical evidence. A quantitative review design was adopted, and relevant studies published between 2020 and 2022 were systematically identified using predefined inclusion and exclusion criteria. A total of 72 quantitative studies involving an estimated 512,486 participants were included in the final analysis. The synthesized evidence covered maternal health indicators, including antenatal care, skilled birth attendance, institutional delivery, postnatal care, emergency obstetric care, and family planning, together with child health indicators such as neonatal healthcare, routine childhood immunization, growth monitoring, nutritional assessment, pediatric outpatient attendance, and healthcare-seeking for common childhood illnesses. Descriptive and inferential statistical approaches were used to summarize healthcare utilization patterns, compare pre-pandemic and pandemic service utilization, and examine determinants of healthcare access. The findings demonstrated a statistically significant decline in maternal and child health service utilization during the COVID-19 pandemic (p < 0.001). Overall essential healthcare utilization decreased by 18.9%, while maternal health services declined by 19.8% and child health services by 17.9%. Preventive healthcare services experienced the greatest reduction (20.6%), whereas curative services declined by 11.7%. Growth monitoring and pediatric outpatient consultations exhibited the largest reductions among child health indicators, while postnatal care showed the greatest decline among maternal health services. Regression analysis indicated that healthcare accessibility, educational attainment, household income, and health system readiness were significant positive predictors of healthcare utilization, whereas transportation barriers and perceived COVID-19 infection risk exerted significant negative effects. The regression model explained 68.4% of the variation in maternal and child health service utilization (Adjusted R² = 0.684).
Copyright (c) 2022 Nishat Jahan, Mst Kaniz Fatema (Author)
A Framework for Cross-Functional Collaboration: Aligning Project Coordination, QA Engineering, and Client Communication to Maximize Project Performance
Cross-functional collaboration has become an essential organizational capability for improving project performance in increasingly complex project-based environments where multidisciplinary teams must coordinate technical activities, maintain quality standards, and satisfy diverse stakeholder expectations. The effectiveness of project coordination, quality assurance engineering, and client communication has therefore emerged as a critical determinant of successful project delivery. This study examined the influence of project coordination, quality assurance engineering, and client communication on project performance within an integrated framework of cross-functional collaboration. A quantitative cross-sectional research design was employed, and data were collected using a structured questionnaire administered to professionals working in engineering, information technology, construction, manufacturing, healthcare, telecommunications, and consulting organizations. A total of 350 questionnaires were distributed, of which 334 were returned. Following data screening, 320 valid responses were retained for statistical analysis, representing a usable response rate of 91.4%. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS Version 29). Descriptive statistics, reliability analysis, Pearson product-moment correlation, multiple linear regression, independent sample t-tests, and one-way analysis of variance were employed to examine the proposed research model. The findings demonstrated that all measurement constructs achieved satisfactory internal consistency, with Cronbach's alpha coefficients ranging from 0.894 to 0.931. Pearson correlation analysis revealed strong positive relationships between project performance and project coordination (r = 0.821), cross-functional collaboration (r = 0.793), quality assurance engineering (r = 0.764), and client communication (r = 0.719), all statistically significant at p < 0.001. Multiple linear regression analysis indicated that project coordination emerged as the strongest predictor of project performance (β = 0.427), followed by quality assurance engineering (β = 0.319) and client communication (β = 0.248). The overall regression model was highly significant (F = 405.83, p < 0.001) and explained 79.4% of the variance in project performance (R² = 0.794; Adjusted R² = 0.792), demonstrating excellent explanatory capability. Comparative analyses further indicated that professional experience, organizational size, and industry sector produced statistically significant differences in project performance, whereas gender did not demonstrate significant variation. The findings confirmed that stronger cross-functional collaboration, supported by effective project coordination, systematic quality assurance engineering, and structured client communication, substantially enhanced schedule adherence, operational efficiency, quality achievement, resource utilization, and stakeholder satisfaction. Overall, the study established that the proposed cross-functional collaboration framework provides a robust quantitative model for explaining project performance and offers empirical evidence supporting integrated organizational management practices across project-based organizations.
Copyright (c) 2026 Rukaiya Sultana (Author)
Assessment of Data Integrity Gaps and Preventive Strategies in QC Laboratories in Compliance with FDA and CGMP Expectations
Laboratory data integrity is a fundamental requirement for ensuring the reliability, accuracy, and traceability of analytical results generated within pharmaceutical quality control (QC) laboratories. Compliance with the United States Food and Drug Administration (FDA) regulations and Current Good Manufacturing Practice (CGMP) expectations requires organizations to maintain complete documentation, validated computerized systems, effective audit trail management, qualified personnel, comprehensive quality oversight, and sustainable preventive quality management practices. This study assessed data integrity gaps and preventive strategies implemented in pharmaceutical QC laboratories and examined their influence on overall laboratory compliance performance. A quantitative cross-sectional research design was employed using a structured questionnaire administered to professionals involved in quality control, quality assurance, validation, regulatory compliance, and laboratory management. The study investigated the relationships among laboratory data integrity gaps, preventive quality management strategies, and compliance performance while evaluating documentation practices, electronic record management, computerized system validation, audit trail review, personnel training, corrective and preventive action (CAPA) implementation, internal audit systems, risk-based monitoring, and management review. Statistical analyses included descriptive statistics, reliability and validity assessment, correlation analysis, and multiple regression analysis to evaluate the proposed conceptual framework and research hypotheses. The findings indicated that laboratory data integrity gaps adversely affected compliance performance, whereas preventive quality management strategies significantly strengthened documentation quality, computerized system reliability, audit readiness, regulatory preparedness, and overall laboratory governance. Preventive measures, including standardized operating procedures, routine audit trail monitoring, effective personnel training, independent quality assurance oversight, timely CAPA implementation, and structured management review, were associated with stronger laboratory compliance and improved data integrity performance. The study concluded that sustainable compliance with FDA and CGMP expectations depends upon an integrated quality management system that combines technical controls, organizational governance, personnel competence, and continuous quality improvement. The findings contribute to pharmaceutical quality management by providing an evidence-based framework for strengthening laboratory data integrity, improving regulatory compliance, enhancing analytical reliability, supporting product quality, and protecting patient safety through proactive organizational quality practices.
