American Journal of Advanced Technology and Engineering Solutions
type: channel
description: The American Journal of Advanced Technology and Engineering Solutions (AJATES) Online ISSN (3067-0470) is a leading international, peer-reviewed, open-access journal dedicated to publishing high-quality research and advancements in technology and engineering. AJATES serves as a platform for researchers, academicians, industry professionals, and innovators to present cutting-edge solutions, novel methodologies, and interdisciplinary studies that address contemporary challenges in engineering and technology. Published monthly, AJATES focuses on fostering innovation and collaboration across diverse disciplines, including mechanical engineering, electrical engineering, computer science, materials science, robotics, and sustainable technologies. By bridging the gap between theory and practice, the journal contributes to the global discourse on technological advancements and their practical applications.
publisher: American Scholarly Publishing Group Inc
language: en
publication name: American Journal of Advanced Technology and Engineering Solutions
issn: 3067-0470
items: A Trust-Centered AI and Security Modeling Approach for Early Cancer Diagnosis, Population-Level Health Analysis, and Secure Deployment in U.S. Healthcare Infrastructure
items: Advanced Computing–Based Modeling of Steel Connection Behavior and Stability Performance using ETABS And STAAD Pro
items: Advanced Computing Frameworks for Real-Time SAP S/4HANA Retail Business Intelligence: Optimizing Data Processing, Latency, and System Reliability
items: Advanced Computing–Enabled Secure Financial Information Systems for Real-Time Fraud Detection in U.S. Digital Payments: A Quantitative Analysis
items: Fraud-Detection Algorithms for Identifying Anomalous Transactions in Retail Banking Networks
items: A Smart Contract Framework for Automated Settlement and Compliance in Renewable Energy and Distributed Energy Resources
items: An Empirical Evaluation of Anomaly Detection Techniques in Smart Grid Systems Using Real-Time Operational Data
items: The Impact of Machine Learning on Cyber Risk Quantification in Financial Services: A Qualitative Evaluation of Threat Scoring Frameworks
items: Quantitative Risk Modeling of VPN Misconfigurations and Firewall Rule Drift in Hybrid Cloud Networks
items: Reliability-Centered Maintenance of Electrical Power and Control Systems Using Manufacturing-Based Asset Management and Quality Models
items: Using Machine Learning to Identify Suicide Risk and Inform Early Therapeutic Interventions in Vulnerable Populations
items: Secure Multi-Institutional Data Integration Models for Strengthening Clinical Research Collaboration in the U.S. Health Sector
items: Artificial Intelligence-Based Decision Support Systems and Managerial Performance
items: Design and Simulation of Sustainable Calibration Systems for Future Industrial Engineering Applications
items: Strengthening Regulatory Compliance and Financial Governance in International Banking Through Blockchain-Enabled Audit Trails and Secure Ledger Systems
items: Artificial Intelligence Based Predictive Analytics for SKU Performance and Revenue Optimization in Competitive Markets
items: Role of Data Science Models in Enhancing Revenue Assurance and Compliance in U.S. Financial Enterprises
items: APPLICATION OF HIGH-DURABILITY ENGINEERING MATERIALS FOR ENHANCING LONG-TERM PERFORMANCE OF RAIL AND TRANSPORTATION INFRASTRUCTURE
items: DEEP LEARNING–ENABLED LIDAR AND MULTISPECTRAL SIGNATURE FUSION FOR FLOOD HAZARD MAPPING AND LAND-SURFACE VULNERABILITY PREDICTION
items: A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE BASED PREDICTIVE SAFETY MODELS FOR REDUCING WORKPLACE INJURIES IN MANUFACTURING AND CONSTRUCTION
items: QUANTITATIVE ASSESSMENT OF CORROSION-FATIGUE IN NUCLEAR POWER PLANT COMPONENTS USING SIMULATION AND EXPERIMENTAL DATA
items: ROLE OF FINTECH ACCOUNTING AUTOMATION IN MINIMIZING MANUAL ERRORS AND SUPPORTING DIGITAL MARKETING DECISION-MAKING IN FINANCIAL OPERATIONS
items: DEEP NEURAL NETWORK MODELS FOR REAL-TIME FINANCIAL FORECASTING AND MARKET INTELLIGENCE
items: ROBUST AND VERIFIABLE LLMS FOR HIGH-STAKES DECISION-MAKING (HEALTHCARE, DEFENSE, FINANCE)
items: QUANTITATIVE ASSESSMENT OF DATA PROTECTION PRACTICES IN U.S. REVENUE CYCLE MANAGEMENT
items: INTEGRATION OF MACHINE LEARNING MODELS AND ADVANCED COMPUTING FOR REDUCING LOGISTICS DELAYS IN PHARMACEUTICAL DISTRIBUTION
items: THE IMPACT OF BIM AND DIGITAL TWIN TECHNOLOGIES ON RISK REDUCTION IN CIVIL INFRASTRUCTURE PROJECTS
items: IRRADIATION-ENHANCED CREEP–FATIGUE INTERACTION IN HIGH-TEMPERATURE AUSTENITIC STEEL: CURRENT UNDERSTANDING AND CHALLENGES
items: DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
items: INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW

A Trust-Centered AI and Security Modeling Approach for Early Cancer Diagnosis, Population-Level Health Analysis, and Secure Deployment in U.S. Healthcare Infrastructure
type: item
description: This study addresses the persistent gap between high-performing cancer AI prototypes and real-world adoption by proposing and testing a trust-centered AI plus security-modeling blueprint for early cancer diagnosis and population-level health analysis within U.S. healthcare infrastructure. The purpose was to quantify how often published “enterprise-ready” capabilities actually co-occur with deploy ability pillars such as interpretability, robustness, security, and equity. Using a quantitative, cross-sectional, case-based review design, each included paper was treated as a case reflecting cloud and enterprise healthcare deployment contexts (for example, multi-site systems, integrated EHR and imaging stacks, or networked inference services). The sample comprised 45 cases (N = 45). Key variables were five Likert-scored readiness dimensions (1–5): clinical validation rigor, interpretability and communication support, robustness and generalization evidence, security and privacy modeling, and fairness and equity evidence, plus composite indicators such as trust-mechanism presence and a rubric-scaled Trust-Centered Deployment Readiness (TDR). The analysis plan applied descriptive statistics (counts, percentages, means), cross-tabs between trust-mechanism grouping and validation readiness, and a composite readiness summary. Headline findings show that radiology and pathology cases dominated (31/45, 68.9%), interpretability appeared in 28/45 (62.2%) but comprehensive interpretability was limited (12/45, 26.7%; mean M = 3.1/5), external validation or multi-site evaluation occurred in only 16/45 (35.6%), and explicit security or privacy-by-design elements were present in 14/45 (31.1%) with the lowest readiness mean (M = 2.4/5). Trust-mechanism studies (19/45, 42.2%) showed higher validation readiness (M = 3.6/5) and more external validation (11/19, 57.9%) than performance-only studies (5/26, 19.2%). Overall, only 8/45 (17.8%) met high composite readiness (≥0.75), while 21/45 (4…
subject: Articles
  topic: section
subject: Trust-Centered AI
  topic: keywords
subject: Early Cancer Diagnosis
  topic: keywords
subject: Security And Privacy Modeling
  topic: keywords
subject: Population Health Analytics
  topic: keywords
subject: Deployment Readiness
  topic: keywords
creator: Md Fokhrul Alam
creator: Md Fardaus Alam
creator: Md Ashraful Alam
rights: Copyright (c) 2020 Md Fokhrul Alam, Md Fardaus Alam, Md Ashraful Alam (Author)
date: 2020-12-03
publication date: 2020-12-03
starting page: 01
ending page: 39
doi: 10.63125/zyvt7f56

Advanced Computing–Based Modeling of Steel Connection Behavior and Stability Performance using ETABS And STAAD Pro
type: item
description: Advanced computational modeling has become an essential tool for evaluating the structural performance of steel frame systems and their connection behavior under complex loading conditions. This study examined the structural behavior and stability performance of steel connections using advanced computing-based modeling through ETABS and STAAD Pro. A quantitative comparative simulation design was adopted to analyze the response of multistory steel frame structures under standardized modeling conditions. A total of 24 three-dimensional steel structural models were developed and analyzed using identical geometric properties, material characteristics, and boundary conditions in both software platforms. The analytical dataset consisted of 960 structural observations derived from multiple load combinations including gravity, wind, and seismic loading scenarios. Key structural performance indicators evaluated in the study included maximum displacement, interstory drift, column axial force, beam shear force, bending moment distribution, and structural stability indices. The findings demonstrated that both ETABS and STAAD Pro produced generally consistent structural response patterns across the simulated models. The average maximum displacement predicted by ETABS was 40.56 mm, compared with 37.12 mm generated by STAAD Pro, indicating slightly greater lateral deformation predicted by ETABS. Similarly, the average interstory drift ratio was 0.0023 in ETABS and 0.0021 in STAAD Pro, confirming similar deformation trends between the two platforms. In contrast, STAAD Pro simulations produced higher internal force values in structural members, with average column axial forces of 1287.66 kN compared with 1218.43 kN in ETABS, and average beam bending moments of 366.88 kN·m compared with 349.72 kN·m in ETABS. Subgroup analysis further showed that structural models with semi-rigid connections produced greater deformation responses, with an average displacement of 42.63 mm compared with…
subject: Articles
  topic: section
subject: Steel connection behavior
  topic: keywords
subject: Structural stability modeling
  topic: keywords
subject: ETABS simulation analysis
  topic: keywords
subject: STAAD Pro modeling
  topic: keywords
subject: Computational structural engineering
  topic: keywords
creator: Md. Mainuddin Faisal
creator: Palash Chandra Das
rights: Copyright (c) 2023 Md. Mainuddin Faisal, Palash Chandra Das (Author)
date: 2023-12-08
publication date: 2023-12-08
starting page: 42
ending page: 86
doi: 10.63125/xfkzrg56

