International Journal of Scientific Interdisciplinary Research
type: channel
description: 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.
publisher: International Journal of Scientific Interdisciplinary Research
language: en
publication name: International Journal of Scientific Interdisciplinary Research
items: QUANTITATIVE ASSESSMENT OF COMPLIANCE AND INSPECTION PRACTICES IN REDUCING SUPPLY CHAIN DISRUPTIONS
items: BLOCKCHAIN-ENABLED SECURITY PROTOCOLS COMBINED WITH AI FOR SECURING NEXT-GENERATION INTERNET OF THINGS (IoT) NETWORKS
items: MULTI-OBJECTIVE THERMO-ECONOMIC AND SUPPLY CHAIN OPTIMIZATION MODELING FOR HYDROGEN ENERGY INTEGRATION IN SMART FACTORIES
items: AI-DRIVEN CRISIS COMMUNICATION AND EMERGENCY RESPONSE: OPTIMIZING NONPROFIT DIGITAL OUTREACH DURING NATIONAL DISASTERS
items: CFD-BASED INVESTIGATION OF HEAT TRANSFER EFFICIENCY IN RENEWABLE ENERGY SYSTEMS
items: INTEGRATION OF STI PREVENTION INTERVENTIONS WITHIN PrEP SERVICE DELIVERY: IMPACT ON STI RATES AND ANTIBIOTIC RESISTANCE
items: CLOUD-NATIVE FRAMEWORKS FOR REAL-TIME THREAT DETECTION AND DATA SECURITY IN ENTERPRISE NETWORKS
items: ENHANCING HFO SEPARATOR EFFICIENCY: A DATA-DRIVEN APPROACH TO PETROLEUM SYSTEMS OPTIMIZATION
items: PREDICTIVE ANALYTICS AND DATA-DRIVEN ALGORITHMS FOR IMPROVING EFFICIENCY IN FULL-STACK WEB SYSTEMS
items: IMPACT OF DATA PRIVACY AND CYBERSECURITY IN ACCOUNTING INFORMATION SYSTEMS ON FINANCIAL TRANSPARENCY
items: THE ROLE OF LARGE LANGUAGE MODELS (LLMS) IN PERSONALIZED ENGLISH LANGUAGE INSTRUCTION
items: INTEGRATING AI-POWERED ROBOTICS IN LARGE-SCALE WAREHOUSE MANAGEMENT: ENHANCING OPERATIONAL EFFICIENCY, COST REDUCTION, AND SUPPLY CHAIN PERFORMANCE MODELS
items: DATA-DRIVEN DECISION SUPPORT IN INFORMATION SYSTEMS: STRATEGIC APPLICATIONS IN ENTERPRISES
items: QUANTITATIVE RISK MODELING FOR DATA LOSS AND RANSOMWARE MITIGATION IN GLOBAL HEALTHCARE AND PHARMACEUTICAL SYSTEMS
items: AI-BASED PORTABLE SUBSTATIONS AND TRANSFORMER DESIGN FOR FAST EV CHARGING: A CRITICAL REVIEW OF INNOVATIONS AND CHALLENGES
items: PHYTOTHERAPEUTIC COMPOUNDS IN DIABETES MANAGEMENT: A COMPARATIVE ANALYSIS OF NATURAL EXTRACTS AND SYNTHETIC DRUGS
items: A COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE-INTEGRATED BI DASHBOARDS FOR REAL-TIME DECISION SUPPORT IN OPERATIONS
items: ADAPTIVE LEARNING SYSTEMS FOR ENGLISH LITERATURE CLASSROOMS: A REVIEW OF AI-INTEGRATED EDUCATION PLATFORMS
items: DATA PRIVACY IN BUSINESS INTELLIGENCE SYSTEMS: ENSURING COMPLIANCE IN HRIS AND ENTERPRISE PLATFORMS
items: INTEGRATION OF PLC AND SMART DIAGNOSTICS IN PREDICTIVE MAINTENANCE OF CT TUBE MANUFACTURING SYSTEMS
items: CYBERSECURITY AND DATA INTEGRITY IN FINANCIAL SYSTEMS: A REVIEW OF RISK MITIGATION AND COMPLIANCE MODELS
items: POWER BI AND DATA ANALYTICS IN FINANCIAL REPORTING: A REVIEW OF REAL-TIME DASHBOARDING AND PREDICTIVE BUSINESS INTELLIGENCE TOOLS
items: AI-DRIVEN OPTIMIZATION AND RISK MODELING IN STRATEGIC ECONOMIC ZONE DEVELOPMENT FOR MID-SIZED ECONOMIES: A REVIEW APPROACH
items: SUSTAINABILITY METRICS IN GREEN INFRASTRUCTURE: A REVIEW OF LEED, LIFECYCLE COSTING, AND WASTE REDUCTION IN CONSTRUCTION
items: QUANTIFYING THE IMPACT OF NETWORK SCIENCE AND SOCIAL NETWORK ANALYSIS IN BUSINESS CONTEXTS: A META-ANALYSIS OF APPLICATIONS IN CONSUMER BEHAVIOR, CONNECTIVITY
items: NEURAL NETWORK–BASED RISK PREDICTION AND SIMULATION FRAMEWORK FOR MEDICAL IOT CYBERSECURITY: AN ENGINEERING MANAGEMENT MODEL FOR SMART HOSPITALS
items: SUPPLY CHAIN TRACEABILITY IN APPAREL MANUFACTURING: A REVIEW OF REAL-TIME ORDER TRACKING, VENDOR MANAGEMENT, AND EXPORT VISIBILITY SYSTEMS
items: BUSINESS BACKGROUND STUDENT'S PERCEPTION ANALYSIS TO UNDERTAKE PROFESSIONAL ACCOUNTING EXAMINATIONS
items: TO WHAT EXTENT DOES THE USE OF PROJECT MANAGEMENT–ORIENTED DIGITAL COLLABORATION TOOLS AFFECT DELIVERY TIMELINES IN REMOTE TECH TEAMS?
items: ENERGY EFFICIENCY MONITORING IN INSTITUTIONAL FACILITIES: A REVIEW OF UI/UX SOLUTIONS FOR IOT DASHBOARDS