Copyright (c) 2024 Muhammad Zahidul Islam (Author)
Development of A Predictive Maintenance Model for Industrial Motor Drives and VFD Systems Using PLC/DCS Operational Telemetry
This study examined the problem of reliability, safety, and compliance gaps in stationary hydrogen storage and distribution infrastructure, where reactive or time-based maintenance may fail to detect early leakage, pressure abnormality, compressor degradation, valve malfunction, sensor drift, pipeline stress, and emergency shutdown delays. The purpose of the study was to quantitatively evaluate how AI-driven predictive maintenance, supported by SCADA/PLC integration and operational telemetry, contributes to operational reliability, real-time fault detection, hydrogen safety risk reduction, and U.S. pipeline safety policy readiness in enterprise hydrogen infrastructure cases, including storage vessels, pipelines, compressors, valves, regulators, sensors, control systems, and cloud-assisted monitoring environments. A quantitative, cross-sectional, case-based design was adopted, using a structured five-point Likert-scale questionnaire. Data were obtained from 185 valid respondents out of 230 distributed questionnaires, representing an 80.4% valid response rate. The sample included engineers, maintenance managers, SCADA/PLC specialists, safety and risk officers, energy-sector professionals, and policy or compliance professionals, with 76.8% reporting high or very high familiarity with hydrogen infrastructure. The key variables were AI-driven predictive maintenance adoption, SCADA/PLC integration, data quality, sensor reliability, real-time fault detection capability, operational reliability, hydrogen safety risk reduction, policy compliance readiness, and predictive maintenance readiness. The analysis plan included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, multiple regression, hypothesis testing, Hydrogen Infrastructure Predictive Maintenance Readiness Index analysis, and hydrogen safety-critical fault detection and policy alignment assessment. The headline findings showed high ratings for SCADA/PLC integration, M = 4.22, real-time fault detection, M = 4.20, hydrogen safety risk reduction, M = 4.19, and AI-driven predictive maintenance, M = 4.18. Reliability was strong, with Cronbach’s alpha values ranging from 0.80 to 0.91. Correlation results showed strong positive relationships, including SCADA/PLC integration and real-time fault detection, r = 0.72, p < 0.01, and real-time fault detection and hydrogen safety risk reduction, r = 0.70, p < 0.01. Regression results showed that predictive maintenance, SCADA/PLC integration, data quality, and sensor reliability explained 57.4% of operational reliability, while real-time fault detection, predictive maintenance, and SCADA/PLC integration explained 61.2% of hydrogen safety risk reduction. The HPMRI score was 4.17, indicating high readiness. The study implies that hydrogen infrastructure operators should strengthen integrated telemetry, sensor reliability, data quality, predictive analytics, digital maintenance records, and policy-aligned safety reporting to improve reliability and risk-based compliance.
Copyright (c) 2024 Mohammad Shoriful Hossan Shohel (Author)
STI and HIV Prevention Through Public Health Digital Twins: A Framework for Personalized Prevention and Adaptive Disease Intervention
Sexually transmitted infections (STIs) and Human Immunodeficiency Virus (HIV) continue to present significant public health challenges worldwide, necessitating innovative prevention approaches that integrate advanced digital technologies with personalized and adaptive intervention strategies. This study investigated the influence of Public Health Digital Twin Capability on STI and HIV Prevention Performance through the mediating effects of Personalized Prevention Effectiveness and Adaptive Intervention Capacity. A quantitative cross-sectional research design was employed, and data were collected from 412 public health professionals, epidemiologists, infectious disease specialists, healthcare administrators, health informatics experts, and STI/HIV program coordinators. The conceptual framework was developed based on Digital Twin Theory, Precision Public Health, and Adaptive Intervention Theory. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 and SPSS 29. The findings revealed that Public Health Digital Twin Capability exerted significant positive effects on Personalized Prevention Effectiveness (β = 0.712, p < 0.001), Adaptive Intervention Capacity (β = 0.684, p < 0.001), and STI and HIV Prevention Performance (β = 0.276, p < 0.001). Personalized Prevention Effectiveness (β = 0.358, p < 0.001) and Adaptive Intervention Capacity (β = 0.412, p < 0.001) also demonstrated significant positive effects on prevention performance. Mediation analysis confirmed significant indirect effects through Personalized Prevention Effectiveness (β = 0.255, p < 0.001) and Adaptive Intervention Capacity (β = 0.282, p < 0.001), with both mediators jointly accounting for 66.06% of the total effect. The structural model explained 69.4% of the variance in STI and HIV Prevention Performance (R² = 0.694), indicating substantial explanatory power. Secondary analyses further identified Predictive Analytics Capability (r = 0.748, p < 0.001) and Intervention Responsiveness (r = 0.756, p < 0.001) as the strongest correlates of prevention performance.
Copyright (c) 2026 Shima Ali Sadia, Sonya Ghosh, Mahbubul Islam (Author)
Reducing False and Improving Response Time in High-Occupancy Buildings: A Quantitative Study of NFPA 72-Complaint Cause-and-Effect Testing Outcomes
This study examined the effectiveness of NFPA 72-compliant cause-and-effect testing in reducing false alarm frequency and improving response time in high-occupancy buildings through a quantitative quasi-experimental design. Data were collected from 28 buildings, including hospitals, educational institutions, commercial complexes, and high-rise office structures, comprising a total of 12,460 recorded alarm events. Pre-testing and post-testing system performance metrics were analyzed to evaluate the impact of structured testing interventions. The results indicated a substantial reduction in false alarm frequency, with mean values decreasing from 14.8 to 7.2 events per building per month, representing an overall reduction of 51.4%. Response time analysis revealed a significant improvement, with mean latency decreasing from 9.2 seconds to 5.6 seconds, reflecting a 39.1% reduction and improved consistency across systems. Statistical analysis confirmed that these changes were significant, with large effect sizes observed for false alarm reduction and moderate-to-large effect sizes for response time improvement. Sub-group analysis demonstrated that commercial complexes achieved the highest reduction in false alarms at 56.3%, while high-rise office buildings showed the most consistent response time improvements. Multi-sensor detection systems outperformed single-sensor technologies, achieving a false alarm reduction of 58.6%, compared to 47.2% and 42.5% for photoelectric and ionization detectors, respectively. Additionally, fully integrated systems exhibited greater performance gains, with response time improvements averaging 44.3%, compared to 31.8% in partially integrated systems. The findings demonstrated that cause-and-effect testing significantly enhanced system reliability by ensuring accurate input-output relationships and reducing configuration errors. The integration of statistical analysis and performance benchmarking provided robust evidence of the effectiveness of structured testing in optimizing fire alarm system performance. The study contributed to the advancement of quantitative fire safety evaluation by highlighting the importance of systematic verification and data-driven analysis in high-occupancy building environments.
