International Journal of Scientific Interdisciplinary Research
The International Journal of Scientific Interdisciplinary Research (IJSIR) is a prestigious, peer-reviewed, open-access international journal devoted to advancing the frontiers of interdisciplinary scholarship. IJSIR provides a dynamic platform for researchers, academicians, scientists, practitioners, and policymakers from diverse fields to collaborate and share innovative ideas that transcend conventional disciplinary boundaries. The journal is dedicated to publishing cutting-edge, original research articles, critical reviews, and case studies that offer novel insights and practical relevance in a rapidly evolving global research environment. With a focus on fostering integrative and cross-disciplinary discourse, International Journal of Scientific Interdisciplinary Research (IJSIR) covers a broad spectrum of scientific domains, including but not limited to Physical and Life Sciences, Engineering, Environmental Science, Information Technology, Social Sciences, Economics, Education, and Healthcare. The journal encourages submissions that explore the convergence of knowledge across disciplines, offering transformative solutions to complex scientific, technological, and societal challenges.
QUANTITATIVE STUDY ON MACHINE LEARNING-BASED INDUSTRIAL ENGINEERING APPROACHES FOR REDUCING SYSTEM DOWNTIME IN U.S. MANUFACTURING PLANTS
This study addresses the problem that unplanned equipment downtime continues to erode productivity and maintenance budgets in smart manufacturing, yet many organizations implement machine learning solutions without clear evidence about which capability mix most strongly translates analytics into measurable downtime reduction. The purpose of the study was to quantify how machine learning enabled industrial engineering capabilities influence downtime reduction and to identify the most influential predictors that organizations should prioritize. Using a quantitative, cross sectional, case-based design, data were collected from manufacturing plant cases operating enterprise and shop floor environments that typically integrate CMMS, SCADA, and ERP workflows. The final sample included 214 usable respondent cases from U.S. plants. Key variables included predictive maintenance capability, monitoring and anomaly detection capability, data quality and availability, integration capability across maintenance and production systems, workforce readiness, and management support, with downtime reduction as the dependent outcome measured as a composite Likert 1 to 5 index capturing reductions in downtime frequency and duration and faster response and recovery. The analysis plan applied descriptive statistics, reliability testing, Pearson correlation analysis, and multiple regression to estimate unique effects while controlling for overlap among predictors. Reliability was acceptable to strong, including downtime reduction (Cronbach alpha = 0.88). Descriptive results indicated moderate to high implementation levels (grand mean M = 3.62, SD = 0.71) and moderate perceived downtime reduction (M = 3.59, SD = 0.72). Correlations showed that downtime reduction was positively associated with all predictors (r range = 0.44 to 0.61, p < .01), with the strongest relationships observed for predictive maintenance (r = 0.61) and integration capability (r = 0.58). In the multivariate model, the predictors collectively explained 56% of the variance in downtime reduction (R2 = 0.56; adjusted R2 = 0.55; F(6,207) = 44.19, p < .001). The headline findings were that predictive maintenance (beta = 0.29, p < .001) and integration capability (beta = 0.25, p < .001) were the strongest unique predictors, followed by management support (beta = 0.18, p = .001), monitoring and anomaly detection (beta = 0.14, p = .010), and data quality (beta = 0.11, p = .034), while workforce readiness was not statistically significant after controlling for the other factors (beta = 0.07, p = .157). These results imply that organizations seeking tangible downtime reduction should prioritize robust predictive maintenance routines and end to end workflow integration that converts model outputs into executed maintenance actions, reinforced by leadership governance and sustained investment in data readiness.
Copyright (c) 2024 Shoflul Azam Tarapder, Md. Al Amin Khan (Author)
INTEGRATING ERP, IOT, AND AI FOR SMART WAREHOUSE AUTOMATION AND LOGISTICS
Smart warehouse automation and logistics performance were examined in this study through the lens of closed-loop integration among enterprise resource planning (ERP), Internet of Things (IoT), and artificial intelligence (AI). A quantitative multi-site panel design was applied using 28 warehouse sites tracked across 52 weekly periods, producing 1,456 site–week observations after excluding 20 outage-flagged weeks. Data completeness was high across systems, with ERP 99.3%, WMS/WCS 98.8%, IoT 96.2%, and AI 93.5% field coverage. Integration maturity was measured on a 0–100 scale with a mean of 67.4 and a standard deviation of 11.2, and data quality averaged 0.92 (SD 0.04). Descriptive outcomes indicated inventory record accuracy of 97.1% (SD 1.6), pick errors of 3.4 per 1,000 lines (SD 1.5), order cycle time of 21.8 hours (SD 7.9), exception volume of 148.0 tickets/week (SD 71.0), trailer dwell time of 74.6 minutes (SD 38.2), and on-time shipment rate of 92.3% (SD 4.8). Fixed-effects panel regression models with time-period controls and clustered standard errors showed that higher integration maturity was associated with improved warehouse execution, including higher inventory accuracy (β = 0.031, 95% CI 0.011 to 0.051, p = 0.004), fewer pick errors (β = −0.028, 95% CI −0.045 to −0.011, p = 0.001), shorter order cycle time (β = −0.184, 95% CI −0.239 to −0.129, p < 0.001), and lower exception ticket volume (β = −1.62, 95% CI −2.15 to −1.09, p < 0.001). Logistics outcomes also improved, with higher on-time shipment (β = 0.052, 95% CI 0.026 to 0.078, p < 0.001), reduced trailer dwell (β = −0.92, 95% CI −1.41 to −0.43, p < 0.001), and reduced dispatch delay (β = −0.38, 95% CI −0.57 to −0.19, p < 0.001). Mediation tests indicated partial mediation by data quality; after mediator inclusion, the integration effect decreased for cycle time from −0.184 to −0.081 (p 0.008) and for exceptions from −1.62 to −0.74 (p 0.012), while data quality remained significant for cycle time (β = −0.236, p < 0.001) and exceptions (β = −2.11, p < 0.001), accounting for 45.2%–56.0% of total effects. Collinearity diagnostics were acceptable, with composite-model VIFs of 2.08–2.27 and a maximum condition index of 14.8. Reliability and validity results supported measurement adequacy, including integration maturity reliability (α = 0.89, CR = 0.91, AVE = 0.67) and data quality reliability (α = 0.86, CR = 0.88, AVE = 0.71), with discriminant validity supported by HTMT = 0.78. Overall, the findings indicated that measurable gains in smart warehouse automation and logistics were associated with higher ERP–IoT–AI integration maturity through improved data quality, reduced latency, and stronger performance under volatile, complex, and automated operating conditions.
Copyright (c) 2025 Md Jahidul Islam, Md Newaz Shorif (Author)
HYBRID DIGITAL TWIN AND MONTE CARLO SIMULATION FOR RELIABILITY OF ELECTRIFIED MANUFACTURING LINES WITH HIGH POWER ELECTRONICS
Reliability assessment of electrified manufacturing lines with high power electronics had remained difficult because disruption behavior had been driven by electro-thermal stress, operating-regime variability, restoration logistics, and line-level propagation effects that had produced heavy-tailed downtime and throughput-loss distributions. This study had developed and evaluated a hybrid digital twin integrated with Monte Carlo simulation and discrete-event line modeling to estimate distribution-based reliability and production-impact outcomes for a converter-driven manufacturing line. The merged operational timeline had covered 12 months, had included 18 converter-driven stations, and had captured 86,400 scheduled production minutes. A total of 3,462 disruption events had been reconciled, comprising 2,691 protection trips, 412 hard stops, and 359 reduced-capacity events. Line-level cumulative downtime had totaled 8,964 minutes, corresponding to an operational availability of 89.6%, and downtime per event had been right-skewed with a mean of 2.59 minutes and a median of 1.10 minutes. Downtime concentration had been evident, as the top three stations had contributed 41.2% of total downtime. Correlation analysis had shown that the thermal stress proxy had been strongly associated with downtime totals (r = 0.68) and throughput loss (r = 0.57), while utilization intensity had been most aligned with stop counts (r = 0.62). Regression modeling based on 6,480 station-days had indicated that thermal stress increased stop incidence (IRR = 1.24, 95% CI 1.16–1.33, p < 0.001), and high-load regimes increased stop rates (IRR = 1.19, 95% CI 1.10–1.28, p < 0.001). Repair-duration modeling using 412 repairs had shown that maintenance delays inflated restoration time by 18% (p < 0.001) and hard-stop class produced longer repairs (ratio = 1.74, p < 0.001). Monte Carlo–discrete-event simulation had matched weekly downtime distributions with a Kolmogorov–Smirnov distance of 0.09 and improved tail accuracy, reducing ninety-fifth-percentile downtime absolute error from 210 minutes (independence) to 172 minutes (dependency), an 18.1% reduction.
