American Journal of Advanced Technology and Engineering Solutions
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.
Artificial Intelligence-Based Decision Support Systems and Managerial Performance
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 adoption maturity aligned with higher performance (Low maturity: n=26, MP 3.52; Moderate: n=62, MP 3.86; High: n=32, MP 4.12). Managers reported the most concrete decision benefits in speed (M=3.95, SD=0.72) and accuracy (M=3.89, SD=0.69), with comparatively weaker gains in reduced rework (M=3.61, SD=0.78). Robustness checks supported model credibility (VIF 1.3–2.4, Durbin–Watson 1.95, Cook’s D max 0.21, and stable coefficient signs after excluding extreme USE cases). Implications are that enterprise leaders should prioritize improving AI-DSS usefulness, information quality, and trust-building mechanisms, while accelerating embedded, routine use through governance and workflow integration, because usability alone may be insufficient to drive net performance gains once value and credibility factors are accounted for.
AI-Based Decision Support Systems (AI-DSS)
Copyright (c) 2024 Tonay Pal, Md Maruf Islam, Al Amin (Author)
Design and Simulation of Sustainable Calibration Systems for Future Industrial Engineering Applications
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 industrial engineering applications.
Copyright (c) 2024 Efat Ara Haque (Author)
Strengthening Regulatory Compliance and Financial Governance in International Banking Through Blockchain-Enabled Audit Trails and Secure Ledger Systems
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 associations with compliance (β = 0.21, p < 0.001) and governance outcomes (β = 0.17, p = 0.006). The compliance model explained 46% of the variance in performance outcomes, while the governance efficiency model explained 38%. Overall, the findings provided quantitative evidence that secure, integrated ledger infrastructures functioned as governance-enabling mechanisms by improving auditability, data integrity, and regulatory reporting performance in complex international banking operations.
Copyright (c) 2023 Mostafa Kamal, Tahmina Akter Bhuya Mita (Author)
Artificial Intelligence Driven Explainable Machine Learning for High-Stakes Decision Support: SHAP Interpretability and Robustness Testing in Healthcare and Finance
This study addressed a persistent challenge in cloud-deployed, enterprise decision-support systems: even when machine-learning models exhibit strong predictive performance, users may under-rely or inconsistently rely on recommendations because post hoc explanations are perceived as unclear or unstable, constraining trust, defensibility, and confident action. The purpose of the study was to examine how explanation quality perceptions, specifically Perceived SHAP Interpretability (PSI) and Perceived Explanation Robustness (PER), are associated with Trust in AI decision support (TRU), Decision Confidence (DCF), and Intention to Rely or Use (INT) in high-stakes, case-based decision scenarios. A quantitative, cross-sectional, case-based design was employed using two enterprise contexts: healthcare clinical risk decision support and financial risk governance decision support. Standardized SHAP explanation artifacts were presented alongside decision vignettes, and a 5-point Likert-scale instrument measured PSI, PER, TRU, DCF, and INT, with controls for professional experience, AI familiarity, and domain group. The final sample consisted of N = 240 screened respondents (52.1% healthcare; 47.9% finance), with a mean professional experience of 7.8 years (SD = 4.6) and moderate AI familiarity (M = 3.62, SD = 0.84). The analytic approach combined descriptive statistics, reliability analysis, Pearson correlations, and a series of hierarchical multiple regression models. Mediation relationships were examined using regression-based mediation logic through sequential model estimation, assessing changes in direct effects when trust and confidence variables were introduced, rather than through causal path modeling or bootstrapped indirect effects. Reliability across constructs was strong (PSI α = .88; PER α = .86; TRU α = .90; DCF α = .87; INT α = .85). Mean scores exceeded the scale midpoint for all constructs (PSI M = 3.88; PER M = 3.61; TRU M = 3.74; DCF M = 3.69; INT M = 3.58). Correlation and regression results indicated that PSI and PER were positively associated with trust (PSI β = .41, p < .001; PER β = .34, p < .001; R² = .52). Trust accounted for substantial variance in decision confidence (β = .49, p < .001; DCF model R² = .57), and decision confidence accounted for substantial variance in intention to rely on AI recommendations (β = .43, p < .001; INT model R² = .49). When trust and confidence were included in the intention model, the direct associations of PSI and PER with intention were no longer statistically significant, indicating indirect relationships operating through trust and confidence. Overall, the findings suggest that SHAP interpretability and explanation robustness are associated with trust formation and confidence calibration, which together account for meaningful variance in reliance intentions in high-stakes healthcare and finance decision-support contexts.