QUANTITATIVE ASSESSMENT OF COMPLIANCE AND INSPECTION PRACTICES IN REDUCING SUPPLY CHAIN DISRUPTIONS
type: item
description: 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 a…
subject: Articles
  topic: section
subject: Supply Chain Disruptions
  topic: keywords
subject: Compliance
  topic: keywords
subject: Inspection Practices
  topic: keywords
subject: Governance Mechanisms
  topic: keywords
subject: Quantitative Analysis
  topic: keywords
creator: Md. Hasan Imam
creator: Rakibul Hasan
rights: Copyright (c) 2024 Md. Hasan Imam, Rakibul Hasan (Author)
date: 2024-06-26
publication date: 2024-06-26
starting page: 301
ending page: 342
doi: 10.63125/db63r616

BLOCKCHAIN-ENABLED SECURITY PROTOCOLS COMBINED WITH AI FOR SECURING NEXT-GENERATION INTERNET OF THINGS (IoT) NETWORKS
type: item
description: 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 …
subject: Articles
  topic: section
subject: Blockchain enabled IoT security
  topic: keywords
subject: AI based threat analytics
  topic: keywords
subject: Next generation IoT networks
  topic: keywords
subject: Cyber risk management
  topic: keywords
subject: IoT security performance
  topic: keywords
creator: Tonoy Kanti Chowdhury
creator: Shaikat Biswas
rights: Copyright (c) 2021 Tonoy Kanti Chowdhury, Shaikat Biswas (Author)
date: 2021-06-28
publication date: 2021-06-28
starting page: 98
ending page: 127
doi: 10.63125/pcdqzw41

MULTI-OBJECTIVE THERMO-ECONOMIC AND SUPPLY CHAIN OPTIMIZATION MODELING FOR HYDROGEN ENERGY INTEGRATION IN SMART FACTORIES
type: item
description: 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 coordin…
subject: Articles
  topic: section
subject: Hydrogen energy integration
  topic: keywords
subject: Smart factories
  topic: keywords
subject: Thermo-economic optimization
  topic: keywords
subject: Hydrogen supply chain optimization
  topic: keywords
subject: Multi-objective modeling
  topic: keywords
creator: Shaikh Shofiullah
creator: Sudipto Roy
rights: Copyright (c) 2022 Shaikh Shofiullah, Sudipto Roy (Author)
date: 2022-06-26
publication date: 2022-06-26
starting page: 163
ending page: 193
doi: 10.63125/p9y8p705