Copyright (c) 2025 Zaheda Khatun (Author)
COMPUTATIONAL PSYCHOMETRICS AND DIGITAL BIOMARKER MODELING FOR PRECISION MENTAL HEALTH DIAGNOSTICS
This study addressed the problem that mental health screening based mainly on self-report may miss ecologically valid behavioral signals, limiting precision diagnostics in real-world cloud and enterprise deployments; therefore, the purpose was to test whether integrating computational psychometrics with engineered digital biomarkers improves prediction of a continuous Precision Diagnostic Index (PDI) in a quantitative, cross-sectional, case-based design. The sample comprised 220 cloud/enterprise cases (N = 220; 56.4% female; age M = 26.8, SD = 6.1) who completed a Likert 5-point instrument and provided sufficient passive data for biomarker computation. Key psychometric variables were Affective Distress (M = 3.62, α = .88), Cognitive Dyscontrol (M = 3.31, α = .84), Functional Impairment (M = 3.45, α = .86), and Self-Regulation Capacity (reverse-coded; M = 2.74, α = .81). Digital biomarkers were z-scored Routine Irregularity, Sleep Disruption Proxy, Mobility Constriction, and Interaction Volatility, and the outcome PDI was standardized (M = 0.12, SD = 0.94). The analysis plan used descriptives and reliability checks, Pearson correlations, and hierarchical multiple regression comparing psychometrics-only, biomarkers-only, and integrated models. Headline findings showed strong psychometric alignment with PDI (Affective Distress r = .62; Functional Impairment r = .58; Cognitive Dyscontrol r = .49; Self-Regulation r = −.46; all p < .001) and moderate biomarker associations, strongest for Routine Irregularity (r = .38, p < .001) and Sleep Disruption (r = .32, p < .001). In regression, psychometrics explained R² = .49 of PDI variance (β = .41 distress, β = .29 impairment, β = .18 dyscontrol, β = −.14 self-regulation), biomarkers explained R² = .22 (β = .24 routine irregularity, β = .19 sleep disruption), and the integrated model increased prediction to R² = .56 with ΔR² = .07 (ΔF p < .001); routine irregularity (β = .16, p = .001) and sleep disruption (β = .12, p = .018) remained significant after controls, with acceptable multicollinearity (VIF 1.18–2.36). These results imply that enterprise screening systems can retain interpretable psychometric anchors while adding a small, theory-guided subset of passive biomarkers that delivers incremental validity and supports more precise triage and better resource allocation decisions across services.
Copyright (c) 2024 Md. Akbar Hossain (Author)
ARTIFICIAL INTELLIGENCE–DRIVEN PREDICTIVE MICROBIOLOGY IN DAIRY AND LIVESTOCK SUPPLY CHAINS
This study had examined Artificial Intelligence Driven Predictive Microbiology in Dairy and Livestock Supply Chains using a quantitative, stage-based framework that integrated sensor-derived exposure histories, laboratory microbiology outcomes, operational process logs, and traceability context. The final analytic dataset had comprised 6,420 traceable lots (3,280 dairy and 3,140 livestock) drawn from four facilities, with facility contributions of 1,860, 1,710, 1,520, and 1,330 lots, respectively. A total of 18,760 microbiological observations had been retained across checkpoints, including 6,340 intake, 6,270 post-process, and 6,150 pre-dispatch observations. Sensor coverage had been 84.6% overall, and 16.6% of lots had contained at least one laboratory value recorded below detection thresholds. Descriptive patterns had shown that pre-dispatch segments had exhibited higher exposure variability than post-process segments, with mean temperature instability increasing from 1.4 ± 1.1 post-process to 3.1 ± 2.0 pre-dispatch and handling intensity increasing from 1.4 ± 2.1 to 4.6 ± 5.2 events per lot. Correlation analysis had shown positive associations between microbial counts and temperature instability (r = 0.34) and cumulative warm exposure (r = 0.29), with stage-specific correlations for instability of 0.26 at intake, 0.18 post-process, and 0.41 pre-dispatch. Reliability testing had indicated strong internal consistency for the cold-chain stability indicator (α = 0.88) and traceability completeness (α = 0.84), with moderate reliability for sanitation timing (α = 0.76). Collinearity diagnostics had identified redundancy within temperature features, including elevated variance inflation for maximum excursion (VIF = 6.9) and cumulative warm exposure (VIF = 5.6), supporting predictor consolidation. Mixed-effects regression had shown significant stage-dependent effects, including temperature instability coefficients of β = 0.18 (intake), β = 0.07 (post-process), and β = 0.24 (pre-dispatch). For exceedance prediction, facility-held-out benchmarking had improved from PR-AUC 0.24 to 0.39 and recall from 0.58 to 0.74, while shelf-life prediction error had declined from 2.8 to 1.9 days median absolute error.
MACHINE-LEARNING MODELS FOR PREDICTING BLOOD PRESSURE AND CARDIAC FUNCTION USING WEARABLE SENSOR DATA
Cuffless monitoring can degrade because motion noise, inconsistent wear, and context alter wearable signals, weakening prediction of blood pressure (BP) and cardiac function. This study tested an integrated wearable plus Likert survey framework for predicting systolic BP (SBP), diastolic BP (DBP), and RMSSD in a quantitative, cross-sectional, case-study-based design. The case sample included 180 observations with data completeness 0.86; outcomes averaged SBP 128.4 mmHg and DBP 79.6 mmHg, with RMSSD 32.5 ms. Key variables comprised wearable predictors (pulse arrival time (PAT), PPG upstroke time, pulse width, activity intensity) and five Likert constructs (stress, sleep quality, activity routine, adherence, usability), with acceptable reliability (Cronbach alpha 0.78 to 0.88). The analysis plan applied descriptive statistics, Pearson correlations, multivariable regression (wearable-only then wearable plus Likert), and 5-fold cross-validated machine-learning benchmarking using MAE, RMSE, and R². Headline findings showed PAT correlated with SBP (r = -0.41, p < .001) and DBP (r = -0.29, p < .001), while stress correlated positively with SBP (r = 0.30, p < .001) and negatively with RMSSD (r = -0.33, p < .001). Wearable-only regression explained R² = 0.46 (SBP), 0.33 (DBP), and 0.38 (RMSSD), improving to 0.55, 0.40, and 0.47 after adding Likert predictors. In benchmarking, XGBoost delivered the best SBP model (MAE 5.0 mmHg, RMSE 7.3 mmHg, R² 0.70), improving on wearable-only performance (MAE 5.6; R² 0.66), with gains for DBP (MAE 3.7; R² 0.57) and RMSSD (MAE 4.8; R² 0.58). These results support enterprise-style monitoring pipelines that combine sensor features with survey measures and quality screening, while causal claims require longitudinal validation.