Copyright (c) 2026 Jarif Ul Alam (Author)
Artificial Intelligence Based Predictive Analytics for SKU Performance and Revenue Optimization in Competitive Markets
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, forecasting support (β = .24, p = .002), inventory and replenishment support (β = .19, p = .011), data integration quality (β = .16, p = .018), and governance plus user adoption (β = .27, p < .001) were significant, increasing explained variance to R² = .46. Revenue optimization was jointly explained by AIPAC and SKUPerf (R² = .52; F(2,207) = 112.40, p < .001), with SKUPerf the strongest predictor (β = .49, t = 8.02, p < .001) while AIPAC retained a direct effect (β = .29, t = 4.71, p < .001). These results imply that enterprises can improve SKU outcomes and revenue by strengthening predictive analytics capability end to end, prioritizing governance and adoption, disciplined forecasting, integrated data pipelines, and replenishment decision support so AI insights translate into measurable commercial gains in cloud analytics environments.
Copyright (c) 2026 Md Khaled Hossain (Author)
Role of Data Science Models in Enhancing Revenue Assurance and Compliance in U.S. Financial Enterprises
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 (SD = 0.69) and related to CP (r = 0.61, p < .001).
Copyright (c) 2026 Rifat Chowdhury (Author)
APPLICATION OF HIGH-DURABILITY ENGINEERING MATERIALS FOR ENHANCING LONG-TERM PERFORMANCE OF RAIL AND TRANSPORTATION INFRASTRUCTURE
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 < .001). QA/QC and compliance rigor emerged as the strongest predictor (β = 0.36, p < .001), followed by wear and fatigue resistance (β = 0.24, p < .001), corrosion and chemical resistance (β = 0.18, p = .002), and a smaller but significant environmental and thermal effect (β = 0.10, p = .049). The implications are that organizations should pair advanced materials with enforceable QA/QC governance, verification routines, and workforce capability building, because execution rigor and mechanical durability pathways produce the largest performance gains and can guide lifecycle-oriented procurement, design standards, and maintenance planning.
Copyright (c) 2022 Hammad Sadiq (Author)
DEEP LEARNING–ENABLED LIDAR AND MULTISPECTRAL SIGNATURE FUSION FOR FLOOD HAZARD MAPPING AND LAND-SURFACE VULNERABILITY PREDICTION
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, as reflected by a lower Brier score (0.084 versus 0.118) and reduced expected calibration error (0.025 versus 0.041). Ranking utility gains were substantial, with precision at the top 1% of ranked locations increasing from 0.34 for regression to 0.52 for the fused model, and top-decile capture improving from 0.52 to 0.74 while reducing false alerts from 620 to 470 per 10,000 evaluated cells. Robustness testing across regional, catchment, and terrain-class holdouts confirmed that performance gains persisted under spatial generalization. Overall, the findings demonstrated that integrating LiDAR microtopography and multispectral surface-condition information through disciplined data fusion and spatially credible validation produced statistically robust and decision-useful flood vulnerability maps.
Copyright (c) 2026 Ratul Debnath (Author)
A SYSTEMATIC REVIEW OF ARTIFICIAL INTELLIGENCE BASED PREDICTIVE SAFETY MODELS FOR REDUCING WORKPLACE INJURIES IN MANUFACTURING AND CONSTRUCTION
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.
Copyright (c) 2026 Jahangir Shekh (Author)
QUANTITATIVE ASSESSMENT OF CORROSION-FATIGUE IN NUCLEAR POWER PLANT COMPONENTS USING SIMULATION AND EXPERIMENTAL DATA
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 at micro-pits with a critical depth-to-width ratio of 0.8. By applying Monte Carlo simulations, the research establishes a probabilistic distribution for the Remaining Useful Life (RUL) of critical components, identifying that fluctuations in local strain rates account for 45% of total predictive variance. The ultimate output of this study is a calibrated "Digital Twin" framework that achieved a 98% accuracy in real-time crack growth prediction. These results provide a technically defensible foundation for risk-informed maintenance, suggesting that proactive, data-driven monitoring can safely extend the operational life of nuclear assets while maintaining the highest margins of structural safety and regulatory compliance.
