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 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)
ENHANCING HFO SEPARATOR EFFICIENCY: A DATA-DRIVEN APPROACH TO PETROLEUM SYSTEMS OPTIMIZATION
The study titled Enhancing HFO Separator Efficiency: A Data-Driven Approach to Petroleum Systems Optimization investigated the quantitative relationships between thermophysical, mechanical, and operational parameters that determine the performance efficiency of heavy fuel oil (HFO) centrifugal separators. Using a data-driven framework, the research analyzed empirical field data supported by an extensive review of 112 scholarly and industrial studies focusing on petroleum separation technologies, machine learning applications in process optimization, and reliability-based maintenance modeling. The quantitative methodology incorporated multiple regression and mixed-effects models to evaluate the influence of inlet temperature, viscosity, flow rate, bowl rotational speed, torque, and vibration amplitude on overall separator efficiency, measured through a composite Efficiency Index (EI) integrating residual water, solids concentration, and energy consumption per ton of fuel treated. The findings revealed that inlet temperature had a significant positive impact on separator efficiency, whereas viscosity, torque, and vibration amplitude exhibited strong negative effects. Bowl rotational speed contributed positively but displayed diminishing marginal returns beyond optimal operational thresholds, while flow rate had minimal direct influence within controlled capacity ranges. The study demonstrated that approximately 75% of the total variation in separator efficiency could be explained by the identified thermophysical and mechanical predictors, confirming that the process is jointly governed by thermal conditioning and mechanical stability. Reliability and validity testing confirmed high measurement accuracy, while multicollinearity diagnostics verified the statistical independence of predictors. The analysis also established that mechanical imbalance, characterized by elevated torque and vibration, directly contributed to reduced efficiency, supporting the integration of condition monitoring and predictive maintenance as essential optimization strategies. The review of prior literature reinforced the study’s empirical findings, revealing convergence between historical theoretical models and modern data analytics in understanding separator behavior under variable operational conditions. Overall, the study concluded that HFO separator efficiency can be substantially enhanced through a unified data-driven optimization framework that integrates real-time monitoring, predictive analytics, and reliability engineering. This approach provides a comprehensive quantitative foundation for petroleum system operators to improve energy efficiency, minimize contaminant carryover, and extend equipment lifespan through adaptive, analytics-based decision-making.
Copyright (c) 2024 Zobayer Eusufzai, Sabuj Kumar Shil (Author)
PREDICTIVE ANALYTICS AND DATA-DRIVEN ALGORITHMS FOR IMPROVING EFFICIENCY IN FULL-STACK WEB SYSTEMS
This study quantitatively examines how predictive analytics and data-driven algorithms improve the operational efficiency of full-stack web systems. Framed within the global expansion of cloud-native, containerized, and microservice architectures, we treat predictive components as embedded control mechanisms for load balancing, caching, and fault tolerance. Using a randomized post-test control design with longitudinal replication across a four-week period, concurrent user sessions were assigned to either a predictive optimization environment or a conventional reactive baseline. Telemetry captured request-level latency, throughput, CPU utilization, cache hit ratio, and model precision at one-minute intervals, under a standardized data quality protocol. Descriptively, the predictive system outperformed the baseline, yielding higher mean throughput (7,856 vs. 6,942 req/s, +13.1%), lower mean latency (182 ms vs. 247 ms, –26.3%), reduced CPU strain (68.3% vs. 72.8%), and improved cache efficiency (91.4% vs. 82.7%). Correlation analyses showed predictive precision was strongly associated with throughput (r = .81, p < .01) and inversely with latency (r = –.77, p < .01). A multiple regression model explained 74% of the variance in throughput (R² = .74; adjusted R² = .72), with predictive precision the dominant predictor (β = .62, p < .001) alongside cache hit ratio (β = .31, p < .01); workload intensity had a modest negative effect (β = –.18, p < .05). Logistic regression indicated systems with higher predictive precision were 2.8× more likely to sustain sub-200 ms latency. Model assumptions were satisfied (DW = 1.94), multicollinearity was acceptable (all VIFs < 3.5), and measurement reliability/validity were high (Cronbach’s α ≥ .89; CFA loadings ≥ .70). Findings establish that predictive precision is a statistically demonstrable driver of efficiency, stabilizing throughput and compressing tail latency via anticipatory resource control. We recommend operationalizing lightweight, hybrid predictive services within orchestration pipelines, instituting continuous feedback-driven recalibration, and adopting precision-linked SLOs to translate modeling gains into durable performance, scalability, and reliability improvements.
