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.

This study employs the Standard Error Estimation technique to investigate the connections between the digitalization of economy, population, trade openness, financial development, and sustainable development across 127 countries from 1990 to 2019. The findings revealed associations between financial development, population growth, trade openness, economic growth, Digitalization development, foreign direct investment (FDI), and sustainable development. Financial development negatively impacts sustainable development, suggesting that countries with advanced financial systems may struggle to maintain sustainability. Trade openness exhibits a negative association with sustainable development, implying that countries with open trade policies may face challenges in maintaining sustainability, possibly due to heightened competition or resource exploitation. These findings highlight the multifaceted relationship between economic factors and sustainable development, underscoring the importance of comprehensive policies and governance mechanisms in fostering sustainability amidst global economic dynamics.

This study investigates the impact of perceived innovative leadership on team innovation performance, with innovation climate acting as a mediating variable. A quantitative research approach, including a survey of team members across various industries, was used to collect data. Analysis through Structural Equation Modeling (SEM) reveals that perceived innovative leadership significantly positively influences team innovation performance, with innovation climate partially mediating this relationship. The findings emphasize the critical role of innovative leadership and a positive innovation climate in fostering organizational innovation, offering valuable insights for management practices. This paper also discusses the study’s limitations and provides directions for future research.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.

The question of whether legal gun ownership is a positive security factor in the Czech Republic is subject to expert debate and depends on several factors, including available crime data, public attitudes, and the legal framework. Some argue that legal gun ownership can dissuade criminals because they know victims may be armed. Many advocates argue that the right to own guns is a fundamental right that should be protected. Sometimes, it is difficult to clearly demonstrate that legal gun ownership directly contributes to crime reduction. Statistical data can be interpreted in different ways. In contrast, the presence of guns can in some situations escalate conflicts that could otherwise be resolved nonviolently. In the Czech Republic, legal gun ownership is relatively strictly regulated. Citizens must meet the conditions established by law, including criminal integrity and passing a theoretical-practical examination of professional competence. This regulation aims to ensure that only responsible and qualified individuals own guns. Therefore, the presented article discusses legal gun ownership as an internal factor of state security. Using statistical data, it analyses the amount of violent crime committed with firearms in relation to the possibility of holding and carrying a gun in the conditions of the Czech Republic and in selected EU countries. Furthermore, with the help of a questionnaire survey, it identifies that legal gun ownership can be considered a positive safety factor in certain situations, if it is associated with strict regulation and a responsible attitude of gun owners. The resulting effect on security depends on a combination of legal frameworks, gun culture, and effective law enforcement.