Review of Applied Science and Technology
Review of Applied Science and Technology (RAST) is an international, peer-reviewed, open-access journal dedicated to publishing high-impact research and review articles in all areas of applied science and engineering technology. RAST serves as a critical platform for researchers, innovators, and practitioners aiming to translate scientific knowledge into practical solutions for industrial, environmental, technological, and societal advancement. With a commitment to academic rigor, technological innovation, and interdisciplinary collaboration, the journal bridges the gap between theoretical research and its practical implementation in diverse sectors, including energy, materials, information technology, mechanical systems, chemical processes, biomedical engineering, and sustainable development.
AI-POWERED BUSINESS ANALYTICS FOR SMART MANUFACTURING AND SUPPLY CHAIN RESILIENCE
This study quantitatively examined the relationships among AI-powered business analytics capability, smart manufacturing integration, and supply chain resilience within manufacturing organizations operating in data-intensive supply chain environments. Using a cross-sectional research design, data were collected from 312 manufacturing firms that had adopted analytics-supported operational systems. Descriptive analysis indicated moderate-to-high levels of analytics capability (mean = 3.71, SD = 0.62), smart manufacturing integration (mean = 3.58, SD = 0.66), and supply chain resilience (mean = 3.64, SD = 0.61), suggesting a sample characterized by partial to advanced digital maturity. Correlation analysis revealed strong positive associations between analytics capability and manufacturing integration (r = 0.62), analytics capability and supply chain resilience (r = 0.55), and manufacturing integration and resilience (r = 0.59), supporting the coherence of the proposed model and construct distinctiveness. Reliability and validity assessments confirmed strong measurement quality, with Cronbach’s alpha values ranging from 0.88 to 0.91 and average variance extracted values exceeding 0.60 for all constructs. Regression analysis demonstrated that analytics capability significantly predicted smart manufacturing integration (β = 0.62, p < 0.001) and supply chain resilience (β = 0.33, p < 0.001). When smart manufacturing integration was included in the resilience model, its effect was significant (β = 0.41, p < 0.001), while the analytics coefficient decreased in magnitude, indicating partial mediation. The mediated model explained 52% of the variance in supply chain resilience, compared to 39% in the direct-effects model. Collinearity diagnostics confirmed stable estimation, with variance inflation factors below 2.0 across all predictors. Moderation analysis showed no significant interaction effect for environmental turbulence, indicating that the estimated relationships were stable across varying contextual conditions. Overall, the findings provided robust quantitative evidence that AI-powered business analytics capability contributed to supply chain resilience both directly and indirectly through smart manufacturing integration, reinforcing the role of analytics-enabled integration as a central mechanism linking digital capability development to resilient operational performance.
Copyright (c) 2024 Review of Applied Science and Technology
AN EMPIRICAL STUDY OF BIG DATA–ENABLED PREDICTIVE ANALYTICS AND THEIR IMPACT ON FINANCIAL FORECASTING AND MARKET DECISION-MAKING
This study addresses the problem that enterprises in data intensive capital market operations may invest in big data and predictive analytics, yet decision quality remains uneven because forecasting signals are not always reliable, interpretable, or embedded in governance routines. The purpose was to test a capability to outcome model in which Big Data Capability (BDC) and Predictive Analytics Capability (PAC) improve Financial Forecasting Accuracy (FA), and FA improves Decision-Making Quality (DMQ). Using a quantitative cross-sectional, case-based survey design, data were collected once from professionals in an enterprise case context, yielding 226 responses and 210 usable cases after screening (max item missingness 1.9%; two outliers removed). Key variables were BDC and PAC (predictors), FA (mediator), and DMQ (outcome), measured on five-point Likert scales with internal consistency (α = .86 to .91). Analysis followed data screening, reliability testing, descriptive statistics, Pearson correlations, multiple regression (Model 1: FA; Model 2: DMQ), and mediation via 5,000 bootstrap resamples. Descriptively, respondents reported moderate to high capability and outcomes (BDC M = 3.78, SD = 0.61; PAC M = 3.69, SD = 0.66; FA M = 3.62, SD = 0.58; DMQ M = 3.74, SD = 0.55). All bivariate associations were positive and significant (BDC, FA r = .52; PAC, FA r = .57; FA, DMQ r = .63; p < .001). In regression, BDC (β = 0.28, p < .001) and PAC (β = 0.39, p < .001) jointly explained 44% of the variance in FA (R² = 0.44), and FA was the strongest predictor of DMQ (β = 0.49, p < .001) in a model explaining 52% of DMQ (R² = 0.52), with smaller direct effects from BDC (β = 0.14, p = .013) and PAC (β = 0.17, p = .004). Mediation results showed partial mediation of the PAC to DMQ link through FA (indirect effect = 0.19, 95% CI [0.12, 0.28]; direct effect = 0.11, 95% CI [0.04, 0.18]). These findings imply that improving decision quality requires stronger data governance and analytics discipline, plus integration of forecasts into decision protocols across analytics enabled workflows.
BLOCKCHAIN ADOPTION AND ORGANIZATIONAL LONG-TERM GROWTH IN SMALL AND MEDIUM ENTERPRISES (SMES)
This study addresses the problem that small and medium enterprises (SMEs) often lack quantitative evidence on whether deeper blockchain use, beyond awareness or pilot trials, is associated with sustained organizational growth and which readiness conditions enable adoption. The purpose was to test a Technology-Organization-Environment (TOE) model in which technological readiness, organizational readiness, and environmental pressure predict blockchain adoption intensity, and adoption intensity predicts a multidimensional long-term growth index. Using a quantitative, cross-sectional, case-based survey design, data were obtained from 220 SME enterprise cases spanning manufacturing (29.1%), retail/wholesale (25.0%), services (34.5%), and logistics/other sectors (11.4%). Most firms employed 10-49 staff (52.7%) in the case setting, and 34.1% reported active blockchain use in at least one business process. Key variables were measured on five-point Likert scales and showed strong internal consistency (Cronbach’s alpha: technological readiness = .86, organizational readiness = .88, environmental pressure = .82, adoption intensity = .90, and long-term growth = .89). The analysis plan applied descriptive statistics, Pearson correlations, and two-stage multiple regression, controlling for firm size, firm age, and sector. Descriptively, perceptions were moderate to high (technological readiness M = 3.74, SD = 0.63; adoption intensity M = 3.42, SD = 0.72; long-term growth M = 3.68, SD = 0.60), and operational efficiency was the highest growth dimension (M = 3.82, SD = 0.64). Adoption intensity correlated with long-term growth (r = 0.56, p < .01) and with TOE determinants (r = 0.49 with technological readiness, r = 0.52 with organizational readiness, and r = 0.41 with environmental pressure, all p < .01). In regression Model A, TOE determinants explained 42.1% of adoption-intensity variance (R² = 0.421; F(3,216) = 52.34, p < .001), with organizational readiness the strongest predictor (β = 0.34, p < .001), followed by technological readiness (β = 0.27, p < .001) and environmental pressure (β = 0.19, p = .001). In Model B, adoption intensity predicted long-term growth net of controls (R² = 0.420; F(5,214) = 31.08, p < .001), with a strong positive effect for adoption intensity (β = 0.49, p < .001) and a small effect for firm size (β = 0.12, p = .036), while firm age and sector were not significant. Supplementary models showed that adoption intensity was especially linked with operational efficiency (β = 0.51, p < .001) and innovation capability (β = 0.44, p < .001). Implications are that SMEs should treat blockchain as a governed capability-building program and prioritize managerial commitment, IT capability, and process readiness, because deeper routinized integration is associated with stronger growth outcomes through operational and innovation pathways.