Copyright (c) 2024 Md Muzahidul Islam, Aditya Dhanekula (Author)
IMPACT OF DATA PRIVACY AND CYBERSECURITY IN ACCOUNTING INFORMATION SYSTEMS ON FINANCIAL TRANSPARENCY
This study investigated the impact of data privacy and cybersecurity in accounting information systems (AIS) on financial transparency, emphasizing how integrated digital governance frameworks influence the accuracy, reliability, and accountability of financial reporting. As the reliance on technology in financial management grows, safeguarding data integrity and maintaining ethical information practices have become vital components of organizational governance. The research adopted a quantitative explanatory design, supported by an extensive review of 135 peer-reviewed studies published between 2010 and 2024, to establish both theoretical and empirical foundations for the analysis. The reviewed literature provided multidimensional perspectives from accounting, information systems, and governance disciplines, highlighting the progressive convergence of privacy compliance and cybersecurity control as determinants of transparent reporting. The empirical phase of the study utilized panel data from organizations disclosing digital governance information across a ten-year period, integrating both cross-sectional and longitudinal observations. Data privacy maturity was operationalized through structured privacy programs, designated data protection officers, and adherence to international compliance certifications, whereas cybersecurity maturity was measured through control frameworks, encryption standards, intrusion detection systems, and incident response readiness. Financial transparency was assessed using a composite index that included disclosure accuracy, reporting timeliness, audit reliability, and error frequency. The results revealed that both data privacy and cybersecurity maturity exerted significant positive effects on financial transparency, confirming that firms with advanced governance and protection mechanisms reported higher levels of accuracy and disclosure clarity. Moreover, the interaction between privacy and cybersecurity demonstrated a complementary effect, indicating that organizations achieving balance between ethical data stewardship and technical resilience attained superior transparency outcomes. The mediation analysis further showed that accounting information system control strength partially mediated these relationships, translating governance practices into tangible improvements in reporting integrity. Moderation effects indicated that firm size and regulatory intensity amplified these relationships, while technological complexity slightly weakened them. Collectively, the findings underscored that financial transparency in the digital era is not solely an accounting outcome but the result of comprehensive governance integration between data privacy, cybersecurity, and internal control systems.
Copyright (c) 2025 Hozyfa Shafa, Ashraful Islam (Author)
THE ROLE OF LARGE LANGUAGE MODELS (LLMS) IN PERSONALIZED ENGLISH LANGUAGE INSTRUCTION
This study addresses a practical gap in English Language Teaching research, namely the lack of quantitative, case-grounded evidence on how large language models deliver personalized instruction at scale. The purpose is to model how LLM personalization and feedback translate into learner outcomes within real classrooms using enterprise cloud deployments. Adopting a quantitative, cross-sectional, case-based design, we sampled 312 learners across three cloud or enterprise cases that had embedded LLM-mediated tasks for 6 to 8 weeks. Key variables were personalization features, feedback quality, perceived usefulness, perceived ease of use, interaction frequency, engagement, self-efficacy, and perceived learning outcomes, each measured with five-point Likert scales. The analysis plan combined descriptive statistics and reliability checks with correlation matrices, hierarchical regressions using case fixed effects and HC3 standard errors, and bootstrapped mediation and moderation tests. Headline findings show that personalization features and ease of use jointly predict perceived usefulness, feedback quality is the strongest predictor of self-efficacy, and engagement and self-efficacy are the primary proximal predictors of outcomes after controls. Mediation results indicate that personalization influences outcomes serially through usefulness and engagement, while feedback quality acts via self-efficacy; moderation shows stronger returns for lower-proficiency learners and a steeper usefulness–outcome link for learners with higher AI familiarity. Implications include prioritizing actionable feedback design, pacing to reduce extraneous load, providing prompt-fluency scaffolds for novices, and governing prompt libraries and data flows as curricular assets so that value perceptions convert into frequent, purposeful use and measurable gains. These results offer a replicable blueprint for enterprise cloud implementations of LLM-enabled English instruction that centers learner engagement and efficacy as the engines of improvement.
Copyright (c) 2022 Elmoon Akhter (Author)
INTEGRATING AI-POWERED ROBOTICS IN LARGE-SCALE WAREHOUSE MANAGEMENT: ENHANCING OPERATIONAL EFFICIENCY, COST REDUCTION, AND SUPPLY CHAIN PERFORMANCE MODELS
This study addresses a practical problem in large-scale fulfillment: organizations invest in AI-powered robotics yet often fail to see reliable gains in efficiency, cost, and service because orchestration across cloud and enterprise systems is uneven. The purpose is to quantify how adoption of AI-robotic warehousing relates to operational efficiency, cost per order, and supply chain performance, and to test whether systems integration and workforce capability condition those effects. We use a quantitative, cross-sectional, case-based design across 24 cloud-enabled enterprise warehouse cases with 268 respondents and harmonized KPIs. Key variables include AI-robotics adoption, systems integration, workforce capability, operational efficiency lines per labor hour, order cycle time, picking accuracy, cost per order, and on-time-in-full. The analysis plan combines descriptive statistics and correlations with hierarchical OLS, moderation via centered interactions Adoption × Integration and Adoption × Workforce, mediation through operational efficiency using bootstrap confidence intervals, and robustness checks with KPI-level models, site-clustered errors, and leave-one-site-out tests. Headline findings show adoption is positively associated with efficiency β = 0.28, p < .001 and the effect strengthens with higher integration βinteraction = 0.12, p = .003; efficiency is linked to lower log cost per order β = −0.31, p < .001 and higher supply chain performance β = 0.27, p < .001; indirect effects from adoption to cost and service through efficiency are significant and grow with integration, with a practical integration threshold around 3.4 on a five-point Likert scale. Implications are to treat robotics as a data-centric control system, prioritize governed APIs and low-latency event flows, instrument KPI lineage, and invest in role-based training to translate algorithmic potential into sustained cadence and quality.
