Emerging Science Journal (ESJ)
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Combining a Moving Average with a Triple EWMA Chart to Improve Detection Performance
Get PDFThis article aims to introduce the novel mixed triple exponentially weighted moving average-moving average (MTEM) chart to accurately detect position changes for both symmetric and non-symmetric distributions. The MTEM chart constructs a moving average (MA) structure to filter out fluctuations in the raw data and then applies triple exponential weighting to improve the ability to identify minor shifts. The average run length (ARL) and median run length (MRL), which are run length profiles derived from the Monte Carlo simulation (MC) strategy, were used to compare the performance of the suggested chart with that of MA, EWMA, TEWMA, and mixed moving average-triple exponentially weighted moving average (MMTE) charts. In addition, the expected average run length (EARL) and expected median run length (EMRL) were also used to rate the overall results. Results of the study indicate that the MTEM chart surpasses competitor charts in detecting minor to moderate changes. The MMTE chart responds slightly slower than the proposed chart. Due to its smoothed and re-averaged structure, it may lose significant information. The MA chart worked better for greater shifts. Furthermore, the MTEM chart competency was applied to two real-world datasets, confirming its practicality
Predicting Dropout in MENA STEM Higher Education Using Explainable AI: A Machine Learning Approach
No full textThis study aims to develop an explainable machine learning–based early warning system to predict dropout risk among Science, Technology, Engineering, and Mathematics (STEM) students in the MENA region. Using longitudinal data from 6,798 undergraduate STEM students enrolled at a major UAE university, we evaluated six supervised classifiers: XGBoost, Gradient Boosting Machine (GBM), Random Forest, CART, Logistic Regression, and K-Nearest Neighbors. Models were trained on institutional student information system (SIS) data spanning ten cohorts (2010–2019), with class imbalance addressed through ROSE sampling. The top-performing models (XGBoost, GBM, and Random Forest) achieved AUC-ROC scores exceeding 0.91 and F1-scores above 0.84, significantly outperforming baseline models. Key predictors of dropout included the number of withdrawn semesters, second-term credit load, academic probation history, and performance in mathematics and physics. To improve interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, enabling both global and individual-level feature attribution. The system offers scalable, real-time predictive capabilities using only routinely available SIS data, with no need for external surveys or learning management system inputs. The novelty of this research lies in its integration of explainable AI into a regional context, enabling early, transparent, and actionable interventions to reduce dropout. These findings contribute to data-driven retention strategies in higher education systems where predictive tools remain underutilized
Policy Recommendations for Enhancing the Green Banking and Sustainable Development
No full textThis study examines the key factors influencing green banking and sustainable development in Vietnam to provide evidence-based policy recommendations to promote the integration of sustainability within financial institutions. A mixed-method approach combining qualitative and quantitative analyses was adopted. The research process began with focus group discussions with 30 banking experts, followed by in-depth interviews with senior managers to refine measurement scales. Subsequently, a structured survey was conducted among 900 commercial bank managers in the Southeast region, and the collected data (n = 845 valid responses) were analyzed using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling (SEM). The findings reveal seven knowledge-driven factors that significantly affect green banking and sustainable development: the legal framework and supporting policies, awareness and trends in sustainable consumption, financial technology, leadership commitment and corporate culture, pressure from investors and international organizations, climate change and environmental risk management, and public-private partnerships. Among them, the legal framework and supporting policies emerged as the most influential drivers. Green banking practices are also shown to directly contribute to sustainable development by financing environmentally friendly projects and integrating ESG principles. The study’s novelty lies in its knowledge-based economy perspective, demonstrating how policy knowledge, financial technology, and organizational learning interact to enhance sustainability. Practical implications highlight the need for regulatory reform, technology adoption, and cross-sectoral collaboration to accelerate Vietnam’s transition to a green economy
Linking Psychological Safety Climate to Dual Innovation Through AI-Enabled Dynamic Capabilities