Hybrid Cloud Security and Compliance in U.S. Enterprises: Addressing Data Privacy Risks and Governance Challenges
Hybrid cloud computing has become a critical operational framework for U.S. enterprises seeking scalability, operational flexibility, and distributed digital infrastructure integration. The increasing dependence on hybrid cloud environments has also intensified concerns related to cybersecurity governance, regulatory compliance, data privacy protection, vendor risk exposure, and operational resilience across interconnected cloud systems. This quantitative study examined the relationships between governance effectiveness, compliance readiness, identity and access management capability, vendor risk governance, operational resilience, cybersecurity preparedness, and enterprise data privacy protection within hybrid cloud infrastructures operating across U.S. enterprises. A cross-sectional quantitative research design grounded in cybersecurity governance and enterprise risk management theory was employed to evaluate governance-security relationships using statistical analysis techniques. Data were collected from 312 cybersecurity professionals, compliance officers, cloud administrators, governance specialists, and information technology managers representing healthcare, finance, retail, manufacturing, education, and information technology sectors. Descriptive statistics, Pearson correlation analysis, multiple regression analysis, and ANOVA procedures were conducted using SPSS and R statistical software to evaluate organizational cybersecurity performance and governance maturity. The findings revealed strong governance implementation and cybersecurity preparedness across participating enterprises, with data privacy protection effectiveness producing the highest mean score (M = 4.21, SD = 0.55). Correlation analysis demonstrated statistically significant positive relationships between governance effectiveness and data privacy protection (r = 0.769, p < 0.001), as well as between cybersecurity preparedness and operational resilience (r = 0.779, p < 0.001). Multiple regression analysis indicated that governance effectiveness, operational resilience, and identity and access management effectiveness significantly predicted enterprise cybersecurity preparedness, with the model explaining 71.6% of the variance (R² = 0.716). Industry-level comparisons further revealed significant differences across sectors, with finance and information technology organizations demonstrating the highest governance maturity and cybersecurity preparedness scores. The study concluded that governance maturity, operational resilience capability, compliance readiness, and access management effectiveness significantly influenced enterprise cybersecurity resilience and data privacy protection outcomes within hybrid cloud systems. The findings contributed quantitative empirical evidence supporting the strategic importance of integrated cybersecurity governance frameworks within contemporary U.S. enterprise cloud infrastructures.
Copyright (c) 2024 H M Mahir Uddin, Risha Alam (Author)
Cybersecurity and Governance in Hybrid Cloud Architectures: Addressing Compliance and Data Privacy Challenges in U.S. Organizations
AI-Enhanced Financial Information Systems for Real-Time Fraud Detection and Cash Flow Optimization in U.S. Logistics and Retail Sectors
This study examined the role of AI-enhanced financial information systems in improving real-time fraud detection and cash flow optimization in U.S. logistics and retail sectors. The central problem addressed was that many traditional financial information systems remain limited to delayed transaction recording and reporting, making them less effective in detecting suspicious transactions, invoice duplication, refund abuse, payment manipulation, and liquidity risks in fast-moving enterprise environments. The purpose of the study was to quantitatively assess how AI-enabled analytics, automation, anomaly detection, real-time monitoring, and financial data integration influence fraud detection capability, cash flow visibility, operational risk reduction, financial control effectiveness, and decision-making efficiency. The study adopted a quantitative, cross-sectional, case-based design using structured five-point Likert-scale survey data from 250 valid respondents drawn from cloud-enabled enterprise financial system cases in logistics and retail organizations, including finance, accounting, IT, risk, analytics, and operations professionals. Key variables included AI-enhanced financial information systems as the independent variable, real-time fraud detection and cash flow optimization as major dependent variables, and fraud alert responsiveness, Cash Flow Visibility Index, financial control effectiveness, operational risk reduction, and decision-making efficiency as supporting constructs. The analysis plan involved descriptive statistics, reliability testing, validity assessment, Pearson correlation, regression analysis, sector-based comparison, and mediation-style regression. Findings showed strong quantitative support for the model: AI-enhanced financial systems recorded a high mean score of 4.18, real-time fraud detection 4.12, Cash Flow Visibility Index 4.15, and cash flow optimization 4.09. Reliability was strong, with an overall Cronbach’s alpha of 0.91. Regression results showed that AI-enhanced systems significantly predicted real-time fraud detection, β = 0.62, R² = 0.46, p < 0.001, while AI analytics predicted cash flow optimization, β = 0.58, R² = 0.41, p < 0.001. Real-time fraud detection also predicted cash flow optimization, β = 0.49, p < 0.001, and partially mediated the AI system and cash flow relationship. The study implies that AI-enhanced financial systems are strategic financial-control tools for improving fraud prevention, liquidity visibility, risk reduction, and managerial decision-making in transaction-intensive enterprise sectors.
AI-enhanced financial information systems
Copyright (c) 2025 Mst Shurovi Akter (Author)
Clinical Psychological Screening and Early Identification of Trauma-Related Disorders Using AI-Supported Assessment Models
This study investigated the persistent problem of delayed, inconsistent, and clinically difficult early identification of trauma-related disorders in psychological screening settings, where symptom overlap, hidden distress, and time-constrained assessment often weaken timely recognition and referral decisions. The purpose of the study was to examine whether AI-supported assessment models improve clinical psychological screening and the early identification of trauma-related disorders through stronger predictive capability, reliability, usability, and clinical trust within a quantitative, cross-sectional, case-based research design. The study drew on a purposive sample of 214 mental health professionals from case-based clinical and enterprise-style digital screening environments, including clinical psychologists, counselors, psychiatrists, and mental health practitioners. Key variables included predictive capability, reliability, usability, clinical trust, screening effectiveness, diagnostic confidence, and early identification of trauma-related disorders. Data were collected through a structured five-point Likert-scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression modeling in SPSS. The findings showed high mean scores across all core constructs, including predictive capability (M = 4.08, SD = 0.61), screening effectiveness (M = 4.11, SD = 0.59), diagnostic confidence (M = 4.03, SD = 0.63), and early identification (M = 4.15, SD = 0.57). Reliability testing confirmed strong internal consistency, with construct alpha values ranging from 0.79 to 0.88 and an overall instrument alpha of 0.91. Correlation results indicated significant positive associations, particularly between screening effectiveness and early identification (r = 0.68, p < .01), and between clinical trust and diagnostic confidence (r = 0.64, p < .01). Regression results showed that predictive capability (β = 0.31, p < .001), reliability (β = 0.24, p = .002), and usability (β = 0.18, p = .011) significantly predicted screening effectiveness, while screening effectiveness strongly predicted early identification (β = 0.54, p < .001); the models explained 57% and 62% of the variance respectively. The study concludes that AI-supported assessment models can substantially strengthen trauma-sensitive early screening when they are accurate, dependable, usable, and trusted, with important implications for digital mental health practice, clinician support, and early intervention planning.
Copyright (c) 2024 Mahmuda Mahmuda (Author)
Contingency-Based Resilience Assessment of Critical Utility Substations: An ETAP Framework for Accelerating Safe Interconnection of High-Density AI Data Center Loads
This study examined the resilience of a critical utility substation under the proposed interconnection of a high-density AI data center load using a contingency-based ETAP framework. The study was motivated by the growing pressure placed on utility substations by concentrated digital infrastructure demand, particularly where safe interconnection depends not only on base-case capacity but also on the ability of the substation to remain within acceptable thermal, voltage, and protection limits during abnormal operating conditions. A quantitative nonexperimental case study design was adopted, and the analytical framework focused on measurable resilience indicators including transformer loading margin, bus voltage deviation, feeder loading utilization, short-circuit current magnitude, breaker duty margin, and contingency violation counts. The modeled system consisted of 2 power transformers, 8 buses, 6 outgoing feeders, 12 evaluated circuit breakers, and 10 credible contingency cases, with a final dataset of 12 retained operating states and 480 complete observations. The findings showed that the proposed AI data center interconnection materially reduced the resilience margin of the hosting substation. Mean transformer loading increased from 61.30% in the base-case condition to 79.80% in the post-interconnection normal condition and 86.70% under post-contingency operation, while the average reserve margin declined from 38.70% to 13.30%. Mean bus voltage deviation rose from 1.90% in the base case to 3.20% after interconnection and 4.80% under contingency, with the lowest recorded bus voltage falling from 0.981 p.u. to 0.941 p.u. Short-circuit current also increased from a mean of 21.70 kA to 24.10 kA and then to 28.10 kA across the same conditions. In parallel, breaker duty margin declined significantly, and contingency-related noncompliance rose from 0.00 violations in the base case to 1.00 in the post-interconnection normal state and 3.50 under post-contingency conditions. The results demonstrated that the substation remained broadly serviceable in selected normal states but experienced substantial reduction in disturbance tolerance after the large-load addition. The study concluded that contingency-based resilience assessment provided a rigorous basis for evaluating the safe interconnection readiness of critical utility substations exposed to high-density AI data center demand.
