American Journal of Scholarly Research and Innovation
type: channel
description: 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.
publisher: American Scholarly Publishing Group Inc
language: en
publication name: American Journal of Scholarly Research and Innovation
issn: 3067-2163
items: Physics-Informed Machine Learning for Predicting Thermal Distortion in Metal Additive Manufacturing
items: Development of a CMMS-Integrated Predictive Maintenance Framework for Industrial PLC and Drive Systems
items: Digital-Twin And IOT-Based Condition Monitoring for Rotating Machinery and Process Equipment in Petrochemical and Industrial Environments
items: A Review of the Impact of Covid-19 On Maternal and Child Health Service Utilization in Bangladesh
items: A Framework for Cross-Functional Collaboration: Aligning Project Coordination, QA Engineering, and Client Communication to Maximize Project Performance
items: Assessment of Data Integrity Gaps and Preventive Strategies in QC Laboratories in Compliance with FDA and CGMP Expectations
items: Development of A Predictive Maintenance Model for Industrial Motor Drives and VFD Systems Using PLC/DCS Operational Telemetry
items: STI and HIV Prevention Through Public Health Digital Twins: A Framework for Personalized Prevention and Adaptive Disease Intervention
items: Reducing False and Improving Response Time in High-Occupancy Buildings: A Quantitative Study of NFPA 72-Complaint Cause-and-Effect Testing Outcomes
items: Hybrid Cloud Security and Compliance in U.S. Enterprises: Addressing Data Privacy Risks and Governance Challenges
items: Cybersecurity and Governance in Hybrid Cloud Architectures: Addressing Compliance and Data Privacy Challenges in U.S. Organizations
items: AI-Enhanced Financial Information Systems for Real-Time Fraud Detection and Cash Flow Optimization in U.S. Logistics and Retail Sectors
items: Clinical Psychological Screening and Early Identification of Trauma-Related Disorders Using AI-Supported Assessment Models
items: Contingency-Based Resilience Assessment of Critical Utility Substations: An ETAP Framework for Accelerating Safe Interconnection of High-Density AI Data Center Loads
items: MPLS-TP and SONET Security Hardening for Utility SCADA Networks: Threat Modeling and Mitigation Strategies for Energy Fiber Infrastructure
items: Data-Driven Detection of Out-of-Specification Trends in Pharmaceutical Production: A Public Health Imperative
items: AI-Augmented Business Intelligence for Campaign Performance Optimization in U.S. Retail and e-Commerce: A Mixed-Methods Study of Marketing ROI
items: Sustainable Infrastructure Through Intelligent Maintenance and Energy Optimization Frameworks
items: Integrated Modeling of Condition Monitoring Data for Predictive Maintenance of Electrical Power Plant Systems
items: IoT-Driven Digital Transformation in Global Supply Chains: Implications for Financial Risk Monitoring and Investment Efficiency
items: Deploying Low-Latency Edge AI in Medical IOT Networks: A Case Study of Secure Real-Time Patient Monitoring Systems
items: Advanced Computing and AI-Driven National Information Systems for Predictive Disaster Risk Management and Economic Loss Mitigation
items: AI-Enabled Enterprise Scorecards for Reducing Operational Errors and Enhancing Supply Chain Consistency
items: Quantitative Evaluation of Snort IDS Rule Effectiveness against DDOS, Brute-Force, and MITM Attacks in a Controlled Lab Network
items: Predictive Analytics for Working Capital Management: Machine Learning Applications in Cash Flow and Liquidity Forecasting
items: Quantitative Impact Assessment of Digital Payment Solutions on Small Business Revenue Panel Data Analysis From 1,200 U.S. SMES
items: Cyber-Resilient Marketing Intelligence Systems for Fraud Detection and Data Integrity in Digital Campaigns
items: AI-Driven Diagnostic Modelling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems
items: Digital Ledger Optimization Techniques for Enhancing Transaction Speed and Reporting Accuracy in Accounting Systems
items: AI Based Quantitative Optimization Models for FMCG Supply Chain Efficiency in High-Demand Markets: A Linear Programming and Mixed-Integer Programming Approach