No full textObjective: This study develops and empirically validates an integrated model that explains how the psychological safety climate influences dual innovation through AI-enabled dynamic capabilities in Chinese design organizations. Methods: A cross-sectional survey was conducted among 281 designers from industry design firms and departments. Data analysis employed partial least squares-structural equation modeling, including mediation bootstrapping analysis, importance-performance map analysis, necessary condition analysis, and quadratic effect analysis. Findings: All hypotheses received strong empirical support. The psychological safety climate has a significant influence on AI-enabled dynamic capabilities, with a path coefficient of 0.452 at p <0.001, and on dual innovation, with a coefficient of 0.383 at p < 0.001. AI-enabled dynamic capabilities have a positive impact on dual innovation, with a coefficient of 0.384 at p < 0.001, and significant mediation effects, indicating an indirect effect of 0.174 at p < 0.001. The model explains 42.7% of the variance in dual innovation. Importance-performance analysis reveals a psychological safety climate as highly important but moderately performing, indicating strategic opportunities for improvement for organizations. Necessary condition analysis confirms both constructs as essential requirements for innovation outcomes. The findings demonstrate that psychological safety climate, as a higher-order cultural resource, enables lower-order AI-enabled dynamic capabilities, supporting socio-technical systems structure for dual innovation. Organizations should prioritize investments in psychological safety while maintaining their AI capabilities. Novelty: This research introduces AI-enabled dynamic capabilities as a second-order formative construct and establishes the meta-capability role of psychological safety climate in AI-enabled dynamic capabilities and dual innovation, thereby extending the resource-based view and dynamic capabilities theories through micro-foundational perspectives
Corrosion Performance of a Novel Aluminium 6061-Sea Sand Composite Under Electrochemical Method
Get PDFThe need for lightweight materials is increasing from year to year. In its application, lightweight and strong materials also need to be corrosion resistant. Corrosion resistance is an important property in automotive, especially in high humidity areas. Al6061-Sea sand material is a novel material that meets the mechanical standards required in the automotive sector. A previous study of Al 6061-sea sand conducted the mechanical properties of the composite. This current research focuses on the development of Al 6061 material with variations in weight fraction of sea sand reinforcement against the corrosion rate under the potentiodynamic method to determine the corrosion resistance of the composite material. The composite fabrication uses the electroless coating method on sea sand and the stir casting method with a melting temperature of 750°C. The agitation process used a four-bladed impeller for 10 minutes at 600 rpm with a stirring depth of ½ of the height of the molten metal. The tests include density testing, microstructure observation, and corrosion rate under the potentiodynamic method using an electrochemical potentiostat. The test result obtained the lowest corrosion rate results in 2% wt sea sand with a corrosion rate of 0.61875 mmpy. The increase in corrosion rate value is directly proportional to the addition of the weight fraction of sea sand
Performance Evaluation of Significant Feature for Interest Flooding Attack Detection on Named Data Networking
No full textOne of the internet architectures of the future that has advantages over the current system is Named Data Networking (NDN). However, Denial of Service (DoS) attacks, such as interest flooding attacks (IFA), can still disrupt the network. Detecting IFA attacks is crucial for preventing further damage. Several approaches to detection systems have been proposed, including a classification approach to detecting attacks with multiple detection parameters or features. However, the many detection system features that can be extracted from the network result in longer computation times for the classification algorithms. This research focuses on enhancing the detection of IFA by evaluating the features of the detection system and identifying significant features to improve detection accuracy and reduce computation time. We employed various feature selection algorithms, including information gain, wrapper naive Bayes, gain ratio, and correlation-based feature selection (CFS). The selected features are tested to detect attacks using several classification algorithms, including naive Bayes, random forest, J48, and Bayesian network. Our proposed method found only three essential features for detecting IFA from 18 features available, resulting in better detection accuracy and increasing by 47.8% the time to build the model. This study enhances NDN security while reducing computational cost, making real-time attack detection more feasible
Extrusion Technology for Complex Processing of Brewery Waste Into Feed Products for Livestock and Poultry
No full textAn energy-efficient extrusion technology for the complex processing of wet brewing waste into feed products for animals and poultry is proposed and evaluated. The study aims to replace traditional energy-intensive drying methods – typically involving natural gas, steam, or boiler exhaust gases – with a more sustainable extrusion process. The approach allows direct utilization of wet brewing by-products, such as brewers’ grains and brewers’ yeast, without preliminary drying, thereby reducing energy consumption by up to 50%. The technological development was based on systems analysis and synthesis of extrusion processes, combining wet brewing waste with dry feed components. The research identified optimal parameters for extrusion: a feed mixture to compound feed component ratio of 1:1.85–2; initial moisture content of 28–30%; extrusion temperature of 140–150 °C; and barrel pressure of 4–8 MPa. The final product was a partially dehydrated mass with a moisture content of 60–65%, suitable for use as a feed additive or complete compound feed. The results demonstrate improved product quality and extended shelf life due to thermal and mechanical treatment during extrusion. The novelty of the approach lies in bypassing the conventional drying step, offering a cost-effective and environmentally friendly way to increase the value of brewing industry waste
