Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. Currently, existing systems use frequency-based methods for feature selection (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)) not to capture the subtleties of emotions expressed in student feedback and do not provide insights into the specific concerns of students via topics or themes. In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. The SSF framework classifies student sentiments and subsequently groups feedback into themes using semantic networks based on word co-occurrence. Our innovative feature selection approach combines TF-IDF with sentiment-based features derived from SentiWordNet and intensifiers, creating a robust feature vector that enhances the dataset’s richness and improves classification accuracy and robustness. In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.

Ethiopia's bottled water industry faces mounting pressure to balance economic growth with environmental responsibility. This study investigates the effect of green supply chain management (GSCM) on Ethiopian bottled water companies' economic performance, with environmental performance as a potential mediator. We employ structural equation modelling (SEM) on survey data from managers of 99 bottled water firms in Ethiopia. The findings revealed that while some GSCM practices indirectly enhance the bottom line through improved environmental impacts, others, like investment recovery initiatives, directly enhance economic performance. Notably, the study demonstrates that GSCM fosters an environmentally sustainable future for Ethiopia's bottled water industry, where environmental responsibility ultimately leads to long-term economic performance. This research offers valuable insights for policymakers and stakeholders seeking to promote balanced environmental and economic growth within the Ethiopian bottled water industry, moving beyond mere “greenwashing” towards genuine sustainability.

Climate change creates considerable issues for watershed management, especially in areas prone to erosion and sediment production. The purpose of this study was to examine and map the sediment yield response to future climatic scenarios in the Songwe Watershed. The Soil and Water Assessment Tool (SWAT), which is integrated with Regional Climate Models (RCM) under Representative Concentration Pathways (RCPs) 8.5, was used to evaluate the possible consequences on sediment transport dynamics within the watershed. The simulated results from the four Regional Climate Models (CCLM4, HIRAM5, RACMO22T, and RCA4 RCMs) showed that sediment yields increased for future estimates from 2011 to 2100 under RCP 8.5, owing mostly to increased rainfall and altered hydrological cycles. The results reveal that the average annual sediment yield could increase by 30-50% under RCP 8.5. scenario. Sediment yield mapping highlights crucial hotspots, notably in steep terrain and places with minimal vegetation cover, that are extremely susceptible to erosion, providing useful insights for focused intervention measures. The study emphasized the need for adaptive watershed management methods to counteract the negative effects of climate change on soil erosion and sediment crusade.

The advent of Industry 4.0 has revolutionized manufacturing, integrating advanced technologies to enhance efficiency and sustainability. However, the transition to sustainable green manufacturing presents numerous challenges. This paper analyzes these challenges using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). By incorporating expert opinions and fuzzy logic, various obstacles are evaluated and prioritized in the implementation of green manufacturing practices in the context of Industry 4.0. The analysis reveals that market uncertainty in the economic landscape ranks as the top challenge, followed by high costs of implementation, maintenance, security, and integration. Uncertain benefits and trade-offs are also found as significant barriers. Key factors include the need for substantial investments, cybersecurity concerns, integration difficulties, and the complexities of predicting returns on investment. From the study, it is also evident that the impact of Industry 4.0 on supply chains and emissions from Electronics manufacturing is also a critical issue. The study provides actionable insights and strategic recommendations for policymakers and industry leaders to facilitate the adoption of sustainable green manufacturing practices in the era of Industry 4.0.

In the era of Industry 4.0, advanced manufacturing systems are increasingly integrating cyber and physical components, making them susceptible to sophisticated cyber-attacks. Addressing these vulnerabilities is crucial for maintaining the integrity and efficiency of manufacturing processes. This study introduces a comprehensive game-theoretic model to tackle cybersecurity challenges in such systems. The interaction between cyber attackers and defenders is modeled as a strategic game, incorporating a cost function that includes production losses, recovery from attacks, and maintaining of defense strategies. Both deterministic and probabilistic approaches are employed: linear programming identifies optimal strategies, achieving Nash equilibrium under ideal conditions, while the Quantal Response Equilibrium (QRE) method captures player behavior under uncertainty. The optimization problem is solved using the CPLEX library in Python, ensuring robust and efficient computation of optimal mixed strategies. The methodology is demonstrated through a numerical example, highlighting the identification of potential vulnerabilities and optimal defense strategies. The analysis reveals that the defender's learning curve is longer and more complex than the attacker's, emphasizing the necessity for advanced and adaptive defense strategies. This comprehensive approach not only predicts attacker behavior but also suggests effective defense mechanisms tailored to specific threats. The findings underscore the importance of strategic decision-making in enhancing the cybersecurity resilience of cyber-physical manufacturing systems, offering valuable insights for mitigating cybersecurity risks effectively. The most significant result indicates the critical need for timely and adaptive defense mechanisms to counter sophisticated cyber threats, ensuring the sustained operation and security of modern manufacturing environments.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.