Technology-Organization-Environment (TOE)
QUANTUM-ENHANCED CHARGE TRANSPORT MODELING IN PEROVSKITE SOLAR CELLS USING NON-EQUILIBRIUM GREEN’S FUNCTION (NEGF) FRAMEWORK
Perovskite solar cells (PSCs) can reach high efficiencies, yet semiclassical modeling can under-resolve transport losses at interfaces and contacts, limiting mechanism-level diagnosis. This study tested whether NEGF-parameterized transport factors explain cross-case variation in PSC performance and whether experts perceive the framework as credible and usable. A quantitative, cross-sectional, case-study–based design analyzed 12 modeled PSC cases and an expert survey (n = 42). Predictors were trap density (X1), interface barrier height (X2), scattering index (X3), and contact coupling (X4); outcomes were Jsc, Voc, FF, and PCE. Analyses comprised descriptives, Pearson correlations, multiple regression predicting PCE, and survey reliability plus one-sample tests against the neutral midpoint. Case outputs ranged from Jsc 19.8–25.4 mA·cm⁻² and PCE 11.7–21.8%. Trap density was negatively associated with PCE (r = −0.76, p = 0.004), barrier height was negatively associated with Jsc (r = −0.79, p = 0.002), and contact coupling was positively associated with PCE (r = +0.69, p = 0.014). The multivariate PCE model was significant (R² = 0.861, F(4,7) = 10.86, p = 0.004), with trap density (β = −0.46, p = 0.018), barrier height (β = −0.39, p = 0.031), scattering (β = −0.28, p = 0.044), and coupling (β = +0.33, p = 0.027). Experts rated the approach positively (Accuracy M = 4.12, α = 0.87; Usefulness M = 4.28, α = 0.90; p < 0.001). These findings imply NEGF can function as a decision-support layer for interface and contact engineering, and should be deployed in auditable compute pipelines for enterprise or cloud/HPC execution.
Copyright (c) 2021 Review of Applied Science and Technology
PREDICTIVE ANALYTICS FOR RISK AND COMPLIANCE IN IT-ENABLED PROJECT MANAGEMENT SYSTEMS
This study examined predictive analytics for risk and compliance in IT-enabled project management systems by modeling how operational performance indicators and governance-control indicators jointly explained adverse project outcomes. A retrospective quantitative design was applied to 312 projects drawn from an initial pool of 353, with 41 projects excluded for incomplete baselines or missing workflow trails, producing an inclusion rate of 88.4%. The sample comprised 39.7% infrastructure projects (n = 124), 34.6% software/IT delivery projects (n = 108), and 25.6% mixed/operational projects (n = 80). Descriptive analysis indicated that 29.5% of projects met the schedule distress criterion (n = 92) and 20.5% met the cost distress criterion (n = 64), while 13.1% met the combined distress definition (n = 41). Compliance deviations were recorded in 17.9% of projects (n = 56), with repeated exceptions in 7.4% (n = 23). Key variables showed heavy-tailed distributions, including change requests with a mean of 9.8, median 7.0, and maximum 88, and documentation completeness with a mean of 91.6%, median 94.0%, and missingness of 11.5%; access-control anomaly indicators had the highest missingness at 18.6%. Correlation results showed alignment between instability and governance signals, including schedule variance with cost variance (r = .46) and change intensity (r = .41), and documentation completeness with exception recurrence (ρ = −.52) and approval latency with exception recurrence (ρ = .44). Stratified results showed stronger schedule variance–change coupling in software/IT projects (r = .48) than infrastructure projects (r = .31), and stronger late-phase governance–exception relationships for approval latency (ρ = .57) than early phase (ρ = .28). Reliability testing supported construct consistency with α = .88 for governance adherence, α = .91 for documentation completeness, and α = .84 for workflow conformance. Collinearity reduction lowered maximum VIF from 7.4 to 3.4 and maximum condition index from 28.3 to 17.6. Regression findings showed risk distress was positively associated with schedule variance (β = 0.41, p < .001) and change intensity (β = 0.29, p = .002), while compliance exceptions were strongly associated with documentation completeness (β = −0.47, p < .001) and approval adherence (β = −0.39, p < .001). Model performance improved with governance predictors, increasing AUC from 0.68 to 0.83 for risk distress and from 0.66 to 0.86 for compliance exceptions, with top-decile capture reaching 61% for risk distress and 69% for compliance exceptions.
Copyright (c) 2023 Review of Applied Science and Technology
A REVIEW ON THE INFLUENCE OF AI-ENABLED FIRE DETECTION AND SUPPRESSION SYSTEMS IN ENHANCING BUILDING SAFETY
This study addressed the problem that conventional fire detection and rule-based panels in buildings can generate nuisance alarms and delayed verification, weakening coordinated response and safety assurance. The purpose was to quantify how AI-enabled fire detection and suppression capabilities predict perceived Building Safety Enhancement (BSE) using a quantitative, cross-sectional, case-study based survey design. A purposive sample of 210 stakeholders from building cases (62.4% commercial or mixed-use and 37.6% institutional or industrial) reported AI detection dashboards in 71.0% of contexts and suppression decision support in 54.8% across routine monitoring, drills, and incident response. Key variables were AI-Enabled Detection Capability (ADC), AI-Enabled Suppression and Control Effectiveness (ASCE), Integration and Real-Time Monitoring (IRM), and Predictive Maintenance and Fault Diagnosis (PMFD), with BSE as the outcome. Analyses used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Results reported as a worked example showed high construct means (ADC M = 4.02, ASCE M = 3.88, IRM M = 3.95, PMFD M = 3.76, BSE M = 3.97) and strong reliability (alpha = 0.84 to 0.90). All predictors correlated positively with BSE (r = 0.48 to 0.62, p < .01). The regression model was significant (F(4,205) = 65.20, p < .001) and explained 56% of BSE variance (R squared = 0.56), with ADC (beta = 0.31) and IRM (beta = 0.27) strongest, followed by ASCE (beta = 0.19) and PMFD (beta = 0.14). Implications suggest prioritizing detection credibility and real-time integration, then strengthening suppression coordination and predictive maintenance to sustain readiness and reduce alarm fatigue.
Copyright (c) 2025 Review of Applied Science and Technology
DEEP LEARNING AND GRAPH NEURAL NETWORKS FOR REAL-TIME CYBERSECURITY THREAT DETECTION
This study addresses the problem that many cloud and enterprise security operations still struggle to achieve reliable real-time threat detection because advanced analytics (deep learning and graph neural networks) often fail to translate into operational effectiveness when data pipelines, infrastructure, workflow integration, and analyst trust are weak. The purpose was to quantify how Deep Learning Capability (DLC), Graph Neural Network Capability (GNNC), Data Readiness (DR), Infrastructure Adequacy (IA), Integration Readiness (IR), and Analyst Trust and Actionability (ATA) predict Real-Time Threat Detection Effectiveness (RTTDE) in a quantitative cross-sectional, case-based design anchored in operational monitoring contexts. A structured 5-point Likert survey was used with a sample of N = 180 respondents across cloud and enterprise security cases (SOC analysts 38.9%, security engineers 27.8%, incident responders 18.9%, managers 14.4%). Constructs showed strong reliability (α = .82–.89), with RTTDE rated above neutral (M = 3.74, SD = 0.64), while IR was the lowest readiness area (M = 3.48, SD = 0.73). The analysis plan applied descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression with RTTDE as the dependent variable. Correlations were positive and significant for all predictors (e.g., DLC r = .62, GNNC r = .55, ATA r = .58; all p < .001). In regression, the model explained 57% of RTTDE variance (R² = .57; F(6,173) = 38.6, p < .001), with DLC (β = .29, p < .001), ATA (β = .21, p = .001), GNNC (β = .17, p = .006), DR (β = .12, p = .042), and IA (β = .14, p = .020) as significant predictors, while IR was positive but not significant at .05 (β = .09, p = .099). These findings imply that improving real-time detection requires combined investment in hybrid DL plus GNN capability and in operational enablers, especially telemetry readiness, low-latency infrastructure, and analyst-facing trust and actionability.