Modeling and Performance Optimization for Complex Workflow in IoT
Get PDFThis study addresses the growing challenge of time scheduling in Internet of Things (IoT) workflows, where efficiency in time utilization and resource profitability is increasingly constrained by uncertainty. Real-world workflows are characterized by non-deterministic activity execution and resource preparation times, yet existing research often neglects these fundamental dynamics when modeling IoT-based processes. To bridge this gap, we propose a comprehensive modeling and performance optimization framework that explicitly incorporates uncertainty. Methodologically, the framework introduces two distinct types of places to represent activities and resources, with resource properties capturing reusability and preparation processes abstracted as specialized activities. For workflow activities, timing functions are defined to model minimum and maximum execution times, enabling the computation of earliest and latest start times and the identification of critical activities driving overall workflow duration. To mitigate resource conflicts during execution, three alternative resolution strategies are developed and systematically evaluated. Results demonstrate that the proposed approach effectively identifies optimal scheduling strategies under uncertainty, enhancing both temporal efficiency and resource utilization. A workflow case study illustrates the applicability of the framework, offering methodological and practical insights for designing resilient IoT workflow scheduling systems in complex, real-world environments
Evaluating Digital Transformation Risks in Logistics and Supply Chain Management with PLS-SEM-ANN-fsQCA
No full textThis study investigates the risks associated with digital transformation (DT) implementation in Vietnam’s logistics and supply chain management (SCM) sector, utilizing a hybrid PLS-SEM-ANN-fsQCA methodology to analyze data from 243 valid questionnaires. Anchored in the Technology-Organization-Environment framework augmented with human factors (TOE+H), the research aims to examine how technological, organizational, environmental, and human factors influence DT adoption and associated risks, including financial, operational, cybersecurity, and reputational risks, while exploring the moderating roles of firm size and digital literacy. Findings reveal that TOE+H factors significantly drive DT implementation, but misalignment, ineffective management, market volatility, and limited digital literacy amplify risks, particularly cybersecurity vulnerabilities. Moderation analyses indicate that high digital literacy, larger firm size, and regulatory compliance mitigate these risks. Artificial neural network (ANN) analysis highlights non-linear relationships, emphasizing technological and human factors as key drivers, while fuzzy-set qualitative comparative analysis (fsQCA) identifies configurations, such as strong technological-human factor alignment, linked to successful DT outcomes. Importance-Performance Map Analysis (IPMA) prioritizes technological and human factors for resource allocation to enhance sustainability. This study advances the TOE+H framework by integrating a hybrid methodology, offering novel insights into DT risk dynamics and practical strategies for sustainable logistics in Vietnam’s SCM sector
The Impact of Climate-Smart Technology Adoption on Agricultural Productivity and Environmental Sustainability
Get PDFThis study provides an empirical assessment of emerging opportunities and offers a conceptual framework for understanding the potential impacts of climate-smart agriculture (CSA) adoption on agricultural productivity and environmental sustainability. Focusing on Uzbekistan, the research employs quantitative analysis of farm-level data, adoption gradient modeling, and return-on-investment (ROI) estimation to examine how CSA technologies influence key farm-level outcomes, including yields, income, resource-use efficiency (RUE), soil erosion, and water quality under world constraints. In cotton-wheat systems, the usage of six or more CSA is associated with a 71% increase in farm income, a 43% rise in crop yields, and a 48% improvement in resource-use efficiency (RUE), compared to farms with low levels of CSA usage. Fertilizer micro-dosing is associated with an average increase in cotton yields of 245.8 kg ha⁻¹ yr⁻¹ and delivers a ROI of 456%. Multivariate regression models account for 57.3% of the variation in yield and 61.8% in farm income, underscoring the explanatory power of CSA adoption patterns. Comparative analyses demonstrate that organic matter-based practices consistently outperform capital-intensive alternatives in both economic and environmental terms. The methodological approach integrates monitoring, reporting, and verification (MRV) indicators, payback period estimations, and threshold analyses tailored to risk-sensitive smallholder contexts. The findings provide robust empirical support for evidence-informed CSA policy formulation, including the design of targeted subsidies, extension services, and investment strategies in Uzbekistan. By reconciling global CSA implementation paradigms with localized constraints, the study generates scalable and empirically validated approaches, offering methodological relevance for analogous agroecological and institutional contexts