Physics-Informed Machine Learning for Predicting Thermal Distortion in Metal Additive Manufacturing
type: item
description: 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 Integ…
subject: Articles
  topic: section
subject: Physics-Informed Machine Learning
  topic: keywords
subject: Thermal Distortion Prediction
  topic: keywords
subject: Metal Additive Manufacturing
  topic: keywords
subject: Thermomechanical Coupling
  topic: keywords
subject: Thermal-Process Data Quality
  topic: keywords
creator: Nasrin Akter Shopno
rights: Copyright (c) 2025 Nasrin Akter Shopno (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 943
ending page: 989
doi: 10.63125/ny974y17

Development of a CMMS-Integrated Predictive Maintenance Framework for Industrial PLC and Drive Systems
type: item
description: 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-p…
subject: Articles
  topic: section
subject: CMMS Integration
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Programmable Logic Controllers
  topic: keywords
subject: Condition-Monitoring Data Quality
  topic: keywords
subject: Industrial Drive Systems
  topic: keywords
creator: Md Mahamud Pervaz Pradhan
rights: Copyright (c) 2025 Md Mahamud Pervaz Pradhan (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 894
ending page: 942
doi: 10.63125/j33gtf31

Digital-Twin And IOT-Based Condition Monitoring for Rotating Machinery and Process Equipment in Petrochemical and Industrial Environments
type: item
description: 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,…
subject: Articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Condition Monitoring
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Operational Performance
  topic: keywords
creator: Mukut Kanti Barua
rights: Copyright (c) 2026 Mukut Kanti Barua (Author)
date: 2026-04-11
publication date: 2026-04-11
starting page: 382
ending page: 428
doi: 10.63125/mwdnjv55

A Review of the Impact of Covid-19 On Maternal and Child Health Service Utilization in Bangladesh
type: item
description: 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, education…
subject: Articles
  topic: section
subject: COVID-19
  topic: keywords
subject: Maternal Health
  topic: keywords
subject: Child Health
  topic: keywords
subject: Healthcare Utilization
  topic: keywords
subject: Bangladesh
  topic: keywords
creator: Nishat Jahan
creator: Mst Kaniz Fatema
rights: Copyright (c) 2022 Nishat Jahan, Mst Kaniz Fatema (Author)
date: 2022-12-29
publication date: 2022-12-29
starting page: 223
ending page: 269
doi: 10.63125/vgv5s742

A Framework for Cross-Functional Collaboration: Aligning Project Coordination, QA Engineering, and Client Communication to Maximize Project Performance
type: item
description: 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 linea…
subject: Articles
  topic: section
subject: Cross-functional Collaboration
  topic: keywords
subject: Project Coordination
  topic: keywords
subject: Quality Assurance
  topic: keywords
subject: Client Communication
  topic: keywords
subject: Project Performance
  topic: keywords
creator: Rukaiya Sultana
rights: Copyright (c) 2026 Rukaiya Sultana (Author)
date: 2026-04-11
publication date: 2026-04-11
starting page: 327
ending page: 381
doi: 10.63125/s3s3zd80

Assessment of Data Integrity Gaps and Preventive Strategies in QC Laboratories in Compliance with FDA and CGMP Expectations
type: item
description: 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 pr…
subject: Articles
  topic: section
subject: Data Integrity
  topic: keywords
subject: Quality Control Laboratories
  topic: keywords
subject: FDA Compliance
  topic: keywords
subject: Current Good Manufacturing Practice
  topic: keywords
subject: Preventive Quality Strategies
  topic: keywords
creator: Muhammad Zahidul Islam
rights: Copyright (c) 2024 Muhammad Zahidul Islam (Author)
date: 2024-06-19
publication date: 2024-06-19
starting page: 243
ending page: 291
doi: 10.63125/a3xjb608