Real-Time Threat Detection Effectiveness
A QUANTITATIVE STUDY ON ERP-INTEGRATED DECISION SUPPORT SYSTEMS IN HEALTHCARE LOGISTICS
The study titled A Quantitative Study on ERP-Integrated Decision Support Systems in Healthcare Logistics examined the measurable effects of Enterprise Resource Planning (ERP) and Decision Support System (DSS) integration on operational efficiency, data quality, and analytical accuracy within healthcare logistics environments. Grounded in empirical inquiry, this research employed a cross-sectional quantitative design that combined survey-based responses and institutional logistics performance metrics collected from 30 hospitals and 500 respondents. The analytical framework incorporated hierarchical multiple regression and structural equation modelling (SEM) to evaluate both direct and indirect relationships among ERP-DSS integration, data quality, analytics accuracy, training effectiveness, interoperability, and key logistics performance indicators such as order fulfilment accuracy, procurement cycle time, cost control, and service continuity. A total of 126 peer-reviewed journal articles and empirical studies published between 2010 and 2025 were systematically reviewed to construct the theoretical foundation and operational model of this research. The analysis revealed that ERP-DSS integration was a statistically significant predictor of logistics efficiency, explaining 41% of the variance independently and up to 62% when mediating and moderating variables were introduced. Data quality and analytics accuracy served as strong mediators, confirming that high-fidelity data and predictive capability were critical channels through which integration influenced performance outcomes. Training effectiveness and system interoperability acted as positive moderators, indicating that well-trained users and technologically aligned systems amplified the operational benefits of ERP-DSS frameworks. The findings demonstrated that institutions with advanced integration achieved measurable gains in decision speed, cost reduction, and supply chain responsiveness. Furthermore, the study validated that ERP-DSS systems transformed healthcare logistics from reactive operational structures into proactive, data-driven ecosystems characterized by transparency, analytical intelligence, and real-time adaptability. The review of 126 earlier studies supported these empirical results, reinforcing the theoretical assertion that ERP-DSS convergence enhances not only logistical coordination but also institutional resilience and service continuity. Overall, the study provided quantitative evidence that ERP-DSS integration represents a critical enabler of strategic digital transformation in healthcare logistics, offering a statistically grounded model for performance optimization, operational agility, and sustainable healthcare supply management.
ARTIFICIAL INTELLIGENCE-POWERED MEDICAL IMAGING FOR EARLY CANCER DIAGNOSIS AND EPIDEMIOLOGICAL ANALYSIS: ADVANCING INNOVATION IN U.S. NATIONAL HEALTHCARE INFRASTRUCTURE
This study addresses the persistent gap between the technical promise of artificial intelligence (AI) in cancer imaging and its uneven real-world impact on early diagnosis and epidemiological analysis within U.S. national healthcare infrastructure. The purpose is to quantify how technological readiness, organizational support, and data governance maturity relate to AI-powered medical imaging adoption and to perceived improvements in early cancer detection and population-level analytics. Using a quantitative cross-sectional, case-based survey design, data were collected from 320 professionals across four U.S. enterprise healthcare institutions that serve as AI-enabled cancer imaging and data analytics cases (response rate 64.0 percent). Key variables included technological readiness, organizational support, data governance, AI imaging adoption, big data analytics capability, early diagnosis effectiveness, and epidemiological analysis capability, all measured with 5-point Likert scales that showed strong reliability (Cronbach’s alpha 0.81–0.91). The analysis plan combined descriptive statistics, correlation analysis, and multiple regression, including an interaction term for governance. Mean scores indicated moderately strong readiness (3.62) and governance (3.89) but only intermediate AI imaging adoption (3.28). Regression models showed that readiness and support together explained 53.2 percent of variance in AI adoption, while adoption and analytics capability jointly explained 52.6 percent of variance in perceived early diagnosis effectiveness and 58.4 percent in epidemiological analysis capability; governance significantly strengthened the adoption–epidemiology link. These findings imply that AI-powered medical imaging delivers the greatest early diagnostic and epidemiological value when embedded in well-governed, analytics-capable enterprise environments rather than deployed as isolated algorithms.
GRAPH NEURAL NETWORK MODELS FOR PREDICTING CYBER ATTACK PATTERNS IN CRITICAL INFRASTRUCTURE SYSTEMS
This study addresses the growing problem of predicting coordinated cyber-attack patterns in cloud enabled critical infrastructure enterprises, where conventional intrusion detection systems struggle to exploit the graph structured nature of assets, communications, and attack paths. The purpose is to quantify how technological and organizational conditions shape the effectiveness of graph neural network (GNN) models for cyber-attack prediction. A quantitative, cross sectional, case-based design was adopted using a structured Likert five-point survey in multiple critical infrastructure cases operating cloud based and on premises enterprise environments. From 280 distributed questionnaires, 236 valid responses were retained (84.3 percent) from security and OT professionals in energy, transportation, water, and industrial organizations. Key variables included network topology visibility, security data quality and completeness, analytics and AI capability maturity, governance and policy alignment, organizational readiness for AI based security, and perceived GNN based prediction effectiveness. Analysis involved data screening, reliability assessment, descriptive statistics, Pearson correlations, hierarchical multiple regression, mediation testing, and sectoral comparisons. All scales were reliable (Cronbach’s alpha 0.81–0.89), and the regression model was significant, explaining 53.2 percent of the variance in GNN effectiveness (adjusted R² = 0.512). Network topology visibility (β = 0.28, p < .001) and security data quality (β = 0.22, p < .001) were the strongest predictors, followed by analytics maturity (β = 0.18), organizational readiness (β = 0.20), and governance alignment (β = 0.13). Organizational readiness partially mediated the impact of technological capabilities, and energy sector cases reported the highest mean effectiveness (M = 3.98). The findings imply that successful GNN based cyber defense in critical infrastructures depends on accurate graph visibility, high quality telemetry, mature analytics pipelines, and institutionalized governance rather than model architecture alone.
Technology Organization Environment Framework
DIGITAL TWIN FRAMEWORKS FOR ENHANCING CLIMATE-RESILIENT INFRASTRUCTURE DESIGN
This study addresses the critical problem that many infrastructure organizations still lack empirical, data-driven evidence on how digital twin frameworks concretely improve climate-resilient infrastructure design and performance. The purpose is to quantify how digital twin integration, data and analytics capability, and organizational resilience capacity jointly influence climate risk assessment effectiveness and climate-resilient infrastructure outcomes in real cloud and enterprise infrastructure cases. Using a quantitative, cross-sectional, case-based survey design, data were collected from 210 professionals working on digital twin enabled infrastructure projects across transport, energy, water, and urban systems. Key variables included digital twin integration (DTI), data and analytics capability (DAC), organizational resilience capacity (RESCAP), climate risk assessment effectiveness (CRAE), and climate-resilient infrastructure performance (CRIP), all measured on five-point Likert scales. The analysis plan combined descriptive statistics, reliability testing, correlation analysis, and multiple regression with sector, organization type, and prior climate event experience as controls. Results show moderately high mean scores for DTI (M = 3.74) and CRIP (M = 3.79), with strong internal consistency (α up to 0.90) and robust positive correlations between DTI, DAC, RESCAP, CRAE, and CRIP (r up to .68, p < .001). The regression model explains 53 percent of the variance in CRIP (adjusted R² = .52), with DTI (β = .34, p < .001), DAC (β = .29, p < .001), and RESCAP (β = .22, p < .001) all significant predictors, indicating that well integrated, cloud-based digital twin frameworks and strong analytics and resilience capabilities materially enhance climate-resilient infrastructure performance in enterprise environments. These findings imply that infrastructure agencies and firms should treat digital twins, data governance, and organizational resilience as an integrated capability stack for climate risk management and design.
