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.