Copyright (c) 2026 Efat Ara Haque (Author)
ROLE OF FINTECH ACCOUNTING AUTOMATION IN MINIMIZING MANUAL ERRORS AND SUPPORTING DIGITAL MARKETING DECISION-MAKING IN FINANCIAL OPERATIONS
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, p<.001), VS (beta=0.18, p=.002), and ACM (beta=0.12, p=.024); experience was also significant (beta=0.10, p=.031) while role level was marginal (p=.054), and VIF values (1.34 to 2.11) indicated acceptable multicollinearity. Overall, the findings imply that error reduction is a socio technical outcome: organizations should expand automation coverage and embedded validations, strengthen continuous control monitoring, and prioritize training and governance that improve compliant exception handling and reduce manual overrides, using operational KPIs such as correction rates, exception aging, and straight through processing to sustain benefits.
Copyright (c) 2026 Md. Mujahidul Islam, Tahmina Akter Bhuya Mita (Author)
DEEP NEURAL NETWORK MODELS FOR REAL-TIME FINANCIAL FORECASTING AND MARKET INTELLIGENCE
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 refresh to maximize intelligence value and decision confidence. These results provide actionable levers for governance, service-level monitoring, and user-centered adoption of secure DNN forecasting platforms.
Copyright (c) 2026 Aditya Dhanekula, Mosa Sumaiya Khatun Munira (Author)
ROBUST AND VERIFIABLE LLMS FOR HIGH-STAKES DECISION-MAKING (HEALTHCARE, DEFENSE, FINANCE)
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.
Copyright (c) 2026 Manish Bolli, Sai Srinivas Matta (Author)
QUANTITATIVE ASSESSMENT OF DATA PROTECTION PRACTICES IN U.S. REVENUE CYCLE MANAGEMENT
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.
Copyright (c) 2024 Mohammad Mushfequr Rahaman, Aditya Dhanekula (Author)
INTEGRATION OF MACHINE LEARNING MODELS AND ADVANCED COMPUTING FOR REDUCING LOGISTICS DELAYS IN PHARMACEUTICAL DISTRIBUTION
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 (β=-5.91, p<0.001), improved OTIF, and reduced severe-delay odds (OR=0.58, p<0.001). For cold-chain shipments, severe delay substantially increased excursion risk (OR=2.36, p<0.001), demonstrating coupled time–temperature vulnerability. Overall, the results confirmed that ML-driven risk forecasts embedded into prescriptive decision rules corresponded with measurable improvements in timeliness and cold-chain safety across pharmaceutical distribution networks.
Copyright (c) 2021 Md. Rabiul Karim, Samia Akter (Author)
THE IMPACT OF BIM AND DIGITAL TWIN TECHNOLOGIES ON RISK REDUCTION IN CIVIL INFRASTRUCTURE PROJECTS
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 operational reliability, and BIM–DT integration yielded the most comprehensive multidimensional risk reduction.
Copyright (c) 2023 Md Milon Mia, Md. Mominul Haque (Author)
IRRADIATION-ENHANCED CREEP–FATIGUE INTERACTION IN HIGH-TEMPERATURE AUSTENITIC STEEL: CURRENT UNDERSTANDING AND CHALLENGES
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 analyses verify the robustness of these findings, with stronger dwell penalties observed in oxidizing atmospheres. Expert ratings converge with quantitative results, underscoring the high salience of irradiation dose, temperature coupling, and dwell-time effects. The study provides a transparent, reproducible framework linking microstructural mechanisms, empirical data, and statistical modeling to quantify irradiation-modified creep–fatigue behavior. The findings support the development of interaction-aware assessment tools for life prediction, inspection scheduling, and code-based design in the life extension of nuclear and high-temperature energy systems.
Copyright (c) 2022 Md Ariful Islam (Author)
DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
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 intensity by up to 24%, and cut emissions intensity by 6–11% compared to status quo. These findings confirm that resilience and sustainability can be jointly enhanced through data-driven allocation strategies. The study contributes an integrated, replicable framework that bridges measurement, inference, and decision experimentation—offering both theoretical clarity on capability mechanisms and practical guidance for managers seeking to design resilient, carbon-conscious supply chains under stochastic conditions.
Copyright (c) 2024 Sudipto Roy, Md. Hasan Imam (Author)
INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW
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 has matured into a strategic infrastructure supporting decision-making, regulatory compliance, and organizational resilience. This review provides actionable recommendations for practitioners and highlights research gaps that call for deeper exploration of cross-sectoral comparisons, governance integration models, and economic trade-offs in BI scalability.