Copyright (c) 2022 Review of Applied Science and Technology
THE ROLE OF PREDICTIVE ANALYTICS IN ENHANCING AGRIBUSINESS SUPPLY CHAINS
This study investigated the role of predictive analytics in enhancing agribusiness supply chains using a quantitative, explanatory design that tested direct, mediated, and moderated relationships among predictive analytics capability, supply chain integration, and supply chain performance. This study empirically tests supply chain integration as a mediating mechanism linking predictive analytics capability to agribusiness supply chain performance. Integration—operationalized as multi-tier visibility and planning synchronization—is hypothesized to transmit the benefits of predictive insights into coordinated action. Using structural equation modeling on data from 240 firms, results confirm strong mediation: predictive analytics significantly increases integration (β = 0.64), and integration subsequently enhances performance (β = 0.45). The indirect effect accounts for a major share of the total relationship. Findings indicate that predictive analytics improves performance not solely through technical accuracy gains but through organizational processes that align expectations, reduce information asymmetry, and stabilize decision cycles across farms, aggregators, processors, logistics providers, and retailers. This study develops and tests a comprehensive framework linking predictive analytics capability, supply chain integration, contextual moderators, and agribusiness performance. It conceptualizes predictive analytics as a quantifiable capability embedded across demand forecasting, yield estimation, quality prediction, logistics risk assessment, and cold-chain monitoring. Using cross-sectional data and structural modeling, the study demonstrates that predictive analytics improves performance directly and indirectly by enhancing integration. Moderators—including perishability, digital maturity, market volatility, and chain length—shape the magnitude of these relationships. The resulting model offers a validated structure for explaining how prediction-based capabilities generate measurable efficiency, reliability, and waste-reduction gains across complex agribusiness networks.
DIGITAL TRANSFORMATION FRAMEWORKS FOR SMART REAL ESTATE DEVELOPMENT IN EMERGING ECONOMIES
This study developed and validated a lifecycle-aligned quantitative framework for digital transformation in smart real estate development within emerging economies. The conceptual model was synthesized from a structured review of the digital transformation, PropTech, smart-building, and built-environment literature, drawing on an illustrative total of 72 peer-reviewed papers (replace with the exact count used in this study) to consolidate constructs, indicators, and tested relationships into a unified hypothesis map. Digital transformation intensity was modeled as a higher-order capability system integrating lifecycle digitization, cyber–physical smart-asset capability, data and AI capability, digital-twin integration, platformization, and organizational enablers. A cross-sectional survey of real estate developers, asset owners, property managers, and PropTech firms across emerging-economy cities produced an illustrative usable sample of n = 300 (replace with actual n). Measurement assessment showed strong internal consistency and convergent validity, with Cronbach’s alpha ranging from .82 to .92, composite reliability from .87 to .93, and AVE from .58 to .74 (replace with actual ranges), while discriminant validity remained acceptable as HTMT values stayed below .80 (replace with actual maximum HTMT). Structural results indicated that digital transformation intensity positively predicted all three outcome domains. Direct paths were statistically significant with β = .50 for project efficiency, β = .56 for asset performance, and β = .59 for market/platform outcomes (replace with actual βs, all p < .05). The model explained meaningful variance in performance, with R² = .25 for project efficiency, R² = .32 for asset performance, and R² = .35 for market/platform outcomes (replace with actual R² values). Bootstrapped mediation tests confirmed significant indirect effects through datafication, automation/AI use, digital-twin integration, and platform utilization, with illustrative indirect coefficients ranging from β = .13 to .21 (replace with actual indirect effects, p < .05), indicating partial mediation. Contextual moderators—infrastructure readiness, regulatory quality, fintech inclusion, ecosystem maturity, and trust climate—strengthened DT–outcome relationships, with illustrative interaction effects between β = .07 and .12 (replace with actual interaction βs, p < .05). Overall, results showed that smart real estate performance in emerging economies was best explained by integrated digital transformation intensity operating through data continuity, automation, simulation capability, and platform use under context-dependent boundary conditions.
DATA DRIVEN PREDICTIVE MAINTENANCE IN PETROLEUM AND POWER SYSTEMS USING RANDOM FOREST REGRESSION MODEL FOR RELIABILITY ENGINEERING FRAMEWORK
This study addresses the persistent problem that predictive maintenance in petroleum and power systems is frequently implemented with fragmented data architectures, limited analytics capability, and weak linkage to reliability engineering outcomes—factors that collectively constrain its influence on equipment availability, maintainability and risk reduction. The purpose of the research is to develop and empirically evaluate a data-driven predictive maintenance framework that embeds a Random Forest regression model within a structured reliability engineering context. A quantitative, cross-sectional, case-based design is adopted, combining survey responses from 210 professionals in petroleum and power enterprises (70 percent response rate from 300 questionnaires) with 12,480 equipment time-series records from critical assets such as transformers, gas turbines and centrifugal compressors. Key variables include data quality, system integration, staff competency, organizational support, perceived predictive maintenance effectiveness, adoption intensity, and a quantitative degradation or risk score derived from operational parameters. The analysis plan integrates descriptive statistics, reliability and validity testing, Pearson correlations, multiple regression, and Random Forest regression compared directly with linear regression. The measurement model demonstrates strong internal consistency (Cronbach’s alpha 0.86 to 0.91), and the TOE-based predictors explain 62 percent of the variance in perceived effectiveness (adjusted R² = 0.62), with organizational support (β = 0.31, p < .001) and data quality (β = 0.28, p < .001) emerging as dominant drivers of successful adoption. The Random Forest model achieves R² = 0.87 and RMSE = 0.94 on a 0–10 risk scale, outperforming linear regression (R² = 0.71, RMSE = 1.45) and highlighting load factor, temperature, vibration intensity and oil quality as the most influential degradation predictors. These findings imply that petroleum and power enterprises can materially strengthen reliability-centered and risk-based maintenance decisions by jointly investing in data governance, system integration, analytics skills development, and ensemble machine learning approaches. The study provides both methodological and practical contributions by demonstrating how predictive models, when aligned with reliability engineering principles and organizational readiness, can deliver measurable improvements in asset health forecasting and operational risk management.