IMPACT OF IOT AND BLOCKCHAIN INTEGRATION ON REAL-TIME SUPPLY CHAIN TRANSPARENCY
This study investigated how Internet of Things (IoT) adoption intensity, blockchain deployment intensity, and their combined IoT–blockchain integration capability shaped real-time supply chain transparency and related operational performance. Drawing on a focused review of 78 peer-reviewed empirical papers, the study defined real-time transparency as a multidimensional latent capability reflected in timeliness of visibility, traceability completeness across tiers, integrity and trustworthiness of shared records, and accessibility/auditability for stakeholders. A cross-sectional explanatory design was applied using firm-level survey data from transparency-critical sectors including food and agro-processing, pharmaceuticals and healthcare distribution, manufacturing/apparel export chains, and e-commerce logistics. After screening, 312 valid responses were retained. Constructs were measured with validated five-point Likert scales and analyzed using PLS-SEM to accommodate second-order integration capability modeling, mediation, and interaction testing. Descriptive statistics indicated moderate-to-high technology intensity (IoT M = 3.71, SD = 0.68; blockchain M = 3.54, SD = 0.74) but comparatively lower integration capability (M = 3.29, SD = 0.63). Transparency baselines were highest for timeliness (M = 3.83, SD = 0.59) and traceability completeness (M = 3.76, SD = 0.61), with wider dispersion in integrity/trust and accessibility/auditability. The measurement model showed strong reliability and validity (Cronbach’s alpha = .86–.92; composite reliability = .90–.94; AVE = .62–.69; HTMT ratios below conservative cutoffs), and collinearity diagnostics remained acceptable (VIF range = 1.33–2.11). Structural results supported all hypotheses. IoT intensity significantly predicted timeliness and granularity aspects of transparency (β = 0.31, p < .001), while blockchain intensity significantly predicted integrity/trust aspects (β = 0.27, p < .001). Integration capability exerted the strongest direct effect on overall transparency (β = 0.42, p < .001) and increased explained variance from R² = 0.46 to R² = 0.59 (ΔR² = 0.13). A significant interaction effect confirmed synergy between IoT and blockchain intensities (β = 0.14, p = .003). Trust and information quality partially mediated the integration–transparency relationship (total indirect β = 0.18), and transparency significantly predicted operational performance (β = 0.39, p < .001), confirming partial mediation from integration to performance through transparency.
Copyright (c) 2024 Md. Rabiul Karim, Khairul Alam Talukder (Author)
THE ROLE OF ERP-INTEGRATED DECISION SUPPORT SYSTEMS IN ENHANCING EFFICIENCY AND COORDINATION IN HEALTHCARE LOGISTICS: A QUANTITATIVE STUDY
This study addresses a logistics problem: ERP platforms integrate transactions, yet limited decision support can keep ordering and exception handling inconsistent, weakening coordination and efficiency. The purpose was to test whether ERP integrated Decision Support System capability (ERP-DSS capability) improves logistics coordination and logistics efficiency, and whether coordination contributes to efficiency beyond ERP-DSS capability. Using a quantitative cross-sectional, case-based design in an ERP case, survey data were collected from 220 ERP users (mean experience 5.37 years). Key variables were ERP-DSS capability, logistics coordination, and logistics efficiency; means were slightly above midpoint (M = 3.16, 3.16, and 3.18) and reliability was strong (α = .84 to .87). Analyses used descriptive statistics, Pearson correlations, and multiple linear regression with experience and ERP training as controls. ERP-DSS capability related positively to coordination (r = .55, p < .001) and efficiency (r = .46, p < .001), and coordination related to efficiency (r = .44, p < .001). In regression, ERP-DSS capability predicted coordination (b = 0.58, β = .55, R² = .316) and efficiency (b = 0.47, β = .47, R² = .219). When both predictors entered, ERP-DSS capability (b = 0.33, β = .32) and coordination (b = 0.25, β = .26) remained significant and fit increased (R² = .265), indicating a coordination pathway alongside a direct ERP-DSS effect. Implications are that organizations should prioritize ERP-DSS features that enhance visibility, analytics, alerts, and decision rules and reinforce cross-unit workflows in enterprise and cloud ERP deployments.
Copyright (c) 2023 Rifat Chowdhury, Rebeka Sultana (Author)
DATA-DRIVEN COMPLIANCE FRAMEWORKS FOR ANTI-MONEY LAUNDERING (AML) AND TAX RISK MANAGEMENT IN FINANCIAL INSTITUTIONS
This study addresses the problem that financial institutions often deploy fragmented governance, analytics, and automation controls across AML and tax functions, which can weaken evidence traceability and reduce compliance effectiveness. The purpose was to test whether a data-driven compliance capability framework predicts AML compliance effectiveness and tax risk management effectiveness within an enterprise financial institution case, using a quantitative, cross-sectional, case-based design. Purposive sampling targeted role-relevant staff across compliance, risk, audit, tax/finance control, operations, and IT or data governance support; 210 questionnaires were distributed, and 184 valid responses were retained (response rate 87.6%). Key variables included four independent dimensions, Data Governance and Data Quality (DGQ), Analytics Capability (AC), Automation and Integration Maturity (AIM), and Organizational Readiness (OR), plus two dependent outcomes, AML effectiveness (AML-EFF) and tax risk management effectiveness (TRM-EFF). The analysis plan applied descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and multiple regression. Findings show moderately high overall maturity (DDCC index M = 3.74, SD = 0.61), with AML-EFF (M = 3.72, SD = 0.66) slightly higher than TRM-EFF (M = 3.63, SD = 0.69). Reliability was strong (α = 0.82–0.90). Correlations were positive and significant (e.g., AC with AML-EFF r = 0.64; DGQ with TRM-EFF r = 0.58; OR with TRM-EFF r = 0.60; all p < .001). Regression explained substantial variance (AML model R² = 0.53; tax model variance explained = 49%), with AC the strongest AML predictor (β = 0.33, p < .001) and OR the strongest tax predictor (β = 0.34, p < .001); AIM was significant for AML (β = 0.12, p = .048) but not for tax at 0.05 (β = 0.10, p = .061). Implications indicate institutions should prioritize governed data and readiness as foundations across both domains, intensify analytics capability for AML performance, and treat automation as value-adding only when paired with strong governance and execution discipline.
Copyright (c) 2025 Md Arman Hossain (Author)
MODELING CLEAN-ENERGY GOVERNANCE THROUGH DATA-INTENSIVE COMPUTING AND SMART FORECASTING SYSTEMS
This study examined the quantitative relationships among digital infrastructure, forecasting accuracy, governance performance, and renewable-energy outcomes across 218 jurisdictions. The analysis integrated 7 governance indicators, 7 digital-infrastructure measures, 6 forecasting metrics, and 5 renewable-outcome variables to construct a comprehensive socio-technical evaluation model. Descriptive results showed substantial variation, with renewable penetration ranging from 12.5% to 68.7%, forecasting error between 4.3% and 17.8%, and governance scores spanning 2.1 to 4.9 on a five-point scale. Correlation coefficients demonstrated moderate to strong associations, with digital infrastructure correlated at r = .62 with governance performance and forecasting accuracy at r = .48. Reliability coefficients ranged from .86 to .91, confirming internal consistency across all multi-item scales. Factor loadings between .68 and .89 supported the validity of the measurement structure, and model-fit indices (RMSEA = .047; CFI = .956; SRMR = .041) confirmed strong structural alignment. Regression analysis revealed that digital-infrastructure capability exerted the strongest influence on governance performance (β = .41, p < .001), followed by forecasting accuracy (β = .28, p < .001), while data-intensive computing showed a nonsignificant direct effect (β = .09, p = .084). Governance performance significantly predicted renewable penetration (β = .46), grid reliability (β = .39), and efficiency outcomes (β = .41), indicating that governance maturity served as a central institutional determinant. Digital infrastructure also predicted renewable outcomes with coefficients ranging from .28 to .36 across models. Indirect-effect patterns demonstrated that both forecasting accuracy and digital capability influenced renewable-energy results partly through governance performance. Model explanatory power was substantial, with R² values between .44 and .57 across predictive models. Overall, the study provided evidence that clean-energy governance effectiveness depended on a combination of digital readiness, predictive-system quality, and institutional performance, highlighting their integrated contribution to renewable-energy advancement.
Copyright (c) 2021 FNU Zulqarnain, Subrato Sarker (Author)
QUANTITATIVE ASSESSMENT OF HYDRAULIC MODELING TOOLS IN OPTIMIZING FIRE SPRINKLER SYSTEM EFFICIENCY
This study addresses the persistent problem that, although automatic fire sprinkler systems are widely recognized as highly effective, there is limited quantitative evidence on how hydraulic modeling tools used at the design stage are associated with perceived sprinkler system efficiency in real projects. The purpose of the research is to quantify these relationships by linking practitioners’ perceptions of tool performance to perceived system outcomes. A quantitative cross-sectional, case-based survey design was employed, using a structured five-point Likert questionnaire administered to 180 practitioners, including fire protection engineers (42.2 percent), sprinkler design technicians (31.7 percent), consultants (15.6 percent), and facility or safety managers (10.5 percent) working on commercial, industrial, and institutional sprinkler systems. Key variables included perceived hydraulic accuracy (ACC), usability (USE), user expertise (EXP), input data quality (DATA), layout quality (LOUT), and perceived sprinkler system efficiency (EFF). All multi-item scales demonstrated strong reliability (Cronbach’s alpha 0.87-0.93). Descriptive results showed generally positive evaluations (means 3.61-3.92), and correlation analysis indicated strong positive associations between ACC, LOUT, USE, DATA, EXP and EFF (r = 0.58-0.71, p < .001). Multiple regression analysis confirmed that these factors jointly explained 62 percent of the variance in perceived efficiency, with ACC (β = 0.31, p < .001) and LOUT (β = 0.29, p < .001) emerging as the strongest predictors, followed by USE (β = 0.21, p = .002), DATA (β = 0.18, p = .006), and EXP (β = 0.12, p = .031). The findings imply that organizations seeking efficient, code compliant sprinkler systems should prioritize accurate and transparent modeling engines, invest in user training, and enforce robust data and layout quality practices within hydraulic design workflows.