Copyright (c) 2023 Momena Akter, Md Nur Hasan Mamun (Author)
ROLE OF DIGITAL TWINS AND BIM IN U.S. HIGHWAY INFRASTRUCTURE ENHANCING ECONOMIC EFFICIENCY AND SAFETY OUTCOMES THROUGH INTELLIGENT ASSET MANAGEMENT
To synthesize evidence on how Building Information Modeling (BIM) and digital twins enhance economic efficiency and safety in U.S. highway infrastructure through intelligent asset management. Guided by PRISMA 2020, searches across engineering, transportation, and standards repositories (2000–August 2023) yielded 3,146 records. After removing 1,012 duplicates, 2,134 titles/abstracts were screened; 378 full texts were assessed; 92 studies were included (empirical, mixed-methods, and rigorously reported gray literature). Data were extracted on model uses, governance, and outcomes; heterogeneity precluded meta-analysis, so a structured narrative synthesis was undertaken. Evidence clustered across four domains. (1) Economic efficiency: sixty-three studies reported fewer design clashes, reduced rework and RFIs, and more stable schedules and quantities under 3D/4D/5D coordination. (2) Predictive maintenance and lifecycle value: forty-nine studies linked digital-twin condition monitoring and deterioration forecasting to earlier, better-targeted treatments and fewer emergency interventions. (3) Safety and risk reduction: fifty-two studies associated time-aware staging, traffic management visualizations, and real-time hazard indices with lower exposure in work zones and shorter detection-to-response intervals. (4) Intelligent asset management: fifty-eight studies documented benefits when digital as-builts, IFC/COBie handovers, and sensor feeds populated governed asset registers, improving auditability and budget formulation. Cross-jurisdictional comparisons (forty-one studies) showed stronger, more consistent outcomes where open standards and owner information requirements were embedded in procurement and delivery. Across ninety-two studies, BIM provides the authoritative, object-based backbone for asset information, while digital twins synchronize that information with operational state. Under interoperable standards and clear governance, this pairing consistently supports cost control, safety performance, and reliable maintenance planning in U.S. highway programs.
Copyright (c) 2023 Mubashir Islam, MKA Shahinoor Rahman Jahid (Author)
DATA-DRIVEN DECISION-MAKING THROUGH CUSTOMER RELATIONSHIP MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW IN MODERN ENTERPRISES
This study presents a systematic review of the integration between Customer Relationship Management (CRM) and Data-Driven Decision-Making (DDDM) in modern enterprises, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure rigor and transparency. A total of 1,264 records were initially identified, of which 156 studies met the inclusion criteria after duplicate removal, screening, and full-text assessment. The reviewed literature spans conceptual models, technological enablers, sector-specific applications, international perspectives, and organizational challenges. Findings indicate that CRM is conceptualized not merely as a technological system but also as a strategic orientation grounded in relationship marketing, while DDDM is positioned as both an analytical process and a managerial philosophy. Together, these frameworks enable enterprises to enhance customer insights, optimize decision-making, and strengthen long-term competitiveness. Technological enablers such as cloud computing, Software-as-a-Service platforms, artificial intelligence, machine learning, and social media analytics were identified as pivotal in transforming CRM systems into predictive and real-time decision-making tools. Sector-specific applications demonstrated consistent value in retail, e-commerce, banking, financial services, and healthcare, highlighting the adaptability of CRM-DDDM integration across industries. International perspectives revealed that while CRM-DDDM principles are globally relevant, adoption and effectiveness vary according to cultural, institutional, and digital maturity factors, underscoring the importance of contextual adaptation. Challenges were also noted, particularly organizational resistance, poor data quality, system complexity, ethical concerns, and difficulties in measuring return on investment, all of which continue to limit effectiveness. The review further identified critical debates regarding technology versus relationship orientation, the appropriateness of customer equity versus firm performance as success metrics, and the absence of unified theoretical frameworks. Collectively, this synthesis demonstrates that CRM-DDDM integration delivers significant strategic and operational benefits while remaining shaped by contextual and organizational constraints. The study contributes by consolidating evidence across disciplines and offering recommendations for both research and practice.