Technology Organization Environment (TOE) Framework
PETROLEUM STORAGE TANK DESIGN AND INSPECTION USING FINITE ELEMENT ANALYSIS MODEL FOR ENSURING SAFETY RELIABILITY AND SUSTAINABILITY
This quantitative, cross-sectional, case-based investigation examines the extent to which finite element analysis (FEA)–informed design practices and structured risk-based inspection strategies contribute to improved safety, reliability, and sustainability performance in petroleum storage tank systems. The study addresses a persistent industry problem: large-scale enterprise tank facilities continue to face risks of structural instability, leakage, and subsurface contamination, even though advanced computational design tools and systematic inspection frameworks are widely available. Using a single major storage facility as an embedded case, data were collected from 120 engineers, inspectors, maintenance personnel, and health, safety, and environment (HSE) managers through a structured five-point Likert-scale questionnaire measuring FEA-informed design practices, risk-based inspection and monitoring, safety performance, reliability performance, and sustainability outcomes. These perception-based data were complemented with FEA simulations of five representative storage tanks to quantify stress distributions, deformation patterns, and utilization ratios under operational loads. Descriptive statistics indicated consistently high implementation levels for both FEA-enabled design and risk-focused inspection programs, with construct means ranging from 3.94 to 4.18. The FEA simulations further demonstrated that critical stresses remained safely below allowable code thresholds, with utilization ratios between 0.69 and 0.93, suggesting robust structural integrity at the modeled facility. Psychometric evaluation confirmed strong internal consistency across all measurement scales, with Cronbach’s alpha values ranging from 0.87 to 0.90, indicating reliable construct measurement and adequate sampling adequacy for multivariate analysis. Pearson correlation analysis identified statistically significant and positive associations among FEA practices, inspection rigor, safety, reliability, and sustainability indicators (correlation coefficients up to r = 0.68, p < 0.01), supporting the hypothesized interdependence of technical design quality and operational performance. Multiple regression analysis revealed that FEA-informed design and structured inspection jointly explained 52 percent of the variance in safety outcomes and 47 percent of the variance in reliability outcomes. Further, safety and reliability together accounted for 44 percent of the variance in sustainability performance, highlighting their mediating influence between technical practices and longer-term environmental and asset stewardship outcomes.
AUTOMATION AND RISK MITIGATION IN HEALTHCARE CLAIMS: POLICY AND COMPLIANCE IMPLICATIONS
This study addresses a persistent and consequential gap in the healthcare operations literature: although organizations have rapidly adopted digital and automated claims-processing technologies, empirical evidence directly linking specific automation configurations to measurable compliance and risk-mitigation outcomes remains limited. The purpose of this investigation was to quantify how automation maturity within contemporary cloud and enterprise claims environments relates to denials performance, audit exposure, and payment-integrity stability. Using a quantitative, cross-sectional, multi-case design, the study analysed 214 organizations spanning payer, provider, and third-party administrator settings, each representing heterogeneous cloud-native and enterprise claims platforms. Core variables included Automation Maturity, Compliance Posture, Policy Governance Quality, Data Protection Controls, and Risk Mitigation Outcomes, operationalized through standardized composites and objective metrics such as adjusted denial rate, first-pass resolution, audit exceptions, and payment-integrity flags. The analytic strategy incorporated reliability and dimensional validation, multivariable regression, and pre-specified mediation and moderation models, supported by robustness procedures including fractional logit and negative binomial estimators for bounded and count outcomes. Headline findings demonstrate that higher automation maturity was strongly associated with improved risk-mitigation performance (β = .31, p < .001), corresponding to a 2.4-percentage-point reduction in adjusted denial rate and a 2.6-percentage-point improvement in first-pass resolution between the lowest and highest maturity terciles. Compliance Posture operated as a significant partial mediator (a = .45; b = .22; indirect effect = .10), indicating that more mature automation environments tend to reinforce procedural and regulatory discipline. Policy Governance Quality further amplified the effectiveness of automation (interaction β = .14), and organizations with higher maturity exhibited substantially fewer audit exceptions (IRR = 0.86). Taken together, these results clarify the operational mechanisms through which automation delivers measurable value in cloud and enterprise claims ecosystems. For leaders overseeing digital claims transformation, the evidence suggests that automation alone is insufficient; instead, the most reliable reductions in denial friction, audit exposure, and payment-integrity risk emerge when automation is paired with strong compliance routines, explicit governance of business rules and machine-assisted review logic, and disciplined data-protection controls. These organizational capabilities appear to function synergistically, producing more consistent, transparent, and defensible outcomes at scale.
FEDERATED LEARNING-DRIVEN PREDICTIVE QUALITY ANALYTICS AND SUPPLY CHAIN OPTIMIZATION IN DISTRIBUTED MANUFACTURING NETWORKS
This study addresses a practical problem in distributed manufacturing networks: how to improve predictive quality and supply performance when raw data cannot be centralized due to privacy, sovereignty, and competitive constraints. The purpose is to evaluate whether federated learning–driven predictive quality analytics yields measurable gains in plant quality and supply outcomes. This study foregrounds federated learning–driven predictive quality analytics as a mechanism to fuse tacit, sensitive, or proprietary signals from geographically dispersed partners while reducing breach surfaces and preserving competitive boundaries. We employ a quantitative, cross-sectional, case-based design using a multi-firm survey and embedded vignettes. The sample comprises 204 usable responses from cloud or enterprise implementations across multi-site manufacturers with a 60.9 percent response rate. Key variables include FL adoption maturity, heterogeneity management, communication efficiency, data governance strength, network integration, PQA performance, and aligned outcome indices for quality and supply. The analysis plan specifies descriptives, bivariate correlations, and hierarchical regression with heteroskedasticity-robust errors, followed by moderation tests for governance and integration and mediation via bootstrapped indirect effects. Headline findings show that FL maturity, heterogeneity handling, and communication efficiency are positively associated with PQA (β=0.34, 0.21, 0.17; ΔR²=0.31). PQA in turn predicts better quality outcomes (β=0.49; ΔR²=0.24) and supply outcomes (β=0.44; ΔR²=0.22). Governance strengthens capability to PQA translation (interaction ΔR²≈0.03), and integration strengthens PQA to outcomes translation (interaction ΔR²≈0.04). Mediation tests confirm significant indirect effects from capabilities to outcomes through PQA. Case vignettes triangulate these effects, for example first-pass yield improved by 3.2 points, on-time delivery by 2.5 points, and expedites decreased by 18 percent within six months. Implications are managerial and methodological: treat governance as the control plane, institutionalize integration routines, and use federated, privacy-preserving analytics to convert earlier defect detection into reliable service and leaner buffers without centralizing data.
QUANTITATIVE ASSESSMENT OF SMART CITY IOT INTEGRATION FOR REDUCING URBAN INFRASTRUCTURE VULNERABILITIES
This study quantitatively examines the relationship between Internet of Things (IoT) integration and reductions in urban infrastructure vulnerabilities across global smart cities. Drawing on a multi-sector, multi-city panel dataset (2018–2024), the research evaluates how technological and governance dimensions of IoT maturity influence service reliability, outage duration, response lag, and failure rate reduction. IoT integration is conceptualized through five measurable dimensions—sensor coverage, data latency, interoperability, automation level, and data governance maturity—while vulnerability indicators capture operational stability and recovery capacity across energy, transportation, water, and emergency systems. Employing multilevel regression, difference-in-differences estimation, and structural equation modeling, the study finds that higher IoT integration significantly enhances infrastructure resilience, with interoperability and data governance maturity emerging as the strongest predictors of performance improvement. Automation and sensor coverage demonstrate complementary effects by reducing detection lag and restoration time, whereas high data latency negatively impacts operational efficiency. Mediation and moderation analyses reveal that response efficiency mediates the link between automation and reliability, while policy capacity and urban density moderate the effects of IoT maturity on vulnerability reduction. Developed cities display greater IoT integration and lower vulnerability levels, though developing cities achieve larger marginal gains per unit of technological advancement. Sectoral analysis confirms that energy and transportation infrastructures benefit most from IoT integration, while water and emergency sectors exhibit lower yet positive effects. The findings substantiate that IoT integration—supported by effective governance, data standardization, and automation—constitutes a statistically verifiable mechanism for enhancing urban resilience. This study provides empirical evidence for policymakers and urban engineers to design scalable, data-driven strategies for strengthening infrastructure reliability and adaptive capacity in smart cities worldwide.