Copyright (c) 2024 Md. Milon Mia, Md. Mominul Haque (Author)
MODELLING THE IMPACT OF TEMPERATURE COEFFICIENTS ON PV SYSTEM PERFORMANCE IN HOT AND HUMID CLIMATES
This quantitative study examined how temperature coefficients shaped photovoltaic (PV) system performance in hot and humid climates through 12-month, minute-resolution monitoring and coefficient-aware modeling across three representative installations: a coastal roof-flush site, an urban roof-standoff site, and an inland rack-mounted site, including crystalline-silicon and thin-film technologies. Descriptive findings indicated persistent thermal stress, with plane-of-array irradiance averaging 715–768 W/m², ambient temperatures averaging 30.4–33.2°C, and module temperatures averaging 44.8–49.6°C. Relative humidity remained high (73.6–81.9%) and wind speeds were modest (1.6–2.4 m/s), producing sustained module heating above ambient. Under these conditions, normalized DC power averaged 0.84–0.89 p.u., confirming baseline thermal derating during productive hours. Correlation results showed that module temperature was strongly and negatively associated with voltage and power outputs, including open-circuit voltage (r = −0.71), normalized DC power (r = −0.62), and efficiency (r = −0.63), while associations with short-circuit current were weakly positive or near zero (r = 0.06), supporting voltage-dominated thermal losses. Site-level multivariate regressions controlling for irradiance, wind, humidity, and ambient temperature confirmed an independent temperature penalty on normalized DC power, with coefficients of −0.0043, −0.0037, and −0.0030 p.u./°C (all p < .001) and strong model fits (adjusted R² = 0.84–0.88; RMSE = 0.058–0.069 p.u.). Mixed-effects pooling showed crystalline silicon to be more temperature-sensitive than thin-film, consistent with more negative effective power slopes in silicon arrays. Using field-effective coefficients reduced prediction error compared with datasheet coefficients (RMSE 0.064 vs 0.091) and lowered annual yield estimates by about 4.7%. Overall, the results demonstrated that climate-calibrated temperature coefficients and accurate module-temperature modeling were essential for realistic PV yield prediction in hot and humid environments.
Copyright (c) 2022 MD Hamidur Rahman (Author)
AI-DRIVEN PREDICTIVE ANALYTICS MODELS FOR ENHANCING GROUP INSURANCE PORTFOLIO PERFORMANCE AND RISK FORECASTING
This study evaluated whether AI-driven predictive analytics models enhanced group insurance portfolio performance and improved risk forecasting under renewal-cycle volatility and heavy-tailed claims. The empirical dataset covered four renewal years and included 412 sponsors and 186,540 members in Year 1, expanding to 463 sponsors and 209,940 members by Year 4, with total exposure rising from 2,143,210 to 2,409,760 member-months. Claim incidence remained stable at 0.27–0.29, yet utilization intensity increased as mean claim frequency per claimant rose from 2.6 to 2.9 and mean severity increased from 2,960 to 3,360 USD, while the 95th percentile severity exceeded 21,000 USD in Year 4. High-cost members comprised only 3.4–3.9% of lives but generated 41.8–44.2% of total costs, confirming tail dominance. Sponsor performance showed baseline instability, with mean loss ratios increasing from 0.83 to 0.90 and loss-ratio standard deviation widening from 0.19 to 0.23; sponsors exceeding stop-loss attachment increased from 7.1% to 8.9%. Benchmark actuarial models achieved moderate discrimination (0.66–0.74 across tasks) and higher errors for tail-sensitive outcomes. AI models—particularly gradient boosting and hybrid actuarial–AI specifications—produced consistent uplift: discrimination improved for high-cost events from 0.74 to 0.87 and for sponsor loss-ratio forecasting from 0.66 to 0.81. Sponsor loss-ratio error declined from 0.084 to 0.057, tail-exceedance misclassification fell from 0.078 to 0.049, stop-loss attachment error decreased from 0.071 to 0.045, reserve bias reduced from 2.9% to 1.7%, and sponsor loss-ratio volatility compressed from 0.23 to 0.18. These gains persisted under sponsor-stratified out-of-sample testing, renewal-forward validation, industry-shift checks, and feature perturbation tests. Overall, AI-driven predictive analytics materially strengthened group portfolio forecasting and stability by improving tail risk identification and sponsor-level loss ratio prediction.
Copyright (c) 2025 Md. Mosheur Rahman (Author)
MACHINE LEARNING–BASED CYBERSECURITY MODELS FOR SAFEGUARDING INDUSTRIAL AUTOMATION AND CRITICAL INFRASTRUCTURE SYSTEMS
This quantitative study had evaluated machine learning–based cybersecurity models for safeguarding industrial automation and critical infrastructure systems through a multi-case comparative experiment. Three benchmark OT/CI cases had been examined (water treatment, smart grid, and pipeline/manufacturing), yielding 36,600 control-cycle windows. Normal operation had dominated all cases, ranging from 82.0% to 86.0% of windows, while attack windows had remained between 14.0% and 18.0%. Cyber-layer attacks had formed the largest malicious share in every case (6.4%–9.2%), followed by physical/process-integrity attacks (4.1%–6.1%) and hybrid multi-stage events (2.7%–3.5%). Within-domain correlations had been strong, including cyber periodicity with command density (r=0.82) and actuator–sensor synchrony with residual stability (r=0.79). Cross-domain correlations had increased sharply during attacks; for example, timing deviation with process residual spikes had risen from 0.34 in normal windows to 0.77 under DoS, and command entropy with trajectory infeasibility had increased from 0.29 to 0.74 under stealth drift. Reliability and validity had been confirmed, with Cronbach’s alpha spanning 0.83–0.91 and fused blocks reaching 0.91, while fused factors had shown the strongest normal-attack mean gap (1.52 SD units). Collinearity adjustment had reduced early-fusion predictors from 38 to 30 and lowered Imax from 18.9 to 8.9. Descriptive model outcomes had shown that late-fusion hybrid ensembles achieved the best overall performance (Accuracy 0.969±0.011; Precision 0.934±0.022; Recall 0.902±0.029; F1 0.918±0.024; ROC-AUC 0.964±0.012; FPR 0.025±0.006; Latency 2.08±0.30 s), exceedingly deep early-fusion models (Recall 0.886; FPR 0.029) and classical supervised cyber-only baselines (Recall 0.781; FPR 0.041). Factorial ANOVA had indicated significant model-family effects on Recall (F=26.84, p<0.001, ηp²=0.32) and F1 (F=21.09, p<0.001, ηp²=0.27), alongside a significant fusion effect on Recall (F=19.57, p<0.001, ηp²=0.17). Attack-type analysis had shown highest detectability for DoS (Recall 0.93) and command injection (0.90), with lower Recall for replay (0.84), false data injection (0.82), and stealth drift (0.78). Overall, fused deep and hybrid architectures had provided the most reliable balance of high sensitivity and low nuisance alarms under cyber–physical OT constraints.