Advanced Computing Frameworks for Real-Time SAP S/4HANA Retail Business Intelligence: Optimizing Data Processing, Latency, and System Reliability
type: item
description: This study addresses the persistent problem that SAP S/4HANA retail business intelligence environments often struggle to achieve simultaneously low latency, high data freshness, and dependable reliability at enterprise scale, particularly when cloud and hybrid deployments introduce additional integration and operational complexity. The purpose was to quantify which advanced computing framework combinations are most consistently associated with improved real-time BI outcomes in retail decision workflows. Using a quantitative cross-sectional, case-based design, the study coded a sample of 60 eligible enterprise and cloud-oriented cases from the reviewed corpus (N = 60). Key variables included framework adoption categories (for example CDC or incremental refresh, in-memory or HTAP, streaming, distributed processing, cloud-native orchestration), outcome constructs measured via a 5-point Likert evidence scale (Latency Improvement Evidence LIE, Processing Efficiency Evidence PEE, Reliability and Continuity Evidence RCE), and workflow clusters (inventory, promotions, fulfillment, anomaly detection). The analysis plan applied frequency distributions and descriptive statistics, then conducted evidence-based hypothesis aggregation using a predefined support rule (at least 60% directional support and mean evidence score at or above 3.50). Findings show strong concentration of CDC or incremental refresh integration at 73.3% (44/60), in-memory or HTAP execution at 68.3% (41/60), streaming at 60.0% (36/60), cloud-native orchestration at 51.7% (31/60), and distributed processing at 48.3% (29/60). Overall evidence strength was high for latency and processing (LIE M = 3.84, SD = 0.71; PEE M = 3.76, SD = 0.69) with moderate-strong reliability (RCE M = 3.62, SD = 0.74). Headline results supported all three hypotheses: hybrid multi-layer stacks outperformed single-layer designs for latency (77.8% supportive, 28/36; hybrid LIE M = 4.08 vs 3.41), CDC and event-driven strategies outperfor…
subject: Articles
  topic: section
subject: SAP S/4HANA
  topic: keywords
subject: Real-Time Business Intelligence
  topic: keywords
subject: Change Data Capture
  topic: keywords
subject: In-Memory Analytics
  topic: keywords
subject: Observability and Failover
  topic: keywords
creator: Zakia Afroz
creator: Khairum Nahar Pinky
rights: Copyright (c) 2022 Zakia Afroz, Khairum Nahar Pinky (Author)
date: 2022-12-08
publication date: 2022-12-08
starting page: 217
ending page: 254
doi: 10.63125/xk5j7g56

Advanced Computing–Enabled Secure Financial Information Systems for Real-Time Fraud Detection in U.S. Digital Payments: A Quantitative Analysis
type: item
description: This study addressed the persistent problem that U.S. digital payment ecosystems require fraud detection decisions in real time, yet many organizations struggle to align scalable advanced computing with secure financial information system controls in ways that measurably improve fraud detection effectiveness while remaining auditable and operationally usable. The purpose was to quantify how advanced computing enablement (ACE) and secure control strength (SCS) predict real-time fraud detection effectiveness (RTFDE), and to examine whether alert trust and actionability (ATA) strengthens practical effectiveness within enterprise and cloud-based operational cases. Using a quantitative, cross-sectional, case-based design, data were collected from a multi-role sample of enterprise digital-payment stakeholders (N = 210), including fraud and risk operations (38.1%), IT and data engineering (34.3%), and security and compliance (27.6%), with mean experience of 6.8 years (SD = 3.9). Key variables were ACE (8 items), SCS (9 items), RTFDE (8 items), ATA (6 items), feature adoption and maturity (FAM), and operational error hotspots (OEH), all measured on 5-point Likert scales with strong internal consistency (ACE α = .88; SCS α = .91; RTFDE α = .89; ATA α = .86). The analysis plan applied descriptive statistics, Pearson correlations, and hierarchical multiple regression with diagnostic checks (VIF 1.31–2.08). Headline findings showed moderately high capability ratings (ACE M = 3.74, SD = 0.61; SCS M = 3.88, SD = 0.55; RTFDE M = 3.69, SD = 0.63) and strong positive associations with effectiveness (ACE–RTFDE r = .62, p < .001; SCS–RTFDE r = .58, p < .001). Regression results indicated ACE alone explained 32% of variance in RTFDE (R² = .32; β = .57, p < .001); adding SCS increased explanatory power to 40% (ΔR² = .08; ACE β = .41, p < .001; SCS β = .29, p < .001); adding ATA raised total explained variance to 44% (ΔR² = .04; ACE β = .32, p < .001; SCS β = .21, p = .002; ATA β = .24, …
subject: Articles
  topic: section
subject: Real-Time Fraud Detection
  topic: keywords
subject: Advanced Computing Enablement
  topic: keywords
subject: Secure Financial Information Systems
  topic: keywords
subject: Digital Payments
  topic: keywords
subject: Regression Analysis
  topic: keywords
creator: Md Mehedi Hasan
creator: Md. Fardous
rights: Copyright (c) 2022 Md Mehedi Hasan, Md. Fardous (Author)
date: 2022-06-09
publication date: 2022-06-09
starting page: 97
ending page: 133
doi: 10.63125/9mv2qd37

Fraud-Detection Algorithms for Identifying Anomalous Transactions in Retail Banking Networks
type: item
description: This study addresses a practical problem in retail banking where fraud-detection algorithms can appear effective yet still produce low-trust alerts, heavy false-positive workload, and weak readiness for shifting fraud patterns, which reduces real operational value. Using a quantitative, cross-sectional, case-study–based design grounded in an enterprise retail banking network, the purpose was to test how six fraud-detection capability dimensions (Data Quality and Feature Readiness, Real-Time Processing, Model Robustness, Explainability, Integration and Scalability, and Monitoring and Updating) predict the primary outcome Anomalous Transaction Identification Performance and three trust-centered outcomes: Alert Quality Index, False-Positive Burden Score, and Drift Readiness Score . The sample comprised N = 200 professionals embedded in fraud operations, risk/compliance, and analytics/IT roles within the case environment. The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression for hypothesis testing. Headline results show capability maturity was highest for Data Quality (M = 3.98, SD = 0.63) and Integration (M = 3.85, SD = 0.69), while the primary outcome ATIP was moderately high (M = 3.81, SD = 0.66); trust outcomes indicated moderate alert quality (AQI M = 3.76, SD = 0.68) but noticeable false-positive burden (FPBS M = 3.12, SD = 0.81) and comparatively weaker drift readiness (DRS M = 3.33, SD = 0.79). Reliability was strong across constructs (α = .81–.88). Correlations with ATIP were strongest for Data Quality (r = .62), Explainability (r = .58), and Monitoring and Updating (r = .55), all p < .001. The regression model explained substantial variance in ATIP (R² = 0.54; F(6,193) = 37.6; p < 0.001), with significant predictors including Data Quality (β = 0.28, p < 0.001), Explainability (β = 0.22, p = 0.002), Monitoring and Updating (β = 0.19, p = 0.006), and Real-Time Processing (β = 0.12, p …
subject: Articles
  topic: section
subject: Fraud Detection
  topic: keywords
subject: Anomalous Transaction Identification
  topic: keywords
subject: Explainability
  topic: keywords
subject: False-Positive Burden
  topic: keywords
subject: Concept Drift Readiness
  topic: keywords
creator: Mahfuj Ahmed Ruzel
creator: Mostafa Kamal
rights: Copyright (c) 2021 Mahfuj Ahmed Ruzel, Mostafa Kamal (Author)
date: 2021-12-08
publication date: 2021-12-08
starting page: 71
ending page: 107
doi: 10.63125/pefa8x59

A Smart Contract Framework for Automated Settlement and Compliance in Renewable Energy and Distributed Energy Resources
type: item
description: This study addresses the persistent problem that renewable-energy and distributed energy resource (DER) transactions generate high-volume, interval-based micro-transactions that strain conventional centralized billing, reconciliation, and compliance reporting, creating delays, disputes, and weak audit trails. The purpose was to evaluate a smart contract framework that integrates automated settlement and compliance evidence generation within cloud-hosted, enterprise-grade DER transaction workflows using a quantitative, cross-sectional, case-based design. A purposive case sample of n = 214 informed stakeholders was drawn from enterprise settlement and compliance settings, including settlement and billing personnel (31.3%), compliance and audit professionals (24.8%), DER operations and aggregation roles (22.4%), and platform/ICT specialists (21.5%). Key variables were modeled as composite 5-point Likert constructs: Smart Contract Quality (SCQ), Data Integrity and Oracle Reliability (DIO), Interoperability (INT), Security and Privacy Controls (SP), Regulatory Alignment (RA), Automated Settlement Effectiveness (ASE), Compliance Automation Performance (CAP), and Operational or Market Performance (OMP). The analysis plan applied descriptive statistics, internal consistency reliability (Cronbach’s alpha), Pearson correlations, and multiple regression hypothesis testing. Findings showed high capability and outcome perceptions, including SCQ M = 4.12 (SD = 0.61), DIO M = 4.05 (SD = 0.67), SP M = 4.18 (SD = 0.58), ASE M = 4.15 (SD = 0.60), and CAP M = 4.22 (SD = 0.55), with strong reliabilities (α = 0.82–0.91 across constructs). Correlations supported the framework pathways, notably SCQ–ASE r = 0.63, DIO–ASE r = 0.58, SP–CAP r = 0.66, RA–CAP r = 0.61, and CAP–OMP r = 0.68 (all p < 0.001). Regression results confirmed that settlement effectiveness was predicted by SCQ (β = 0.34, p < 0.001), DIO (β = 0.29, p < 0.001), and INT (β = 0.21, p = 0.002) with R² = 0.496, while complian…
subject: Articles
  topic: section
subject: Smart Contracts
  topic: keywords
subject: Automated Settlement
  topic: keywords
subject: Compliance Automation
  topic: keywords
subject: Distributed Energy Resources (DER)
  topic: keywords
subject: Oracle And Data Integrity
  topic: keywords
creator: Tasnim Kabir
creator: Zaheda Khatun
rights: Copyright (c) 2023 Tasnim Kabir, Zaheda Khatun (Author)
date: 2023-04-30
publication date: 2023-04-30
starting page: 31
ending page: 69
doi: 10.63125/fvdjpn66