Copyright (c) 2025 Md Rezaul Karim, Md.Kamrul Khan (Author)
DATA-DRIVEN DECISION SUPPORT IN INFORMATION SYSTEMS: STRATEGIC APPLICATIONS IN ENTERPRISES
This systematic review synthesizes contemporary evidence on data-driven decision support tools in Information Systems with a focus on strategic applications in service-oriented enterprises and enterprise planning. Following a registered protocol aligned to PRISMA, we searched major scholarly databases and screened studies using prespecified inclusion, exclusion, and quality appraisal criteria suited to quantitative, qualitative, and design-science designs. In total, 115 peer-reviewed papers were included, spanning budgeting, forecasting, sales and operations planning, capacity and portfolio planning across service-intensive contexts. We map the field along three layers data architecture and integration, analytics capabilities from descriptive and diagnostic to predictive and prescriptive, and presentation and governance mechanisms that embed outputs into organizational routines. The corpus shows that predictive analytics improves planning accuracy and bias when evaluated with time-aware procedures and hierarchical reconciliation, while prescriptive optimization and decision automation deliver disproportionate gains in service-level attainment, cost-to-serve, and decision latency when coupled with governed, service-oriented or microservices architectures. Event-driven dataflows and explicit stewardship, lineage, and policy controls are consistently associated with faster, more auditable plan changes and higher acceptance of model recommendations. Organizational scaffolds effective-use practices, peer support, and structured reconciliation forums amplify technical benefits by converting insights into shared commitments on a reliable cadence. The review integrates quantitative estimates with thematic synthesis to produce a taxonomy of tools, an evidence map of outcomes, and configuration paths that explain why multiple coherent capability bundles can achieve strong planning performance. Practical guidance prioritizes sequencing investments in governance, predictive reliability, prescriptive services, and workflow embedding to align analytics with enterprise planning cadence.
Copyright (c) 2021 Md. Musfiqur Rahman, Saba Ashfaq (Author)
QUANTITATIVE RISK MODELING FOR DATA LOSS AND RANSOMWARE MITIGATION IN GLOBAL HEALTHCARE AND PHARMACEUTICAL SYSTEMS
This study addresses the escalating problem of data loss and ransomware in globally networked healthcare and pharmaceutical ecosystems, where cross-border cloud and enterprise interdependencies amplify tail risk. The purpose is to develop and test a quantitative risk model that links measurable control maturity to three outcomes: perceived 12-month ransomware likelihood, expected data-loss severity, and expected financial loss. Using a quantitative, cross-sectional, case-based design, we surveyed security, IT, and governance leaders from cloud-enabled and on-premise enterprise cases across providers, payers, pharmaceutical manufacturers, and CROs, and triangulated results with purposively selected organizational cases. Key variables captured six capability domains on five-point Likert scales: Security Control Maturity, Backup and Recovery Readiness, Network Segmentation and Zero-Trust, Security Awareness and Training, Third-Party Risk Management, and Regulatory Compliance Posture. The analysis plan comprised descriptives, correlations, ordered logit or probit for ordinal outcomes, and log-linear OLS for transformed financial-loss bands, with interactions for architectural and governance complementarities, and cluster-robust or heteroskedasticity-consistent errors plus fixed effects for segment and region. Headline findings show that Backup and Recovery Readiness is the strongest predictor of lower severity, Network Segmentation and Zero-Trust most reduces ransomware likelihood, and Third-Party Risk Management, especially when paired with auditable compliance posture, yields the largest percentage reduction in expected financial loss, while size, cloud intensity, and IT or OT coupling raise baseline risk but are partially offset by these controls. Implications prioritize immutable, routinely tested backups, micro-segmentation with least privilege, enforceable vendor governance, and board-visible resilience metrics that translate coefficients into Expected Annual Loss and Expected Shortfall for capital allocation.
Copyright (c) 2023 Md. Tarek Hasan (Author)
AI-BASED PORTABLE SUBSTATIONS AND TRANSFORMER DESIGN FOR FAST EV CHARGING: A CRITICAL REVIEW OF INNOVATIONS AND CHALLENGES
This study addresses the pressing engineering problem of how to deliver high-power EV fast charging at the grid edge without compromising reliability, power quality, deployment speed, or cost. The purpose is to quantify how AI integration and transformer or portable-substation design choices relate to real-world field performance. We conduct a quantitative, cross-sectional, case-based investigation on 10 operating fast-charging sites that are cloud connected and enterprise managed, and pair it with a systematic literature review of 68 publications to ground constructs and measures. The sample comprises cloud or enterprise cases instrumented with SCADA and power-quality telemetry. Key independent variables include AI integration, modularity and standardization of portable skids, transformer technology and cooling class, with contextual moderators such as feeder short-circuit strength and ambient temperature. Outcomes include availability, energy throughput, total harmonic distortion, voltage deviation events, normalized hottest-spot temperature rise, installed cost per kW, and time-to-energize. The analysis plan follows three tiers: descriptive profiling, correlation screening, and multivariable modeling using OLS with heteroskedasticity-robust errors for symmetric outcomes and Gamma log-link GLMs for positive skewed cost and schedule metrics, with pre-registered interaction tests. Headline findings show that stronger AI integration is associated with higher availability and throughput and with lower distortion and thermal stress, modular standardized skids reduce installed cost and commissioning time, and solid-state transformer packages deliver clearer power-quality benefits on weak feeders. Implications for operators and OEMs include prioritizing standardized portable designs, instrumented data access, and an AI operations stack to optimize ramp management and predictive maintenance across heterogeneous sites.