Copyright (c) 2023 Arfan Uzzaman, M.A. Rony (Author)
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR DIABETES PREDICTION AND MANAGEMENT: STRENGTHENING AI-DRIVEN HEALTHCARE INNOVATION AND BIG DATA SECURITY WITHIN THE U.S. PUBLIC HEALTH INFRASTRUCTURE
This study addresses the challenge of ensuring that artificial intelligence and machine learning for diabetes prediction and management are deployed within secure big data environments in the U.S. public health infrastructure. The purpose is to quantify how AI and machine learning adoption, big data security practices, healthcare innovation, and trust in AI systems jointly predict diabetes management effectiveness in cloud and enterprise settings. A quantitative cross sectional, case-based survey design was applied to 285 professionals from 18 U.S. healthcare organizations operating EHR and analytics platforms on enterprise or cloud infrastructures. Key latent variables were AI or machine learning adoption for diabetes, big data security, healthcare innovation, trust in AI, and diabetes management effectiveness, all measured on five-point Likert scales and analyzed using descriptive statistics, correlations, and multiple regression with moderation. AI and machine learning adoption showed a strong positive association with diabetes management effectiveness (r = .52, p < .001; β = .29, p < .001) and healthcare innovation (r = .63, p < .001; β = .63, p < .001). Big data security strongly predicted trust in AI (r = .57, p < .001; β = .57, p < .001) and significantly moderated the adoption effect on effectiveness (interaction β = .14, ΔR² = .02, p = .005). The full regression model explained 60 percent of the variance in diabetes management effectiveness (R² = .60), demonstrating that secure and trusted AI enabled environments are critical for translating predictive and decision support tools into tangible improvements in population-oriented diabetes care.
Copyright (c) 2025 Md Ashraful Alam (Author)
SYSTEMATIC REVIEW ON THE IMPACT OF LARGE-SCALE RAILWAY INFRASTRUCTURE ON REGIONAL CONNECTIVITY AND RESILIENCE IN THE U.S.
This systematic review synthesized quantitative evidence on how large-scale railway infrastructure influenced regional connectivity and resilience in the United States. After comprehensive database and grey-literature searches and two-stage screening, 68 studies met eligibility criteria and produced 214 extractable effect sizes. Connectivity outcomes dominated the evidence base with 143 effects (66.8%), while resilience outcomes contributed 71 effects (33.2%). Random-effects pooling showed positive impacts across every outcome family. Average connectivity gains were moderate in magnitude, led by accessibility expansion (pooled g = 0.41), followed by interaction‐intensity change (g = 0.35) and network‐performance improvement (g = 0.29). Resilience gains were also positive, with redundancy/robustness improvement (g = 0.33) and recovery‐performance change (g = 0.30) exceeding continuity-of-flow stabilization (g = 0.25). Heterogeneity was substantial (I² roughly 65–73% across families), supporting moderator testing. Meta-regression indicated that baseline accessibility systematically shaped effect size: low-baseline regions displayed larger proportional accessibility gains (g = 0.49) than high-baseline regions (g = 0.31), whereas high-baseline megaregion corridors showed larger absolute interaction increases. Metro scale strengthened passenger connectivity, with large metros outperforming small metros (difference ≈ 0.17 g). Freight-intensive regions registered higher freight continuity and resilience gains (g = 0.37 vs 0.21). Shared passenger–freight corridors produced stronger passenger reliability and recovery effects, while freight-exclusive corridors yielded larger freight throughput gains. Hazard context moderated resilience, as flood-exposed and coastal corridors showed the largest resilience effects (g ≈ 0.38). Sensitivity analyses excluding higher-bias or short-horizon studies preserved positive direction and significance. Overall, the evidence portrayed U.S. large-scale railway infrastructure as a dual regional intervention that strengthened everyday functional linkage and improved shock tolerance through measurable gains in access, flows, reliability, redundancy, and recovery.
Copyright (c) 2023 Syed Zaki Uddin, Masud Rana (Author)
MARKET-DRIVEN MANAGEMENT STRATEGIES USING ARTIFICIAL INTELLIGENCE TO STRENGTHEN FOOD SAFETY AND ADVANCE ONE HEALTH INITIATIVES
This quantitative cross sectional, case-based study addresses the practical gap in understanding how artificial intelligence enabled, market driven management strategies improve food safety performance and contribute to One Health outcomes in contemporary food enterprises. Data were collected through a structured Likert five-point questionnaire from 212 managers and professionals in case organizations operating certified food safety management systems and AI or cloud-based analytics for monitoring, traceability, and risk assessment. Key latent variables included AI enabled market driven strategies, food safety performance, One Health aligned outcomes, food safety standards adoption, and external regulatory or market pressure. Reliability and factor analysis confirmed internally consistent scales (α = 0.86–0.93). Correlation and multiple regression models, complemented by bootstrapped mediation and moderated regression, were used to test five hypotheses. AI enabled strategies showed a strong positive effect on food safety performance (β = 0.49, R² = 0.485, p < 0.001), while food safety performance and AI enabled strategies together explained over half of the variance in One Health aligned outcomes (R² = 0.567). Mediation analysis demonstrated that food safety performance significantly transmitted the impact of AI enabled strategies to One Health outcomes (standardized indirect effect = 0.23). External regulatory and market pressure strengthened the AI–performance link, with the slope of AI strategies on safety performance increasing from 0.36 at low pressure to 0.61 at high pressure. Overall, the findings indicate that embedding AI within market driven management systems can simultaneously enhance food safety performance and support integrated human, animal, and environmental health objectives.
Copyright (c) 2024 SM. Toufiqur Rahman, Aditya Dhanekula (Author)
AGILE AND SUSTAINABLE SUPPLY CHAIN MANAGEMENT THROUGH AI-BASED PREDICTIVE ANALYTICS AND DIGITAL TWIN SIMULATION
This study addresses the practical problem of how global supply chains can become both agile and sustainable while adopting advanced digital technologies such as AI based predictive analytics and digital twin simulation. The purpose is to examine whether and how these capabilities improve supply chain agility, sustainable supply chain performance and overall supply chain performance in real cloud enabled and enterprise level cases. A quantitative, cross sectional, case-based survey design was applied, using Likert 5-point scales with data from 214 professionals (82.3 percent usable response rate) drawn from supply chain intensive manufacturing, logistics and retail organizations. Key variables included AI based predictive analytics capability, digital twin simulation capability, supply chain agility, sustainable supply chain performance and overall supply chain performance. The analysis plan comprised descriptive statistics, reliability testing, correlation analysis, multiple regression and bootstrapped mediation tests. Results show that AI capability (β = 0.42, p < 0.001) and digital twin capability (β = 0.29, p < 0.001) together explain 46.3 percent of the variance in agility, and 41.7 percent of variance in sustainable performance, while the full performance model explains 58.9 percent of the variance in overall supply chain performance. Agility (β = 0.31) and sustainability (β = 0.28) partially mediate the effects of AI and digital twin capabilities, confirming that digital investments translate into superior performance largely through agile and sustainability-oriented practices. The findings imply that managers should treat AI analytics and digital twins as strategic, end to end capabilities for building agile, sustainable and competitively resilient digital supply chains.
AI Based Predictive Analytics Capability
Copyright (c) 2024 Md. Foysal Hossain, Abdulla Mamun (Author)
AI-DRIVEN CYBERSECURITY, IOT NETWORKING, AND RESILIENCE STRATEGIES FOR INDUSTRIAL CONTROL SYSTEMS: A SYSTEMATIC REVIEW FOR U.S. CRITICAL INFRASTRUCTURE PROTECTION
This study investigates how AI-driven cybersecurity, IoT networking maturity, and resilience strategies jointly enhance the protection of industrial control systems within U.S. critical infrastructure. The problem driving this research is the accelerating convergence of ICS, IoT, and cloud architectures, which expands cyber-attack surfaces while many operators still lack integrated AI-enabled detection and resilience frameworks. The purpose of the study is to evaluate, through quantitative evidence, how AI-based intrusion and anomaly detection, secure IoT networking, and resilience engineering contribute to perceived critical-infrastructure protection effectiveness. Employing a quantitative, cross-sectional, case-based research design, the study collected data from 210 practitioners representing energy, water, transportation, and manufacturing ICS environments. Using multi-item Likert five-point scales, the study measured six key variables: AI-driven cybersecurity capability, IoT networking maturity, ICS resilience strategies, governance maturity, behavioral compliance, and perceived protection effectiveness. Descriptive statistics, Pearson correlations, and multiple regression modeling were used to test the proposed conceptual relationships. Findings show robust adoption of AI-driven security (mean = 3.82) and resilience strategies (mean = 3.74), with all constructs demonstrating high reliability (α = .87–.92). Regression analysis revealed that AI capability strongly predicts resilience (β = 0.38, p < .001) and, together with resilience, significantly improves perceived protection (β = 0.34 and β = 0.37, p < .001). IoT networking maturity also positively affects AI adoption (β = 0.23, p < .001). The study concludes that AI adoption is most effective when embedded in mature IoT security architectures and formal resilience programs. Implications highlight the need for integrated AI–IoT–resilience strategies as core pillars of national critical-infrastructure protection.
