Energy-saving design is becoming a trending topic and top-priority over the past decades due to high energy costs, limited available resources and growing urban development. Buildings are alluded to as the major contributors of energy consumption and environmental emissions across the globe. This calls for the development of precise forecasting models of energy consumption and carbon emissions. Hence, this research paper harnesses the implementation of several contemporary metaheuristics to accurately project heating and cooling energy (HEN and CEN) in residential buildings. In this respect, black widow optimization, dandelion optimization, dingo optimization, sparrow search, and red-tailed hawk optimization are among the studied metaheuristics in this research study. The prediction accuracies of the developed models are assessed stepping on the measures of i) relative absolute error (RAE), ii) mean absolute error (MAE), iii) mean absolute percentage error (MAPE), iv) root mean squared error (RMSE) and v) Nash-Sutcliffe efficiency (NSE). It is shown that the developed red-tailed hawk optimization-based model succeeded in accomplishing the most precise results of HEN and CEN. In this context, it predicted HEN with RAE (0.201), MAE (1.838), MAPE (7.626%), RMSE (2.826), and NSE (0.921). Besides, it anticipated CEN with RAE (0.234), MAE (2.009), MAPE (7.519%), RMSE (3.246), and NSE (0.883). It can be argued that this research study could benefit architects and designers in creating more energy-efficient buildings at an early stage.

Malaysia’s food industry is facing escalating pressure from rising population demands, resource scarcity, and the mounting effects of climate change. As agriculture remains vital to national food security and economic stability, managing its environmental footprint—particularly CO₂ emissions—has become an urgent priority. Despite growing global attention to sustainability, limited empirical research has explored how internal organizational dynamics and energy transition efforts influence emissions outcomes in the Malaysian context. This study aims to address this gap by examining the effects of agriculture industry operations, renewable energy consumption, and government initiatives on CO₂ emissions management, with organizational perception acting as a mediating factor. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) on pilot survey data from food industry stakeholders, the findings highlight that organizational perception significantly mediates the relationship between renewable energy consumption and government initiatives on CO₂ emissions outcomes. While agricultural operations do not directly or indirectly influence emissions through organizational perception, both renewable energy use and proactive government policies foster a stronger environmental orientation within organizations—leading to improved emissions management. The study contributes to the discourse on sustainable development in emerging economies by emphasizing the critical role of organizational mindset in translating external sustainability drivers into tangible environmental outcomes. These insights offer practical implications for industry leaders and policymakers seeking to enhance sustainability strategies within Malaysia’s agri-food sector.

One of the utmost global trends in project management nowadays is sustainable project management (SPM), which researchers have linked to project success (PS). Hence, this paper aims to conduct systematic literature review of published articles on the effect of SPM on PS from 2013 to June 2024. By means of the Scopus database and PRISMA 2020 declaration, this study critically extracted, examined, and appraised relevant literature from the nineteen sampled papers with regard to theory, context, methodology and characteristics. From the outcomes of the content analysis, this study identified that SPM influences PS since none of them recorded a negative impact. It also concluded that the relationship is inadequately addressed from the past works. The review also enumerates future research directions and implications for practice on the correlation between SPM and PS. Overall, the conclusions of this study present a solid foundation for scholars to investigate these constructs and contribute to the extant field of information.

This study explores the imperative of profit maximization within the context of an apparel industry in Bangladesh. The research centers on the application of the Linear Programming (LP) approach along with Cost engineering and rigorous data analysis including multiple forecasting techniques to devise a systematic and quantitative strategy for maximizing profits in this multifaceted sector. The study recognizes the challenges posed by the industry's multiproduct nature and aspires to contribute valuable insights that resonate with the unique dynamics of the Bangladeshi apparel landscape. By formulating a comprehensive LP model, the research aims to address critical aspects such as resource allocation, production planning, process planning, transportation and demand considerations. The objectives extend to profit analysis based periodic forecasting of five months to assess the model's robustness, adaptability and profit pattern to dynamic business scenarios. This study shows a comparison between the profit based on the company’s present scenario and model-based analysis. Through this research, we aspire to provide a practical and accessible guide for industry practitioners, policymakers, and researchers seeking to enhance profit maximization strategies within Bangladesh's apparel industry, ultimately contributing to the sector's sustainable growth and competitiveness.

Bangladesh being one of the largest textile producers in the world, the apparel manufacturing industry has to deal with the pressure to produce the maximum possible in the face of low product quality and sustainability standards. This paper examines a case of process improvement in a Bangladeshi apparel industry, which has a middle-scale, by implementing proper use of Value Stream Mapping (VSM), which is a strong Lean Manufacturing tool. A current-state VSM was established through careful time studies and data collection of a T-shirt production line and indicated the many non-value added activities that occurred such as the high waiting time, the unbalanced workflow, and the large amount of work-in-progress (WIP) inventory. On the basis of these observations, a future-state VSM has been put forward with the incorporation of lean intervention like line balancing, standardized work, and better information flow. Future state simulation has shown that there was the possibility that the total lead time would be reduced by 35 percent and overall production efficiency to improve by 25 percent. The current study demonstrates the empirical viability of VSM in the Bangladesh apparel manufacturing industry and gives recommendations that can be implemented by factory managers and policymakers to achieve competitive advantage in the rapidly-changing international market. The paper also fills a research gap in that it portrays an empirical documented case in academia to fill the gap hitherto, in the literature of Lean implementation in the apparel and other manufacturing industries in developing countries.

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.