An Empirical Evaluation of Anomaly Detection Techniques in Smart Grid Systems Using Real-Time Operational Data
type: item
description: This study addresses the problem that smart grid operators increasingly rely on enterprise and cloud enabled monitoring platforms, yet anomaly detection outputs are often difficult to trust due to data quality variability, limited alarm explainability, weak workflow integration, and false alarm overload. The purpose was to quantify which technical and organizational factors most strongly predict perceived detection effectiveness and adoption readiness for anomaly detection techniques in a real smart grid case. A quantitative cross sectional, case-based design was applied using a structured 5-point Likert survey administered to N = 182 stakeholders drawn from enterprise smart grid environments spanning AMI and smart metering, SCADA and EMS or DMS, PMU or WAMS, and supporting IT and cybersecurity functions (operations 28.6%, metering 20.3%, data and IT 20.9%, protection 15.9%, cybersecurity 14.3%). Key variables included Data Quality Adequacy (DQ), Robustness and Adaptability (RB), Explainability of Alarms (EX), System Integration Capability (SI), Perceived Detection Effectiveness (PDE), Adoption Readiness and Trust (ART), and False Alarm Burden and Operational Impact Index (FABOI). The analysis plan used descriptive statistics, scale reliability (Cronbach alpha .81 to .88), Pearson correlations, and multiple regression models. Headline findings showed moderately high perceptions of DQ (M = 3.92, SD = 0.61) and PDE (M = 3.81, SD = 0.62), while false alarm burden was nontrivial (FABOI M = 3.41, SD = 0.73). PDE correlated with DQ (r = .52) and RB (r = .48), while ART correlated most strongly with EX (r = .54) and PDE (r = .58), and decreased with FABOI (r = −.49), all p < .01. Regression confirmed PDE was predicted by DQ (β = .31, p < .001) and RB (β = .27, p < .001), R² = .39, whereas ART was driven by EX (β = .33, p < .001), PDE (β = .29, p < .001), SI (β = .21, p = .002) and reduced by FABOI (β = −.24, p < .001), R² = .47. Implications suggest utilities should priori…
subject: Articles
  topic: section
subject: Smart grid
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: Explainability
  topic: keywords
subject: False alarm burden
  topic: keywords
subject: Technology Organization Environment (TOE)
  topic: keywords
creator: S M Shakil
creator: Alamgir Hossain
creator: Md Mijanur Rahman
rights: Copyright (c) 2025 S M Shakil, Alamgir Hossain, Md Mijanur Rahman (Author)
date: 2025-10-08
publication date: 2025-10-08
starting page: 95
ending page: 134
doi: 10.63125/2xcry064

The Impact of Machine Learning on Cyber Risk Quantification in Financial Services: A Qualitative Evaluation of Threat Scoring Frameworks
type: item
description: This study quantitatively evaluated the impact of machine learning–enhanced threat scoring frameworks on cyber risk quantification within a regulated financial services environment. Using a cross-sectional comparative design, 18,742 security event records were analyzed, including 1,964 confirmed malicious events (10.48%) and 16,778 benign events (89.52%). Multiple model families were benchmarked under standardized preprocessing and time-aware validation protocols to assess predictive discrimination, calibration quality, and monetized risk alignment. Results demonstrated substantial improvements associated with ML-based frameworks. The ML-enhanced models achieved a mean area under the ROC curve (AUC) of 0.912 compared to 0.781 for baseline scoring systems, with higher precision (0.842 vs. 0.694) and recall (0.817 vs. 0.628). Calibration error was significantly reduced from 0.067 in conventional models to 0.028 in ML-based models, indicating stronger probability alignment. Regression analyses further showed that ML-derived threat scores exhibited a stronger association with log-transformed financial loss outcomes (β = 0.64, p < .001) compared to baseline scores (β = 0.38, p < .001). The ML model explained 42.6% of the variance in loss magnitude (Adjusted R² = 0.426), representing a statistically significant improvement over the baseline model (Adjusted R² = 0.248). High-risk decile stratification under ML scoring produced a mean financial loss of $126,840 compared to $74,390 under conventional scoring, demonstrating enhanced concentration of severe loss events. Sensitivity analyses confirmed stability across alternative sampling and imbalance handling conditions. Collectively, the findings demonstrated that ML-enhanced threat scoring frameworks provided statistically and practically significant improvements in predictive performance and financial alignment, supporting more accurate and economically meaningful cyber risk quantification in financial services environment…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Cyber Risk Quantification
  topic: keywords
subject: Threat Scoring
  topic: keywords
subject: Financial Services
  topic: keywords
subject: Operational Risk
  topic: keywords
creator: Ishtiaque Ahmed
creator: Rajib Sarkar
rights: Copyright (c) 2025 Ishtiaque Ahmed, Rajib Sarkar (Author)
date: 2025-09-08
publication date: 2025-09-08
starting page: 58
ending page: 94
doi: 10.63125/7aqqac69

Quantitative Risk Modeling of VPN Misconfigurations and Firewall Rule Drift in Hybrid Cloud Networks
type: item
description: This study addresses the persistent security problem that hybrid cloud networks often accumulate VPN misconfigurations and firewall rule drift, which jointly weaken trust-boundary enforcement and increase exposure to unintended reachability and lateral movement. The purpose was to quantify how VPN misconfiguration and firewall rule drift predict hybrid cloud risk exposure, and to examine whether Protection Motivation Theory–based governance (protection motivation) is associated with lower exposure. Using a quantitative cross-sectional, case-based design, data were collected from cloud and enterprise hybrid-network operational cases with N = 132 valid practitioner responses spanning network engineering, security operations, cloud administration, and governance roles. Key variables were VPN Misconfiguration (VMS), Firewall Rule Drift (FDS), Risk Exposure (RE), and Protection Motivation (PMS) measured via multi-item 5-point Likert constructs with strong reliability (α = .88 VMS, α = .91 FDS, α = .87 RE, α = .85 PMS). The analysis plan applied descriptive statistics, Pearson correlations, and multiple regression with multicollinearity checks (VIFs within acceptable ranges). Baseline levels were above neutral for VMS (M = 3.62, SD = 0.71), FDS (M = 3.74, SD = 0.66), and RE (M = 3.58, SD = 0.69), with moderate PMS (M = 3.41, SD = 0.62). Headline findings showed strong positive associations between exposure and both VMS (r = .61, p < .001) and FDS (r = .68, p < .001), while PMS was negatively associated with exposure (r = −.42, p < .001). Regression indicated substantial explanatory power (R² = .58, Adj. R² = .56, F = 58.7, p < .001) with significant effects for VMS (β = .33, p < .001) and FDS (β = .46, p < .001) and a protective effect for PMS (β = −.21, p = .001). Segment analysis localized higher composite exposure to the remote-access VPN zone (CREI M = 3.81) and the on-prem to cloud interconnect boundary (CREI M = 3.73). Implications indicate that drift reduction shou…
subject: Articles
  topic: section
subject: Hybrid cloud security
  topic: keywords
subject: VPN misconfiguration
  topic: keywords
subject: Firewall rule drift
  topic: keywords
subject: Quantitative risk modeling
  topic: keywords
subject: Risk exposure prediction
  topic: keywords
creator: Md. Fardous
creator: MD Zahedul Islam
rights: Copyright (c) 2022 Md. Fardous, MD Zahedul Islam (Author)
date: 2022-12-21
publication date: 2022-12-21
starting page: 182
ending page: 216
doi: 10.63125/fa4qdz07

Reliability-Centered Maintenance of Electrical Power and Control Systems Using Manufacturing-Based Asset Management and Quality Models
type: item
description: This study tackles a practical reliability problem in enterprise electrical power and control systems: many reliability-centered maintenance (RCM) programs remain uneven in execution, so plants experience repeat failures, nuisance trips, and slow restoration despite having documented maintenance intentions. The purpose was to quantify whether governance and process discipline predict stronger RCM execution and, in turn, better reliability performance by testing a capability chain where Asset Management Discipline (X1) and Quality-Model Discipline (X2) predict RCM Execution Maturity (M), which predicts Reliability Outcomes (Y). A quantitative, cross-sectional, case-based design was applied in an enterprise case setting using a structured Likert-scale instrument and a multi-role sample of N = 162 respondents (42.6% electrical maintenance, 24.1% automation/control, 18.5% reliability/engineering, 14.8% operations/supervision). Key variables were X1, X2, M, and Y; construct reliability was strong (α = 0.88 for X1, α = 0.86 for X2, α = 0.90 for M, α = 0.84 for Y). Analysis followed (1) descriptive profiling (X1 M = 3.74, X2 M = 3.58, M = 3.49, Y M = 3.61), (2) Pearson correlations, and (3) multiple regression for hypothesis testing. Correlations supported the proposed linkages (X1–M r = 0.62; X2–M r = 0.55; M–Y r = 0.66; all p < .001). The headline finding was a well-fitting regression model predicting reliability outcomes, explaining 54% of the variance (R² = 0.54; adjusted R² = 0.53; F(3,158) = 62.10, p < .001), with the strongest effect from RCM Execution Maturity (β = 0.43, p < .001), followed by Asset Management Discipline (β = 0.28, p < .001) and Quality-Model Discipline (β = 0.19, p = .004). Practical triangulation reinforced these results: compared with the low-maturity tertile, the high-maturity tertile reported lower downtime frequency (2.41 vs 3.26) and repeat failure frequency (2.58 vs 3.33), and higher restoration speed (3.94 vs 3.10). Implications are that r…
subject: Articles
  topic: section
subject: Reliability-Centered Maintenance (RCM)
  topic: keywords
subject: Asset Management Discipline
  topic: keywords
subject: Quality-Model Discipline
  topic: keywords
subject: Execution Maturity
  topic: keywords
subject: Reliability Outcomes
  topic: keywords
creator: K M Tanvir Anjum Anick
creator: Tasnim Kabir
rights: Copyright (c) 2022 K M Tanvir Anjum Anick, Tasnim Kabir (Author)
date: 2022-09-30
publication date: 2022-09-30
starting page: 29
ending page: 59
doi: 10.63125/xq6a0793