Development of A Predictive Maintenance Model for Industrial Motor Drives and VFD Systems Using PLC/DCS Operational Telemetry
type: item
description: 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-ti…
subject: Articles
  topic: section
subject: AI-driven predictive maintenance
  topic: keywords
subject: SCADA/PLC integration
  topic: keywords
subject: Hydrogen infrastructure
  topic: keywords
subject: Real-time fault detection
  topic: keywords
subject: Pipeline safety policy readiness
  topic: keywords
creator: Mohammad Shoriful Hossan Shohel
rights: Copyright (c) 2024 Mohammad Shoriful Hossan Shohel (Author)
date: 2024-06-18
publication date: 2024-06-18
starting page: 198
ending page: 242
doi: 10.63125/pv1js496

STI and HIV Prevention Through Public Health Digital Twins: A Framework for Personalized Prevention and Adaptive Disease Intervention
type: item
description: 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…
subject: Articles
  topic: section
subject: Public Health Digital Twins
  topic: keywords
subject: STI-HIV Prevention
  topic: keywords
subject: Personalized Prevention
  topic: keywords
subject: Adaptive Interventions
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Shima Ali Sadia
creator: Sonya Ghosh
creator: Mahbubul Islam
rights: Copyright (c) 2026 Shima Ali Sadia, Sonya Ghosh, Mahbubul Islam (Author)
date: 2026-04-10
publication date: 2026-04-10
starting page: 284
ending page: 326
doi: 10.63125/rjgna882

Reducing False and Improving Response Time in High-Occupancy Buildings: A Quantitative Study of NFPA 72-Complaint Cause-and-Effect Testing Outcomes
type: item
description: 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 …
subject: Articles
  topic: section
subject: Fire Alarm Systems
  topic: keywords
subject: False Alarms
  topic: keywords
subject: Response Time
  topic: keywords
subject: NFPA 72
  topic: keywords
subject: Cause-Effect Testing
  topic: keywords
creator: Amir Razaq
rights: Copyright (c) 2025 Amir Razaq (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 856
ending page: 893
doi: 10.63125/s18ss614

Hybrid Cloud Security and Compliance in U.S. Enterprises: Addressing Data Privacy Risks and Governance Challenges
type: item
description: 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 regressi…
subject: Articles
  topic: section
subject: Hybrid Cloud Security
  topic: keywords
subject: Cybersecurity Governance
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Compliance Readiness
  topic: keywords
creator: H M Mahir Uddin
creator: Risha Alam
rights: Copyright (c) 2024 H M Mahir Uddin, Risha Alam (Author)
date: 2024-06-18
publication date: 2024-06-18
starting page: 153
ending page: 197
doi: 10.63125/qcy0cj46

Cybersecurity and Governance in Hybrid Cloud Architectures: Addressing Compliance and Data Privacy Challenges in U.S. Organizations
type: item
description: 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 regressi…
subject: Articles
  topic: section
subject: Hybrid Cloud Security
  topic: keywords
subject: Cybersecurity Governance
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Compliance Readiness
  topic: keywords
subject: Operational Resilience
  topic: keywords
creator: Siful Islam
rights: Copyright (c) 2026 Siful Islam (Author)
date: 2026-03-17
publication date: 2026-03-17
starting page: 239
ending page: 283
doi: 10.63125/qymjmr39

AI-Enhanced Financial Information Systems for Real-Time Fraud Detection and Cash Flow Optimization in U.S. Logistics and Retail Sectors
type: item
description: 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. Regressio…
subject: Articles
  topic: section
subject: AI-enhanced financial information systems
  topic: keywords
subject: Real-time fraud detection
  topic: keywords
subject: Cash flow optimization
  topic: keywords
subject: Financial data integration
  topic: keywords
subject: Logistics and retail sectors
  topic: keywords
creator: Mst Shurovi Akter
rights: Copyright (c) 2025 Mst Shurovi Akter (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 813
ending page: 855
doi: 10.63125/bjqkc150