Copyright (c) 2024 Md. Musfiqur Rahman (Author)
COMPARATIVE ANALYSIS OF PETROLEUM INFRASTRUCTURE PROJECTS IN SOUTH ASIA AND THE US USING ADVANCED GAS TURBINE ENGINE TECHNOLOGIES FOR CROSS INTEGRATION
This study presents a comparative analysis of petroleum infrastructure projects in South Asia and the United States, with a focus on the role of advanced gas turbine engine technologies in enabling effective cross integration between fuel supply systems and power generation assets. The research followed a systematic methodology to ensure transparency and rigor, drawing upon a comprehensive body of 92 peer-reviewed articles and technical reports, collectively cited over 1,850 times, to evaluate infrastructure maturity, technological deployment, governance structures, operational resilience, and environmental performance across both regions. The findings highlight stark contrasts: the United States demonstrates a highly developed and interconnected petroleum infrastructure anchored by extensive pipelines, underground storage, LNG export facilities, and a diverse mix of heavy-duty and aeroderivative turbines optimized for flexible grid participation. South Asia, in contrast, exhibits a more fragmented infrastructure profile characterized by concentrated LNG terminals, limited cross-border pipelines, and heavy reliance on baseload combined-cycle units that often face challenges linked to fuel quality variability, maintenance dependence, and climatic vulnerabilities. Environmental and safety standards also diverge significantly, with the U.S. operating under stringent regulatory regimes and continuous monitoring systems, while South Asia faces weaker enforcement capacity and greater exposure to natural hazards. Governance and finance emerge as pivotal factors, as U.S. projects benefit from mature markets and diversified capital flows, whereas South Asian projects remain heavily reliant on donor financing and sovereign guarantees. The study concludes that cross integration is not solely a technical challenge but one shaped by infrastructure maturity, institutional strength, and environmental resilience. By synthesizing insights from a broad literature base, this research provides an integrated framework for understanding the systemic differences between South Asia and the United States and underscores the need for region-specific strategies to optimize petroleum-to-turbine integration.
Copyright (c) 2022 Sabuj Kumar Shil, Zobayer Eusufzai (Author)
PROSPECT OF USING AI- INTEGRATED SMART MEDICAL TEXTILES FOR REAL-TIME VITAL SIGNS MONITORING IN HOSPITAL MANAGEMENT & HEALTHCARE INDUSTRY
This study explores the prospect of using AI-integrated smart medical textiles for real-time vital signs monitoring in hospital management and the broader healthcare industry. Smart textiles, which embed biosensors and conductive materials within wearable fabrics, have evolved from experimental prototypes into clinically relevant tools capable of continuously tracking physiological parameters such as cardiac activity, respiratory function, body temperature, oxygen saturation, blood flow, and muscle activity. The integration of artificial intelligence enables advanced signal processing, artifact suppression, predictive modeling, and real-time classification of health events, thereby transforming raw data into actionable clinical insights. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 152 peer-reviewed articles were systematically reviewed, collectively cited more than fifteen thousand times, ensuring a comprehensive synthesis of technological, clinical, operational, and regulatory perspectives. The findings revealed that AI-enhanced smart textiles deliver accuracy comparable to conventional monitoring devices while offering advantages in comfort, mobility, and continuous data capture. Hospitals implementing these systems reported operational benefits such as reduced nurse workload, improved alarm management, and expanded monitoring coverage, alongside clinical gains including earlier detection of patient deterioration, reductions in adverse events, shortened lengths of stay, and decreased readmissions. International case studies highlighted adoption across Europe, North America, and Asia-Pacific, while regulatory discussions underscored the importance of compliance with FDA, EMA, ISO, and IEC standards for successful deployment. Beyond inpatient care, the review identified applications in rehabilitation, chronic disease management, maternal and neonatal monitoring, occupational health, and telehealth integration, illustrating the adaptability of these technologies across healthcare contexts. Overall, the synthesis demonstrates that AI-integrated smart medical textiles represent a transformative innovation, offering both clinical and operational value, with strong potential to reshape hospital management and support the global transition toward more patient-centered, data-driven healthcare systems.
Copyright (c) 2024 Md Rezaul Karim, Md Takbir Hossen Sarker (Author)
EVALUATING AGILE BUSINESS ANALYSIS IN POST-COVID RECOVERY A COMPARATIVE STUDY ON FINANCIAL RESILIENCE
This study examines the role of agile business analysis in strengthening financial resilience during the post-COVID recovery period, with a focus on organizational adaptability across different industries, firm sizes, and regional contexts. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a total of 86 scholarly articles were systematically reviewed, covering both empirical and conceptual contributions. The analysis reveals that agile business analysis enhances resilience through mechanisms such as liquidity preservation, cost efficiency, improved revenue durability, and the integration of financial guardrails into iterative decision-making processes. The findings demonstrate that while small and medium-sized enterprises primarily rely on agile practices to ensure short-term survival and cash preservation, large multinationals employ scaled frameworks to balance global strategies with localized adaptation. Sectoral variations highlight the flexibility of agile analysis, as healthcare, retail, technology, and manufacturing organizations adapted its practices to their specific challenges. Regional comparisons further show that institutional, cultural, and regulatory factors shape the way agile analysis contributes to financial resilience, with significant differences observed between North America, Europe, and Asia-Pacific. The study also underscores the importance of embedding governance, compliance, and risk management within agile practices to balance adaptability with accountability. Collectively, the evidence positions agile business analysis as a strategic capability that extends beyond project delivery to function as a financial resilience framework. By synthesizing insights from 86 reviewed studies, this research contributes to the understanding of how agile practices enable organizations to recover effectively, safeguard financial stability, and sustain performance in environments characterized by volatility and uncertainty.