Using Machine Learning to Identify Suicide Risk and Inform Early Therapeutic Interventions in Vulnerable Populations
type: item
description: This study examined the predictive relationships between key study constructs and the dependent variable using a quantitative cross-sectional design supported by multivariate regression analysis. The primary objective was to determine the extent to which the independent constructs significantly explained variance in the outcome and to evaluate hypothesis support based on regression coefficients, confidence intervals, and statistical decision rules. Data were collected from a final usable sample of 412 respondents, with demographic analysis indicating a moderately balanced gender distribution (55.3% male, 42.7% female) and strong representation of respondents holding at least a bachelor’s degree (76.7%). Most participants were employed full-time (65.0%), and the sample was predominantly urban (63.3%). Descriptive construct results indicated moderate-to-high levels across all constructs, with means ranging from 3.48 to 3.92 and standard deviations ranging from 0.66 to 0.77, confirming adequate variability for predictive modeling. Reliability testing demonstrated acceptable to strong internal consistency across constructs, with Cronbach’s alpha coefficients ranging from 0.74 to 0.88, supporting measurement stability and suitability for regression analysis. The regression model was statistically significant (F(5, 406) = 82.14, p < .001) and explained a substantial proportion of variance in the dependent variable (R² = 0.494; adjusted R² = 0.488). Four predictors demonstrated statistically significant positive effects on the outcome: Construct 1 (β = 0.321, p < .001), Construct 2 (β = 0.238, p < .001), Construct 3 (β = 0.089, p = .032), and Construct 5 (β = 0.276, p < .001). Construct 4 did not reach statistical significance (β = 0.034, p = .424), indicating no unique direct effect in the multivariate model. Multicollinearity was not problematic, with all variance inflation factor values remaining below 2.0, supporting stable coefficient interpretation. Overall, hypothes…
subject: Articles
  topic: section
subject: Quantitative analysis
  topic: keywords
subject: Multiple regression
  topic: keywords
subject: Reliability testing
  topic: keywords
subject: Predictive modeling
  topic: keywords
subject: Hypothesis testing
  topic: keywords
creator: Amena Begum Sumi
creator: Md. Nazmul Haque
rights: Copyright (c) 2021 Amena Begum Sumi, Md. Nazmul Haque (Author)
date: 2021-12-17
publication date: 2021-12-17
starting page: 43
ending page: 70
doi: 10.63125/jht6jb26

Secure Multi-Institutional Data Integration Models for Strengthening Clinical Research Collaboration in the U.S. Health Sector
type: item
description: This study examined Secure Multi-Institutional Data Integration (SMIDI) maturity as a measurable organizational capability and evaluated its association with collaboration performance in U.S. clinical research environments using a quantitative, institution-level design. A simulated sample of 96 healthcare and research institutions was analyzed to assess how variation in integration maturity, governance quality, security and privacy control maturity, and analytical infrastructure readiness corresponded with observable collaboration outcomes. SMIDI maturity was operationalized through composite indicators capturing interoperability intensity, access governance strength, auditability, privacy enforcement, and evidence automation, while collaboration performance was measured using a multidimensional index encompassing throughput, efficiency, network engagement, and output quality. Descriptive results indicated moderate-to-high dispersion across institutions, with SMIDI maturity indicators such as interface coverage averaging 46.8 interfaces (SD = 20.9), mapping completeness averaging 82.6% (SD = 9.8), and automated evidence rates averaging 69.8% (SD = 14.6). Collaboration performance measures showed substantial variability, including an average of 19.8 multi-site studies per year (SD = 10.6), a mean dataset build time of 37.6 days (SD = 16.9), and replication consistency averaging 86.2% (SD = 8.9). Reliability testing of reflective subscales produced Cronbach’s alpha values ranging from 0.79 to 0.89, supporting acceptable internal consistency for constructs used in regression models. Multivariate regression analysis demonstrated that SMIDI maturity was positively associated with collaboration performance in the baseline model (B = 0.42, SE = 0.09, p < .001), explaining 34% of outcome variance. After adjusting for governance quality and security/privacy control maturity, the SMIDI effect remained significant (B = 0.31, SE = 0.08, p < .001), and model fit improved (Adjust…
subject: Articles
  topic: section
subject: Secure Data Integration
  topic: keywords
subject: Clinical Research
  topic: keywords
subject: Collaboration Performance
  topic: keywords
subject: Analytics Readiness
  topic: keywords
creator: Jinnat Ara
creator: Molla Al Rakib Hasan
rights: Copyright (c) 2023 Jinnat Ara, Molla Al Rakib Hasan (Author)
date: 2023-09-02
publication date: 2023-09-02
starting page: 82
ending page: 120
doi: 10.63125/qqe4sh98

Artificial Intelligence-Based Decision Support Systems and Managerial Performance
type: item
description: This quantitative, cross-sectional, case-based study addresses the persistent problem that organizations invest in cloud-deployed, enterprise AI-based decision support systems (AI-DSS) yet often lack clear, measurable evidence about which adoption and perception factors actually translate into better managerial performance; accordingly, the purpose was to test a Technology Acceptance Model (TAM) aligned explanatory model linking managers’ perceptions and use of AI-DSS to decision-linked performance outcomes in an enterprise case context. The sample comprised 120 managers across core enterprise functions (Operations 30.0%, Finance 23.3%, Sales/Marketing 18.4%, HR/Admin 15.0%, IT/Analytics 13.3%) with mixed seniority (Supervisors 33.3%, Mid-level 48.3%, Senior 18.4%), and most had received AI-DSS training (61.7%). Key variables included Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Trust in AI-DSS (TRUST), Information Quality (INFOQ), and AI-DSS Use/Adoption Maturity (USE) as predictors, with Managerial Performance (MP) as the outcome. The analysis plan applied descriptive statistics, scale reliability, Pearson correlations, and multiple regression, followed by robustness diagnostics (multicollinearity, residual independence, influence, and stability tests). Results showed generally favorable perceptions and outcomes (PU M=4.02, SD=0.62; PEOU M=3.69, SD=0.66; TRUST M=3.74, SD=0.73; INFOQ M=3.91, SD=0.60; USE M=3.78, SD=0.71; MP M=3.88, SD=0.58). Correlations with MP were positive and statistically strong (PU r=0.62, INFOQ r=0.58, TRUST r=0.55, USE r=0.49, PEOU r=0.41, all reported as significant at p<.001). The regression model explained 53% of variance in managerial performance (R²=0.53, F=38.7, p<0.001), with PU (β=0.29, p<0.001), INFOQ (β=0.24, p=0.002), TRUST (β=0.21, p=0.004), and USE (β=0.17, p=0.011) emerging as significant unique predictors, while PEOU was not significant in the full model (β=0.08, p=0.143). Consistent with these effects, higher ado…
subject: Articles
  topic: section
subject: AI-Based Decision Support Systems (AI-DSS)
  topic: keywords
subject: Managerial Performance
  topic: keywords
subject: Perceived Usefulness
  topic: keywords
subject: Trust In AI
  topic: keywords
subject: Information Quality
  topic: keywords
creator: Tonay Pal
creator: Md Maruf Islam
creator: Al Amin
rights: Copyright (c) 2024 Tonay Pal, Md Maruf Islam, Al Amin (Author)
date: 2024-12-30
publication date: 2024-12-30
starting page: 154
ending page: 184
doi: 10.63125/wmz8dc67

Design and Simulation of Sustainable Calibration Systems for Future Industrial Engineering Applications
type: item
description: This study examined the design and simulation implications of sustainable calibration systems by quantitatively analyzing the role of digital calibration information flow within industrial engineering contexts. Using archival operational data from 1,620 calibration events, the analysis evaluated how certificate digitization, interoperability, data integrity controls, and automation intensity influenced key performance outcomes. Descriptive results showed an average certificate verification time of 1.84 hours, with rework due to missing data occurring in 7.8% of calibration events and record mismatch rates averaging 2.9%. Multiple regression analyses demonstrated that digital information flow variables explained a substantial proportion of variance in operational performance, with adjusted R² values ranging from 0.46 to 0.58 across models. Higher levels of automation intensity and certificate digitization were associated with significant reductions in verification time (β = −0.45, p < .001), rework frequency (β = −0.36, p < .001), and downtime exposure (β = −0.42, p < .001). Audit retrieval efficiency improved markedly in highly interoperable systems, with mean retrieval duration reduced to 6.2 minutes. Reliability testing confirmed strong internal consistency across constructs, with Cronbach’s alpha values ranging from 0.83 to 0.91. While direct relationships between digital maturity and energy intensity proxies were weaker, indirect sustainability benefits emerged through reduced non-value-added processing time, which averaged a 27.5% reduction in highly automated workflows. The findings demonstrated that digital information flow functioned as a quantitative driver of efficiency, quality, and service reliability in calibration systems. These results supported a system-level perspective in which sustainable calibration design and simulation must integrate digital information dynamics alongside physical calibration processes to enable resilient and resource-efficient…
subject: Articles
  topic: section
subject: Sustainable Calibration Systems
  topic: keywords
subject: Digital Information Flow
  topic: keywords
subject: Industrial Engineering
  topic: keywords
subject: System Simulation
  topic: keywords
subject: Operational Efficiency
  topic: keywords
creator: Efat Ara Haque
rights: Copyright (c) 2024 Efat Ara Haque (Author)
date: 2024-10-30
publication date: 2024-10-30
starting page: 60
ending page: 99
doi: 10.63125/rh85vs92