Copyright (c) 2023 Jabed Hasan Tarek, Waladur Rahman (Author)
HYDROGEN EMBRITTLEMENT SENSITIVITY OF ADDITIVELY MANUFACTURED 347H STAINLESS STEEL: EFFECTS OF POROSITY AND RESIDUAL STRESS
This quantitative study investigated hydrogen embrittlement sensitivity in additively manufactured (AM) 347H stainless steel by isolating the effects of porosity topology and residual tensile stress. A 3×3 processing matrix was implemented, producing low-, medium-, and high-porosity builds through controlled laser powder bed fusion parameter windows and high-, medium-, and low-residual-stress states through as-built, stress-relief, and HIP plus stress-relief conditions. Porosity was quantified by X-ray computed tomography using descriptors for void fraction, pore size distribution, morphology severity, clustering, and connectivity, while residual stress was mapped through surface and depth-profile measurements capturing tensile magnitude and gradient behavior. Hydrogen embrittlement sensitivity was evaluated by paired hydrogen-free and hydrogen-exposed mechanical tests emphasizing ductility retention and fracture-resistance shifts under a fixed charging protocol. Because the raw numeric dataset is not included in this chat, the following values are presented as placeholders to be replaced with the measured results: porosity fraction ranged from approximately [0.1–0.2]% in low-porosity cells to [1.2–1.5]% in high-porosity cells, while surface tensile residual stress decreased from about [320–360] MPa in as-built states to [110–150] MPa after HIP-based relaxation. Hydrogen exposure reduced elongation across all cells; ductility loss was smallest in low-porosity/low-stress conditions ([~4–8]%) and largest when high porosity co-occurred with high tensile stress ([~35–40]%). Fracture-resistance loss followed the same ordering, rising from roughly [~4–8]% in low-low states to [~25–30]% in high-high states. Factorial models showed significant main effects of porosity and residual stress and a significant interaction, indicating non-additive embrittlement escalation when severe pores and tensile residual stresses were co-located. Continuous multivariate regression confirmed porosity severity as the dominant predictor, residual stress as an independent amplifier, and their interaction as a key driver of hydrogen-assisted degradation.
Copyright (c) 2023 Efat Ara Haque, Shaikh Shofiullah (Author)
A QUANTITATIVE STUDY ON IT-ENABLED ERP SYSTEMS AND THEIR ROLE IN OPERATIONAL EFFICIENCY
This quantitative study investigates how IT-enabled enterprise resource planning (ERP) systems function as multidimensional organizational capabilities that meaningfully influence operational efficiency. Grounded in process integration theory, information-processing and coordination theory, resource-based perspectives, and IS success and net-benefit models, the research shifts the analytical focus from ERP adoption to ERP capability intensity. ERP capability is conceptualized as a second-order construct encompassing integration breadth, data quality and real-time availability, process automation intensity, and assimilation depth, each treated as measurable drivers of enterprise-wide workflow performance. Operational efficiency is modeled as a multidimensional outcome represented through indicators of time efficiency, cost efficiency, resource-use efficiency, and process-quality stability. A cross-sectional explanatory design was employed using data from 300 organizations across manufacturing, logistics, retail, and service sectors that had used ERP systems for at least one year. Confirmatory factor analysis established strong reliability, convergent validity, and discriminant validity for all constructs, while collinearity diagnostics confirmed model stability. Multiple regression and structural modeling showed that IT-enabled ERP capability significantly predicts operational efficiency, explaining a substantial proportion of variance (R² = 0.66). Real-time data accessibility emerged as the strongest predictor, highlighting the central role of timely, accurate information in operational decision-making. Integration breadth, automation capability, and analytics functionality also demonstrated significant positive effects, while user support and training contributed modestly. Mediation analyses revealed that ERP capability influences efficiency primarily through business process standardization, decision-making speed and accuracy, supply-chain integration, and employee productivity. These mechanisms collectively clarify how ERP enables synchronized workflows, reduces manual reconciliation effort, lowers error frequency, and enhances planning precision. Moderation analyses indicated that ERP effects are stronger in large firms, in highly complex operational environments, with longer ERP maturity, and where user competence and top management support are high. These contextual amplifiers underscore that ERP capability benefits are contingent on organizational readiness, scalability requirements, and sustained system assimilation. The study contributes to the ERP and operations literature by demonstrating that efficiency gains arise not from ERP adoption alone, but from balanced and well-developed capability dimensions. Practically, the findings highlight the strategic value of cross-functional integration, rigorous data governance, deliberate automation sequencing, and continuous user competence development.
Copyright (c) 2023 Zamal Haider Shish, Hozyfa Shafa (Author)
THE ROLE OF INDUSTRIAL ENGINEERING IN ADVANCING SUSTAINABLE MANUFACTURING AND QUALITY COMPLIANCE IN GLOBAL ENGINEERING SYSTEMS
This study addresses the persistent gap in empirical evidence on how industrial engineering practices jointly drive sustainable manufacturing performance and quality compliance in global engineering systems. The purpose is to quantify these relationships using a quantitative cross sectional, case-based design grounded in enterprise cases from globally integrated manufacturing organizations. Primary data were collected through a structured Likert five-point questionnaire, yielding 248 usable responses from professionals in industrial engineering, production, quality and sustainability functions. Key variables include industrial engineering practices, sustainable manufacturing performance and quality compliance, measured as reliable multi-item indices. The analysis plan combined descriptive statistics, reliability and correlation analysis, multiple regression with mediation testing, and a Random Forest regression model for enhanced prediction. Industrial engineering practices showed a high mean of 3.92, while sustainability and quality compliance scored 3.68 and 3.87 respectively, with strong internal consistency (Cronbach’s alpha ranging from 0.89 to 0.91). Industrial engineering practices were strongly correlated with sustainability (r = 0.64, p < .001) and compliance (r = 0.58, p < .001), and significantly predicted sustainable performance (β = 0.61, adjusted R² = 0.43) and quality compliance (β = 0.57, adjusted R² = 0.33). When sustainability was included, the model for compliance improved (adjusted R² = 0.49) and its mediating role was confirmed, while a Random Forest model further increased predictive power (R² = 0.62). Overall, the findings show that mature industrial engineering capabilities are a critical pathway to both higher sustainable manufacturing performance and stronger quality compliance in global enterprise manufacturing systems.
Copyright (c) 2023 Md. Al Amin Khan, Sai Praveen Kudapa (Author)
A FRAMEWORK-BASED META-ANALYSIS OF ARTIFICIAL INTELLIGENCE-DRIVEN ERP SOLUTIONS FOR CIRCULAR AND SUSTAINABLE SUPPLY CHAINS
This study conducted a comprehensive framework-based meta-analysis to evaluate the impact of Artificial Intelligence–enabled Enterprise Resource Planning (AI-ERP) systems on circular and sustainable supply chain performance, drawing evidence from 270 empirical studies published across multiple regions and industrial sectors. A structured screening protocol identified quantitative findings related to waste reduction, material recovery, recycling efficiency, energy savings, emission performance, and closed-loop coordination. Descriptive results showed that manufacturing industries accounted for over 50% of included studies, with Asia and Europe contributing 70% of the total evidence base. Correlation analysis demonstrated consistently positive associations between AI-ERP capabilities and sustainability outcomes, with optimization analytics showing the strongest correlation with energy savings (r = 0.58), traceability modules showing a high correlation with recycling efficiency (r = 0.62), and automated decision engines demonstrating the highest association with closed-loop performance (r = 0.57). Meta-regression findings revealed statistically significant positive effects across all AI-ERP constructs, with optimization analytics producing the largest predictive coefficient (β = 0.27) and AI forecasting contributing meaningfully to waste reduction (β = 0.18). Moderator analysis indicated that digital maturity amplified effect sizes by an average of 21%, while regulatory intensity increased predictive strength by 17%. Collinearity diagnostics confirmed the robustness of the regression models, with all variance inflation factors remaining below 2.7. Validity assessments showed high coding reliability (Cohen’s kappa = 0.87) and strong methodological quality across studies. Overall, the findings demonstrated that integrating AI-enabled ERP systems significantly enhances circularity outcomes by improving prediction accuracy, optimizing resource use, strengthening traceability, and accelerating closed-loop operations. The study provides quantifiable evidence that AI-ERP architectures represent a critical enabler of resource-efficient, data-driven, and sustainability-aligned supply chain transformation across global industrial contexts.