AI-DRIVEN CRISIS COMMUNICATION AND EMERGENCY RESPONSE: OPTIMIZING NONPROFIT DIGITAL OUTREACH DURING NATIONAL DISASTERS
type: item
description: 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)…
subject: Articles
  topic: section
subject: AI Adoption
  topic: keywords
subject: Crisis Communication
  topic: keywords
subject: Nonprofit Digital Outreach
  topic: keywords
subject: Message Relevance and Trust
  topic: keywords
subject: Disaster Readiness
  topic: keywords
creator: Maniruzzaman Bhuiyan
rights: Copyright (c) 2025 Maniruzzaman Bhuiyan (Author)
date: 2025-10-27
publication date: 2025-10-27
starting page: 293
ending page: 326
doi: 10.63125/j4h9ns25

CFD-BASED INVESTIGATION OF HEAT TRANSFER EFFICIENCY IN RENEWABLE ENERGY SYSTEMS
type: item
description: 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 eff…
subject: Articles
  topic: section
subject: Computational fluid dynamics (CFD)
  topic: keywords
subject: Performance evaluation criterion (PEC)
  topic: keywords
subject: Heat-transfer efficiency
  topic: keywords
subject: Reynolds number
  topic: keywords
subject: Nusselt number
  topic: keywords
creator: Saikat Sarkar
rights: Copyright (c) 2022 Saikat Sarkar (Author)
date: 2022-01-12
publication date: 2022-01-12
starting page: 129
ending page: 162
doi: 10.63125/ttw40456

INTEGRATION OF STI PREVENTION INTERVENTIONS WITHIN PrEP SERVICE DELIVERY: IMPACT ON STI RATES AND ANTIBIOTIC RESISTANCE
type: item
description: 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 necessitati…
subject: Articles
  topic: section
subject: Pre-Exposure Prophylaxis (PrEP)
  topic: keywords
subject: Sexually Transmitted Infections (STIs)
  topic: keywords
subject: Antibiotic Resistance
  topic: keywords
subject: Integrated Prevention Services
  topic: keywords
subject: Public Health Intervention
  topic: keywords
creator: Ferdous Ara Ahmed
rights: Copyright (c) 2021 Ferdous Ara Ahmed (Author)
date: 2021-06-27
publication date: 2021-06-27
starting page: 63
ending page: 97
doi: 10.63125/65143m72

CLOUD-NATIVE FRAMEWORKS FOR REAL-TIME THREAT DETECTION AND DATA SECURITY IN ENTERPRISE NETWORKS
type: item
description: 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 pe…
subject: Articles
  topic: section
subject: Cloud-Native Security
  topic: keywords
subject: Real-Time Threat Detection
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Observability
  topic: keywords
subject: Data-Security Posture
  topic: keywords
creator: Abdulla Mamun
creator: Md. Jobayer Ibne Saidur
rights: Copyright (c) 2021 Abdulla Mamun, Md. Jobayer Ibne Saidur (Author)
date: 2021-06-24
publication date: 2021-06-24
starting page: 34
ending page: 62
doi: 10.63125/0t27av85

ENHANCING HFO SEPARATOR EFFICIENCY: A DATA-DRIVEN APPROACH TO PETROLEUM SYSTEMS OPTIMIZATION
type: item
description: 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 c…
subject: Articles
  topic: section
subject: HFO Separator Efficiency
  topic: keywords
subject: Thermophysical Optimization
  topic: keywords
subject: Mechanical Reliability
  topic: keywords
subject: Data-Driven Modeling
  topic: keywords
subject: Petroleum Systems
  topic: keywords
creator: Zobayer Eusufzai
creator: Sabuj Kumar Shil
rights: Copyright (c) 2024 Zobayer Eusufzai, Sabuj Kumar Shil (Author)
date: 2024-06-24
publication date: 2024-06-24
starting page: 261
ending page: 300
doi: 10.63125/2tzaap28