Clinical Psychological Screening and Early Identification of Trauma-Related Disorders Using AI-Supported Assessment Models
type: item
description: 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 …
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Clinical Psychological Screening
  topic: keywords
subject: Trauma-Related Disorders
  topic: keywords
subject: Early Identification
  topic: keywords
subject: Diagnostic Confidence
  topic: keywords
creator: Mahmuda Mahmuda
rights: Copyright (c) 2024 Mahmuda Mahmuda (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 472
ending page: 510
doi: 10.63125/zxs94b13

Contingency-Based Resilience Assessment of Critical Utility Substations: An ETAP Framework for Accelerating Safe Interconnection of High-Density AI Data Center Loads
type: item
description: 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 conditio…
subject: Articles
  topic: section
subject: Resilience
  topic: keywords
subject: Substation
  topic: keywords
subject: Interconnection
  topic: keywords
subject: Contingency
  topic: keywords
subject: ETAP
  topic: keywords
creator: Md. Sultan Mahamud
rights: Copyright (c) 2024 Md. Sultan Mahamud (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 422
ending page: 471
doi: 10.63125/5vn2r379

MPLS-TP and SONET Security Hardening for Utility SCADA Networks: Threat Modeling and Mitigation Strategies for Energy Fiber Infrastructure
type: item
description: 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 …
subject: Articles
  topic: section
subject: MPLS-TP
  topic: keywords
subject: SONET
  topic: keywords
subject: Utility SCADA Security
  topic: keywords
subject: Security Hardening
  topic: keywords
subject: Energy Fiber Infrastructure
  topic: keywords
creator: Abu Naser Md Golam Mosharraf
rights: Copyright (c) 2025 Abu Naser Md Golam Mosharraf (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 775
ending page: 812
doi: 10.63125/7czfg639

Data-Driven Detection of Out-of-Specification Trends in Pharmaceutical Production: A Public Health Imperative
type: item
description: 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 …
subject: Articles
  topic: section
subject: Pharmaceutical Analytics
  topic: keywords
subject: OOS Detection
  topic: keywords
subject: OOT Monitoring
  topic: keywords
subject: Process Control
  topic: keywords
subject: Public Health
  topic: keywords
creator: Beatrice Onyinyechi Mkpa
creator: Ferdous Ara Ahmed
rights: Copyright (c) 2026 Beatrice Onyinyechi Mkpa, Ferdous Ara Ahmed (Author)
date: 2026-03-16
publication date: 2026-03-16
starting page: 198
ending page: 238
doi: 10.63125/ng0x8j42

AI-Augmented Business Intelligence for Campaign Performance Optimization in U.S. Retail and e-Commerce: A Mixed-Methods Study of Marketing ROI
type: item
description: 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 i…
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: Business intelligence
  topic: keywords
subject: Campaign performance optimization
  topic: keywords
subject: Marketing ROI
  topic: keywords
subject: Retail and e-commerce
  topic: keywords
creator: Md Aminul Islam
creator: Zakia Afroz
rights: Copyright (c) 2025 Md Aminul Islam, Zakia Afroz (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 732
ending page: 774
doi: 10.63125/h9j70a40

Sustainable Infrastructure Through Intelligent Maintenance and Energy Optimization Frameworks
type: item
description: 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 per…
subject: Articles
  topic: section
subject: Sustainable Infrastructure
  topic: keywords
subject: Intelligent Maintenance Systems
  topic: keywords
subject: Energy Optimization Frameworks
  topic: keywords
subject: Infrastructure Performance
  topic: keywords
subject: Systems Theory
  topic: keywords
creator: K M Tanvir Anjum Anick
rights: Copyright (c) 2026 K M Tanvir Anjum Anick (Author)
date: 2026-02-05
publication date: 2026-02-05
starting page: 156
ending page: 197
doi: 10.63125/zdb6zb58