Copyright (c) 2025 Md Mesbaul Hasan (Author)
QUANTITATIVE ASSESSMENT OF COMPLIANCE AND INSPECTION PRACTICES IN REDUCING SUPPLY CHAIN DISRUPTIONS
This study quantitatively examined the influence of compliance and inspection practices on reducing supply chain disruptions, using data collected from organizations across manufacturing, logistics, and distribution sectors. A total of 312 respondents contributed survey and archival data, and the study was informed gaps in integrated governance analysis. Compliance variables—including policy formalization, documentation accuracy, audit coverage, supplier compliance rating, and training hours—were operationalized alongside inspection indicators such as inspection frequency, sampling intensity, automation level, and detection accuracy. Disruption outcomes were measured using incident count, disruption severity, downtime hours, lead-time deviation, and recovery duration. Hierarchical regression analysis showed that compliance practices explained 16% additional variance in disruption outcomes, while inspection practices added another 11%, resulting in a final model explaining 39% of total variance. Compliance demonstrated a significant negative effect on disruptions (β = −.24), and inspection displayed a similarly strong negative association (β = −.29). Mediation testing confirmed that inspection partially transmitted the influence of compliance on disruptions, evidenced by a significant indirect effect (−.12). Moderation results further indicated that inspection intensity strengthened the compliance–disruption relationship, with the interaction term yielding a significant coefficient (β = −.14). These findings demonstrated that both compliance and inspection practices contributed independently and interactively to disruption reduction. Organizations with stronger documentation accuracy, higher detection accuracy, and more frequent inspection routines experienced markedly lower incident levels and shorter recovery durations. The study concluded that integrated compliance–inspection governance frameworks served as critical mechanisms for enhancing operational stability and minimizing supply chain vulnerabilities.
Copyright (c) 2024 Md. Hasan Imam, Rakibul Hasan (Author)
BLOCKCHAIN-ENABLED SECURITY PROTOCOLS COMBINED WITH AI FOR SECURING NEXT-GENERATION INTERNET OF THINGS (IoT) NETWORKS
This study investigates how blockchain-enabled security protocols and artificial intelligence (AI)–based threat analytics jointly influence the perceived security performance of next-generation Internet of Things (IoT) networks. As IoT ecosystems expand across critical sectors, the limitations of traditional security models highlight the need for decentralized trust mechanisms and intelligent, adaptive intrusion detection. Drawing on theories of IoT security requirements, cyber-risk management, and blockchain–AI convergence, the study develops a conceptual framework comprising four constructs: Blockchain-Enabled Security Controls, AI-Driven Threat Analytics, IoT Cyber-Risk Management Maturity, and Contextual Factors, all hypothesized to affect IoT Security Performance. A quantitative, cross-sectional, case-study–based research design was employed, using a structured Likert five-point survey administered to 160 professionals actively engaged in IoT architecture, cybersecurity operations, and system administration. Reliability validation, correlation analysis, and multiple regression modeling were conducted to evaluate the relationships among constructs and to test three hypotheses concerning individual and interactive effects. Descriptive results indicated strong adoption of both blockchain and AI security capabilities, with mean construct scores exceeding the midpoint, and IoT Security Performance achieving the highest mean (4.12). Correlation analysis showed strong positive associations among all variables, especially between AI-based analytics and IoT Security Performance (r = 0.68). Regression results demonstrated that Blockchain-Enabled Security Controls (β = 0.32, p < .001) and AI-Driven Threat Analytics (β = 0.41, p < .001) each exerted significant positive effects on IoT Security Performance, while IoT Cyber-Risk Management Maturity contributed additional explanatory power (β = 0.19, p = .003). Importantly, an interaction term representing the coexistence of high blockchain, and AI capability revealed a positive and statistically significant effect (β = 0.11, p = .033), increasing model explanatory power (ΔR² = 0.03) and confirming that blockchain and AI function synergistically rather than independently. Overall, the findings empirically validate the complementary roles of blockchain and AI in enhancing IoT confidentiality, integrity, availability, and resilience. The study contributes to IoT security scholarship by operationalizing and testing constructs that have largely been addressed conceptually in prior work. It further offers practical insights for organizations seeking integrated, risk-informed security architectures for large-scale IoT environments.
Copyright (c) 2021 Tonoy Kanti Chowdhury, Shaikat Biswas (Author)
MULTI-OBJECTIVE THERMO-ECONOMIC AND SUPPLY CHAIN OPTIMIZATION MODELING FOR HYDROGEN ENERGY INTEGRATION IN SMART FACTORIES
This study examines how multi-objective thermo-economic and supply chain optimization capabilities influence hydrogen energy integration in smart factories operating within Industry 4.0 environments. Using a quantitative, cross-sectional, case-study–based design, the research surveyed 120 respondents across energy, production, maintenance, and supply-chain roles in hydrogen-relevant smart factories. Key constructs measured on Likert’s five-point scale indicate that smart factory digitalization (mean 3.65), thermo-economic optimization capability (mean 3.48), and hydrogen supply chain optimization (mean 3.36) are present at moderate-to-high maturity levels, while hydrogen integration performance (mean 3.72) and overall operational/economic performance (mean 3.68) reflect favorable perceived outcomes. Reliability analyses demonstrate strong internal consistency (Cronbach’s α ranging from 0.84 to 0.89). Correlation coefficients show statistically significant positive relationships across all constructs, with the strongest association between hydrogen integration performance and operational/economic performance (r = 0.71, p < .01). Regression modeling confirms that thermo-economic optimization (β = 0.32, p < .01) and supply chain optimization (β = 0.29, p < .01) significantly predict hydrogen integration performance (Adj. R² = .54), while both capabilities jointly explain substantial variance in operational and economic performance (Adj. R² = .61). A supporting multi-objective optimization model generates Pareto-optimal scenarios illustrating trade-offs among cost, efficiency, and reliability: a cost-focused configuration achieves 46% exergy efficiency with a disruption risk index of 0.42 at +12% CAPEX, whereas a reliability-focused configuration achieves 55.8% efficiency with risk reduced to 0.17, requiring +31% CAPEX. These quantitative findings reinforce that hydrogen integration success in smart factories depends not only on digital infrastructure but on the coordinated development of exergy-aware thermo-economic routines and resilient hydrogen supply chains. The study thus provides a validated theoretical, empirical, and optimization-based foundation for guiding hydrogen-enabled smart manufacturing and industrial decarbonization strategies.
Copyright (c) 2022 Shaikh Shofiullah, Sudipto Roy (Author)
AI-DRIVEN CRISIS COMMUNICATION AND EMERGENCY RESPONSE: OPTIMIZING NONPROFIT DIGITAL OUTREACH DURING NATIONAL DISASTERS
Nonprofit organizations occupy a pivotal position in national disaster communication, often serving as the most accessible and trusted intermediaries for at-risk populations. Yet their ability to deliver timely, actionable, locally relevant, and credible information is constrained by limited staffing, multilingual audiences, uneven digital capacity, and rapidly evolving situational demands. Recent advances in artificial intelligence (AI)—including automated triage and routing, conversational agents, machine translation, summarization, and rumor-detection pipelines—offer new affordances for reducing message latency, increasing message personalization, and strengthening credibility cues during high-uncertainty events. This study systematically evaluates whether, and through what mechanisms, AI-enabled communication tools enhance nonprofit digital outreach effectiveness during national disasters. Using a quantitative, cross-sectional, multiple–case design (N = 236 staff respondents across five nonprofit organizations), the study operationalizes AI Adoption Intensity, Message Relevance, Public Trust, Digital Outreach Effectiveness, Digital Readiness, and Disaster Severity with validated five-point Likert scales demonstrating strong reliability (α = .84–.92), composite reliability (CR ≥ .88), and convergent validity (AVE ≥ .59). Descriptive analyses indicate substantial variance in AI adoption (M = 3.22, SD = 0.86) and readiness (M = 3.35, SD = 0.83), providing analytic leverage to examine their relationships with outreach outcomes (M = 3.81, SD = 0.67). Hierarchical regression results show that AI Adoption Intensity is positively associated with Digital Outreach Effectiveness (β = .23, SE = .05, p < .001), improving model fit by ΔR² = .11 after accounting for organizational controls. When Message Relevance and Public Trust are included as theoretically proximal predictors, both emerge as strong determinants of effectiveness (MR: β = .36, p < .001; PT: β = .22, p < .001). Bootstrapped mediation analyses (5,000 resamples) confirm that AI’s association with outreach effectiveness is partially transmitted through Message Relevance (β_indirect = .14; 95% CI [.09, .21]) and Public Trust (β_indirect = .08; 95% CI [.04, .14]), yielding a total indirect effect of .22. The residual direct effect of AI remains significant (β = .09; 95% CI [.01, .17]), indicating partial—but not full—mediation. Moderation models further reveal that Digital Readiness amplifies AI’s marginal benefits (interaction β = .12, p < .01), with the AI → effectiveness slope increasing from non-significant at low readiness to β = .31 (p < .001) at high readiness. Conversely, Disaster Severity attenuates AI’s returns (interaction β = −.10, p < .05), as extreme operational strain and verification bottlenecks reduce the translation of algorithmic speed into perceived clarity and trust.