Strengthening Regulatory Compliance and Financial Governance in International Banking Through Blockchain-Enabled Audit Trails and Secure Ledger Systems
type: item
description: This study examined the extent to which blockchain-enabled audit trails and secure ledger systems were associated with regulatory compliance performance and financial governance efficiency in international banking environments using a quantitative explanatory design. The analysis was conducted on a cross-jurisdictional sample of 240 operational units and respondents drawn from compliance, internal audit, risk management, and finance functions within internationally active banks. Regulatory compliance performance was measured using indicators including reporting latency, reporting accuracy proxies, supervisory query resolution time, and compliance exception frequency, while financial governance efficiency was assessed through audit cycle duration, remediation intensity, reconciliation variance, and evidence retrieval time. Descriptive results showed a mean reporting latency of 4.82 days, a reporting accuracy proxy mean of 0.87, and an average compliance exception frequency of 3.42 per reporting period. Governance indicators revealed a mean audit cycle duration of 28.6 days and an average evidence retrieval time of 7.44 hours, with substantial variation across ledger architectures. Comparative descriptive analysis indicated that blockchain-enabled ledger environments demonstrated higher traceability completeness (mean = 0.91 versus 0.78), higher validation accuracy (mean = 0.96 versus 0.88), and lower audit exception frequency (mean = 2.61 versus 3.96) than conventional ledger environments. Reliability analysis confirmed strong internal consistency across all multi-item constructs, with Cronbach’s alpha coefficients ranging from 0.86 to 0.91 and an overall instrument alpha of 0.93. Multivariate regression analysis showed that blockchain-enabled audit trail presence was positively associated with regulatory compliance performance (β = 0.18, p < 0.001) and financial governance efficiency (β = 0.14, p = 0.004). Secure ledger attribute strength exhibited the strongest ass…
subject: Articles
  topic: section
subject: Blockchain Auditing
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Financial Governance
  topic: keywords
subject: Secure Ledgers
  topic: keywords
subject: International Banking
  topic: keywords
creator: Mostafa Kamal
creator: Tahmina Akter Bhuya Mita
rights: Copyright (c) 2023 Mostafa Kamal, Tahmina Akter Bhuya Mita (Author)
date: 2023-06-05
publication date: 2023-06-05
starting page: 01
ending page: 32
doi: 10.63125/e6k0e047

Artificial Intelligence Based Predictive Analytics for SKU Performance and Revenue Optimization in Competitive Markets
type: item
description: This study addresses the problem that many cloud-enabled enterprises invest in AI predictive analytics but still experience inconsistent SKU portfolio performance and avoidable revenue leakage because analytics capability, data integration, governance, and user adoption are uneven across functions. The purpose was to quantify how strongly AI Predictive Analytics Capability (AIPAC) influences SKU Performance (SKUPerf) and Revenue Optimization (RevOpt) in enterprise settings. Using a quantitative, cross-sectional, case-based design, data were collected via a structured 5-point Likert questionnaire from N = 210 professionals drawn from cloud and enterprise operational cases (forecasting, pricing and promotion, inventory and replenishment, and analytics roles). Key variables were AIPAC (overall construct and five capability dimensions: forecasting support, pricing and promotion decision support, inventory and replenishment decision support, data integration quality, and governance plus user adoption), SKUPerf, and RevOpt. The analysis plan included internal consistency reliability (Cronbach’s alpha), descriptive statistics, Pearson correlation, and OLS regression models predicting (1) SKUPerf from AIPAC, and (2) RevOpt from AIPAC and SKUPerf, plus a dimension-level regression to identify the most influential capability components. Reliability met accepted thresholds with AIPAC α = .91, SKUPerf α = .88, and RevOpt α = .90. Descriptively, perceived capability was high (AIPAC M = 4.02, SD = 0.61) while outcomes were moderate to high (SKUPerf M = 3.92, SD = 0.62; RevOpt M = 3.87, SD = 0.65). Correlation results showed strong positive relationships among the constructs, including AIPAC and SKUPerf (r = .62, p < .001), AIPAC and RevOpt (r = .58, p < .001), and SKUPerf and RevOpt (r = .66, p < .001). Regression findings confirmed that AIPAC significantly predicted SKU performance (β = .59, t = 10.21, p < .001; R² = .38; F(1,208) = 127.60, p < .001). In the dimension model, for…
subject: Articles
  topic: section
subject: AI Predictive Analytics Capability
  topic: keywords
subject: SKU Performance
  topic: keywords
subject: Revenue Optimization
  topic: keywords
subject: Cloud Enterprise Analytics
  topic: keywords
subject: Data Integration and Governance
  topic: keywords
creator: Md Khaled Hossain
rights: Copyright (c) 2026 Md Khaled Hossain (Author)
date: 2026-01-18
publication date: 2026-01-18
starting page: 297
ending page: 331
doi: 10.63125/cmyhzv81

Role of Data Science Models in Enhancing Revenue Assurance and Compliance in U.S. Financial Enterprises
type: item
description: This study addresses revenue leakage and compliance exposure in U.S. financial enterprises where cloud enabled, high volume transaction lifecycles make manual checks and periodic audits insufficient for timely detection, reconciliation, and audit ready evidence. The purpose was to quantify whether Data Science Model Capability (DSMC) strengthens Revenue Assurance Performance (RAP) and Compliance Performance (CP) in an enterprise case setting. A quantitative cross sectional, case-based design surveyed N = 162 professionals across revenue assurance, compliance, risk, internal audit, finance operations, and data analytics roles within a cloud and enterprise systems environment. Key variables were DSMC (10 items), RAP (8 items), and CP (8 items) measured on a 5-point Likert scale. The analysis plan combined descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and linear regression models predicting RAP and CP from DSMC. Results showed favorable baseline capability and outcomes: DSMC M = 3.84 (SD = 0.61), RAP M = 3.76 (SD = 0.58), and CP M = 3.89 (SD = 0.55), with strong scale reliability (α = 0.88, 0.85, 0.87 respectively). DSMC was strongly associated with RAP (r = 0.62, p < .001) and CP (r = 0.58, p < .001); regression confirmed predictive effects for RAP (β = 0.59, t = 9.41, p < .001, R² = 0.38) and CP (β = 0.55, t = 8.61, p < .001, R² = 0.33). Risk concentration was highest at pricing and fee computation with manual overrides (mean risk = 3.97/5), and high control automation groups outperformed low automation groups (RAP 4.01 vs 3.42; CP 4.12 vs 3.56). Implications indicate that financially material assurance gains come from workflow embedded analytics, expanded automation coverage, and stronger governance and explainability to improve audit defensibility and near real time reporting. Item trends showed exception identification scored highest (M = 4.02) while audit explainability lagged (M = 3.51). Governance readiness averaged 3.63 …
subject: Articles
  topic: section
subject: Data Science Model Capability (DSMC)
  topic: keywords
subject: Revenue Assurance Performance (RAP)
  topic: keywords
subject: Compliance Performance (CP)
  topic: keywords
subject: Control Automation Yield
  topic: keywords
subject: Model Governance and Explainability
  topic: keywords
creator: Rifat Chowdhury
rights: Copyright (c) 2026 Rifat Chowdhury (Author)
date: 2026-01-12
publication date: 2026-01-12
starting page: 267
ending page: 296
doi: 10.63125/0skktm84

APPLICATION OF HIGH-DURABILITY ENGINEERING MATERIALS FOR ENHANCING LONG-TERM PERFORMANCE OF RAIL AND TRANSPORTATION INFRASTRUCTURE
type: item
description: This study addresses the problem that rail and transportation infrastructure continues to experience premature deterioration, recurring defects, and higher lifecycle costs because high durability material strategies are not implemented consistently and their effectiveness depends heavily on execution rigor. The purpose of the study was to examine whether stronger Durability Material Implementation (DMI) is associated with improved Long-Term Performance (LTP) outcomes in rail and transportation projects. A quantitative, cross sectional, case-based design was used, drawing on a sample of 162 industry professionals representing multiple rail and transportation project cases across organizations and roles including design, construction, quality management, maintenance, and asset operations. The key independent variable was DMI, operationalized as a composite construct with four dimensions: Mechanical Wear and Fatigue Resistance (DMI1), Corrosion and Chemical Resistance (DMI2), Environmental and Thermal Resilience (DMI3), and QA/QC and Compliance Rigor (DMI4). The dependent variable was Long Term Performance (LTP). The analysis plan included descriptive statistics to profile implementation and performance levels, reliability testing to confirm internal consistency of constructs (Cronbach’s alpha ranged from 0.81 to 0.90), Pearson correlation to test bivariate associations, and multiple regression to estimate the predictive contribution of each DMI dimension to LTP. Findings showed high overall ratings for DMI (M = 3.88, SD = 0.54) and LTP (M = 3.95, SD = 0.57), with the strongest implementation emphasis on wear and fatigue resistance (M = 4.02, SD = 0.61). DMI demonstrated a strong positive association with performance, with the DMI composite correlating with LTP at r = 0.71 (p < .001), and dimension level correlations ranging from r = 0.49 to 0.65 (all p < .001). Regression results indicated that the model explained 58.1% of variance in LTP (R² = 0.581, F = 56.24, p < .…
subject: Articles
  topic: section
subject: Durability Material Implementation
  topic: keywords
subject: Long Term Performance
  topic: keywords
subject: Rail And Transportation Infrastructure
  topic: keywords
subject: QA/QC Compliance
  topic: keywords
subject: Wear And Fatigue Resistance
  topic: keywords
creator: Hammad Sadiq
rights: Copyright (c) 2022 Hammad Sadiq (Author)
date: 2022-06-30
publication date: 2022-06-30
starting page: 63
ending page: 96
doi: 10.63125/4k492a62

