This study evaluates the health and sustainability of higher education systems in nine countries: the USA, UK, Australia, Germany, Canada, China, Brazil, India, and South Africa. Using a multi-level analysis model and principal component analysis (PCA), nine key factors—such as international student numbers, academic levels, and graduate employment rates—were identified, capturing over 90% of the cumulative impact on higher education systems. India, scoring 6.2036 initially, shows significant room for improvement. The study proposes policies to increase graduate employment, promote international faculty collaboration, and enhance India’s educational expenditure, which surpasses 9.8% of GDP. Post-policy simulations suggest India’s score could rise to 8.7432. The paper also addresses the impact of COVID-19 on global education, recommending a hybrid model and increased graduate enrollment in China to reduce unemployment by 5.4%. The research aims to guide sustainable development in higher education globally.

This study focuses on enhancing the maintenance processes of centrifugal pumps at Soekarno-Hatta Airport’s Water Treatment Unit in Indonesia, crucial for meeting the clean water needs of the airport, which served around 19.8 million passengers in 2022. Using a qualitative methodology, the research involved focus group discussions with the unit’s operators, technicians, and engineers to pinpoint maintenance challenges and devise solutions. Key findings reveal issues such as insufficient routine maintenance, unplanned repairs, and inadequate staffing, leading to operational disruptions and pump failures. The study highlights the role of Total Productive Maintenance (TPM) in reducing machine breakdowns and improving efficiency. It emphasizes the critical role of centrifugal pumps in the airport’s water supply system. The research proposes several corrective measures, adhering to the 5W + 1H framework, including regular lubrication, bearing replacements, hiring more staff, and advanced training on PLC systems. These actions aim to rectify immediate maintenance problems and establish a foundation for the long-term effectiveness of the pump systems. Conclusively, the study underscores the need for a comprehensive maintenance strategy that aligns with standard operating procedures and preventive maintenance. This approach is essential for boosting the operational performance and reliability of the Water Treatment Unit. It has broader implications for similar infrastructure facilities, underscoring the importance of efficient maintenance management.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

In casting industries, issue of spent molding sand disposal is the origin of molding sand reclamation. Among from all reclamation concepts the thermal reclamation method is better for no-bake sand system. This study focuses on the evaluation of sand quality by considering physical and chemical characteristics of molding sand, which is reclaimed by thermal reclamation method. Electric fuel and fluidization mechanism is used in thermal reclamation system. Effect of reclamation temperature, soaking period and sand quantity on % reclamability, grain size, ADV and on LOI is investigated. The average grain size, low ADV, low LOI and acceptable % reclamability of thermally reclaimed sand are studied.

One of the biggest environmental problems that has affected the planet is global warming, due to high concentrations of carbon (CO₂), which has led to crops such as coffee being affected by climate change caused by greenhouse gases (GHG), especially by the increase in the incidence of pests and diseases. However, carbon sequestration contributes to the mitigation of GHG emissions. The objective of this work was to evaluate the carbon stored in above and below ground biomass in four six-year-old castle coffee production systems. In a trial established under a Randomized Complete Block Design (RCBD) with the treatments Coffee at free exposure (T1), Coffee-Lemon (T2), Coffee-Guamo (T3) and Coffee-Carbonero (T4), at three altitudes: below 1,550 masl, between 1,550 and 2,000 masl and above 2,000 masl. Data were collected corresponding to the stem diameters of coffee seedlings and shade trees with which allometric equations were applied to obtain the carbon variables in the aerial biomass and root and the carbon variables in leaf litter and soil obtained from their dry matter. Highly significant differences were obtained in the four treatments evaluated, with T4 being the one that obtained the highest carbon concentration both in soil biomass with 100.14 t ha^-1 and in aerial biomass with 190.42 t ha^-1.