Water physico-chemical parameters, such as pH and salinity, play an important role in the larval development of Aedes aegypti, the primary vector of dengue fever. although the role of these two factors is known, the interaction between pH and salinity in various aquatic habitats is still not fully understood, especially in the context of endemic areas. this study explored how the interaction between pH and salinity affects the development of Aedes aegypti larvae in dengue hemorrhagic fever (DHF) endemic areas. this study used a pure experimental design with a posttest-only control group approach. Aedes aegypti instar iv larvae were obtained from eggs collected in north kolaka regency, a dhf endemic area. the independent variables tested were pH (6 and 8) and salinity (0.4 gr/L and 0.6 gr/L), with the control group using pH 7 and no salinity. a two-way anova test was used to evaluate the interaction between pH and salinity, followed by tukey’s hsd post-hoc test to compare treatment groups. the results showed that, independently, pH and salinity had no significant effect on larval survival. however, the interaction between the two variables had a significant effect (p < 0.001). the combination of pH 8 and salinity 0.4 gr/L resulted in the highest survival rate, while pH 6 and salinity 0.6 gr/L caused a significant decrease in larval survival. the combination of alkaline pH (pH 8) and low salinity (0.4 gr/L) is the optimal condition for Aedes aegypti larval survival. the results of this study highlight the importance of considering the interaction between pH and salinity in environmental-based vector control strategies in endemic areas. further research is needed to explore other factors, such as aquatic microbiota and environmental variations, that may affect mosquito larval development.

This research examines the interplay between human dignity and the pursuit of knowledge within Islamic thought, using insights from the Quran. It explores how Islamic epistemology emphasizes the harmonious integration of divine revelation and human reason, underscoring the importance of knowledge as a key factor in both intellectual and spiritual development. By analyzing the contributions of classical Islamic scholars, such as Al-Farabi, Ibn Sina, and Al-Ghazali, alongside Western epistemological traditions, the study highlights complementary and contrasting approaches to understanding knowledge and its role in shaping ethics and governance. Furthermore, the research draws on contemporary case studies, such as the Marrakesh Declaration and Masdar City, to illustrate how Quranic principles of cooperation, justice, and environmental stewardship can inform modern societal frameworks. Ultimately, the study argues for the continued relevance of Islamic thought in addressing contemporary global challenges, emphasizing that the pursuit of knowledge not only advances scientific discovery but also promotes human dignity, justice, and societal well-being.

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.

Educational quality policies are a basic principle that every Peruvian university educational institution pursues in accordance with Law No. 30220, with the objective of training highly competent professionals who contribute to the development of the country. This study to analyzes educational quality policies with the student’s satisfaction of public and private universities in Peru, according to social variables. The study was descriptive-comparative, quantitative, non-experimental, and cross-sectional. One thousand (1000) students from two Peruvian universities, one public (n = 500) and one private (n = 500), were purposively selected by quota using the SERVQUALing instrument. The findings indicate a moderate level of satisfaction reported by 49.2% of participants, with a notable tendency towards high satisfaction observed in 40.9% of respondents. These results suggest that most students perceive that the actual state of service quality policies are in a developmental stage. The results, therefore, indicate that regulatory measures, including university laws, licensing, and accreditation, significantly influence outcomes. These measures are essential for the effective functioning of universities. In addition, the analysis revealed that female and male students at private universities showed higher levels of satisfaction with the educational services offered. It is concluded that educational quality policies in Peru are still being executed, because the implementation of the University Law is in process, according to the satisfaction of the student, this must be improved in central aspects such as optimizing human resources, infrastructure, equipment, curricular plans that differ from the public to the private university, In addition, this should lead to improving and redefining current policies on educational quality and the economic policies that finance the educational service.

One of the most frequently debated subjects in international forums is economic growth, which is regarded as a global priority. Consequently, researchers have turned their attention from conventional economic growth at a single average coefficient to divisible economic growth at levels of its value. Although the existing literature has discussed several determinants of economic growth, our article contributes to examining the sources of economic growth in African countries during the generations of reforms from 1990 to 2019 and in the context of economic vulnerability. The variables used in the analysis are gross domestic product, trade openness, financial development, and economic vulnerability. The study uses a quantile regression econometric model to examine these variables at different stages of reform. Quantile regression (QR) estimates for quantiles 0.05 to 0.95 showed mixed results: financial development is favorable to African economic growth at all quantile levels. However, economic vulnerability is a major impediment to economic growth at all quantile levels. In addition, it was found that a high degree of trade openness has a detrimental effect on African economic growth from quantile 0.5 of the dependent variable. Finally, another important result proves that financial development is a remedy for decision-makers against economic vulnerability.

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.