Integrated Modeling of Condition Monitoring Data for Predictive Maintenance of Electrical Power Plant Systems
type: item
description: 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 maint…
subject: Articles
  topic: section
subject: Predictive maintenance
  topic: keywords
subject: Data integration
  topic: keywords
subject: Condition monitoring
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Power plants
  topic: keywords
creator: Md. Shahinur Islam
rights: Copyright (c) 2025 Md. Shahinur Islam (Author)
date: 2025-12-28
publication date: 2025-12-28
starting page: 695
ending page: 731
doi: 10.63125/p203c011

IoT-Driven Digital Transformation in Global Supply Chains: Implications for Financial Risk Monitoring and Investment Efficiency
type: item
description: 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 equ…
subject: Articles
  topic: section
subject: IoT Adoption
  topic: keywords
subject: Digital Monitoring
  topic: keywords
subject: Financial Risk
  topic: keywords
subject: Supply Chain
  topic: keywords
subject: Investment Efficiency
  topic: keywords
creator: Mahfuj Ahmed Ruzel
rights: Copyright (c) 2024 Mahfuj Ahmed Ruzel (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 375
ending page: 421
doi: 10.63125/7ywwk960

Deploying Low-Latency Edge AI in Medical IOT Networks: A Case Study of Secure Real-Time Patient Monitoring Systems
type: item
description: 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 responsivenes…
subject: Articles
  topic: section
subject: Edge AI
  topic: keywords
subject: Medical IoT
  topic: keywords
subject: Low Latency
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Healthcare Systems
  topic: keywords
creator: Istiaq Ahmed
rights: Copyright (c) 2024 Istiaq Ahmed (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 337
ending page: 374
doi: 10.63125/x8255a80

Advanced Computing and AI-Driven National Information Systems for Predictive Disaster Risk Management and Economic Loss Mitigation
type: item
description: 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 Forecastin…
subject: Articles
  topic: section
subject: Advanced Computing Capability
  topic: keywords
subject: AI-Driven Forecasting
  topic: keywords
subject: National Information System Integration
  topic: keywords
subject: Predictive Disaster Risk Management
  topic: keywords
subject: Economic Loss Mitigation
  topic: keywords
creator: Md. Mehedi Hasan
creator: Khairum Nahar Pinky
rights: Copyright (c) 2024 Md. Mehedi Hasan, Khairum Nahar Pinky (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 296
ending page: 336
doi: 10.63125/4sbz5j45

AI-Enabled Enterprise Scorecards for Reducing Operational Errors and Enhancing Supply Chain Consistency
type: item
description: 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 cont…
subject: Articles
  topic: section
subject: AI-Enabled Enterprise Scorecards
  topic: keywords
subject: Operational Error Reduction
  topic: keywords
subject: Supply Chain Consistency
  topic: keywords
subject: Alert Actionability
  topic: keywords
subject: Alert Trust
  topic: keywords
creator: Md Khaled Hossain
creator: Md. Morshedul Islam
rights: Copyright (c) 2024 Md Khaled Hossain, Md. Morshedul Islam (Author)
date: 2024-06-16
publication date: 2024-06-16
starting page: 117
ending page: 152
doi: 10.63125/fa50dw13

Quantitative Evaluation of Snort IDS Rule Effectiveness against DDOS, Brute-Force, and MITM Attacks in a Controlled Lab Network
type: item
description: 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…
subject: Articles
  topic: section
subject: Intrusion Detection Systems
  topic: keywords
subject: Snort IDS
  topic: keywords
subject: Cyberattack Detection
  topic: keywords
subject: Network Security
  topic: keywords
subject: Rule-Based Detection
  topic: keywords
creator: Md. Morshedul Islam
creator: MD Zahedul Islam
rights: Copyright (c) 2024 Md. Morshedul Islam, MD Zahedul Islam (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 254
ending page: 295
doi: 10.63125/6wrx6y24