Copyright (c) 2025 M. A. Masud Khan (Author)
PHYTOTHERAPEUTIC COMPOUNDS IN DIABETES MANAGEMENT: A COMPARATIVE ANALYSIS OF NATURAL EXTRACTS AND SYNTHETIC DRUGS
This study directly engages a critical practice gap in contemporary type 2 diabetes management by systematically evaluating the comparative effectiveness of phytotherapeutic regimens against conventional antihyperglycemic medications within real-world outpatient settings. As treatment decisions increasingly reflect a personalized balance between metabolic outcomes, medication tolerability, patient adherence, and lived usability, there remains a lack of rigorous comparative evidence on whether botanical therapies—either alone or in combination with standard synthetic drugs—offer clinically meaningful advantages. The primary purpose of this investigation was to determine whether phytotherapy, administered as a standalone regimen or as an adjunct to evidence-based pharmaceuticals, is associated with superior glycemic control and enhanced patient-reported outcomes when compared to synthetic therapy alone. To address this objective, a quantitative, cross-sectional, multi-case analytical design was implemented across five geographically distributed outpatient clinics providing standardized diabetes care. The final sample consisted of 782 adult participants with clinically diagnosed type 2 diabetes who had been maintained on a stable treatment regimen for a minimum duration of six months, ensuring physiological equilibrium and mitigating acute treatment-transition effects. The conceptual framework was informed by a targeted literature synthesis of 44 peer-reviewed empirical studies, which guided variable selection, validated construct definitions, and underpinned instrument reliability. The statistical analysis plan involved multi-tiered modeling procedures. Combination therapy involving standardized phytotherapeutic agents administered alongside guideline-recommended synthetic medications produced significantly lower adjusted HbA1c levels and increased odds of meeting the <7% target compared to synthetic therapy alone. Importantly, phytotherapy-only regimens achieved comparable glycemic performance relative to synthetic monotherapy yet demonstrated markedly superior tolerability and patient-reported satisfaction. Regression mediation analysis demonstrated that each one-point increase in adherence score was associated with clinically meaningful reductions in HbA1c, indicating that adherence partially mediated the enhanced effectiveness of combination therapy These findings substantively contribute to the evidence base by demonstrating that quality-assured botanical formulations, when integrated within conventional therapeutic frameworks, can deliver dual advantages—improved metabolic outcomes and enhanced patient acceptability—without compromising clinical safety.
Copyright (c) 2024 Sadia Tasnim (Author)
A COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE-INTEGRATED BI DASHBOARDS FOR REAL-TIME DECISION SUPPORT IN OPERATIONS
This study presents a systematic review of artificial intelligence–integrated business intelligence dashboards and their role in real-time decision support across multiple operational contexts. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a comprehensive search and screening process across major academic databases yielded 96 high-quality studies spanning manufacturing, healthcare, supply chain, services, and utilities. The review synthesizes how advances in data architectures—such as cloud data warehouses, streaming platforms, and lakehouse integration—combine with predictive and prescriptive analytics to transform dashboards from static reporting tools into adaptive decision-support ecosystems. Findings reveal that well-designed dashboards lead to measurable improvements, including defect reductions, cycle time compression, increased service reliability, and enhanced resource utilization. However, technical sophistication alone proved insufficient; effective dashboards depended heavily on robust data governance, organizational readiness, user training, and the integration of explainable AI to ensure trust and adoption. Global comparisons highlighted significant adoption gaps between developed and emerging economies, influenced by infrastructure maturity, regulatory frameworks, and cultural decision-making norms. Human factors, including cognitive load management, usability, and escalation practices, emerged as decisive enablers of actionable intelligence. This review contributes theoretically by extending established frameworks such as the Technology Acceptance Model, Information Systems Success Model, and Resource-Based View to include model lifecycle management and explainability as key dimensions of success. Practically, it provides actionable recommendations for organizations to build trusted, transparent, and workflow-integrated dashboards that convert data into timely operational insight. Collectively, the study offers a comprehensive and evidence-based foundation for understanding and advancing AI-driven dashboards as strategic tools for real-time decision-making across sectors.
Copyright (c) 2024 Momena Akter, Sai Praveen Kudapa (Author)
ADAPTIVE LEARNING SYSTEMS FOR ENGLISH LITERATURE CLASSROOMS: A REVIEW OF AI-INTEGRATED EDUCATION PLATFORMS
This study systematically examined the role of adaptive learning systems integrated with artificial intelligence in English Literature classrooms, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure rigor and transparency. A total of 94 peer-reviewed papers were reviewed, representing a diverse range of educational contexts including native-English, EFL/ESL, bilingual, and resource-constrained environments. The analysis revealed that adaptive platforms consistently improved cognitive outcomes such as reading comprehension, device recognition, interpretive accuracy, and intertextual connections, while also strengthening writing outcomes through enhanced thesis clarity, evidence integration, and iterative revision practices. Metacognitive development and self-regulated learning were supported by adaptive dashboards, reflective prompts, and progress-monitoring tools, which in turn fostered greater engagement, time on task, and enjoyment of literature. Equity and accessibility were also key findings, with adaptive systems demonstrating the ability to reduce performance gaps by providing linguistic scaffolds, disability-inclusive features, and mobile-first offline capabilities for underserved learners. Teacher roles were redefined from evaluators of routine performance to facilitators of interpretive dialogue and mentors of higher-order analysis, illustrating how adaptive systems complement rather than replace human expertise. Comparative analysis further highlighted differences across platform typologies, with text-first systems excelling in close reading, writing-first systems advancing argumentative development, and hybrid suites offering the most comprehensive integration of reading and writing outcomes. Collectively, the findings affirm that adaptive learning systems, when aligned with curricular standards and implemented with attention to equity, accessibility, and teacher agency, represent a transformative innovation in literature education, providing both methodological rigor and pedagogical relevance to the teaching and learning of English Literature.