PREDICTIVE ANALYTICS AND DATA-DRIVEN ALGORITHMS FOR IMPROVING EFFICIENCY IN FULL-STACK WEB SYSTEMS
type: item
description: 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 light…
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Computational Efficiency
  topic: keywords
subject: Throughput
  topic: keywords
subject: Latency
  topic: keywords
subject: Scalability
  topic: keywords
creator: Md Muzahidul Islam
creator: Aditya Dhanekula
rights: Copyright (c) 2024 Md Muzahidul Islam, Aditya Dhanekula (Author)
date: 2024-09-14
publication date: 2024-09-14
starting page: 226
ending page: 260
doi: 10.63125/q75tbj05

IMPACT OF DATA PRIVACY AND CYBERSECURITY IN ACCOUNTING INFORMATION SYSTEMS ON FINANCIAL TRANSPARENCY
type: item
description: 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 e…
subject: Articles
  topic: section
subject: Data Privacy
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Accounting Information Systems
  topic: keywords
subject: Financial Transparency
  topic: keywords
subject: Digital Governance
  topic: keywords
creator: Hozyfa Shafa
creator: Ashraful Islam
rights: Copyright (c) 2025 Hozyfa Shafa, Ashraful Islam (Author)
date: 2025-10-14
publication date: 2025-10-14
starting page: 254
ending page: 292
doi: 10.63125/xs0xt970

THE ROLE OF LARGE LANGUAGE MODELS (LLMS) IN PERSONALIZED ENGLISH LANGUAGE INSTRUCTION
type: item
description: 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 eng…
subject: Articles
  topic: section
subject: Large Language Models (LLMs)
  topic: keywords
subject: Personalized English Instruction
  topic: keywords
subject: Enterprise Cloud Deployment
  topic: keywords
subject: Feedback Quality
  topic: keywords
subject: Learner Engagement
  topic: keywords
creator: Elmoon Akhter
rights: Copyright (c) 2022 Elmoon Akhter (Author)
date: 2022-12-17
publication date: 2022-12-17
starting page: 97
ending page: 128
doi: 10.63125/86jf4136

INTEGRATING AI-POWERED ROBOTICS IN LARGE-SCALE WAREHOUSE MANAGEMENT: ENHANCING OPERATIONAL EFFICIENCY, COST REDUCTION, AND SUPPLY CHAIN PERFORMANCE MODELS
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-Powered Robotics
  topic: keywords
subject: Warehouse Management
  topic: keywords
subject: Systems Integration
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: Cost Per Order
  topic: keywords
subject: Supply Chain Performance
  topic: keywords
creator: Md Rezaul Karim
creator: Md.Kamrul Khan
rights: Copyright (c) 2025 Md Rezaul Karim, Md.Kamrul Khan (Author)
date: 2023-12-10
publication date: 2023-12-10
starting page: 01
ending page: 30
doi: 10.63125/mszb5c17

DATA-DRIVEN DECISION SUPPORT IN INFORMATION SYSTEMS: STRATEGIC APPLICATIONS IN ENTERPRISES
type: item
description: 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 rel…
subject: Articles
  topic: section
subject: Data-Driven Decision Support
  topic: keywords
subject: Business Intelligence And Analytics
  topic: keywords
subject: Enterprise Planning
  topic: keywords
subject: Service-Oriented Enterprises
  topic: keywords
subject: Prescriptive Optimization
  topic: keywords
creator: Md. Musfiqur Rahman
creator: Saba Ashfaq
rights: Copyright (c) 2021 Md. Musfiqur Rahman, Saba Ashfaq (Author)
date: 2021-06-12
publication date: 2021-06-12
starting page: 01
ending page: 33
doi: 10.63125/cfvg2v45

QUANTITATIVE RISK MODELING FOR DATA LOSS AND RANSOMWARE MITIGATION IN GLOBAL HEALTHCARE AND PHARMACEUTICAL SYSTEMS
type: item
description: 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…
subject: Articles
  topic: section
subject: Ransomware
  topic: keywords
subject: Data Loss
  topic: keywords
subject: Healthcare Cybersecurity
  topic: keywords
subject: Pharmaceutical Gxp
  topic: keywords
subject: Quantitative Cross-Sectional
  topic: keywords
subject: Multi-Case
  topic: keywords
subject: Cloud and Enterprise Cases
  topic: keywords
creator: Md. Tarek Hasan
rights: Copyright (c) 2023 Md. Tarek Hasan (Author)
date: 2023-10-25
publication date: 2023-10-25
starting page: 87
ending page: 116
doi: 10.63125/8wk2ch14