Predictive Analytics for Working Capital Management: Machine Learning Applications in Cash Flow and Liquidity Forecasting
type: item
description: 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 …
subject: Articles
  topic: section
subject: Predictive analytics
  topic: keywords
subject: Working capital management
  topic: keywords
subject: Cash flow forecasting
  topic: keywords
subject: Liquidity forecasting
  topic: keywords
subject: Machine learning governance
  topic: keywords
creator: Md. Fardous
rights: Copyright (c) 2025 Md. Fardous (Author)
date: 2025-12-27
publication date: 2025-12-27
starting page: 662
ending page: 694
doi: 10.63125/xrfrsz89

Quantitative Impact Assessment of Digital Payment Solutions on Small Business Revenue Panel Data Analysis From 1,200 U.S. SMES
type: item
description: 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.
subject: Articles
  topic: section
subject: Digital Payments
  topic: keywords
subject: SMEs
  topic: keywords
subject: Revenue
  topic: keywords
subject: Panel Data
  topic: keywords
subject: Adoption
  topic: keywords
creator: Iftekhar Ahmed
creator: Md Tohidul Islam
rights: Copyright (c) 2024 Iftekhar Ahmed, Md Tohidul Islam (Author)
date: 2024-12-29
publication date: 2024-12-29
starting page: 217
ending page: 253
doi: 10.63125/zy98jx29

Cyber-Resilient Marketing Intelligence Systems for Fraud Detection and Data Integrity in Digital Campaigns
type: item
description: 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 pla…
subject: Articles
  topic: section
subject: Cyber Resilience
  topic: keywords
subject: Marketing Intelligence
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Digital Campaigns
  topic: keywords
creator: Molla Al Rakib Hasan
creator: Md. Towhidul Islam
rights: Copyright (c) 2023 Molla Al Rakib Hasan, Md. Towhidul Islam (Author)
date: 2023-12-28
publication date: 2023-12-28
starting page: 485
ending page: 518
doi: 10.63125/b53pgr11

AI-Driven Diagnostic Modelling Frameworks for Enhancing Accuracy and Privacy Protection in U.S. Healthcare Analytics Systems
type: item
description: 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…
subject: Articles
  topic: section
subject: AI Diagnostics
  topic: keywords
subject: Privacy
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Calibration
  topic: keywords
subject: HER
  topic: keywords
creator: Jinnat Ara
creator: Samiha Binte Abdullah
rights: Copyright (c) 2026 Jinnat Ara, Samiha Binte Abdullah (Author)
date: 2026-02-04
publication date: 2026-02-04
starting page: 109
ending page: 155
doi: 10.63125/1dz58p94

Digital Ledger Optimization Techniques for Enhancing Transaction Speed and Reporting Accuracy in Accounting Systems
type: item
description: 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 e…
subject: Articles
  topic: section
subject: Digital Ledger Optimization
  topic: keywords
subject: Transaction Speed
  topic: keywords
subject: Reporting Accuracy
  topic: keywords
subject: Accounting Systems
  topic: keywords
subject: Information Quality
  topic: keywords
creator: Faysal Khan
creator: Shamsunnahar Chadni
rights: Copyright (c) 2022 Faysal Khan, Shamsunnahar Chadni (Author)
date: 2022-12-29
publication date: 2022-12-29
starting page: 171
ending page: 222
doi: 10.63125/33t06k57

AI Based Quantitative Optimization Models for FMCG Supply Chain Efficiency in High-Demand Markets: A Linear Programming and Mixed-Integer Programming Approach
type: item
description: 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 resu…
subject: Articles
  topic: section
subject: AI analytics capability
  topic: keywords
subject: Supply chain efficiency
  topic: keywords
subject: Forecasting effectiveness
  topic: keywords
subject: Inventory optimization
  topic: keywords
subject: LP and MIP optimization
  topic: keywords
creator: Md Shahab Uddin
rights: Copyright (c) 2026 Md Shahab Uddin (Author)
date: 2026-02-03
publication date: 2026-02-03
starting page: 66
ending page: 108
doi: 10.63125/nmr5ew86