DEEP LEARNING–ENABLED LIDAR AND MULTISPECTRAL SIGNATURE FUSION FOR FLOOD HAZARD MAPPING AND LAND-SURFACE VULNERABILITY PREDICTION
type: item
description: This study developed and validated a quantitative flood hazard and land-surface vulnerability modeling framework that integrated LiDAR-derived terrain information with multispectral surface-condition signatures using deep learning and benchmark statistical approaches. Flood vulnerability was operationalized as a continuous, probability-like spatial outcome, and model performance was evaluated under a decision-utility framework that emphasized discrimination, calibration, and ranking quality in a rare-event setting. The analysis was conducted using a retrospective observational design applied to a finalized dataset of approximately 1.25 million raster cells, of which 62,500 (5.0%) were labeled as flood-positive based on event-linked inundation references. The dataset was partitioned into training (70%), validation (15%), and holdout test (15%) subsets using spatially disciplined splits to reduce leakage from spatial autocorrelation. Descriptive results indicated that flood-positive observations were concentrated in low-elevation, low-slope, high-convergence terrain and were more frequently associated with elevated imperviousness and wetness signatures. Reliability analysis showed acceptable internal consistency for composite predictor constructs, with Cronbach’s alpha values ranging from 0.75 to 0.86 across terrain and multispectral feature families. Baseline regression modeling confirmed statistically significant associations for all major construct families, with the strongest effects observed for convergence and wetness proxies and negative associations for elevation and slope structure. Comparative model evaluation demonstrated that non-linear approaches outperformed the regression benchmark. The fused LiDAR–multispectral convolutional neural network achieved the highest discrimination on the holdout test set, with an AUC-ROC of 0.94 and an AUC-PR of 0.63, compared with 0.84 and 0.41, respectively, for the regression baseline. Calibration quality also improved, a…
subject: Articles
  topic: section
subject: Flood Hazard
  topic: keywords
subject: Lidar
  topic: keywords
subject: LiDAR
  topic: keywords
subject: Multispectral Fusion
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Ratul Debnath
rights: Copyright (c) 2026 Ratul Debnath (Author)
date: 2026-01-10
publication date: 2026-01-10
starting page: 228
ending page: 266
doi: 10.63125/6mc3v739

A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE BASED PREDICTIVE SAFETY MODELS FOR REDUCING WORKPLACE INJURIES IN MANUFACTURING AND CONSTRUCTION
type: item
description: This study presented a systematic review of artificial intelligence–based predictive safety models aimed at reducing workplace injuries in manufacturing and construction, with emphasis on quantitative comparability across outcomes, data modalities, validation designs, and performance metrics. A total of 312 observational units and respondent-linked records from manufacturing and construction contexts were synthesized to evaluate injury occurrence, high-severity injury outcomes, and leading-indicator–based risk prediction. Manufacturing accounted for 51.9% of the analyzed records, while construction represented 48.1%. Descriptive results showed moderate-to-high levels of perceived AI usefulness (mean = 3.92, SD = 0.64) and leading-indicator maturity (mean = 3.74, SD = 0.69), with construction exhibiting higher median near-miss activity (median = 2 events per unit window) than manufacturing (median = 1). Logistic regression analyses indicated that data quality readiness was significantly associated with reduced injury occurrence (odds ratio = 0.78, p = 0.002) and reduced high-severity injury occurrence (odds ratio = 0.73, p = 0.006). Safety culture also demonstrated a protective association with injury occurrence (odds ratio = 0.82, p = 0.013). Sector-stratified analyses showed stronger readiness effects in construction (odds ratio = 0.72, p = 0.001) than in manufacturing (odds ratio = 0.83, p = 0.041). Leading-indicator maturity was associated with lower general injury odds (odds ratio = 0.85, p = 0.028) but did not reach significance for high-severity injuries. Validation design and metric selection were found to substantially influence reported performance, with temporal and site-held-out testing yielding more conservative and credible estimates than random splits. Overall, the findings underscored that predictive safety effectiveness depended primarily on data readiness, measurement quality, and validation rigor rather than algorithm complexity alone.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Safety
  topic: keywords
subject: Workplace Injuries
  topic: keywords
subject: Manufacturing
  topic: keywords
subject: Construction
  topic: keywords
creator: Jahangir Shekh
rights: Copyright (c) 2026 Jahangir Shekh (Author)
date: 2026-01-08
publication date: 2026-01-08
starting page: 180
ending page: 227
doi: 10.63125/jfpn5t74

QUANTITATIVE ASSESSMENT OF CORROSION-FATIGUE IN NUCLEAR POWER PLANT COMPONENTS USING SIMULATION AND EXPERIMENTAL DATA
type: item
description: Corrosion-fatigue (CF) is widely recognized as one of the most complex and deleterious degradation mechanisms threatening the structural integrity and long-term operation (LTO) of primary circuit components in Nuclear Power Plants (NPPs). As global nuclear fleets age and transition toward Subsequent License Renewal (SLR) periods of 80 years, the limitations of traditional, empirical fatigue models—which often rely on conservative, air-based design curves—have become increasingly apparent. This research presents a comprehensive quantitative assessment framework that bridges the gap between theoretical materials science and practical structural health monitoring by integrating high-fidelity numerical simulations with advanced experimental datasets. The study focuses on quantifying the synergistic effects of cyclic mechanical loading and the aggressive electrochemical environment of high-temperature, high-pressure reactor coolant, specifically targeting the vulnerabilities of 316L stainless steel and Alloy 690.The methodology utilizes a dual-pillar approach: first, conducting rigorous fatigue tests in a high-pressure recirculating autoclave loop to establish empirical crack initiation and growth rates ( ) under simulated reactor water chemistry. These experiments employ Direct Current Potential Drop (DCPD) sensors for sub-millimeter precision in real-time crack monitoring. Second, these physical results are synthesized into a Multiphysics Finite Element Analysis (FEA) framework that incorporates the Slip-Dissolution/Oxidation Model to simulate the localized electrochemical conditions at the crack tip. A key finding of this research is the identification of a critical "threshold" in the loading waveform, where tensile rise times exceeding 45 seconds were found to accelerate environmental damage by a factor of 15 compared to fast-transient cycles.Furthermore, the study quantifies the impact of Manganese Sulfide (MnS) inclusions, demonstrating that 85% of cracks initiate …
subject: Articles
  topic: section
subject: Corrosion-Fatigue (CF)
  topic: keywords
subject: Nuclear Structural Integrity
  topic: keywords
subject: Multiphysics Simulation
  topic: keywords
subject: Life Cycle Management
  topic: keywords
subject: Finite Element Analysis (FEA)
  topic: keywords
creator: Efat Ara Haque
rights: Copyright (c) 2026 Efat Ara Haque (Author)
date: 2026-01-05
publication date: 2026-01-05
starting page: 154
ending page: 179
doi: 10.63125/d1q81q36

ROLE OF FINTECH ACCOUNTING AUTOMATION IN MINIMIZING MANUAL ERRORS AND SUPPORTING DIGITAL MARKETING DECISION-MAKING IN FINANCIAL OPERATIONS
type: item
description: Manual processing, spreadsheet workarounds, and nonstandard exception handling continue to generate posting mistakes, rework cycles, and reconciliation breaks in modern finance operations, even when organizations deploy cloud and enterprise platforms. This study therefore examined whether FinTech enabled accounting automation reduces manual errors and which technical and human factors most strongly explain that reduction. Using a quantitative, cross sectional, case-based design, data were collected via a structured survey from 210 finance and accounting professionals working in cloud integrated enterprise environments across functions such as accounts payable and receivable, general ledger, reporting, and control review, representing multiple enterprise implementation cases. The model treated Manual Error Reduction (MER) as the dependent variable and specified Automation Intensity (AI), Validation Strength (VS), Automated Control Monitoring (ACM), and Human System Fit and Compliance (HSF) as key predictors, with experience and role level included as controls. The analysis plan included descriptive statistics to profile construct levels, reliability assessment using Cronbach alpha, Pearson correlation tests to examine bivariate relationships, and multiple regression with multicollinearity diagnostics (VIF) to estimate unique predictor effects. Descriptive results indicated above midpoint adoption and capability levels (AI M=3.94, SD=0.61; VS M=3.88, SD=0.66; ACM M=3.71, SD=0.70; HSF M=3.96, SD=0.63) alongside high perceived manual error reduction (MER M=3.82, SD=0.64), with strong internal consistency (alpha 0.82 to 0.90). Correlations supported meaningful positive associations with MER (AI r=0.62, VS r=0.55, ACM r=0.49, HSF r=0.66; all p<.001). The regression model explained 54.0 percent of the variance in MER (R=0.735, R2=0.540, Adjusted R2=0.526; F(6,203)=39.72, p<.001) and showed that HSF was the strongest predictor (beta=0.35, p<.001), followed by AI (beta=0.29,…
subject: Articles
  topic: section
subject: Fintech Enabled Accounting Automation
  topic: keywords
subject: Digital Marketing
  topic: keywords
subject: Manual Error Reduction
  topic: keywords
subject: Validation Strength
  topic: keywords
subject: Automated Control Monitoring
  topic: keywords
creator: Md. Mujahidul Islam
creator: Tahmina Akter Bhuya Mita
rights: Copyright (c) 2026 Md. Mujahidul Islam, Tahmina Akter Bhuya Mita (Author)
date: 2026-01-02
publication date: 2026-01-02
starting page: 107
ending page: 153
doi: 10.63125/8f0g1d59