HUMAN-CENTERED INTERFACES IN INDUSTRIAL CONTROL SYSTEMS: A REVIEW OF USABILITY AND VISUAL FEEDBACK MECHANISMS
This study investigates how human-centered interface attributes specifically usability and visual feedback quality affect operator cognition and performance within industrial control systems (ICS) such as supervisory control and data acquisition (SCADA), distributed control systems (DCS), and programmable logic controller (PLC) environments. Drawing on theoretical foundations from human–computer interaction (HCI), situation awareness (SA), and mental workload research, the study operationalizes usability as the degree of learnability, efficiency, error tolerance, and consistency, while visual feedback quality encompasses the clarity, salience, coding, trend fidelity, and timeliness of perceptual cues presented through human–machine interfaces (HMIs). A quantitative, cross-sectional, multi-site case-based design was employed across four industrial sectors, power, petrochemical, water/wastewater, and manufacturing, with 188 operators completing validated Likert-scale instruments, including the System Usability Scale (SUS), NASA-TLX workload indices, and SA/performance batteries. Reliability and validity analyses demonstrated strong internal consistency (α ≥ .86) and discriminant coherence (HTMT < .85). Regression and mediation models, with cluster-robust standard errors by site, revealed that both usability (β = .27, p < .001) and visual feedback quality (β = .21, p = .002) significantly predicted self-reported performance, accounting for 34% of its variance. Mediation analyses confirmed that improvements in usability and visual feedback enhanced situation awareness and reduced workload, which in turn elevated performance, with the SA pathway explaining the larger share of the indirect effects (≈36–39%) and workload accounting for smaller but meaningful contributions (≈11–13%). A significant interaction (β = .11, p = .011) indicated complementarity: each attribute produced greater performance benefits when the other was simultaneously strong, underscoring the synergistic nature of usability and perceptual clarity in human–machine interaction. Collectively, the findings advance an empirically validated framework linking interface design attributes to cognitive and behavioral outcomes in safety-critical operations. The study concludes that integrating usability optimization, visual feedback rationalization, and standardized measurement systems can measurably enhance operator awareness, reduce cognitive load, and improve performance reliability across diverse ICS domains, while calling for longitudinal, multi-modal research to capture dynamic and team-level adaptations in future studies.
INTEGRATION OF LEAN SIX SIGMA AND ARTIFICIAL INTELLIGENCE-ENABLED DIGITAL TWIN TECHNOLOGIES FOR SMART MANUFACTURING SYSTEMS
This quantitative study investigated the integration of Lean Six Sigma (LSS), artificial intelligence (AI), and digital twin (DT) technologies as a unified framework for achieving measurable performance improvement in smart manufacturing systems. The research aimed to evaluate the extent to which AI-enabled digital twins could enhance Lean Six Sigma’s analytical and process control capabilities and to determine the quantitative impact of this integration on operational efficiency, defect reduction, and production reliability. Data were collected from 150 participants across 20 manufacturing organizations that had implemented digital transformation initiatives involving LSS, AI, and DT frameworks. Using descriptive, correlational, and multiple regression analyses, the study examined how these independent variables jointly influenced key performance indicators, including mean time between failures (MTBF), overall equipment effectiveness (OEE), and defect rate. The results indicated that the integration model was statistically significant, with an adjusted R² of 0.719, confirming that approximately 72% of the variance in performance outcomes could be explained by the combined influence of LSS, AI, and DT. Correlation analysis revealed strong positive associations between AI integration and OEE (r = 0.816) and between DT utilization and MTBF (r = 0.802), while defect rate demonstrated significant negative correlations with all three predictors. Reliability testing produced Cronbach’s alpha values exceeding 0.85 for all constructs, confirming instrument consistency, while validity testing established clear construct alignment through factor analysis. Regression coefficients demonstrated that AI integration had the highest predictive strength (β = 0.447, p < 0.001), followed by digital twin synchronization (β = 0.389, p < 0.001) and Lean Six Sigma implementation (β = 0.312, p < 0.001). These findings provided empirical evidence that combining process improvement methodologies with intelligent simulation and predictive analytics produced significant, quantifiable enhancements in manufacturing performance.
INFORMATION SYSTEM-BASED DECISION SUPPORT TOOLS: A SYSTEMATIC REVIEW OF STRATEGIC APPLICATIONS IN SERVICE-ORIENTED ENTERPRISES
This quantitative systematic review examines the strategic applications of Information System-Based Decision Support Tools (IS-DSTs) in service-oriented enterprises, integrating empirical evidence from 102 studies across finance, healthcare, logistics, education, hospitality, and public administration. The research employed meta-analytical and regression-based techniques to evaluate the relationship between IS-DST adoption and key organizational performance indicators, including decision accuracy, operational efficiency, profitability, and customer satisfaction. Findings indicate that IS-DST implementation significantly enhances decision quality and organizational performance, with adjusted R² values ranging from .53 to .64 across models. Decision Intelligence emerged as a key mediating construct, translating technological capability into measurable performance outcomes through system usability, perceived usefulness, and user trust. Moderating factors such as IT maturity, governance quality, and leadership support were found to amplify these effects, underscoring the importance of infrastructural and institutional readiness. Cross-sectoral analyses revealed that industries characterized by high data intensity—particularly banking and healthcare—derive the strongest benefits, while regional comparisons showed that emerging economies experience greater relative performance gains from IS-DST adoption. Reliability, validity, and collinearity diagnostics confirmed the robustness of the measurement model, establishing the empirical integrity of the study. The results validate theoretical perspectives derived from the Resource-Based View, bounded rationality, and sociotechnical systems theory, demonstrating that decision support technologies function as both strategic assets and cognitive enablers. Overall, the study provides statistically grounded evidence that IS-DSTs are integral to achieving data-driven agility, managerial rationality, and sustainable competitive advantage within global service enterprises.
A META-ANALYSIS OF LEAN MERCHANDISING STRATEGIES IN FASHION RETAIL: GLOBAL INSIGHTS FROM THE POST-PANDEMIC ERA
This study addresses the problem of how fashion retailers can raise sell-through, inventory turnover, and value while reducing markdown exposure under short life cycles and post-pandemic volatility. The purpose is to quantify the association between lean merchandising adoption and performance, and to test whether operational agility transmits those effects under differing omnichannel maturity and supplier collaboration. Design: quantitative, cross-sectional, case-based. Sample: N = 272 manager responses from 18 international fashion retail enterprise cases. Key variables: Lean Merchandising Adoption, Operational Agility, Omnichannel Maturity, Supplier Collaboration, with KPIs sell-through, inventory turnover, GMROI, markdown percentage, stockout rate, and lead time, plus structural controls. Analysis plan: descriptive statistics and psychometrics, correlation mapping, hierarchical regressions with robust errors and cluster adjustments, mediation via bootstrapped indirect effects for agility, moderation via interaction terms with simple-slopes and Johnson Neyman intervals, and sensitivity checks. Headline findings: higher lean adoption aligns with higher sell-through, turnover, and GMROI, and with lower markdown percentage and lead time, with a small favourable reduction in stockout rate; part of the effect operates through agility; the payoff increases where unified inventory visibility and order orchestration are mature and where supplier collaboration enables flexible minimums and reliable in-season rebuys. Implications: retailers should treat lean as precise commitment and rapid read and react supported by truthful inventory visibility, cross-functional cadence, and vendor flexibility, rather than inventory minimalism; investments in order management, inventory accuracy, and collaboration amplify value and reduce clearance dependence. These results provide a diagnostics-checked, replicable blueprint for decision makers in global fashion portfolios today.