The footwear industry is booming with the increasing demand for leather-based products and manufacturers from the Asia region are leading the market. In the meantime, the negative impact on the environment of footwear goods and manufacturing processes of footwear-based products concerns the buyers and consumers as the global environmental situation is worsening day by day. For this reason, footwear industries are bound to use environmentally friendly production techniques to cope with the conditions as well as demands of the buyers otherwise the manufacturer can face the drop of order which can be affected financially. The Bangladeshi footwear industry is most unlikely to adopt sustainable manufacturing and thus lagging in the competitive market containing manufacturers from other Asian countries. The reason behind not implementing sustainable manufacturing practices should be explored to ensure this practice. In the study, Graph Theory and Matrix Approach (GTMA) was utilized along with the best and worst matrix using the weighted values as the diagonal and off-diagonal elements which was found from the two surveys after consultation with experts from the Bangladeshi footwear industry to gather responsible barriers for both large-scale and small-medium sized enterprises. Ranking based on impact was developed for two scales and an index value was found from the calculations of the matrix formed by permanent functional value found for each of the barriers and their best-worst values also which was another determinant of this study for expressing the fitness level for this certain adaptation. Similarity coefficient was another factor to be determined for the decision making required for the first objective of this research.

This study examines the impact of internal and external green supply chain management on consumers’ environmentally friendly purchase behavior. Green supply chain management (GSCM) has emerged as a key approach for enterprises seeking to become environmentally sustainable. This study targets all sectors to investigate the impact of internal and external GSCM on consumers’ environmentally friendly purchase behavior. This paper proposes to study and emphasize GSCM practices and their impact by using quantitative methods. Responses were collected from different organizational sectors and analyzed by 50 people using regression and CFA. Internal and external green supply chain management positively impacts consumer purchasing behavior.

For decades, due to the increasing generation of waste and its negative environmental impacts, efficient municipal solid waste management (MSWM) has become a major concern of sustainable development. On the other hand, selecting an appropriate strategy remains a critical issue for municipal authorities without applying a multi-criteria decision-making (MCDM) approach. This study aims to select the best MSWM strategy from five potential MSW management strategies under various factors that influence waste strategy selection, such as economic, social, technical, and environmental factors. This study used three fuzzy integrated MCDM methods, namely the Decision-Making Trail and Evaluation Laboratory (DMATEL), the Analytic Network Process (ANP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select best MSWM strategies. The study used data from a survey of experts who had knowledge of waste management. The survey data was then analyzed using the DMATEL, ANP and TOPSIS methods under a fuzzy environment. The Fuzzy DEMATEL method is used to identify the causal relationships among the criteria and sub-criteria, while ANP is applied to determine the relative weights of MSWM strategy selection sub-criterion. Finally, TOPSIS is used to rank the waste management strategies depending on established sub-criteria. The result of fuzzy TOPSIS demonstrated that recycling of municipal solid waste is the best alternative which has the highest closeness coefficient followed by reuse. Thus, the result of this study revealed that MSWM strategies are ranked as: waste recycling (A1), reuse (A2), reduce (A1), energy recovery (A4), and disposal (A5), with closeness coefficient values of 0.80, 0.61, 0.60, 0.47, and 0.17, respectively.

The complexity and vulnerability of supply chains in today's globalized economy have heightened the significance of understanding and managing disruptions. Supply chain disruptions, ranging from natural disasters to pandemics, can lead to substantial delays, increased costs, and reduced quality, thereby impacting the operational performance and stability of businesses. Considering the theoretical sophistication and substantial relevance of the field, this research seeks to conduct an extensive bibliometric analysis of supply chain disruption. The goal is to offer a thorough summary of recent advancements and ongoing progress in this specific area. Additionally, it aims to pinpoint emerging research patterns that can guide future studies. We performed a bibliometric review of the structure and global trends of disruptions among supply chains, from 1996 to 2024 using techniques such as analysis of (1) the dataset, (2) sources, (3) authors, (4) countries and institutions, (5) documents, and (6) keywords. A total of 1356 bibliographic records from Web of Science databases were analyzed to generate the study’s research data through Scientometrics networks. The paper concludes by discussing the strengths and limitations of the study and suggesting potential avenues for future research, aiming to guide and inform future studies in this critical area of supply chain management. The findings of this study will enhance understanding of the prevailing patterns in this topic, thus laying the groundwork for future research initiatives in this field.