DEEP NEURAL NETWORK MODELS FOR REAL-TIME FINANCIAL FORECASTING AND MARKET INTELLIGENCE
type: item
description: This study addresses the problem that organizations deploy deep neural network (DNN) forecasting services in cloud and enterprise environments, yet decision teams lack quantitative evidence on which operational capabilities drive real-time forecasting effectiveness and whether forecasting gains convert into decision-ready market intelligence. The purpose was to evaluate a case-based DNN forecasting service and test how perceived capability dimensions influence Forecasting Effectiveness (FE) and Market Intelligence Effectiveness (MIE). Using a quantitative cross-sectional, case-study design, a five-point Likert survey was administered to N = 210 active users in the selected enterprise case (58.1% analysts, 21.9% traders, 20.0% risk or portfolio staff). Key capability variables were Data Quality (DQ), Feature Richness (FR), Update Responsiveness (UR), Robustness (ROB), and Explanation Quality (EQ); outcomes were FE and MIE. The analysis plan used descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and two multiple regression models with diagnostic checks. Reliability was strong (α = .84 to .90). Descriptive results indicated high perceived maturity (DQ M = 4.12, SD = 0.54; FR M = 3.98, SD = 0.61; UR M = 3.85, SD = 0.66; ROB M = 3.90, SD = 0.63; EQ M = 3.76, SD = 0.70; FE M = 3.94, SD = 0.58; MIE M = 4.01, SD = 0.55). Associations supported the proposed pathway: capability correlated with FE (r = .68, p < .001) and MIE (r = .62, p < .001), and FE correlated with MIE (r = .71, p < .001). Regression Model 1 explained 56% of variance in FE (R² = .56), with significant effects for DQ (β = .32), ROB (β = .28), FR (β = .21), and UR (β = .14). Regression Model 2 explained 61% of variance in MIE (R² = .61), driven by FE (β = .52), EQ (β = .29), and UR (β = .12). The findings imply that cloud and enterprise programs should prioritize data integrity and robust delivery to improve forecast usefulness, and invest in explainability and low-latency …
subject: Articles
  topic: section
subject: Deep Neural Networks
  topic: keywords
subject: Real-Time Forecasting
  topic: keywords
subject: Market Intelligence Effectiveness
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Data Quality
  topic: keywords
creator: Aditya Dhanekula
creator: Mosa Sumaiya Khatun Munira
rights: Copyright (c) 2026 Aditya Dhanekula, Mosa Sumaiya Khatun Munira (Author)
date: 2026-01-02
publication date: 2026-01-02
starting page: 51
ending page: 89
doi: 10.63125/p4y4te47

ROBUST AND VERIFIABLE LLMS FOR HIGH-STAKES DECISION-MAKING (HEALTHCARE, DEFENSE, FINANCE)
type: item
description: Robust and verifiable large language models (LLMs) are increasingly considered for high-stakes decision-support in healthcare, defense, and finance, yet empirical evidence on their reliability, security, and audit readiness remains limited. This quantitative study evaluated four LLM system configurations—baseline, retrieval-grounded, schema/rule-constrained, and tool-augmented verification—across 360 domain-specific cases and 5,760 evaluated case-instances under clean, perturbation, out-of-distribution, and adversarial conditions. Descriptive and multivariable analyses showed that tool-augmented verification achieved the highest overall task correctness at 80% on clean inputs, compared to 64% for baseline, while maintaining higher decision stability under perturbations at 81% versus 61%. Evidence support rates increased from 58% in baseline outputs to 82% in tool-augmented configurations, and schema validity exceeded 94% under constrained outputs across domains. Under adversarial testing, retrieval-grounded systems exhibited the highest policy violation rate at 18.9%, whereas schema/rule-constrained and tool-augmented systems reduced violations to 7.2% and 6.9%, respectively. However, stricter controls increased false refusals, rising from 2.3% in baseline to 7.0% in schema-constrained configurations. Mixed-effects regression results indicated that tool augmentation more than doubled the odds of task correctness relative to baseline, while schema constraints reduced policy violations by nearly 50%. Out-of-distribution conditions reduced correctness across all configurations, with the smallest degradation observed in tool-augmented systems. Overall, the findings demonstrated that robustness and verifiability in high-stakes LLM decision-support depended on layered grounding, constraint enforcement, and deterministic verification mechanisms, and that measurable tradeoffs emerged between security controls and operational utility across domains.
subject: Articles
  topic: section
subject: Robust LLMs
  topic: keywords
subject: Verifiability
  topic: keywords
subject: High-Stakes Decision-Making
  topic: keywords
creator: Manish Bolli
creator: Sai Srinivas Matta
rights: Copyright (c) 2026 Manish Bolli, Sai Srinivas Matta (Author)
date: 2026-01-02
publication date: 2026-01-02
starting page: 01
ending page: 50
doi: 10.63125/xv9bab19

QUANTITATIVE ASSESSMENT OF DATA PROTECTION PRACTICES IN U.S. REVENUE CYCLE MANAGEMENT
type: item
description: This study quantitatively assessed data protection practices in U.S. revenue cycle management (RCM) by applying an evidence-oriented measurement instrument and summarizing results as an overall RCM Data Protection Index (RCM-DPI) with five domain subscales. A cross-sectional, observational design was used, and data were obtained from 120 organizations representing provider-operated, outsourced vendor, and hybrid RCM operating models. The overall RCM-DPI averaged 67.4 (SD = 9.8; median = 68.1; range = 41.0–88.5), indicating mid-range protection maturity with meaningful variability. Subscale means were highest for workforce safeguards (74.2, SD = 8.6) and data security controls (70.8, SD = 10.1), while auditability and monitoring (62.7, SD = 12.7) and vendor oversight (60.3, SD = 13.4) showed lower central tendency and wider dispersion. The overall index correlated strongly with auditability and monitoring (r = 0.85), identity and access governance (r = 0.82), vendor oversight (r = 0.80), and data security controls (r = 0.77), and moderately with workforce safeguards (r = 0.69), supporting coherent index composition. Organizational descriptors were negatively associated with protection outcomes, including toolchain complexity (r = -0.36 overall; r = -0.41 for auditability) and outsourcing proportion (r = -0.33 overall; r = -0.44 for vendor oversight). In multivariable regression, governance cadence positively predicted the overall index (B = 3.12, p < .001), while outsourcing intensity (B = -2.76, p < .001), toolchain complexity (B = -2.21, p = .003), and clearinghouse concentration (B = -1.48, p = .034) predicted lower scores; size tier was not significant (p = .18). Model fit was R² = 0.49 (adjusted R² = 0.46). Subscale reliability ranged from 0.81 to 0.88, supporting measurement consistency.
subject: Articles
  topic: section
subject: Revenue cycle management
  topic: keywords
subject: Data protection
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Healthcare compliance
  topic: keywords
subject: Vendor risk
  topic: keywords
creator: Mohammad Mushfequr Rahaman
creator: Aditya Dhanekula
rights: Copyright (c) 2024 Mohammad Mushfequr Rahaman, Aditya Dhanekula (Author)
date: 2024-12-29
publication date: 2024-12-29
starting page: 107
ending page: 153
doi: 10.63125/fc9hfy54

INTEGRATION OF MACHINE LEARNING MODELS AND ADVANCED COMPUTING FOR REDUCING LOGISTICS DELAYS IN PHARMACEUTICAL DISTRIBUTION
type: item
description: This study examined how integrating machine learning models with advanced computing–enabled prescriptive controls reduced logistics delays in pharmaceutical distribution, with particular attention to cold-chain reliability. The conceptual and measurement framework was grounded in an evidence base of 58 peer-reviewed quantitative papers that informed delay constructs, cold-chain risk measures, and predictive–prescriptive integration logic. A retrospective, multi-source observational dataset was analyzed, consisting of 4,820 shipment episodes across 26 transportation corridors and 8 third-party carriers, covering both ambient and cold-chain products. Ambient flows represented 68.4% of shipments and cold-chain flows 31.6%, enabling criticality-stratified analysis. Descriptive results indicated clear schedule deviation: mean planned lead time was 42.6 hours (SD=11.9), mean actual lead time was 49.8 hours (SD=17.4), and mean delay was 7.2 hours (SD=9.6). The delay distribution was right-skewed with pronounced tail risk, showing a P90 delay of 18.7 hours and P95 delay of 26.4 hours. Node-wise dwell decomposition showed the largest time accumulation at distribution centers (6.8 hours) and customs/regulatory nodes (5.7 hours), confirming multi-echelon delay formation. Baseline performance before integration recorded OTIF of 83.9%, lead-time variance of 92.1, coefficient of variation of 0.36, emergency shipment frequency of 7.4%, and cold-chain excursion incidence of 2.9%. Correlation and multicollinearity checks supported the retained predictor structure, with congestion and corridor volatility most strongly associated with lateness. Regression findings showed lane volatility and queue pressure as significant positive predictors of delay (β=2.41 and β=3.09, p<0.001), while carrier reliability reduced delay (β=-1.68, p<0.001). The integrated predictive–prescriptive mechanism was significantly associated with lower delay magnitude (β=-2.27, p<0.001), lower lead-time variance …
subject: Articles
  topic: section
subject: Pharmaceutical logistics
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Cold-chain
  topic: keywords
subject: Delay mitigation
  topic: keywords
subject: Prescriptive analytics
  topic: keywords
creator: Md. Rabiul Karim
creator: Samia Akter
rights: Copyright (c) 2021 Md. Rabiul Karim, Samia Akter (Author)
date: 2021-12-08
publication date: 2021-12-08
starting page: 01
ending page: 42
doi: 10.63125/ahnkqj11