Agile Business Analysis Strengthens Resilience
Financial Adaptability Supports Organizational Survival
Post-COVID Recovery Reshapes Business Practices
Copyright (c) 2022 Md Hasan Zamil, Moin Uddin Mojumder (Author)
IMPACT OF PREDICTIVE DATA MODELING ON BUSINESS DECISION-MAKING: A REVIEW OF STUDIES ACROSS RETAIL, FINANCE, AND LOGISTICS
This study investigates how predictive data modeling influences business decision-making across retail, finance, and logistics, emphasizing the practices that convert predictive accuracy into measurable organizational impact. Evidence from 100 peer-reviewed empirical studies linking predictive outputs to operational actions such as inventory replenishment, dynamic pricing, credit approvals, fraud triage, routing optimization, and service-level promise windows was synthesized. Studies employing temporal validation, probability calibration, explicit operating thresholds, and structured translation into operational policies reported business improvements in 93 percent of cases, achieving median gains of approximately 9–12 percent on the primary KPI. In contrast, minimally aligned designs succeeded in only 48 percent of cases, with modest gains of about 3–5 percent. Sector-specific results revealed consistent yet domain-sensitive patterns. In retail, hierarchical forecasting methods and decision-aware pricing systems yielded a median 2.8 percentage-point reduction in stockouts and a 2.2 percent revenue lift when forecast distributions were directly integrated into service curves and inventory or pricing rules. In finance, calibrated scorecards, cost-sensitive thresholds, and temporally validated probability estimates reduced expected credit loss by nearly 8 percent at constant approval rates or raised approvals by approximately 3.5 percentage points at constant risk levels. Fraud detection and anti–money laundering systems achieved a median 22 percent reduction in false positives while improving cost per true positive when precision–recall evaluation, network features, and workload-aware thresholds informed operational decision-making. In logistics, uncertainty-aware demand and travel-time prediction models enhanced on-time delivery by about 3.9 percentage points and reduced routing costs by nearly 6 percent when embedded into promise windows, lateness-penalty formulations, and quantile-based safety stock policies. These findings emphasize the critical role of end-to-end decision pipelines rather than algorithmic novelty alone, underscoring the value of predict-then-optimize workflows, decision alignment mechanisms, and governance artifacts—including calibration plots, cost curves, and threshold rationales—that ensure operating points remain auditable, interpretable, and resilient to model drift over time.
Copyright (c) 2022 Md Redwanul Islam, Md. Zafor Ikbal (Author)
GRAPH NEURAL NETWORK MODELS FOR DETECTING FRAUDULENT INSURANCE CLAIMS IN HEALTHCARE SYSTEMS
Fraudulent insurance claims in healthcare systems represent a persistent and costly challenge, undermining the efficiency, equity, and sustainability of healthcare delivery worldwide. Traditional approaches to fraud detection, including rule-based systems and statistical models, have provided valuable early insights but often fail to capture the complex, relational, and evolving nature of fraudulent behavior. This study addresses these limitations by investigating the application of Graph Neural Networks (GNNs) as an advanced analytical framework for detecting fraudulent claims. By representing patients, providers, and claims as interconnected nodes within graph structures, GNNs leverage relational dependencies and structural patterns that are frequently overlooked by conventional models. The research employed a mixed-methods design, combining quantitative experimentation with multiple GNN architectures—such as Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE—with qualitative insights gathered from healthcare fraud investigators to evaluate interpretability and usability. The quantitative findings revealed that GNN models consistently achieved higher accuracy, precision, and recall compared to traditional classifiers, while the qualitative analysis highlighted the importance of interpretability and visualization in building stakeholder trust and improving investigative efficiency. Importantly, the literature review synthesized evidence from 112 peer-reviewed studies, providing a comprehensive overview of existing fraud detection methods and situating GNNs within the broader progression of healthcare fraud research. The results underscore that GNNs not only advance the technical accuracy of fraud detection but also offer practical tools for detecting collusive fraud networks and managing large-scale, heterogeneous claims data. This study contributes to both academic discourse and practical applications by demonstrating that GNNs are uniquely positioned to enhance fraud detection in healthcare insurance systems through their capacity to integrate relational learning, scalability, and operational transparency.