AI-BASED PORTABLE SUBSTATIONS AND TRANSFORMER DESIGN FOR FAST EV CHARGING: A CRITICAL REVIEW OF INNOVATIONS AND CHALLENGES
type: item
description: 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.
subject: Articles
  topic: section
subject: EV Fast Charging
  topic: keywords
subject: Portable Substation
  topic: keywords
subject: Solid-State Transformer
  topic: keywords
subject: AI Integration
  topic: keywords
subject: Power Quality
  topic: keywords
subject: Thermal Performance
  topic: keywords
creator: M. A. Masud Khan
rights: Copyright (c) 2025 M. A. Masud Khan (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 219
ending page: 253
doi: 10.63125/kkjgve78

PHYTOTHERAPEUTIC COMPOUNDS IN DIABETES MANAGEMENT: A COMPARATIVE ANALYSIS OF NATURAL EXTRACTS AND SYNTHETIC DRUGS
type: item
description: 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 comp…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Optimization
  topic: keywords
subject: Risk Modeling
  topic: keywords
subject: Strategic Economic Zones
  topic: keywords
subject: Mid-Sized Economies
  topic: keywords
creator: Sadia Tasnim
rights: Copyright (c) 2024 Sadia Tasnim (Author)
date: 2024-10-10
publication date: 2024-10-10
starting page: 192
ending page: 225
doi: 10.63125/04hvs766

A COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE-INTEGRATED BI DASHBOARDS FOR REAL-TIME DECISION SUPPORT IN OPERATIONS
type: item
description: 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, t…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Dashboards
  topic: keywords
subject: Real-Time Decision Support
  topic: keywords
subject: Operations
  topic: keywords
creator: Momena Akter
creator: Sai Praveen Kudapa
rights: Copyright (c) 2024 Momena Akter, Sai Praveen Kudapa (Author)
date: 2024-10-17
publication date: 2024-10-17
starting page: 158
ending page: 191
doi: 10.63125/47jjv310

ADAPTIVE LEARNING SYSTEMS FOR ENGLISH LITERATURE CLASSROOMS: A REVIEW OF AI-INTEGRATED EDUCATION PLATFORMS
type: item
description: 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…
subject: Articles
  topic: section
subject: Adaptive Learning
  topic: keywords
subject: English Literature
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Equity
  topic: keywords
subject: Pedagogy
  topic: keywords
creator: Md Arif Uz Zaman
creator: Elmoon Akhter
rights: Copyright (c) 2023 Md Arif Uz Zaman, Elmoon Akhter (Author)
date: 2023-09-10
publication date: 2023-09-10
starting page: 56
ending page: 86
doi: 10.63125/a30ehr12

DATA PRIVACY IN BUSINESS INTELLIGENCE SYSTEMS: ENSURING COMPLIANCE IN HRIS AND ENTERPRISE PLATFORMS
type: item
description: 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 choic…
subject: Articles
  topic: section
subject: Data privacy
  topic: keywords
subject: Business intelligence
  topic: keywords
subject: HRIS
  topic: keywords
subject: GDPR compliance
  topic: keywords
subject: Data governance
  topic: keywords
subject: RBAC/ABAC
  topic: keywords
subject: Row-level Security
  topic: keywords
creator: Md Mostafizur Rahman
creator: Omar Muhammad Faruk
creator: Sheratun Noor Jyoti
rights: Copyright (c) 2025 Md Mostafizur Rahman, Omar Muhammad Faruk, Sheratun Noor Jyoti (Author)
date: 2025-04-05
publication date: 2025-04-05
starting page: 97
ending page: 136
doi: 10.63125/527rnx08

INTEGRATION OF PLC AND SMART DIAGNOSTICS IN PREDICTIVE MAINTENANCE OF CT TUBE MANUFACTURING SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: CT Tubes
  topic: keywords
subject: Programmable Logic Controllers
  topic: keywords
subject: Smart Diagnostics
  topic: keywords
subject: Medical Device Manufacturing
  topic: keywords
creator: Md Mahamudur Rahaman Shamim
creator: Rezwanul Ashraf Ruddro
rights: Copyright (c) 2022 Md Mahamudur Rahaman Shamim, Rezwanul Ashraf Ruddro (Author)
date: 2022-12-10
publication date: 2022-12-10
starting page: 62
ending page: 96
doi: 10.63125/gspb0f75