Copyright (c) 2024 Md. Sultan Mahamud (Author)
MPLS-TP and SONET Security Hardening for Utility SCADA Networks: Threat Modeling and Mitigation Strategies for Energy Fiber Infrastructure
This study addresses the problem that utility SCADA communication backbones using MPLS-TP, SONET, and hybrid transport environments remain highly critical to grid operations, yet transport-layer security is often assessed less rigorously than broader SCADA cybersecurity despite exposure to unauthorized access, misconfiguration, weak IT/OT segmentation, denial-of-service, insider misuse, and physical fiber threats. The purpose of the study was to examine how threat modeling, security hardening controls, and mitigation strategies influence security performance and resilience in utility energy fiber infrastructures through a quantitative, cross-sectional, case-based design grounded in cloud and enterprise style utility communication cases. Data were collected through a structured five-point Likert questionnaire from professionals involved in SCADA operations, utility telecom, OT security, and infrastructure management. Of 150 distributed questionnaires, 132 were returned and 126 usable responses were analyzed, yielding an 84.0% usable response rate. The sample included respondents from MPLS-TP-only cases (22.2%), SONET-only cases (19.0%), and hybrid MPLS-TP/SONET cases (58.7%). Key variables were threat modeling practices, security hardening controls, mitigation strategies, and security and resilience outcomes. Analysis was conducted using descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression. Reliability was strong across constructs, with alpha values ranging from 0.86 to 0.90. Descriptive findings showed high mean scores for threat modeling (M = 4.08, SD = 0.62), security hardening (M = 4.21, SD = 0.57), mitigation strategies (M = 4.16, SD = 0.60), and security outcomes (M = 4.19, SD = 0.55). Correlation results indicated significant positive relationships with security outcomes for threat modeling (r = 0.61), security hardening (r = 0.74), and mitigation strategies (r = 0.68), all at p < .01. Regression results showed that the model was significant, F = 41.87, p < .001, explaining 50.7% of the variance in security and resilience outcomes (R² = 0.507), with security hardening as the strongest predictor (β = 0.41), followed by mitigation strategies (β = 0.29) and threat modeling (β = 0.24). The study implies that utility operators should prioritize protocol-aware hardening, especially in hybrid environments, to strengthen resilience, continuity, and infrastructure protection.
Copyright (c) 2025 Abu Naser Md Golam Mosharraf (Author)
Data-Driven Detection of Out-of-Specification Trends in Pharmaceutical Production: A Public Health Imperative
Ensuring the consistent quality of pharmaceutical products is central to public health protection, as undetected deviations in production can translate into substandard medicines reaching patients. This study examined the effectiveness of data analytics in detecting out-of-specification (OOS) trends in pharmaceutical production, with the objective of strengthening quality assurance systems and mitigating downstream public health risks. A quantitative longitudinal design was adopted, drawing on 1,248 pharmaceutical production batches and 9,732 critical quality observations extracted from manufacturing execution and laboratory information management systems. Descriptive analysis indicated that 93.8% of observations remained within specification limits, while 6.2% were classified as out-of-trend (OOT) and 0.9% were confirmed as OOS events. Statistical process control (SPC) flagged 214 instances of process instability, and predictive analytics successfully anticipated 79 of 87 OOS cases, yielding a detection accuracy of 92.1%, compared with 79.4% achieved through traditional monitoring methods. Multivariate modelling further enhanced analytical performance, raising sensitivity to 85.3% and increasing variance explained to 68.5%, relative to 51.2% under univariate approaches. Predictive models also demonstrated a lead detection advantage of 3.4 batches, with sensitivity and specificity values of 91.5% and 92.8%, respectively. Secondary analyses revealed meaningful variability across production lines, with OOT frequencies ranging from 4.7% to 8.9% and 62.5% of deviations clustering within specific operational periods, suggesting systemic rather than random origins. Regression findings identified temperature variation (effect size = 0.61) and mixing time (effect size = 0.48) as the most influential predictors of deviation occurrence, while visualization of impurity profiles revealed variability increases of up to 14.6%. Collectively, these findings confirm that integrated data analytics substantially enhances early detection, reduces false negatives, and improves overall process control in pharmaceutical manufacturing. The study supports the adoption of advanced analytical systems, encompassing SPC, predictive modelling, and multivariate diagnostics, as a strategic mechanism for strengthening quality assurance and, by extension, safeguarding public health.
Copyright (c) 2026 Beatrice Onyinyechi Mkpa, Ferdous Ara Ahmed (Author)
AI-Augmented Business Intelligence for Campaign Performance Optimization in U.S. Retail and e-Commerce: A Mixed-Methods Study of Marketing ROI
This study investigates the growing problem that many U.S. retail and e-commerce firms invest in digital marketing technologies and data systems but still struggle to convert campaign data into consistently optimized performance and stronger marketing return on investment. The purpose of the research was to examine whether AI-augmented business intelligence improves campaign performance optimization and marketing ROI, and to identify which analytical dimensions matter most in practice. The study adopted a quantitative, cross-sectional, case-based design focused on cloud-enabled and enterprise retail and e-commerce campaign environments, using survey evidence from 214 valid professional respondents drawn from campaign managers, BI analysts, e-commerce managers, and digital marketing specialists. The key independent variables were predictive analytics capability, real-time insight capability, customer segmentation intelligence, and decision automation support, while the main dependent variables were campaign performance optimization and marketing ROI. Data were analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression. The findings showed strong measurement quality, with Cronbach’s alpha values ranging from 0.77 to 0.88, and high construct means, including AI-augmented BI capability (M = 4.08, SD = 0.61), campaign performance optimization (M = 4.14, SD = 0.57), and marketing ROI (M = 4.02, SD = 0.64). Correlation results indicated that AI-augmented BI was strongly associated with campaign performance optimization (r = .710, p < .001) and marketing ROI (r = .640, p < .001), while campaign optimization was also strongly related to ROI (r = .680, p < .001). Regression analysis revealed that AI-BI dimensions explained 58.4% of the variance in campaign optimization (R² = .584, F = 73.48, p < .001), with predictive analytics capability emerging as the strongest predictor (β = .310, p < .001), followed by customer segmentation intelligence (β = .270, p < .001), real-time insight capability (β = .220, p = .002), and decision automation support (β = .140, p = .018). AI-BI and campaign optimization jointly explained 52.1% of the variance in marketing ROI (R² = .521, F = 114.06, p < .001). The study implies that firms can improve campaign precision, responsiveness, budget efficiency, and financial returns by strategically embedding AI-enhanced BI into campaign decision processes rather than using BI only for descriptive reporting.