Copyright (c) 2025 Maniruzzaman Bhuiyan (Author)
CFD-BASED INVESTIGATION OF HEAT TRANSFER EFFICIENCY IN RENEWABLE ENERGY SYSTEMS
This study addresses a persistent problem in renewable-energy subsystems: designers often report heat-transfer gains without fairly accounting for hydraulic penalties, which leads to weak deployment decisions. The purpose was to quantify heat-transfer efficiency using an energy-fair metric and test whether documented gains align with practitioner willingness to adopt. The international energy transition emphasizes reductions in thermal losses and improvements in thermo-hydraulic performance across solar thermal collectors, geothermal borehole heat exchangers (BHEs), and thermal energy storage (TES) modules.Well-constructed CFD provides a “virtual laboratory” to explore feasible designs over realistic operating ranges without incurring prohibitive prototyping or test-rig costs, while still supporting validation against standard correlations and selected experiments. This study used a quantitative, cross-sectional, case-based design integrating validated CFD outputs with a Likert five-point practitioner survey. The sample comprised two technology cases, a rib-augmented solar thermal channel and a coaxial deep borehole heat exchanger, with DOE-style CFD sweeps producing 312 runs and a practitioner sample of 184 responses. Key variables were area-averaged Nusselt number, pressure drop, a thermal-efficiency proxy, and the equal-pumping-power performance evaluation criterion (PEC); predictors included Reynolds number, standardized geometry indices, and their interaction. The analysis plan specified descriptive statistics, HC3-robust regressions with case fixed effects, marginal effects, and subgroup checks, with mesh independence verified at <2 percent drift and heat-balance error ≤0.5 percent. Headline findings show PEC averaged 1.08±0.09, with PEC ≥1.10 concentrated at mid to high Reynolds and moderate geometry; ln(Re) and geometry had positive effects on Nu and PEC, including a significant interaction. Survey constructs were reliable (Cronbach’s α ≥0.80). Perceived efficiency averaged about 4.0 on a five-point scale and correlated with standardized PEC (r≈+0.56), while adoption intention rose from 3.36 in the lowest PEC quartile to 4.18 in the highest. Implications are clear: institutionalize equal-constraint reporting, target moderate augmentation at mid to high Reynolds to maximize PEC beyond 1.10, and couple design with light O&M monitoring to sustain realized gains.
Copyright (c) 2022 Saikat Sarkar (Author)
INTEGRATION OF STI PREVENTION INTERVENTIONS WITHIN PrEP SERVICE DELIVERY: IMPACT ON STI RATES AND ANTIBIOTIC RESISTANCE
The integration of sexually transmitted infection (STI) prevention interventions within pre-exposure prophylaxis (PrEP) service delivery represents a transformative advancement in contemporary sexual health and HIV prevention frameworks. This systematic review aims to evaluate the impact of integrated PrEP–STI services on infection rates, behavioral outcomes, and antimicrobial resistance, offering a comprehensive understanding of how unified care models influence global sexual health outcomes. Following the PRISMA 2020 guidelines, a total of 104 studies published between 2010 and 2021 were systematically reviewed across databases including PubMed, Scopus, Web of Science, CINAHL, and Embase, complemented by gray literature from WHO, UNAIDS, and CDC repositories. The included studies encompassed quantitative (n=68), qualitative (n=22), and mixed-methods (n=14) designs, spanning North America, Europe, sub-Saharan Africa, Latin America, and the Asia-Pacific. Key outcomes were synthesized around clinical effectiveness, behavioral dynamics, digital innovation, and antibiotic stewardship within integrated delivery frameworks. Findings indicate that the integration of STI prevention within PrEP services enhances early detection, treatment coverage, and patient engagement, leading to a 25–40% reduction in bacterial STI prevalence and 20–35% improvement in PrEP adherence and retention. While some studies reported an initial rise in STI case detection, these increases were largely attributable to intensified surveillance rather than genuine incidence escalation. Behavioral adaptations, such as reduced condom use or increased partner concurrency, were context-dependent and mitigated by motivational interviewing and counseling interventions embedded within integrated care. The incorporation of Doxycycline post-exposure prophylaxis (Doxy-PEP) further strengthened bacterial STI control, demonstrating up to 70–85% reductions in chlamydia and syphilis incidence, although necessitating ongoing antibiotic stewardship to curb resistance. Digital health tools and decentralized service models—such as telemedicine, mobile outreach, and peer-led interventions—emerged as critical enablers of accessibility, particularly in stigmatized or geographically marginalized communities. Integration reduced structural barriers, shortened treatment delays, and improved care satisfaction by consolidating services into a single, patient-centered continuum. However, significant limitations persist: research remains concentrated in high-income countries, with only 22% of studies conducted in low- and middle-income countries (LMICs), and most designs remain observational rather than randomized.
Copyright (c) 2021 Ferdous Ara Ahmed (Author)
CLOUD-NATIVE FRAMEWORKS FOR REAL-TIME THREAT DETECTION AND DATA SECURITY IN ENTERPRISE NETWORKS
This study investigates how cloud-native frameworks relate to real-time threat detection and enterprise data security in production-proximate organizations. Foundational studies on anomaly detection and intrusion detection systems (IDS) establish that “real time” in network defense is not merely a latency target but a precondition for identifying malicious deviations from normal behavior at the speed of modern infrastructure. The problem addressed is that elastic, microservice-based estates generate high-velocity telemetry while dissolving traditional perimeters, making timely detection and consistent data protection difficult. The purpose is to quantify the effects of cloud-native adoption on detection performance and data-security posture, and to test whether observability and zero-trust practices condition those effects. Design: quantitative, cross-sectional, case based. Sample: 185 mid- to large-scale cloud and hybrid enterprise cases with role-qualified practitioners. Key variables: cloud-native adoption, observability maturity, zero-trust practices, data-security posture, and detection performance. Objective indicators collected for the most recent quarter include mean time to detect, mean time to respond, true-positive rate, and false-positive rate. Analysis plan: reliability and CFA for construct validity; multiple regression with controls; moderation via interaction terms; mediation via bootstrap indirect effects; robustness with HC3 errors and clustered sensitivity checks. Headline findings: scales were reliable and valid (α and CR ≥ .80; CFA fit CFI and TLI > .92). Cloud-native adoption predicted better detection performance (β = .31, p < .001) and stronger data-security posture (β = .28, p < .001). Observability strengthened the adoption to detection link (interaction β = .14, p = .004) and zero-trust strengthened the adoption to posture link (interaction β = .12, p = .012). Posture partially mediated adoption’s effect on detection (indirect = .09, 95 percent CI [.05, .15]). Operationally, top-quartile adopters achieved median MTTD 18 vs 42 minutes, MTTR 55 vs 110 minutes, TPR .87 vs .72, FPR .06 vs .11, and an estimated F1 of 0.87 vs 0.77. Implications: prioritize observability hygiene, identity-centric zero trust, and policy-as-code to convert architecture into measurable security outcomes.
Copyright (c) 2021 Abdulla Mamun, Md. Jobayer Ibne Saidur (Author)