CYBERSECURITY AND DATA INTEGRITY IN FINANCIAL SYSTEMS: A REVIEW OF RISK MITIGATION AND COMPLIANCE MODELS
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Risk Mitigation
  topic: keywords
subject: Compliance Models
  topic: keywords
creator: Md Hasan Zamil
creator: Md Omar Faruq
rights: Copyright (c) 2022 Md Hasan Zamil, Md Omar Faruq (Author)
date: 2022-12-10
publication date: 2022-12-10
starting page: 27
ending page: 61
doi: 10.63125/azwznv07

POWER BI AND DATA ANALYTICS IN FINANCIAL REPORTING: A REVIEW OF REAL-TIME DASHBOARDING AND PREDICTIVE BUSINESS INTELLIGENCE TOOLS
type: item
description: 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 fut…
subject: Articles
  topic: section
subject: Power BI
  topic: keywords
subject: Financial Reporting
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Real-Time Dash boarding
  topic: keywords
creator: Danish Mahmud
creator: Md. Zafor Ikbal
rights: Copyright (c) 2024 Danish Mahmud, Md. Zafor Ikbal (Author)
date: 2024-10-12
publication date: 2024-10-12
starting page: 125
ending page: 157
doi: 10.63125/yg9zxt61

AI-DRIVEN OPTIMIZATION AND RISK MODELING IN STRATEGIC ECONOMIC ZONE DEVELOPMENT FOR MID-SIZED ECONOMIES: A REVIEW APPROACH
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Optimization
  topic: keywords
subject: Risk Modeling
  topic: keywords
subject: Strategic Economic Zones
  topic: keywords
subject: Mid-Sized Economies
  topic: keywords
creator: MKA Shahinoor Rahman Jahid
rights: Copyright (c) 2025 MKA Shahinoor Rahman Jahid (Author)
date: 2025-08-10
publication date: 2025-08-10
starting page: 185
ending page: 218
doi: 10.63125/31wna449

SUSTAINABILITY METRICS IN GREEN INFRASTRUCTURE: A REVIEW OF LEED, LIFECYCLE COSTING, AND WASTE REDUCTION IN CONSTRUCTION
type: item
description: 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 rec…
subject: Articles
  topic: section
subject: LEED
  topic: keywords
subject: Lifecycle Costing
  topic: keywords
subject: Waste Reduction
  topic: keywords
subject: Sustainability Metrics
  topic: keywords
subject: Green Infrastructure
  topic: keywords
creator: Mubashir Islam
creator: Abdul Rehman
creator: Moin Uddin Mojumder
rights: Copyright (c) 2024 Mubashir Islam, Abdul Rehman, Moin Uddin Mojumder (Author)
date: 2024-10-11
publication date: 2024-10-11
starting page: 90
ending page: 124
doi: 10.63125/0rjhb708

QUANTIFYING THE IMPACT OF NETWORK SCIENCE AND SOCIAL NETWORK ANALYSIS IN BUSINESS CONTEXTS: A META-ANALYSIS OF APPLICATIONS IN CONSUMER BEHAVIOR, CONNECTIVITY
type: item
description: 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.
subject: Articles
  topic: section
subject: Network Science
  topic: keywords
subject: Social Network Analysis (SNA)
  topic: keywords
subject: Consumer Behavior
  topic: keywords
subject: Connectivity Optimization
  topic: keywords
subject: Civic Engagement
  topic: keywords
creator: Istiaque Mahmud
creator: Dipon Das Rahul
creator: Hasan Aqif
creator: Samia Akter
creator: Sayer Bin Shafi
rights: Copyright (c) 2024 Istiaque Mahmud, Dipon Das Rahul, Hasan Aqif, Samia Akter, Sayer Bin Shafi (Author)
date: 2024-10-10
publication date: 2024-10-10
starting page: 58
ending page: 89
doi: 10.63125/vgkwe938