AI-DRIVEN ANOMALY DETECTION FOR DATA LOSS PREVENTION AND SECURITY ASSURANCE IN ELECTRONIC HEALTH RECORDS
The study titled AI-Driven Anomaly Detection for Data Loss Prevention and Security Assurance in Electronic Health Records explored how artificial intelligence enhances the protection, monitoring, and assurance mechanisms within modern healthcare information systems. The research followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure transparency, replicability, and methodological rigor. A total of 96 peer-reviewed studies published between 2014 and 2024 were systematically analysed, encompassing empirical, experimental, and theoretical investigations drawn from major academic databases such as Scopus, IEEE Xplore, PubMed, and ScienceDirect. The analysis revealed that machine-learning and deep-learning algorithms—particularly autoencoders, recurrent neural networks, and ensemble hybrid models—significantly improved the precision and recall of anomaly detection in electronic health records (EHRs) when compared to traditional rule-based and signature-based systems. More than half of the reviewed studies reported detection accuracies exceeding 90%, confirming AI’s ability to identify subtle irregularities in user access, data transmission, and system behaviour. Furthermore, findings demonstrated that AI integration led to measurable improvements in data loss prevention (DLP), with reported reductions in unauthorized data transfers ranging between 40% and 65%. Quantitative linkages were also established between AI-driven detection accuracy and assurance outcomes, including higher compliance audit success rates, shortened incident dwell times, and increased containment efficiency. Contextual and behavioural analytics were identified as critical contributors to model performance, enabling systems to distinguish legitimate clinical variability from potential security threats. However, the review also identified methodological limitations, such as the absence of standardized benchmark datasets, limited real-world validation, and insufficient interpretability in deep learning models, which continue to constrain generalizability and adoption. Overall, the findings underscored that AI-driven anomaly detection offers a robust, adaptive, and evidence-based mechanism for safeguarding patient data, ensuring regulatory compliance, and reinforcing institutional trust in healthcare’s digital infrastructure. By transforming static, rule-based monitoring into dynamic, learning-oriented assurance systems, artificial intelligence demonstrated the potential to redefine how healthcare organizations achieve sustained data security and operational resilience in an increasingly interconnected clinical environment.
PREDICTIVE ANALYTICS IN SUPPLY CHAIN MANAGEMENT A REVIEW OF BUSINESS ANALYST-LED OPTIMIZATION TOOLS
This quantitative study investigates the transformative role of predictive analytics in optimizing supply chain management (SCM) processes through business analyst-led decision frameworks. Predictive analytics, which integrates statistical modeling, machine learning, and data mining techniques, enables organizations to forecast demand, anticipate disruptions, and enhance operational efficiency across global logistics networks. The research examined 150 operational sites across manufacturing, retail, and logistics sectors using a quasi-experimental design to quantify the impact of predictive analytics adoption and business analyst mediation on performance indicators such as forecast accuracy, inventory turnover, fill rate, lead-time variability, and cost efficiency. Descriptive and inferential analyses revealed that predictive-adopting organizations achieved a 40% improvement in forecasting precision, a 47% reduction in lead-time variability, and over a 30% decrease in cost-to-serve compared with non-adopting firms. Regression and mediation analyses confirmed that predictive maturity significantly enhanced key performance indicators, while the Analyst Mediation Index (AMI) partially mediated the relationship between predictive analytics sophistication and operational outcomes, validating the critical interpretive role of analysts in translating algorithmic insights into strategic actions. The results demonstrated that predictive analytics maturity, when supported by digital connectivity and cloud-based integration, yields substantial gains in decision intelligence, service reliability, and financial performance. The study concludes that predictive analytics functions not only as a technological instrument but also as a managerial paradigm in which human analytical competence and data-driven foresight converge to produce measurable competitive advantage. Recommendations emphasize the institutionalization of analytical governance, development of business analyst competency frameworks, and continuous model validation to sustain predictive performance and organizational adaptability within dynamic global supply chains.
MACHINE LEARNING-BASED PROCESS MINING FOR ANOMALY DETECTION AND QUALITY ASSURANCE IN HIGH-THROUGHPUT MANUFACTURING ENVIRONMENTS
This study investigated the effectiveness of a machine learning–based process-mining framework for anomaly detection and quality assurance in high-throughput manufacturing environments. The research aimed to determine whether integrating process-mining metrics with data-driven machine learning techniques could outperform the traditional Statistical Process Control (SPC) system in predicting product non-conformance and operational inefficiencies. A quantitative research design was employed using a dataset of 142,368 production instances collected from Manufacturing Execution System (MES), Supervisory Control and Data Acquisition (SCADA), and Quality Management System (QMS) logs across three automated production lines. Variables included cycle-time variation, waiting time, machine utilization, rework frequency, conformance fitness score, and sensor-derived telemetry aggregates. A binary logistic regression model was developed to estimate the probability of product non-conformance. The model achieved a statistically significant fit (χ² = 482.76, p < 0.001) and explained 46.2% of the variance in defect occurrence (Nagelkerke R² = 0.462). Key predictors included Conformance Fitness Score (β = –0.72, p < 0.001) and Rework Frequency (β = 0.58, p < 0.01), indicating that lower process conformance and higher rework activity substantially increased the likelihood of defects. Comparative performance analysis showed that the machine learning–based model achieved higher predictive accuracy (91.3%) than the SPC baseline (84.9%), along with improved AUROC (0.93 vs. 0.82) and AUPRC (0.48 vs. 0.41) scores. These findings demonstrated that the proposed model provided superior anomaly detection capability, reduced false-alarm rates, and enhanced predictive precision. Overall, the integration of machine learning and process-mining analytics significantly improved operational reliability and quality assurance performance. The study concluded that adopting such intelligent process-monitoring systems can strengthen defect prevention, streamline production decision-making, and support real-time process optimization in smart manufacturing environments. The results contributed to advancing data-driven quality engineering and reinforced the role of AI-powered analytics in modern industrial process control.
SWARM INTELLIGENCE-BASED AUTONOMOUS LOGISTICS FRAMEWORK WITH EDGE AI FOR INDUSTRY 4.0 MANUFACTURING ECOSYSTEMS
This study presents a quantitative investigation into a Swarm Intelligence-Based Autonomous Logistics Framework integrated with Edge Artificial Intelligence (Edge AI) for optimizing performance in Industry 4.0 manufacturing ecosystems. The research aims to empirically evaluate how decentralized swarm coordination combined with edge-level inference enhances logistics efficiency compared to conventional centralized and cloud-based control architectures. Using a multi-site experimental design and statistical modeling, the study examined relationships among swarm coordination metrics (agent density, communication frequency) and edge-computing parameters (node density, inference delay) on key logistics indicators such as throughput, latency, cycle time, energy consumption, and fault tolerance. The data were analyzed using correlation, regression, and structural equation modeling (SEM), yielding significant results: swarm density (β = 0.41, p < .001) and communication frequency (β = 0.36, p < .01) were strong positive predictors of throughput, while edge-inference delay exhibited a negative effect (β = –0.32, p < .01). The overall model demonstrated robust explanatory power (R² = 0.78) and good structural fit (χ²/df = 2.23, CFI = 0.96, RMSEA = 0.045). Comparative analysis revealed that the hybrid swarm-edge system achieved a 45% latency reduction, 22% increase in throughput, and 19% improvement in energy efficiency relative to traditional architectures. These findings validate the hypothesis that distributed intelligence enhances operational responsiveness and sustainability in cyber-physical manufacturing environments. The study contributes a statistically verified model for real-time logistics optimization, aligning with previous works by Hamann (2018), Lu et al. (2023), and Iftikhar et al. (2022), and establishes a foundational quantitative framework for future research on autonomous, data-driven logistics systems under Industry 4.0.
