In injection molding manufacturing, selection of the optimal machine from various alternatives is a crucial strategy for enhancing productivity, cost-effectiveness, and maintaining performance standards. This article presents an approach that combines two techniques to make the best choice from three presented options. Firstly, it employs the Analytic Hierarchy Process method to determine the weights of five main criteria and eleven sub-criteria, considering both cost and performance. Secondly, it utilizes the Technique for Order Preference by Similarity to Ideal Solution to rank the three machines by comparing each machine's performance against ideal and anti-ideal solutions to determine their relative suitability. The final model is validated through three distinct scenarios, illustrating how key criteria such as cost breakdown and scrap rate can influence the ultimate selection ranking. Through a presented numerical example, the paper provides decision-makers with a scientifically robust decision support system, aiding in strategic and complex decision-making processes for selecting the most suitable machine.

Currently, the lack of water threatens the lives of billions of people around the world, and due to population growth, economic growth, and climate change, this trend is expected to continue and become more acute in the future. Therefore, the desire to curb excessive water consumption through identifying the main factors affecting consumer behavior has become very important in the management of drinking water resources. By understanding this necessity, the aim of the current research is to identify and investigate the causal relationships and prioritize the factors influencing the pattern of water consumption in the domestic sector. In this regard, by reviewing the literature in the field of water and interviewing industry experts, factors affecting consumption were identified and monitored in various criteria. Through the fuzzy Delphi method to investigate the action factors and finally 7 main economic, social, cultural, technical and engineering, legal and managerial, spatial and temporal criteria with 26 sub-criteria were identified. Then, through the fuzzy Dematel method, cause and effect relationships were investigated and these factors were evaluated and ranked using the fuzzy TOPSIS technique. The results obtained from the research on family awareness, use of water-reducing devices, advertising and education, consumption motivation, water tariff and pricing, and the use of new faucets ranked first to sixth. The findings of the research show that among various factors, it is possible to have a high impact on water consumption management by increasing the awareness of households on how to use water and changing the consumption pattern.

This study examines the efficiency of health-oriented APIs in Iran, analyzing their performance across various categories. Using a combined approach of Data Envelopment Analysis (DEA), machine learning techniques, and statistical analysis, we evaluated 149 APIs to determine their efficiency scores and identify areas for improvement.The DEA analysis revealed that many APIs, particularly those in the "Health and Wellness" and "Genetic Data" categories, operate at high-efficiency levels. The scores were calculated using an input variable derived from Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA), while the output was determined using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The Kruskal-Wallis test showed significant differences in efficiency scores among the macro-categories, with "Clinical and Patient Management" demonstrating notable superiority. Pairwise comparisons confirmed these differences, indicating the need for improvement in some categories.We applied a k-means clustering algorithm to classify the APIs into efficiency gradients. Validation through logistic regression confirmed the significant influence of categories on efficiency, supported by SHAP analysis. The results suggest that "Patient Management" APIs are the most efficient.Future implications include optimizing less efficient APIs and adopting more advanced techniques. These findings provide valuable guidance for improving technological performance and optimizing efficiency in the healthcare sector, contributing to a more innovative and responsive system.