NEURAL NETWORK–BASED RISK PREDICTION AND SIMULATION FRAMEWORK FOR MEDICAL IOT CYBERSECURITY: AN ENGINEERING MANAGEMENT MODEL FOR SMART HOSPITALS
type: item
description: 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 mat…
subject: Articles
  topic: section
subject: Medical Internet of Things (MIoT)
  topic: keywords
subject: Neural Network
  topic: keywords
subject: Cybersecurity Risk Prediction
  topic: keywords
subject: Smart Hospitals
  topic: keywords
subject: Engineering Management
  topic: keywords
creator: Md Tawfiqul Islam
creator: Sabbir Ahmad
creator: Md Anikur Rahman
creator: Md Arifur Rahaman
rights: Copyright (c) 2024 Md Tawfiqul Islam, Sabbir Ahmad, Md Anikur Rahman, Md Arifur Rahaman (Author)
date: 2024-10-07
publication date: 2024-10-07
starting page: 30
ending page: 57
doi: 10.63125/g0mvct35

SUPPLY CHAIN TRACEABILITY IN APPAREL MANUFACTURING: A REVIEW OF REAL-TIME ORDER TRACKING, VENDOR MANAGEMENT, AND EXPORT VISIBILITY SYSTEMS
type: item
description: 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 emer…
subject: Articles
  topic: section
subject: Supply Chain Traceability
  topic: keywords
subject: Real-Time Order Tracking
  topic: keywords
subject: Vendor Management Systems
  topic: keywords
subject: Export Visibility
  topic: keywords
subject: Apparel Manufacturing
  topic: keywords
creator: Golam Qibria Limon
rights: Copyright (c) 2022 Golam Qibria Limon (Author)
date: 2022-12-07
publication date: 2022-12-07
starting page: 01
ending page: 26
doi: 10.63125/dj9fgy65

BUSINESS BACKGROUND STUDENT'S PERCEPTION ANALYSIS TO UNDERTAKE PROFESSIONAL ACCOUNTING EXAMINATIONS
type: item
description: 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 …
subject: Articles
  topic: section
subject: Business Students
  topic: keywords
subject: Business Student’s Perception
  topic: keywords
subject: Accounting Profession
  topic: keywords
subject: Professionally Qualified Accountants
  topic: keywords
subject: CA
  topic: keywords
subject: CMA
  topic: keywords
creator: Md. Rasel Ahmed
rights: Copyright (c) 2023 Md. Rasel Ahmed (Author)
date: 2023-09-07
publication date: 2023-09-07
starting page: 30
ending page: 55
doi: 10.63125/bbwm6v06

TO WHAT EXTENT DOES THE USE OF PROJECT MANAGEMENT–ORIENTED DIGITAL COLLABORATION TOOLS AFFECT DELIVERY TIMELINES IN REMOTE TECH TEAMS?
type: item
description: 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.
subject: Articles
  topic: section
subject: Project Management Tools
  topic: keywords
subject: Remote Tech Teams
  topic: keywords
subject: Delivery Timelines
  topic: keywords
subject: Digital Collaboration
  topic: keywords
subject: TAM
  topic: keywords
subject: TTF
  topic: keywords
subject: Mixed-Methods Research
  topic: keywords
subject: Communication Efficiency
  topic: keywords
creator: Mohammad Hasan Imam
creator: Mahruba Chowdhury
rights: Copyright (c) 2025 Mohammad Hasan Imam, Mahruba Chowdhury (Author)
date: 2025-03-19
publication date: 2025-03-19
starting page: 163
ending page: 184
doi: 10.63125/aq819t42

ENERGY EFFICIENCY MONITORING IN INSTITUTIONAL FACILITIES: A REVIEW OF UI/UX SOLUTIONS FOR IOT DASHBOARDS
type: item
description: 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 p…
subject: Articles
  topic: section
subject: Energy Efficiency
  topic: keywords
subject: IoT Dashboards
  topic: keywords
subject: Institutional Facilities
  topic: keywords
subject: UI/UX Design
  topic: keywords
subject: Smart Building Management
  topic: keywords
creator: Rezwanul Ashraf Ruddro
rights: Copyright (c) 2025 Rezwanul Ashraf Ruddro (Author)
date: 2025-03-20
publication date: 2025-03-20
starting page: 137
ending page: 162
doi: 10.63125/yhr4g345