Copyright (c) 2025 Md Aminul Islam, Zakia Afroz (Author)
Sustainable Infrastructure Through Intelligent Maintenance and Energy Optimization Frameworks
This study investigated how intelligent maintenance systems and energy optimization frameworks improve sustainable infrastructure performance in operational infrastructure environments, addressing the persistent problem that many infrastructure systems continue to rely on fragmented maintenance routines and disconnected energy management practices, which weaken asset reliability, increase inefficiency, and reduce long-term sustainability outcomes. The purpose of the study was to examine the individual and joint effects of intelligent maintenance systems and energy optimization frameworks on sustainable infrastructure performance using a quantitative, cross-sectional, case-based research design. Primary data were collected through a structured five-point Likert scale questionnaire from 200 valid respondents selected from infrastructure-related case environments, including public infrastructure agencies, institutional and commercial facilities, utility and service organizations, and transport-related facilities. The sample included engineers, maintenance officers, facility managers, energy management staff, and technical supervisors. The key variables were intelligent maintenance systems and energy optimization frameworks as independent variables, and sustainable infrastructure performance as the dependent variable. Data were analyzed using descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, and multiple regression analysis in SPSS. The findings showed high respondent agreement across all constructs, with composite means of 4.08 for intelligent maintenance systems, 4.15 for energy optimization frameworks, and 4.21 for sustainable infrastructure performance. Reliability values were strong, with Cronbach’s alpha coefficients of 0.861, 0.884, and 0.892 respectively, and 0.901 for the overall instrument. Correlation analysis revealed significant positive relationships between intelligent maintenance systems and sustainable infrastructure performance (r = 0.680, p < .01), energy optimization frameworks and sustainable infrastructure performance (r = 0.730, p < .01), and intelligent maintenance systems and energy optimization frameworks (r = 0.610, p < .01). Regression results confirmed that both intelligent maintenance systems (β = 0.340, t = 5.92, p < .001) and energy optimization frameworks (β = 0.490, t = 8.11, p < .001) significantly predicted sustainable infrastructure performance, with the model explaining 62.4% of the variance (R² = 0.624; F = 163.410, p < .001). The study concludes that sustainable infrastructure is significantly strengthened when maintenance intelligence and energy optimization operate as integrated management capabilities, with important implications for infrastructure managers, policymakers, and facility operators seeking greater efficiency, reliability, asset longevity, and environmental sustainability.
Copyright (c) 2026 K M Tanvir Anjum Anick (Author)
Integrated Modeling of Condition Monitoring Data for Predictive Maintenance of Electrical Power Plant Systems
This study examined the effectiveness of integrated modeling of condition monitoring data for predictive maintenance in electrical power plant systems using a quantitative, quasi-experimental research design. The analysis was conducted on a dataset comprising 12,480 observations collected over an 18-month monitoring period across turbines, generators, transformers, and auxiliary systems. The study integrated multiple condition monitoring variables, including vibration, temperature, and electrical load, to develop predictive models using machine learning techniques. The results demonstrated that integrated models significantly outperformed single-source models, achieving a classification accuracy of 92.6% compared to 81.4%. Precision and recall values also improved, reaching 91.8% and 93.4%, respectively, indicating enhanced fault detection capability. Regression analysis revealed a substantial reduction in prediction error, with root mean square error decreasing from 0.58 to 0.42 and mean absolute error from 0.45 to 0.31. Additionally, integrated models identified faults approximately 36 hours in advance compared to 22 hours for single-source models, representing a 63.6% improvement in early detection capability. Component-level analysis indicated that turbines and generators achieved the highest predictive accuracy at 94.2% and 92.8%, respectively, while auxiliary systems showed comparatively lower accuracy at 87.3% due to higher operational variability. Statistical testing confirmed that these improvements were significant at a probability level below 0.05, with large effect sizes observed across key performance metrics. The findings also highlighted the importance of data quality, feature engineering, and model optimization in achieving reliable predictive outcomes. Overall, the study demonstrated that integrated condition monitoring significantly enhances predictive maintenance performance by improving accuracy, reducing uncertainty, and enabling proactive maintenance planning. These results provide strong empirical support for the adoption of integrated data-driven maintenance strategies in electrical power plant systems and contribute to the advancement of intelligent industrial maintenance practices.
Copyright (c) 2025 Md. Shahinur Islam (Author)
IoT-Driven Digital Transformation in Global Supply Chains: Implications for Financial Risk Monitoring and Investment Efficiency
The increasing complexity of global supply chains has intensified the need for advanced digital monitoring systems capable of improving financial risk monitoring and investment efficiency. This study examined the influence of Internet of Things (IoT) technologies and digital monitoring infrastructures on financial risk monitoring effectiveness and investment performance within globally integrated supply chain environments. A quantitative research design was employed to investigate the relationships among IoT technology adoption, digital monitoring capability, operational visibility, financial risk monitoring effectiveness, and investment efficiency. Data were collected from 214 professionals working in supply chain management, logistics coordination, procurement operations, and financial risk management roles across multiple industries, including manufacturing, logistics, retail distribution, and international trading organizations. Descriptive and inferential statistical analyses were conducted using correlation analysis, multiple regression, and structural equation modeling to evaluate the proposed conceptual relationships. The descriptive findings indicated relatively high levels of digital technology adoption across participating organizations, with 46.3% of firms reporting high levels of IoT adoption, while 36.9% demonstrated moderate adoption levels. Correlation analysis revealed strong positive relationships among the main constructs, with the highest correlation observed between digital monitoring capability and operational visibility (r = 0.71). Regression results further demonstrated that IoT technology adoption significantly predicted digital monitoring capability (β = 0.61, p < 0.001), explaining 37% of the variance in monitoring capability across organizations. Digital monitoring capability also exerted a significant influence on financial risk monitoring effectiveness (β = 0.63, p < 0.001) and investment efficiency (β = 0.58, p < 0.001). Structural equation modeling confirmed that monitoring capability functioned as a mediating factor linking IoT adoption with improved financial outcomes. The structural model demonstrated satisfactory fit indicators, including CFI = 0.94, TLI = 0.92, and RMSEA = 0.056, confirming the adequacy of the proposed model. The findings indicated that organizations with stronger IoT-enabled monitoring infrastructures demonstrated greater operational transparency, improved financial risk detection, and more efficient allocation of supply chain investments. Industries characterized by complex logistics operations, particularly manufacturing and transportation sectors, exhibited the strongest benefits from digital monitoring systems. Overall, the study provided empirical evidence that integrated IoT-based monitoring technologies significantly enhance financial oversight and investment decision-making within global supply chain networks.