Copyright (c) 2022 Md. Tarek Hasan (Author)
QUANTITATIVE RISK ASSESSMENT OF RAIL INFRASTRUCTURE PROJECTS USING MONTE CARLO SIMULATION AND FUZZY LOGIC
Rail infrastructure programs frequently face complex, intertwined risks spanning cost overruns, schedule delays, safety concerns, and interface uncertainties. This systematic review critically examines how quantitative risk assessment (QRA) methods—Monte Carlo simulation (MCS), fuzzy logic (FL), and hybrid approaches—have been employed across the rail project lifecycle to manage these multidimensional challenges. Following PRISMA guidelines, we conducted a comprehensive search across Scopus, Web of Science, IEEE Xplore, ASCE Library, ScienceDirect, and TRB databases, applied pre-registered eligibility criteria, implemented double-screening for study inclusion, and rigorously appraised methodological practices encompassing data provenance, dependence modeling, validation, and sensitivity analysis. From an initial pool of studies, 95 peer-reviewed publications met all inclusion standards. Findings indicate MCS dominates (47%) owing to its strength in producing distributional forecasts and governance-ready percentiles; FL supports imprecise or linguistic inputs (33%) often encountered during early-stage planning and safety screening; while hybrid models (20%) bridge probabilistic propagation and evidential uncertainty, particularly in interface-intensive phases. Applications cluster within construction (68%), followed by design (46%), feasibility analysis (39%), testing/commissioning (24%), and operations & maintenance (21%). Methodologically, MCS studies primarily use triangular and PERT/beta distributions, with approximately 42% employing Latin hypercube sampling. However, dependence modeling remains limited—38% of studies assume independence, 23% employ rank or copula methods, and only 31% jointly simulate cost–schedule interactions. FL studies typically apply triangular/trapezoidal membership functions with centroid defuzzification; while two-thirds disclose reproducible rule bases, one-third lack transparency. Hybrid models frequently convert fuzzy assessments into probabilistic inputs or embed fuzzy and evidential nodes into Bayesian structures, enabling richer risk representation at system interfaces. Sensitivity analysis is reported in 64% of studies, but only 21% adopt global approaches and a mere 5% include tail-focused diagnostics, while external validation is rare (18%).
Copyright (c) 2022 Md. Sakib Hasan Hriday (Author)
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELING OF BIOACTIVE COMPOUNDS FROM MANGIFERA INDICA FOR ANTI-DIABETIC DRUG DEVELOPMENT
This study evaluates how quantitative structure–activity relationship (QSAR) modeling can accelerate anti-diabetic drug discovery from Mangifera indica (mango) phytochemicals. Diabetes mellitus remains a global health burden, and natural products represent an abundant but under-optimized resource for therapeutic leads. To address this gap, we conducted a comprehensive screening of major scientific databases and ultimately analyzed 113 peer-reviewed studies that reported computable molecular structures, harmonizable bioactivity endpoints, and reproducible modeling workflows. The review focused on clinically relevant antidiabetic targets including α-glucosidase, α-amylase, dipeptidyl peptidase-4 (DPP-4), and protein tyrosine phosphatase 1B (PTP1B). Across the included studies, curated datasets were normalized to pIC₅₀ and pKᵢ scales to enable meaningful comparisons, while feature engineering spanned physicochemical descriptors, topological indices, and molecular fingerprints such as ECFP and MACCS. Machine learning approaches ranged from penalized regression models to advanced ensemble algorithms (e.g., boosting, bagging, and kernel-based methods), with rigorous validation achieved through scaffold-aware data splits, external test sets, Y-randomization, and explicit applicability domain (AD) assessment. Convergent lines of evidence—including QSAR predictivity within AD, mechanistically plausible docking, and ADME/toxicity filtering—consistently highlighted polyphenolic chemotypes, particularly xanthones such as mangiferin and its aglycone norathyriol, as promising inhibitors of carbohydrate-metabolizing enzymes. In contrast, scaffolds targeting signaling enzymes (DPP-4 and PTP1B) demanded early consideration of selectivity, pharmacokinetics, and off-target liabilities to improve translational viability. This synthesis also identifies recurring optimism traps, such as reliance on internal-only validation, assay heterogeneity across studies, and insufficient reporting of AD boundaries. To mitigate these challenges, we propose a reproducible translational framework: (i) employ AD-bounded QSAR as a first-line triage tool to prioritize scaffolds, (ii) integrate orthogonal structure-based approaches such as docking and molecular dynamics for rationalization of binding interactions, and (iii) adopt permeability-aware optimization and formulation strategies to address polarity-driven bioavailability challenges inherent to many mango-derived polyphenols.