Copyright (c) 2023 Md Arif Uz Zaman, Elmoon Akhter (Author)
DATA PRIVACY IN BUSINESS INTELLIGENCE SYSTEMS: ENSURING COMPLIANCE IN HRIS AND ENTERPRISE PLATFORMS
This systematic literature review examines how data privacy can be engineered and governed as an intrinsic property of business intelligence (BI) programs that consume Human Resource Information Systems (HRIS) and wider enterprise platforms. Following a registered PRISMA protocol, we searched multidisciplinary databases, screened records in duplicate, appraised quality with design-appropriate tools, and synthesized heterogeneous evidence narratively and thematically. In total, 115 peer-reviewed articles and standards met the inclusion criteria. The synthesis maps regulatory obligations such as purpose limitation, minimization, storage limitation, transparency, rights handling, accountability, and integrity-confidentiality to concrete controls across the analytics lifecycle. We find that privacy outcomes improve when governance is encoded in code via policy-as-code approvals, slim ingestion schemas, retention rules, and purpose tags; least-privilege access is most durable when role-based baselines are refined with attribute-based context and enforced as row- and column-level security at the semantic layer; protection works best as a choreography that assigns encryption, tokenization, pseudonymization, and masking to precise pipeline stages; and verification hinges on engineered lineage, runtime observability, and tamper-evident audit logs that make “what happened to whose data and why” answerable under audit. We also surface operational patterns for DSAR fulfillment, retention and erasure in warehouses and lakehouses, machine unlearning for models, and cross-border and vendor risk mitigations using customer-managed keys, regionalization, and purpose-aware authorization. Contributions include a regulation-to-engineering crosswalk, a reference architecture, a maturity model, DPIA prompt templates tailored to analytics, and a compact assurance metric set for continuous monitoring. Collectively, the review translates legal principles into testable, auditable design choices that enable compliant, trustworthy HRIS-to-BI analytics at scale.
Copyright (c) 2025 Md Mostafizur Rahman, Omar Muhammad Faruk, Sheratun Noor Jyoti (Author)
INTEGRATION OF PLC AND SMART DIAGNOSTICS IN PREDICTIVE MAINTENANCE OF CT TUBE MANUFACTURING SYSTEMS
This systematic review examines the integration of programmable logic controllers (PLCs) and smart diagnostics within predictive maintenance frameworks for computed tomography (CT) tube manufacturing systems. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 87 studies were analyzed to synthesize evidence on maintenance complexity, diagnostic strategies, modeling approaches, regulatory dimensions, and economic implications. Findings highlight that CT tube manufacturing is characterized by multi-domain degradation processes, including vacuum instability, rotor-bearing wear, and thermal fatigue, which traditional preventive maintenance cannot adequately address. Recurrent failure modes—such as filament thinning, anode cracking, and vacuum leakage—were consistently associated with measurable health indicators like vibration signals, acoustic emissions, thermal gradients, and arc frequency. Prognostic modeling approaches revealed that physics-based models offer mechanistic insight, data-driven methods provide adaptive accuracy, and hybrid frameworks balance both strengths while meeting regulatory validation requirements. The review also identified unresolved challenges in interoperability, data governance, and compliance with ISO 13485, ISO 14971, IEC 60601, and FDA 21 CFR Part 11, which continue to constrain large-scale implementation. Conceptual anchors derived from the synthesis—deterministic PLC control, validated diagnostic science, and regulatory alignment—provide a structured foundation for advancing predictive maintenance in regulated medical device manufacturing.
Copyright (c) 2022 Md Mahamudur Rahaman Shamim, Rezwanul Ashraf Ruddro (Author)
CYBERSECURITY AND DATA INTEGRITY IN FINANCIAL SYSTEMS: A REVIEW OF RISK MITIGATION AND COMPLIANCE MODELS
Cybersecurity and data integrity have become central to financial stability as institutions increasingly depend on digital infrastructures. Cyber incidents such as data breaches, payment disruptions, and manipulation of trading systems generate not only direct financial losses but also systemic contagion, reputational harm, and loss of consumer trust. This review evaluates organizational, governance, and compliance models designed to mitigate such risks, highlighting cybersecurity as a structural determinant of resilience in modern financial systems. The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive database search initially identified 1,486 records, of which 127 met inclusion criteria after screening and quality assessment. These studies covered domains including enterprise risk management (ERM), board-level accountability, incident response strategies, regulatory frameworks, and cyber insurance. Together, they accounted for more than 24,000 citations, reflecting the scholarly and policy relevance of this research area. Findings revealed five key themes. First, cyber incidents consistently disrupt liquidity and settlements, triggering systemic ripple effects. Second, case studies such as the Bangladesh Bank SWIFT attack and the Capital One breach illustrate vulnerabilities in governance and third-party reliance. Third, breaches of data integrity significantly damage consumer trust and institutional reputation, often persisting beyond financial recovery. Finally, regulatory and compliance models, including GDPR, Dodd-Frank, and TIBER-EU, enhanced transparency, accountability, and systemic learning when embedded into organizational culture. In conclusion, cybersecurity and data integrity represent systemic imperatives. Integrated approaches combining governance, compliance, resilience planning, and risk transfer are vital to securing financial stability in the digital era.