Copyright (c) 2024 Mahfuj Ahmed Ruzel (Author)
Deploying Low-Latency Edge AI in Medical IOT Networks: A Case Study of Secure Real-Time Patient Monitoring Systems
This study examined the deployment of low-latency Edge Artificial Intelligence (Edge AI) within Medical Internet of Things (MIoT) networks to enhance secure real-time patient monitoring systems through a quantitative experimental case study design. The research aimed to evaluate system performance across key indicators, including latency, throughput, predictive accuracy, energy consumption, and security overhead, by comparing edge-based and cloud-based architectures under controlled operational conditions. A structured dataset comprising 480 experimental iterations was analyzed, incorporating physiological data streams such as heart rate, oxygen saturation, respiratory rate, and electrocardiographic signals. The findings demonstrated that the edge-based system significantly reduced end-to-end latency, achieving a mean delay of 37.8 milliseconds compared to 109.6 milliseconds in the cloud configuration, representing a reduction of approximately 65.5%. Throughput performance was also improved, with the edge system processing 6.2 MB per minute versus 5.5 MB per minute in the cloud system. Predictive accuracy remained high, reaching 96.4% in the edge environment compared to 94.1% in the cloud setup. Energy efficiency analysis indicated that overall system energy consumption was reduced, with edge devices averaging 2.9 watts compared to 3.7 watts for cloud-based processing. Statistical analysis confirmed that these differences were significant at p < 0.05, with large effect sizes observed for latency and processing time improvements. Additionally, the edge system maintained stable performance under high workload and low bandwidth conditions, demonstrating enhanced scalability and network resilience. Security evaluation revealed that encryption overhead introduced a smaller latency increase in the edge system, further supporting its suitability for real-time applications. These results provided strong quantitative evidence that Edge AI significantly improved responsiveness, efficiency, and reliability in MIoT-based patient monitoring systems, offering a robust framework for optimizing healthcare technology deployment in latency-sensitive environments.
Copyright (c) 2024 Istiaq Ahmed (Author)
Advanced Computing and AI-Driven National Information Systems for Predictive Disaster Risk Management and Economic Loss Mitigation
This study addresses the persistent problem that many national disaster management systems remain fragmented, reactive, and weakly integrated despite rising disaster complexity, which limits early warning, coordinated response, and the reduction of economic losses. The purpose of the research was to examine how advanced computing and AI-driven national information systems influence predictive disaster risk management and how this, in turn, supports economic loss mitigation. Using a quantitative, cross-sectional, case-based design, the study drew on 210 valid responses from disaster management officials, ICT and system professionals, emergency response coordinators, policy and planning officers, and analysts involved in national disaster information environments, following 240 distributed questionnaires, 218 returns, and an effective response rate of 87.5%. The key variables were Advanced Computing Capability, AI-Driven Forecasting Capability, National Information System Integration, Real-Time Decision Support, Predictive Disaster Risk Management, and Economic Loss Mitigation. Data were analyzed through descriptive statistics, reliability testing, Pearson correlation, and regression analysis using SPSS. The results showed high mean scores across the main constructs, including Advanced Computing Capability (M = 4.18, SD = 0.64), AI-Driven Forecasting Capability (M = 4.11, SD = 0.69), Predictive Disaster Risk Management (M = 4.14, SD = 0.62), and Economic Loss Mitigation (M = 4.02, SD = 0.71). Reliability was strong, with an overall Cronbach’s alpha of 0.903. Correlation results indicated that Predictive Disaster Risk Management was strongly associated with AI-Driven Forecasting Capability (r = 0.724, p < .01), Real-Time Decision Support (r = 0.701, p < .01), and Economic Loss Mitigation (r = 0.756, p < .01). Multiple regression showed that the four predictors explained 62.8% of the variance in Predictive Disaster Risk Management (R² = 0.628), with AI-Driven Forecasting Capability emerging as the strongest predictor (β = 0.312, p = 0.001). Predictive Disaster Risk Management also explained 57.1% of the variance in Economic Loss Mitigation (R² = 0.571; β = 0.756, p < .001). The study implies that governments should prioritize AI forecasting, integrated national systems, and real-time decision support to strengthen predictive readiness and reduce disaster-related economic disruption.
Copyright (c) 2024 Md. Mehedi Hasan, Khairum Nahar Pinky (Author)
AI-Enabled Enterprise Scorecards for Reducing Operational Errors and Enhancing Supply Chain Consistency
This study examined why supply chains still face preventable execution errors and inconsistent KPI performance despite dashboards, because reporting visibility does not consistently translate exceptions into coordinated corrective action. The purpose was to test whether AI-enabled enterprise scorecard capability improves operational error reduction and supply chain consistency, and whether AI alert trust and actionability act as mechanisms in an enterprise case setting. A quantitative, cross-sectional, case-based design used a five-point Likert survey of N = 210 scorecard users from planning, procurement, warehouse, logistics, and BI or reporting functions. Key variables were Scorecard Capability (SC_CAP), Alert Trust (TRUST), Alert Actionability (ACT), Operational Error Reduction (OER), and Supply Chain Consistency (SCC). Analysis combined descriptive statistics, reliability testing, Pearson correlations, and multiple regression for hypotheses and mechanisms. Reliability was acceptable to excellent (α = .86–.91; SC_CAP α = .89; OER α = .91; SCC α = .90), supporting composite-index modeling. Respondents reported moderately high capability (SC_CAP M = 3.86, SD = 0.62) and broad availability of KPI dashboards (82%), drill-down traceability (74%), and automated exception alerts (69%). Correlations supported the expected direction: SC_CAP related to OER (r = .62, p < .001) and SCC (r = .58, p < .001), and OER related to SCC (r = .55, p < .001). In regression, SC_CAP predicted OER (β = .47, p < .001, R² = .43) and SCC (β = .34, p < .001, R² = .38), and OER predicted SCC (β = .29, p < .001, R² = .31). Adding TRUST and ACT increased explained variance (OER R² = .51; SCC R² = .49) and showed partial mediation: SC_CAP’s OER effect reduced to β = .36 while TRUST (β = .18, p = .004) and ACT (β = .21, p = .001) remained significant, and ACT was especially influential for SCC (β = .24, p < .001). Implications are that organizations should manage AI scorecards as operational control infrastructure by strengthening KPI governance and drill-down traceability and by improving alert credibility and usability through clear ownership and response playbooks, so exceptions lead to repeatable actions that reduce errors and stabilize execution.