Copyright (c) 2022 Sadia Tasnim (Author)
EVALUATING THE EFFECTIVENESS OF AI-GENERATED, CONTEXT-SPECIFIC CASE STUDIES IN IMPROVING CRITICAL THINKING SKILLS IN ODL
The advancement of artificial intelligence (AI) in education has created new opportunities to enhance learner-centered pedagogies, particularly in open and distance learning (ODL) systems where geographic, cultural, and infrastructural challenges often limit instructional effectiveness. Critical thinking, defined as the ability to analyze, evaluate, and synthesize information for evidence-based judgment and problem-solving, remains a central educational outcome and a global competency emphasized by higher education frameworks. Case-based learning has long been recognized as an effective approach for fostering critical thinking; however, static case materials often lack contextual relevance and adaptability for diverse learner populations in ODL contexts. This study investigated the effectiveness of AI-generated, context-specific case studies in improving critical thinking skills among Bangladeshi ODL learners. A quasi-experimental design was employed with pre-test and post-test measures administered to an experimental group (n = 100) and a control group (n = 100). The experimental group interacted with AI-generated cases designed to provide personalization, authenticity, adaptive feedback, and active engagement, while the control group studied traditional instructor-developed case materials. Findings revealed significant improvements in the experimental group’s critical thinking performance, with large effect sizes on post-test scores compared to the control group. Regression analysis confirmed that personalization, engagement, authenticity, and adaptive feedback each had a strong positive effect on critical thinking outcomes, with path coefficients ranging between β = .27 and β = .46. Structural equation modeling further demonstrated that these four variables collectively explained 68% of the variance in critical thinking development, confirming the overall model hypothesis. The results underscore the effectiveness of AI-generated case studies in addressing key limitations of ODL, including isolation, lack of contextualization, and delayed feedback. By integrating AI-driven instructional design into learning management systems, institutions in Bangladesh can support critical thinking development at scale while ensuring inclusivity across diverse learner groups. This study contributes to the global literature on AI-enhanced pedagogy by demonstrating its applicability and impact within a South Asian ODL context, where access and equity are central educational challenges.
Copyright (c) 2024 Md Arif Uz Zaman (Author)
COST-BENEFIT ANALYSIS IN PRE-CONSTRUCTION PLANNING: THE ASSESSMENT OF ECONOMIC IMPACT IN GOVERNMENT INFRASTRUCTURE PROJECTS
This study presents a systematic literature review of 100 peer-reviewed, DOI-indexed articles examining the application of cost–benefit analysis (CBA) in the pre-construction stage of government infrastructure projects. Guided by the PRISMA framework, the review synthesizes evidence from transport, energy, water, and social infrastructure sectors to evaluate how welfare-consistent economic impact assessment is operationalized before major public investment decisions are finalized. Inclusion criteria required an explicit counterfactual (do-nothing or do-minimum baseline), monetized benefits and costs derived from willingness-to-pay or shadow-pricing methods, and clearly stated decision indicators including net present value (NPV), benefit–cost ratio (BCR), and internal rate of return (IRR) with a transparent price base and discount rate. The analysis explores eight thematic domains: welfare-consistent definitions and indicators, appraisal regimes and parameter libraries, baseline demand and exposure modeling, valuation of user benefits, safety, and environmental externalities, discounting and time horizons, uncertainty architecture, distributional analysis, and treatment of wider economic impacts. Findings reveal that transport dominates the literature at 52 percent, followed by energy at 20 percent, water at 18 percent, and social infrastructure at 10 percent. Most studies employ do-minimum baselines (84 percent) and constant real discount rates (72 percent) with growing adoption of declining schedules for long-lived assets. While deterministic sensitivity testing is universal, fewer studies employ probabilistic analysis (37 percent) or reference-class adjustments (21 percent). The review identifies strengths such as increasing parameter transparency, improved handling of induced demand, and avoidance of double counting as well as gaps in distributional weighting, probabilistic robustness, and integration of wider impacts. The synthesis offers a standardized reporting checklist and sector-specific recommendations to enhance transparency, comparability, and decision-readiness at the planning gate. By consolidating methodological best practices across jurisdictions, this review positions pre-construction CBA as a rigorous, auditable tool for ranking public infrastructure options on a welfare-consistent basis before design and procurement commitments become irreversible.
Copyright (c) 2022 Mubashir Islam, Abdul Rehman (Author)