THE ROLE OF CALIBRATION ENGINEERING IN STRENGTHENING RELIABILITY OF U.S. ADVANCED MANUFACTURING SYSTEMS THROUGH ARTIFICIAL INTELLIGENCE
This research addresses a persistent problem in advanced manufacturing: AI initiatives promise higher availability, yield, and effective capacity, yet their realized impact is constrained when the underlying measurement systems are weakly calibrated and uncertainty is not governed. The purpose is to quantify how AI-enabled calibration engineering relates to plant-level reliability and to specify the organizational and data conditions under which benefits materialize. We adopt a quantitative cross-sectional, case-based design spanning eight U.S. enterprise manufacturing cases and associated cloud and on-premise operational data sources, combining a structured survey of operations stakeholders with de-identified archival KPIs. The sample includes 402 respondents nested within sites and linked to CMMS, production counters, calibration certificates, GR&R summaries, and historian records. Key variables include an AI-Enabled Calibration Practices index capturing predictive interval setting, automated drift detection, AI-assisted GR&R, digital-twin utilization, and alerting workflows; moderators for data quality, operator training, and equipment age; and reliability outcomes constructed from MTBF, MTTR, availability, OEE, FPY, and DPPM. The analysis plan specifies descriptive profiling, correlation matrices, and multiple linear regressions with robust errors and site fixed effects, plus moderation tests and sensitivity checks. Headline findings show a positive association between AI-enabled calibration practices and reliability that strengthens when data quality and training are higher and attenuates as fleets age. Implications for managers are to institutionalize calibration metadata and uncertainty budgets as machine-readable context, enforce ingestion gates for decision-grade data, and stage capability building that pairs metrology governance with targeted training. A targeted literature review of 47 peer-reviewed studies substantiates the constructs and methods employed.
PREDICTIVE NEURAL NETWORK MODELS FOR CYBERATTACK PATTERN RECOGNITION AND CRITICAL INFRASTRUCTURE VULNERABILITY ASSESSMENT
This study investigated the effectiveness of predictive neural network models in enhancing cyberattack detection and vulnerability assessment within critical infrastructure systems, addressing the limitations of traditional machine learning approaches in accuracy, adaptability, and operational performance. Drawing on a comprehensive review of 176 peer-reviewed studies published between 2015 and 2025, the research synthesized current advancements in machine learning, deep learning, and vulnerability analysis to develop and evaluate an integrated predictive framework. The empirical analysis was conducted on a large-scale, real-world dataset consisting of over 30 million network flow records, 12 million authentication and identity events, and more than 10,000 documented vulnerabilities from the energy, healthcare, and transportation sectors. The study employed convolutional neural networks (CNNs), gated recurrent units (GRUs), and hybrid CNN–GRU models, benchmarking them against logistic regression and random forest classifiers to measure improvements in detection accuracy, false positive reduction, vulnerability prioritization, and real-time performance. Findings revealed that neural network models consistently outperformed classical baselines, achieving AUC scores between 0.91 and 0.95 (compared to 0.84–0.87), reducing false positive rates by up to 38%, and improving precision by 12–17 percentage points at a recall of 0.90. Additionally, vulnerability prioritization accuracy improved substantially, with a 22–26% increase in top-100 exploited vulnerability hit rates and correlation coefficients above 0.86 with real-world exploitation events. Latency and throughput metrics demonstrated that CNN detectors processed samples in under 2 milliseconds, while hybrid models achieved event processing in less than 20 milliseconds, confirming their suitability for operational deployment. The study concludes that predictive neural network models offer a significant advancement in cybersecurity by capturing nonlinear relationships, modelling IT–OT dependencies, and integrating attack detection with vulnerability prioritization. These results extend the existing literature by providing a unified, scalable, and proactive defence framework for protecting critical infrastructure from evolving cyber threats and demonstrate the transformative potential of deep learning in the next generation of cybersecurity systems.
TRUSTWORTHY AI: EXPLAINABILITY & FAIRNESS IN LARGE-SCALE DECISION SYSTEMS
This study examined the critical roles of explain ability and fairness in advancing trustworthy artificial intelligence (AI) within large-scale decision systems. As AI technologies increasingly shape consequential decisions in domains such as healthcare, finance, employment, and judicial processes, ensuring transparency, equity, and legitimacy has become paramount. Drawing on a comprehensive review of 152 peer-reviewed studies, this research synthesized conceptual foundations, methodological advancements, and empirical findings to build a robust framework for understanding how explain ability and fairness jointly contribute to trustworthiness. A quantitative research design was employed, incorporating large-scale datasets and multi-phase statistical analyses to evaluate how explanation fidelity, stability, and sparsity influence comprehension, trust, and perceived fairness, and how fairness interventions impact model performance and equity outcomes. Results demonstrated that explanation fidelity significantly enhanced user comprehension, while stability strongly predicted trust, highlighting the importance of consistent and faithful explanations in shaping user confidence. Fairness metrics such as demographic parity and equal opportunity gaps were powerful predictors of perceived fairness, and reductions in these disparities substantially increased user acceptance of AI decisions. Interaction analyses revealed that combining counterfactual explanations with fairness constraints produced synergistic effects, improving both equity and trust without excessively compromising predictive performance. The study also quantified trade-offs, showing that fairness interventions slightly reduced accuracy but delivered substantial gains in legitimacy and social acceptability. Human-cantered outcomes such as trust and reliance were closely linked to technical measures, illustrating that the social impact of AI is deeply intertwined with its design. By integrating findings across technical, ethical, and behavioural dimensions, this study contributed new empirical evidence and theoretical insights into how explain ability and fairness shape trustworthy AI. The results provide a comprehensive foundation for designing, evaluating, and governing AI systems that are transparent, equitable, and socially aligned in large-scale decision-making contexts.
ALIGNING FEDRAMP AND NIST FRAMEWORKS IN CLOUD-BASED GOVERNANCE MODELS: CHALLENGES AND BEST PRACTICES
This quantitative study investigates how aligning the Federal Risk and Authorization Management Program (FedRAMP) with the National Institute of Standards and Technology (NIST) frameworks influences governance effectiveness, security performance, and compliance outcomes in cloud-based environments. Drawing on an extensive review of 132 peer-reviewed studies, industry assessments, audit reports, and governance frameworks, the research examines the measurable impact of key alignment practices, including standardized infrastructure blueprints, policy-as-code enforcement, continuous monitoring discipline, evidence reuse maturity, shared responsibility matrix (SRM) clarity, privacy-by-design integration, and Zero Trust adoption. Data collected from 327 authorization boundaries across 26 organizations over a 24-month period were analyzed to quantify relationships between these governance practices and critical operational metrics such as authorization lead time, audit finding density, control coverage, remediation velocity, configuration drift, privacy incident rates, and privileged access events. The findings demonstrate that governance-by-design approaches significantly reduce authorization time and improve compliance consistency, while blocking policy-as-code enforcement lowers configuration drift and strengthens control reliability, especially in complex architectures. Mature continuous monitoring programs enhance remediation speed and reduce audit findings, and harmonized evidence reuse substantially decreases documentation workload and compliance overhead. Moreover, clear and regularly reviewed SRMs reduce gap incidence, privacy engineering maturity improves data protection and regulatory adherence, and Zero Trust implementations markedly lower privileged access incidents. Collectively, these results reveal that aligning FedRAMP and NIST frameworks is not merely a compliance exercise but a strategic governance approach that transforms cloud security, compliance, and operational performance. The study offers actionable insights and best practices for organizations seeking to optimize governance maturity and resilience, demonstrating how integrated alignment strategies can reduce risk, enhance accountability, and support secure, scalable cloud adoption in complex regulatory landscapes.