Copyright (c) 2024 Md Khaled Hossain, Md. Morshedul Islam (Author)
Quantitative Evaluation of Snort IDS Rule Effectiveness against DDOS, Brute-Force, and MITM Attacks in a Controlled Lab Network
Intrusion detection systems play a critical role in protecting network infrastructures from increasingly sophisticated cyber threats. This study quantitatively evaluated the effectiveness of Snort intrusion detection system rules in detecting three common categories of cyberattacks, namely distributed denial-of-service attacks, brute-force authentication attempts, and man-in-the-middle interception attacks within a controlled laboratory network environment. A quantitative experimental design was implemented in which simulated attack scenarios were executed across multiple experimental runs while network traffic was monitored using Snort IDS. The dataset consisted of packet capture logs, IDS alert records, and rule activation events generated during 30 experimental attack sessions and 10 baseline monitoring sessions. Across all sessions, a total of approximately 1,245,600 network packets were captured and analyzed, producing 360 confirmed attack events used to evaluate IDS detection performance. Detection effectiveness was measured using several quantitative indicators including detection rate, alert frequency, rule trigger counts, false positive events, and false negative occurrences. The results indicated that detection performance differed significantly across the three attack categories. Distributed denial-of-service attacks achieved the highest detection rate at 94.2%, with 113 out of 120 attack events successfully identified by the IDS. Brute-force authentication attacks produced a detection rate of 88.3%, with 106 detected events out of 120 attempts. In contrast, man-in-the-middle attack simulations produced a lower detection rate of 76.7%, with 92 detected events and 28 undetected attack instances. The IDS generated a total of 1,128 alerts and 1,542 rule activation events during monitoring, with DDoS scenarios producing the highest alert frequency due to the high-volume traffic patterns associated with flooding attacks. Statistical analysis confirmed that the differences in detection performance across attack categories were statistically significant. Effect size analysis further demonstrated a large practical difference between DDoS and MITM detection performance. The findings indicated that rule-based intrusion detection systems were highly effective in identifying volumetric and repetitive attack patterns but showed comparatively lower performance in detecting subtle packet interception behaviors. These results contribute empirical evidence regarding the operational strengths and limitations of rule-based intrusion detection systems in controlled network security environments.
Copyright (c) 2024 Md. Morshedul Islam, MD Zahedul Islam (Author)
Predictive Analytics for Working Capital Management: Machine Learning Applications in Cash Flow and Liquidity Forecasting
This study addresses the persistent problem that finance teams in cloud-enabled and ERP-integrated enterprises often lack decision-grade visibility into short-horizon cash flow and liquidity, because working-capital timing is driven by heterogeneous receivables and payables behaviors that are poorly captured by simple rules or baseline statistical forecasts. The purpose was to quantify, using a quantitative cross-sectional, case-based evidence map, how predictive analytics and machine learning improve cash flow and liquidity forecasting for working capital management across enterprise cases documented in the literature. The study design treated each documented application instance as a “case” and synthesized N = 35 cases spanning cloud and enterprise data ecosystems (ERP, treasury workflows, and banking-adjacent settings), with sector-identifiable enterprise contexts reported in 18 of 35 cases (51.4%). Key variables included forecasting target (AR cash-in, cash position, AP cash-out, liquidity shortfall), model family (boosting/ensembles, regression, SVM, deep sequence, hybrid), feature strategy (aging, calendar, behavioral history, bank-reconciliation signals), and implementation conditions (governance, explainability, workflow integration, monitoring). The analysis plan used frequency distributions, cross-tabulation-style comparisons, and hypothesis-aligned evidence-strength scoring, plus improvement-rate coding versus baselines. Headline findings show ML outperformed statistical or rule baselines in 24/35 studies (68.6%) with strongest support for H1 (4.2/5), while tree-based ensembles were most prevalent (57.1%) and had the highest within-family improvement rate (75.0%). Feature enrichment mattered: among cases using aging structures, 82.6% reported better performance when behavioral or process-state features were added (evidence strength 4.0/5). For liquidity stress handling, hybrids were tested in 40.0% of cases and outperformed single models under volatility proxies in 71.4% of that subset (evidence strength 3.6/5). Practical implications are that enterprises gain the most when ML forecasting is embedded into governance and workflows: governance detail appeared in 42.9% of cases with 80.0% uptake, and workflow integration appeared in 40.0% with 78.6% uptake, indicating that trust and integration convert accuracy gains into operational impact.
Quantitative Impact Assessment of Digital Payment Solutions on Small Business Revenue Panel Data Analysis From 1,200 U.S. SMES
This study assessed the revenue impact of Digital Payment Solutions (DPS) using a longitudinal panel dataset of 1,200 U.S. small and medium-sized enterprises (SMEs) observed over 14,400 firm-period observations. The sample included SMEs from retail (30.0%), food service (20.0%), personal services (17.5%), professional services (16.7%), and repair-based industries (15.8%), with 70.0% employing fewer than 20 employees. Descriptive results showed mean logged revenue of 10.42 (SD = 0.88) and an average digital sales share of 49.6% (SD = 24.3%). DPS adoption increased from 68.0% in the early period to 85.0% by the final period. Reliability analysis confirmed strong internal consistency for the main constructs, including DPS intensity (α = 0.86), breadth (α = 0.79), and integration (α = 0.82). Two-way fixed effects regression models controlling for firm size, age, sector, location, channel mix, and competitive density indicated that DPS adoption was positively associated with revenue (β = 0.082, p < 0.001), corresponding to an average revenue increase of approximately 8.2% relative to pre-adoption levels. Usage intensity demonstrated the strongest effect (β = 0.214, p < 0.001). Payment breadth was also significant (β = 0.036, p = 0.010), while integration showed a moderate positive effect (β = 0.051, p = 0.020). Dynamic models confirmed revenue persistence (lagged revenue β = 0.641, p < 0.001) and maintained coefficient stability. The baseline model explained 42% of within-firm revenue variation (within R² = 0.42). Overall, findings provided robust quantitative evidence that DPS adoption, particularly when used intensively and integrated operationally, was positively associated with SME revenue performance in the U.S. context.
Copyright (c) 2024 Iftekhar Ahmed, Md Tohidul Islam (Author)
Cyber-Resilient Marketing Intelligence Systems for Fraud Detection and Data Integrity in Digital Campaigns
This quantitative study examined the role of cyber-resilient marketing intelligence system capability in shaping data integrity reliability and fraud detection performance within digital campaign analytics environments. Drawing on data collected from 300 organizations operating multi-platform digital marketing campaigns, the study employed multivariate regression and integrated modeling techniques to evaluate direct, indirect, and conditional relationships among cyber resilience capability, data integrity reliability, and fraud detection performance while accounting for marketing intelligence capability, platform complexity, and contextual controls. Descriptive findings indicated moderate-to-high levels across core constructs, with mean values of 3.78 for cyber-resilient marketing intelligence capability, 3.81 for data integrity reliability, and 3.59 for fraud detection performance on a five-point scale, suggesting relatively advanced analytics and resilience maturity within the sample. Correlation analysis showed significant positive associations between cyber resilience capability and data integrity reliability (r = 0.62), cyber resilience capability and fraud detection performance (r = 0.54), and data integrity reliability and fraud detection performance (r = 0.58), providing preliminary support for the proposed relationships without indicating problematic construct overlap. Regression results demonstrated that cyber resilience capability was the strongest predictor of data integrity reliability (β = 0.48, p < .001) and fraud detection performance (β = 0.29, p < .001), even after controlling for marketing intelligence capability and platform complexity. Mediation analysis revealed that data integrity reliability partially mediated the relationship between cyber resilience capability and fraud detection performance, with a statistically significant indirect effect (β = 0.17, p < .001) alongside a remaining direct effect. Moderation analysis further showed that platform complexity strengthened the positive effect of cyber resilience capability on fraud detection performance (interaction β = 0.11, p = .018), indicating greater resilience benefits in more complex, multi-platform environments. Overall, the findings demonstrated that cyber-resilient marketing intelligence systems were associated with more reliable data handling, reduced integrity degradation, and more stable fraud detection outcomes, supporting an integrated quantitative perspective on resilience, integrity, and analytical performance in digital marketing campaigns.