Copyright (c) 2022 Md Hasan Zamil, Md Omar Faruq (Author)
POWER BI AND DATA ANALYTICS IN FINANCIAL REPORTING: A REVIEW OF REAL-TIME DASHBOARDING AND PREDICTIVE BUSINESS INTELLIGENCE TOOLS
This systematic review explores the transformative role of Power BI and modern data analytics tools in reshaping the domain of financial reporting. In an era where organizations are increasingly prioritizing real-time decision-making, predictive insights, and compliance automation, the integration of business intelligence (BI) platforms into financial workflows has become both a strategic imperative and a technological necessity. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review critically analyzes 61 peer-reviewed studies published between 2010 and 2025, sourced from academic databases such as Scopus, Web of Science, IEEE Xplore, and Google Scholar. The review identifies eight core thematic areas: technological foundations of BI in finance, real-time dashboarding, predictive analytics integration, performance measurement, regulatory compliance, data pipeline architecture, organizational adoption, and theoretical-methodological insights. The findings reveal that Power BI is widely adopted across industries for streamlining financial disclosures, enabling interactive dashboards, and integrating predictive modeling capabilities through R, Python, and Azure Machine Learning. Furthermore, the review highlights how self-service analytics, automation of ETL pipelines, and real-time KPI tracking have enhanced both the operational efficiency and strategic agility of finance departments. In addition, the study uncovers common barriers such as skill gaps, resistance to change, and fragmentation in data governance. By comparing the reviewed findings with prior literature, the paper presents an updated understanding of how Power BI is transitioning from a reporting tool to a core financial intelligence system. The study concludes with targeted recommendations for practitioners, researchers, and policymakers to maximize the benefits of business intelligence in financial reporting, while also identifying areas for future inquiry, including longitudinal impacts, ethical considerations, and cross-industry comparisons.
Copyright (c) 2024 Danish Mahmud, Md. Zafor Ikbal (Author)
AI-DRIVEN OPTIMIZATION AND RISK MODELING IN STRATEGIC ECONOMIC ZONE DEVELOPMENT FOR MID-SIZED ECONOMIES: A REVIEW APPROACH
This study investigates the integration of artificial intelligence (AI)-driven optimization and quantitative risk modeling in the planning and management of Strategic Economic Zones (SEZs) in mid-sized economies, focusing on measurable efficiency gains, resilience enhancement, and strategic value creation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 84 scholarly and applied studies—cited collectively 3,912 times—were systematically reviewed. AI applications, including predictive analytics, multi-variable optimization, and real-time monitoring, were shown to improve operational efficiency by 12% to 28%, while structured risk modeling reduced operational disruptions by over 15% and enhanced investor confidence, contributing to stronger foreign direct investment commitments. Thirty-eight studies on integrated frameworks demonstrated combined efficiency improvements of up to 35% and return-on-investment gains averaging 7% higher than non-integrated approaches. Geographic patterns indicated that 54% of the reviewed studies focused on mid-sized or emerging economies, demonstrating the adaptability of integrated AI-risk strategies to resource-constrained contexts. Temporal trends revealed rapid growth in interdisciplinary research over the past five years, reflecting the increasing recognition of these tools in economic zone governance. The findings confirm that AI-driven optimization and risk modeling, when applied systematically, provide a robust, data-driven foundation for improving SEZ operational performance, strengthening resilience against disruptions, and enhancing the competitive positioning of mid-sized economies in the global economic landscape.
Copyright (c) 2025 MKA Shahinoor Rahman Jahid (Author)
SUSTAINABILITY METRICS IN GREEN INFRASTRUCTURE: A REVIEW OF LEED, LIFECYCLE COSTING, AND WASTE REDUCTION IN CONSTRUCTION
This study presents a comprehensive meta-analytical review of sustainability metrics employed in green infrastructure, focusing on three dominant evaluative domains: Leadership in Energy and Environmental Design (LEED) certification, Lifecycle Costing (LCC), and construction waste reduction strategies. Drawing upon a systematic analysis of 67 peer-reviewed articles published between 2000 and 2024, the review synthesizes the methodological frameworks, empirical outcomes, and sectoral applications of these metrics across various geographical and economic contexts. The study adheres to PRISMA guidelines to ensure transparency and reproducibility in article selection, data extraction, and synthesis. Findings indicate that while LEED remains the most widely adopted and globally recognized standard, its performance varies significantly across regions, with notable differences in adaptation and outcomes in North America, Southeast Asia, and Europe. Lifecycle costing emerges as a crucial economic lens that enhances decision-making and long-term financial planning in green building projects, yet its integration remains limited due to institutional inertia and data complexity. Waste reduction metrics—anchored in circular economy principles—demonstrate strong environmental and operational benefits, particularly when aligned with modular construction and adaptive reuse strategies. The review also uncovers significant redundancies among metric frameworks, a lack of standardized post-certification performance tracking, and an urgent need for harmonization across sustainability evaluation tools. Through cross-comparative analysis, this study provides evidence-backed insights into the synergies and trade-offs among sustainability metrics and identifies critical knowledge gaps that must be addressed to support integrated, context-sensitive green infrastructure development. The results underscore the importance of multi-criteria evaluation approaches and offer policy and practice recommendations for stakeholders seeking to enhance the environmental, economic, and material performance of infrastructure projects in a measurable and scalable manner.