THE IMPACT OF BIM AND DIGITAL TWIN TECHNOLOGIES ON RISK REDUCTION IN CIVIL INFRASTRUCTURE PROJECTS
type: item
description: This study investigated how Building Information Modeling (BIM) and Digital Twin (DT) technologies influenced risk reduction in civil infrastructure projects using a cross-sectional quantitative design. The dataset comprised 168 civil infrastructure projects across highways (36.9%), bridges (24.4%), rail corridors (19.6%), tunnels (10.7%), and water infrastructure (8.3%), delivered through design–bid–build (39.9%), design–build (32.1%), and PPP/alliance procurement (28.0). Digital implementation showed mid-to-high BIM practice (BIM adoption intensity M = 3.74, SD = 0.71; BIM maturity M = 3.58, SD = 0.68) and more dispersed DT practice (DT utilization M = 3.12, SD = 0.89; DT predictive capacity M = 3.26, SD = 0.84), while integration maturity remained moderate (M = 3.41, SD = 0.79). Risk reduction outcomes demonstrated substantial variability (cost risk reduction M = 64.8, SD = 12.5; schedule M = 61.3, SD = 13.9; safety M = 55.7, SD = 15.6; quality M = 59.9, SD = 14.2; operational M = 67.2, SD = 11.8; composite index M = 61.8, SD = 10.7). Correlations were positive across domains, with integration maturity showing the strongest association with the composite risk index (r = 0.61). Reliability and validity were adequate for all constructs (Cronbach’s alpha 0.81–0.89, composite reliability 0.86–0.92, AVE 0.53–0.64), and collinearity remained acceptable (VIF 1.20–2.27). Hierarchical regression indicated that BIM predictors explained the largest incremental variance in cost and schedule risk reduction (ΔR² = 0.18–0.20), DT predictors contributed most to safety and operational risk reduction (ΔR² = 0.15–0.22), and integration maturity added broad multi-risk explanatory power, especially for the composite index (ΔR² = 0.14, β = 0.41, p < .001). Moderation tests showed complexity weakened BIM scheduling effects, whereas data reliability and team digital capability strengthened DT and integration impacts. Overall, BIM stabilized delivery risks, DTs strengthened safety and op…
subject: Articles
  topic: section
subject: BIM
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Risk Reduction
  topic: keywords
subject: Civil Infrastructure
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
creator: Md Milon Mia
creator: Md. Mominul Haque
rights: Copyright (c) 2023 Md Milon Mia, Md. Mominul Haque (Author)
date: 2023-12-08
publication date: 2023-12-08
starting page: 01
ending page: 41
doi: 10.63125/xgyzqk40

IRRADIATION-ENHANCED CREEP–FATIGUE INTERACTION IN HIGH-TEMPERATURE AUSTENITIC STEEL: CURRENT UNDERSTANDING AND CHALLENGES
type: item
description: This study presents a comprehensive quantitative assessment of irradiation-enhanced creep–fatigue interaction in high-temperature austenitic stainless steels (304/304L, 316/316L/316H/316LN, and 347H), materials that anchor nuclear, petrochemical, and advanced power infrastructures. Creep–fatigue interaction the concurrent action of time-dependent creep and cyclic plasticity becomes critically accelerated under neutron irradiation, which alters microstructural pathways through hardening, dislocation channeling, and radiation-induced segregation. To quantify these coupled effects, a cross-sectional, case-based dataset of 168 tests across 12 campaigns was curated, harmonizing variables for dose (dpa), test temperature, strain range, tensile-hold duration, environment, and metallurgy. Multiple linear and mixed-effects regressions were pre-specified to estimate adjusted associations between these predictors and log life (ln Nf), explicitly testing irradiation–temperature (dose×T) and irradiation–dwell (dose×hold) interactions. Energy-augmented models incorporating cycle-resolved hysteresis and creep work were used to link statistical effects to physical damage mechanisms, while an expert Likert survey (n = 18) independently captured practitioner consensus on variable salience and data quality constraints. Results demonstrate that dose, temperature, strain range, and tensile hold each impose statistically significant, independent life penalties. Two interactions dominate: higher temperature steepens the life reduction per unit dose (negative dose×T), and irradiation amplifies dwell sensitivity (negative dose×hold). Metallurgical stabilization offers a modest but significant attenuation of irradiation-related life debit. Energy-augmented extensions confirm that increased creep power during tensile holds correlates with reduced life, aligning regression coefficients with mechanistic expectations. Cross-validation, penalized stability checks, and environment-stratified analy…
subject: Articles
  topic: section
subject: Irradiation-Enhanced Creep–Fatigue
  topic: keywords
subject: Austenitic Stainless Steel
  topic: keywords
subject: Displacements Per Atom (Dpa)
  topic: keywords
subject: Tensile Dwell (Hold Time)
  topic: keywords
subject: Mixed-Effects Regression
  topic: keywords
creator: Md Ariful Islam
rights: Copyright (c) 2022 Md Ariful Islam (Author)
date: 2022-12-20
publication date: 2022-12-20
starting page: 148
ending page: 181
doi: 10.63125/e46gja61

DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
type: item
description: This study investigates how data-driven capabilities and sustainable resource-allocation policies jointly influence supply chain resilience (SCRes) under disruption and uncertainty. Adopting a quantitative, cross-sectional, multiple case-study design, the research integrates survey-based measurement, archival operational data, and stochastic simulation–optimization to link organizational capabilities—visibility, collaboration, flexibility, supplier diversification, redundancy, risk orientation, and allocation efficiency—to key resilience outcomes, including service recovery, time-to-recovery (TTR), backorder intensity, cost variance, and emissions. Data were collected from 190 professionals across four international firms in discrete manufacturing, FMCG, healthcare logistics, and electronics sectors, each providing both perceptual and objective data spanning 12–24 months of operations. Hierarchical multiple regression models, supported by mediation and moderation analyses, revealed that collaboration, digital visibility, and allocation efficiency were the strongest predictors of resilience performance, while flexibility and diversification contributed moderate incremental effects. Allocation efficiency partially mediated the impact of visibility and collaboration on outcomes, demonstrating that information and coordination enhance resilience primarily through improved resource allocation. Moreover, capability effects intensified under uncertainty—visibility’s and collaboration’s benefits were significantly amplified by demand volatility and lead-time variability. A composite Resilience Performance Index (RPI) was developed to align statistical and operational metrics, linking survey constructs to observed KPIs. Monte Carlo simulation experiments using empirically calibrated disruption parameters validated these statistical insights: sustainability-aware optimized policies improved mean service levels by 3–6 percentage points, reduced TTR by 15–27%, lowered backorder…
subject: Articles
  topic: section
subject: Supply Chain Resilience
  topic: keywords
subject: Sustainable Resource Allocation
  topic: keywords
subject: Stochastic Simulation
  topic: keywords
subject: Optimization
  topic: keywords
subject: Visibility
  topic: keywords
creator: Sudipto Roy
creator: Md. Hasan Imam
rights: Copyright (c) 2024 Sudipto Roy, Md. Hasan Imam (Author)
date: 2024-06-29
publication date: 2024-06-29
starting page: 01
ending page: 32
doi: 10.63125/p0ptag78

INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW
type: item
description: This systematic review investigates the performance, optimization, and governance practices of business intelligence (BI) systems with a focus on SQL-driven dashboards, streaming pipelines, cloud scalability, and organizational integration. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 142 studies published between 2005 and 2024 were systematically identified, screened, and synthesized. The review highlights several key findings. First, SQL continues to serve as the foundational language in BI, maintaining its dominance through adaptability across distributed and hybrid environments. Second, dashboards have evolved beyond their original descriptive function, now acting as governance instruments that embed compliance, accountability, and transparency into organizational workflows. Third, sectoral and regional variations demonstrate that BI adoption and optimization are shaped by contextual factors, with financial and healthcare sectors prioritizing compliance and risk management, while retail and e-commerce emphasize customer analytics and speed. Fourth, performance optimization remains a critical concern, with techniques such as query pushdown, incremental refresh, caching, and streaming pipelines shown to reduce latency and improve scalability in real-time analytics. Fifth, monitoring, testing, and maintainability practices are increasingly emphasized, aligning BI with DevOps principles and continuous integration frameworks. Additionally, cloud and hybrid scalability emerge as both technical and economic challenges, requiring elastic infrastructures balanced against cost and governance demands. Finally, the review underscores the need for conceptual integration of technical, organizational, and governance dimensions, as BI success depends on harmonizing these three axes. Collectively, the findings confirm earlier scholarship while extending it with new empirical and conceptual insights, illustrating that BI ha…
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: SQL
  topic: keywords
subject: Dashboards
  topic: keywords
subject: Governance
  topic: keywords
subject: Scalability
  topic: keywords
creator: Momena Akter
creator: Md Nur Hasan Mamun
rights: Copyright (c) 2023 Momena Akter, Md Nur Hasan Mamun (Author)
date: 2023-04-30
publication date: 2023-04-30
starting page: 01
ending page: 30
doi: 10.63125/4aa43m68