Copyright (c) 2023 Molla Al Rakib Hasan, Md. Towhidul Islam (Author)
AI-Driven Diagnostic Modelling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems
This study examined AI-Driven Diagnostic Modeling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems using a framework-level quantitative approach that integrated predictive performance, calibration reliability, and privacy-risk evaluation. Structured evidence mapping process reviewed 72 peer-reviewed papers to define constructs, metrics, and privacy threat considerations, after which a retrospective multi-site design was implemented to compare centralized non-private modeling, differentially private training, federated learning, and hybrid privacy configurations under standardized cohort rules and leakage-resistant validation. The analytic dataset included 48,620 adult patients contributing 162,904 encounters across three health systems, with a mean age of 57.8 years (SD = 16.4) and 52.6% female representation. Median encounter density was 3.0 encounters per patient (IQR = 2.0–5.0), and 18.9% of patients were classified as low-contact (≤1 encounter during the lookback window). Data completeness varied by domain, with missingness of 6.8% for vital signs, 18.4% for core laboratories, and 9.6% for medication indicators. Overall diagnostic outcome prevalence was 8.6%, ranging from 7.5% to 9.6% across sites. Correlation analysis indicated a strong relationship between encounter density and measurement frequency (r = 0.58) and a moderate association between comorbidity burden and outcome occurrence (r = 0.34). Collinearity diagnostics showed elevated redundancy among utilization predictors, including a variance inflation factor of 6.8 for total encounters and 5.9 for inpatient admissions, supporting composite consolidation before regression. Multivariable regression showed that differentially private training was associated with a modest reduction in discrimination (ΔAUC = −0.012) and increased calibration error (+0.021), while federated learning showed minimal average discrimination change (ΔAUC = −0.004) but greater cross-site dispersion. Privacy-risk evaluation indicated reduced membership inference leakage under privacy-preserving configurations, with leakage reductions of −0.083 for differentially private training and −0.071 for hybrid training relative to baseline. Overall, accuracy and privacy outcomes co-varied as system-level properties shaped by data quality, institutional heterogeneity, and framework design choices.
Copyright (c) 2026 Jinnat Ara, Samiha Binte Abdullah (Author)
Digital Ledger Optimization Techniques for Enhancing Transaction Speed and Reporting Accuracy in Accounting Systems
This quantitative study examined how digital ledger optimization techniques influenced transaction speed and reporting accuracy in accounting systems using archival operational data. A panel dataset comprising 360 unit–period observations from 30 accounting environments observed over 12 reporting periods was analyzed. Optimization intensity was measured across three layers—data, process, and control—while transaction speed was captured through posting latency, throughput, and queue waiting time, and reporting accuracy was measured using reconciliation mismatch rates, journal error rates, and correction entry frequency and value. Descriptive results showed that median posting latency was 9.6 seconds, while tail latency reached 44.2 seconds at the 95th percentile, confirming pronounced peak-load effects. Regression findings indicated that data-layer optimization was strongly associated with reduced posting latency (β = –0.41, p < .001), while process-layer optimization was most strongly associated with increased throughput (β = 0.39, p < .001). Control-layer optimization demonstrated the strongest relationships with reporting accuracy outcomes, including lower reconciliation mismatch rates (β = –0.51, p < .001) and reduced correction entry frequency (β = –0.53, p < .001). Moderation analysis revealed that validation strictness reduced the magnitude of batching-related speed gains (interaction β = 0.13, p = .020), while automation maturity strengthened the relationship between optimization intensity and reporting accuracy (interaction β = –0.15, p = .009). Robustness checks confirmed that results remained stable when alternative dependent-variable definitions were applied, including tail latency and mismatch value. Overall, the findings demonstrated that transaction speed improvements were most strongly associated with data- and process-layer optimization, while reporting accuracy improvements were primarily associated with control-layer practices. The study provided empirical evidence that layered digital ledger optimization contributed to faster processing and more reliable financial reporting under real operational conditions.
Copyright (c) 2022 Faysal Khan, Shamsunnahar Chadni (Author)
AI Based Quantitative Optimization Models for FMCG Supply Chain Efficiency in High-Demand Markets: A Linear Programming and Mixed-Integer Programming Approach
This study addressed a persistent supply chain performance gap in fast moving consumer goods environments where AI forecasting and visibility tools exist, yet efficiency declines under demand surges because replenishment and distribution decisions are not optimized under real capacity and lead time constraints. The purpose was to quantify how AI enabled analytics capability and related planning practices influence supply chain efficiency and to validate the statistical results with prescriptive optimization outcomes. A quantitative, cross sectional, case-based design was used, combining survey evidence from 168 supply chain professionals with cloud and enterprise planning cases modeled under normal and high demand conditions. Key variables included AI analytics capability, forecasting effectiveness, inventory optimization practice, logistics and distribution planning quality, supplier coordination and lead time reliability. The analysis plan applied reliability testing, descriptive statistics, Pearson correlation, and multiple regression, followed by linear programming and mixed integer programming scenario evaluation with feasibility checks and forecast noise sensitivity testing. Reliability was acceptable across constructs (Cronbach alpha 0.79 to 0.88). Descriptive results showed moderately high AI analytics capability (M 3.84, SD 0.62) and forecasting effectiveness (M 3.71, SD 0.66), while overall efficiency remained moderate (M 3.52, SD 0.67). Supply chain efficiency correlated strongly with AI analytics capability (r 0.62, p < 0.001). The regression model explained 54 percent of variance in efficiency (R2 0.54, p < 0.001), with AI analytics capability as the strongest predictor (beta 0.34, p < 0.001), followed by forecasting effectiveness (beta 0.21, p = 0.001), inventory optimization practice (beta 0.17, p = 0.005), and supplier coordination (beta 0.14, p = 0.017); logistics planning was positive but not statistically significant (p = 0.075). Optimization results reinforced these findings: under normal demand, total cost decreased 11.8 percent (1.72M to 1.52M), service level improved from 92.1 to 96.0 percent, and stockouts reduced 18.4 percent; under high demand, cost decreased 9.3 percent (2.05M to 1.86M), service improved from 88.4 to 93.2 percent, and stockouts reduced 15.1 percent, with a 0.8 percent optimality gap and solutions remaining feasible across scenarios. With plus or minus 10 percent forecast noise, cost rose only 2.1 percent and service declined 0.9 points, indicating robustness. The findings imply that enterprises should prioritize analytics and forecasting governance, enforce disciplined inventory policy execution, and embed constraint-based optimization into routine planning to sustain cost and service performance during demand peaks.
Copyright (c) 2026 Md Shahab Uddin (Author)