Copyright (c) 2024 Mubashir Islam, Abdul Rehman, Moin Uddin Mojumder (Author)
QUANTIFYING THE IMPACT OF NETWORK SCIENCE AND SOCIAL NETWORK ANALYSIS IN BUSINESS CONTEXTS: A META-ANALYSIS OF APPLICATIONS IN CONSUMER BEHAVIOR, CONNECTIVITY
This meta-analysis investigates the strategic impact of network science and social network analysis (SNA) across diverse business contexts, focusing on consumer behavior, connectivity optimization, and civic engagement. Drawing from 89 peer-reviewed studies published between 2005 and 2023, the study synthesizes empirical evidence to assess how network-based methodologies influence decision-making, communication strategies, and operational efficiency. The analysis reveals that SNA enhances the understanding of consumer preferences, brand influence, and word-of-mouth dynamics by mapping relational structures and identifying key opinion leaders. In connectivity optimization, network science facilitates improved resource allocation, supply chain coordination, and knowledge dissemination by leveraging centrality, clustering, and modularity metrics. Civic engagement, especially in public-facing enterprises and CSR campaigns, benefits from SNA by detecting community clusters, tracking campaign diffusion, and promoting inclusive dialogue. Subgroup analyses indicate significant variations in outcomes based on platform type (e.g., Twitter, LinkedIn, enterprise intranets), network size, and industry verticals. The findings suggest that the integration of network science into business analytics not only enhances the granularity of stakeholder insights but also drives adaptive strategies for engagement, innovation diffusion, and service personalization. This study provides a comprehensive evidence base for practitioners and researchers seeking to harness the predictive and diagnostic power of network-based tools in data-driven environments.
Copyright (c) 2024 Istiaque Mahmud, Dipon Das Rahul, Hasan Aqif, Samia Akter, Sayer Bin Shafi (Author)
NEURAL NETWORK–BASED RISK PREDICTION AND SIMULATION FRAMEWORK FOR MEDICAL IOT CYBERSECURITY: AN ENGINEERING MANAGEMENT MODEL FOR SMART HOSPITALS
The rapid digitalization of healthcare through the adoption of Medical Internet of Things (MIoT) technologies has given rise to smart hospital ecosystems that are highly efficient yet increasingly vulnerable to cybersecurity threats. As MIoT devices become integral to patient monitoring, diagnostics, and treatment, the risk of cyberattacks—ranging from ransomware and data breaches to insider threats and Distributed Denial of Service (DDoS) attacks—has grown substantially. In response, this study conducts a structured meta-analysis to evaluate the effectiveness of neural network–based risk prediction and simulation frameworks in securing smart hospital environments. Using the PRISMA 2020 methodology, the review systematically screened and synthesized findings from 112 peer-reviewed studies published between 2010 and 2024, encompassing various experimental setups, real-world hospital case studies, and benchmark datasets. The meta-analysis focused on comparing performance metrics such as detection accuracy, false positive rates, real-time responsiveness, and attack versatility between traditional cybersecurity systems and advanced neural network architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid deep learning models. The findings indicate that neural network–based intrusion detection systems (NN-IDS) consistently outperform rule-based and statistical models, achieving higher accuracy in identifying both known and novel cyber threats. Additionally, these models demonstrate significant reductions in false positive rates and enhanced responsiveness under real-time operational constraints, which are critical for patient safety in clinical environments. These simulation tools support data-driven decision-making and engineering management by forecasting breach impacts, operational disruptions, and compliance risks. Moreover, the adaptability and scalability of NN-IDS across different hospital sizes and digital maturity levels position them as suitable for wide-scale deployment in healthcare systems globally. Overall, this research offers a comprehensive evaluation of neural network–enabled cybersecurity solutions and establishes their practical and strategic value in developing resilient, intelligent, and secure smart hospital infrastructures.
Copyright (c) 2024 Md Tawfiqul Islam, Sabbir Ahmad, Md Anikur Rahman, Md Arifur Rahaman (Author)
SUPPLY CHAIN TRACEABILITY IN APPAREL MANUFACTURING: A REVIEW OF REAL-TIME ORDER TRACKING, VENDOR MANAGEMENT, AND EXPORT VISIBILITY SYSTEMS
The complexity and global dispersion of modern apparel supply chains have intensified the need for robust traceability systems that can ensure ethical sourcing, operational efficiency, and regulatory compliance. This study presents an in-depth meta-analysis of traceability mechanisms in apparel manufacturing, focusing specifically on three interconnected dimensions: real-time order tracking, vendor management, and export visibility systems. Drawing upon 113 empirical studies published between 2005 and 2022, the analysis synthesizes technological, organizational, and governance-related approaches to supply chain traceability, with particular attention to digital platforms such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Manufacturing Execution Systems (MES), Transportation Management Systems (TMS), blockchain-enabled infrastructures, and Traceability-as-a-Service (TaaS) models. The findings reveal that while real-time order tracking is well-established at the Tier 1 level through ERP and SCM systems, traceability significantly deteriorates beyond Tier 2 due to subcontracting opacity, fragmented governance, and limited technological integration. Vendor management practices remain largely coercive and audit-driven, which leads to short-term compliance but does not foster sustained transparency or collaborative data sharing. Export visibility tools, while effective in managing container movement and customs documentation, often operate in isolation from broader traceability platforms, resulting in data silos and verification gaps. Furthermore, the study highlights the central role of governance models in shaping traceability outcomes, with collaborative and hybrid approaches demonstrating higher success rates in multi-tier visibility and supplier engagement. It also underscores the growing relevance of blockchain and EDI-based systems in export traceability, emphasizing the benefits and limitations of each. TaaS platforms are identified as emerging enablers of flexible, modular traceability infrastructure, particularly for small and mid-sized brands; however, their effectiveness is often constrained by digital readiness and uneven supplier participation. The meta-analysis concludes that traceability in apparel manufacturing is not merely a technical challenge but a deeply institutional and organizational endeavor. Effective traceability systems require alignment across governance frameworks, platform interoperability, and equitable stakeholder involvement to move beyond surface-level compliance and achieve meaningful transparency across the entire apparel value chain.
Copyright (c) 2022 Golam Qibria Limon (Author)
BUSINESS BACKGROUND STUDENT'S PERCEPTION ANALYSIS TO UNDERTAKE PROFESSIONAL ACCOUNTING EXAMINATIONS
The primary objective of this study is to investigate the perceptions of business students toward the accounting profession and to identify the key factors influencing their intention to pursue professional accounting qualifications. Existing literature has consistently shown that career decision- making among students is significantly shaped by personal perceptions, social influences, and contextual factors. Building upon this foundation, the current research sought to examine eight specific variables that potentially impact students’ professional intentions: salary, job security or stability, prestige or status, opportunity for advancement, students’ perception of the profession, advisory received, financial assistance, and grit personality traits. These constructs were operationalized into a structured questionnaire comprising twenty items, which was administered to 176 final-year business students from multiple public and private universities across Bangladesh. Quantitative data were collected through the survey instrument and analyzed using both descriptive statistics and multiple regression analysis. The results revealed that among the eight independent variables, only three demonstrated a statistically significant association with students’ intention to undertake professional accounting examinations. The findings underscore the importance of personalized guidance, internal motivation, and realistic exposure to the demands and rewards of the accounting profession. These insights offer practical implications for academic institutions, accounting educators, and professional bodies aiming to increase the number of qualified professional accountants in Bangladesh. The results particularly highlight that student tenacity and perseverance—hallmarks of grit—play a vital role in determining whether students remain committed to the rigorous path of professional qualification. This study contributes to the broader discourse on career development in the accounting field and provides an evidence-based framework for targeted interventions to support students’ professional growth.
Copyright (c) 2023 Md. Rasel Ahmed (Author)
TO WHAT EXTENT DOES THE USE OF PROJECT MANAGEMENT–ORIENTED DIGITAL COLLABORATION TOOLS AFFECT DELIVERY TIMELINES IN REMOTE TECH TEAMS?
This study explores the impact of project management–oriented digital collaboration tools—specifically Asana, Trello, and Jira—on project delivery timelines in remote technology teams, with a particular focus on mid-sized firms (50–500 employees). Leveraging an exploratory sequential mixed-methods approach grounded in a pragmatic paradigm, the research integrates qualitative thematic analysis of secondary case studies with quantitative survey data from remote team professionals. Drawing on the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) framework, the study examines how tool usage intensity, organizational support, tool integration, and communication efficiency influence project delivery outcomes. Findings suggest that higher levels of digital tool adoption are associated with reduced timeline deviations, particularly when communication is efficient and supported by structured onboarding and platform integration. The study identifies communication efficiency as a key mediating factor and underscores the moderating roles of organizational training and task-tool alignment. Regression analysis further confirms that tool usage alone is not sufficient—its impact is conditioned by contextual variables such as team size, project complexity, and geographic dispersion. By focusing on mid-sized tech firms, the study fills a critical gap in existing literature, which often generalizes findings across organizations of varying scale. The research contributes to theory by extending TAM and TTF to outcome-focused variables such as delivery timelines and offers practical insights for managers aiming to optimize remote workflows. It also holds significance for software developers, policymakers, and scholars interested in digital transformation, remote work, and agile project management.
Copyright (c) 2025 Mohammad Hasan Imam, Mahruba Chowdhury (Author)
ENERGY EFFICIENCY MONITORING IN INSTITUTIONAL FACILITIES: A REVIEW OF UI/UX SOLUTIONS FOR IOT DASHBOARDS
As energy consumption becomes a critical concern for institutional facilities such as universities, hospitals, and government buildings, there is an increasing shift toward leveraging Internet of Things (IoT) technologies to enhance energy efficiency and operational sustainability. A key enabler in this transformation is the energy monitoring dashboard—an interface that aggregates and visualizes data from distributed smart meters, sensors, and gateways. However, the success of these dashboards hinges not only on their technical capacity but also on the quality of their user interface (UI) and user experience (UX) design, which directly influence user comprehension, engagement, and decision-making. This study presents a comprehensive and systematic literature review of 122 peer-reviewed articles published between 2015 and 2024, with the objective of identifying the design elements, interaction patterns, and functional features that determine UI/UX effectiveness in institutional energy dashboard systems. The review examines how UI/UX frameworks, such as human-centered design and usability engineering, have been integrated into institutional dashboards to support real-time energy monitoring, data-driven decision-making, and cross-departmental coordination. Particular attention is paid to the ways in which visual design elements—such as interactive charts, heatmaps, and modular components—enhance user navigation and situational awareness. The study synthesizes empirical findings that illustrate how dashboards offering customization, role-based access, and device adaptability can increase user satisfaction and operational responsiveness across different institutional roles. It also investigates the impact of inclusive design strategies, such as ADA-compliant features and language localization, which are critical for ensuring that diverse user groups—including those with limited technical literacy—can benefit from energy data insights. Furthermore, the review identifies persistent challenges such as data overload, inconsistent feedback loops, and lack of interoperability with building automation or enterprise resource systems. Case studies demonstrate that dashboards which address these challenges through cognitive simplicity, predictive visualization, and system integration yield significantly better outcomes in terms of energy savings, user engagement, and policy compliance. Overall, this review provides a holistic and multidisciplinary perspective on the role of UI/UX design in institutional energy dashboards, offering practical insights for designers, energy managers, and policymakers seeking to optimize the utility of smart monitoring systems for sustainable facility operations.
Copyright (c) 2025 Rezwanul Ashraf Ruddro (Author)