Research and reporting on university rankings serve as valuable tools for students in evaluating universities and understanding their current performance status. Within academic literature, university rankings are established using diverse criteria across various domains, each carrying varying degrees of importance. This study adopts a multi-criteria decision-making (MCDM) perspective to analyze the academic performance ranking of Turkish Universities in 2023. Data sourced from the 2023 reports of sixty-one universities from Times Higher Education (THE) serve as the basis for this research, with THE indicators—teaching, research, citations, industry income, and international outlook—considered as primary research criteria. The Method based on the Removal Effects of Criteria (MEREC) method is employed to ascertain criterion weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) method is utilized for university ranking. The study identifies "citations" as the criterion of highest significance. Notably, the top-performing universities in the ranking include Çankaya University, Fırat University, and Bahçeşehir University. Furthermore, by comparing the rankings from this study with THE university rankings, the research offers tailored suggestions for universities. This study underscores the importance of deriving criterion weights from university performance datasets rather than relying on fixed weights, facilitating a more nuanced approach to university rankings. Moreover, it presents THE performance rankings for sixty-one Turkish universities, offering valuable insights for strategic planning within the university sector.

Diabetes can lead to various health problems and complications, such as cardiovascular disease, kidney damage (nephropathy), eye issues, neuropathy, and foot ailments. Therefore, early diagnosis of diabetes can be immensely beneficial in preventing the development of these conditions. Utilizing machine learning is one method to detect diabetes in individuals at an early stage. In this study, we compare the performance of nine machine-learning classification models in predicting diabetes. These models include XGBoost, gradient boosting, AdaBoost, logistic regression, decision tree, KNN, perceptron, random forest, and naïve bayes. We utilize several evaluation metrics, focusing on the f1-score, area under the curve (AUC), and computational runtime. Our comparison reveals that complex tree-based models exhibit the highest f1-score and AUC, albeit with longer execution times.

The existing sustainable supplier selection methods are not sufficient to deal with the problem of sustainable food supplier selection with the interaction of criteria under uncertainty. Therefore, this paper proposes a method of sustainable food supplier selection based on an extended decision model. Firstly, a processing method for supplier evaluation information is constructed using the Pythagorean fuzzy set has the function of processing complex uncertain information. Secondly, to obtain the objective weights of decision experts, a Pythagorean fuzzy weighted distance measure model is constructed, and an expert information fusion method based on a weighted power mean operator is proposed to construct the group decision matrix. Then, the decision experiment and evaluation experiment methods are integrated with the traditional MARCOS method, to construct a sustainable food supplier selection method considering the interaction of factors. This method can effectively deal with the complicated and uncertain problem of sustainable food supplier selection with interactive factors. Finally, the feasibility of the proposed method is verified by an example of sustainable food supplier selection. In addition, parameter sensitivity analysis and multi-method comparative analysis verified the rationality of the proposed selection method for sustainable food supplier selection.

Fermatean fuzzy sets (FFSs), as one of the representative variants of fuzzy sets, have broad application prospects. FFSs have advantages in modeling uncertain information and therefore have been widely applied. However, how to perfectly quantify the differences between FFS remains an open question. This paper introduces a new distance measure for FFSs, utilizing triangular divergence. The proposed measure is designed to rectify the limitations in the current measure, offering a more effective solution for analyzing FFSs. Moreover, we demonstrate that the proposed distance measure satisfies some basic properties and further show its effectiveness through several numerical examples. Finally, we explore the performance of the proposed distance measure in a medical diagnosis application, and the results show that the proposed distance measure can well overcome the limitations of the current measure.

To evaluate the performance of the logistics safety standard system, we propose an evaluation framework based on the performance evaluation theory. First, we construct the performance evaluation indicator of the safety standard system for logistics enterprises. It is based on the existing performance evaluation indicator and combined with the construction goal of the logistics safety standard system. Second, we combine the triangular fuzzy number and prospect theory to determine the indicator state according to the characteristics of performance evaluation indicators. Then, we use the Choquet integral, fuzzy method, and Shapley value methods to evaluate the information, which considers the interaction of indicators. Third, we use the entropy and fuzzy analytic hierarchy process to determine the expert weight. The performance evaluation information of the logistic enterprise’s safety standard system is aggregated to obtain the assessment results. Finally, the proposed framework is validated by an example analysis. The results show that the proposed framework can be used to evaluate the performance of logistics enterprise safety standard systems.

Spherical fuzzy sets are an enhanced framework of the fuzzy set (FS), intuitionistic fuzzy set (IFS), Pythagorean fuzzy set (PyFS), and picture fuzzy set (PFS) with the restriction that the total square sum of the membership, indeterminacy, and non-membership degrees must be in 0 and 1. In contrast, the Z-number, a revolutionary idea that captures both the restriction and the reliability of evaluation, is more significant than fuzzy numbers in the fields of decision-making (DM), risk assessment, etc. However, there are still few and insufficient discussions of how to effectively deal with the limitations and reliability of the literature currently in existence. To address this, we first introduced the spherical fuzzy Einstein Z-numbers (SFEZNs), those elements are pairwise comparisons of the decision-makers options. It can be used effectively to make truly ambiguous judgments, reflecting the fuzzy nature, flexibility, and applicability of decision-making data. We present the spherical fuzzy Einstein Z-number weighted aggregation operators and the spherical fuzzy Einstein Z-number weighted geometric operators. We develop a model for spherical fuzzy Einstein Z-number aggregation operators. The main focus of this study is on a technique for handling the issue of multi-attribute decision-making (MADM) effectively and based on one's preferences. We also developed the algorithms for ranking the best options. Finally, we developed the relative comparison and discussion analysis to show the practicability and efficacy of the suggested operators and approaches. The study's findings and implications are discussed.

With the rapid development of technological informatization, competition among enterprises is gradually transitioning from being "production-centered" to being "customer-centric," making service-oriented enterprises increasingly important. In addition to this, as global manufacturing advances in the process of intelligent manufacturing (IM), there is growing attention on the integration of manufacturing and the service industry, which has garnered the interest of numerous experts and scholars in the field of intelligent manufacturing services (IMS). This article combines intelligent manufacturing enterprises, intelligent service nodes, and consumers. Based on the background of intelligent manufacturing services, it collected risk factors within the smart supply chain (SSC) that connect different service nodes. These factors were evaluated by experts using a proposed linguistic q-rung orthopair fuzzy weighted averaging (Lq-ROFWA) operator in combination with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method for aggregation operations. Finally, we obtain the conclusions that the most influential factor affecting other risk factors is the inadequate identification of core customer needs; and the most important risk factor for smart supply chains oriented to intelligent manufacturing services is the leakage of customer information. After analyzing the relevant data, we will provide some theoretical and managerial implications for IM enterprises.

Renewable energy sources, particularly solar energy, play a vital role in achieving the 2030 agenda and sustainable development goals (SDGs) in Africa. While not explicitly addressed in the millennium development goals (MDGs), renewable energy, including solar energy technologies, indirectly contributed to MDGs targets in Africa. Nevertheless, the absence of quantifiable assessments regarding the impacts of solar energy technologies, potentially attributed to limited implementation in the developing world, motivates this paper to evaluate their potential impact in Africa. The analysis, based on a two-level criteria framework and utilizing the interval-valued Fermatean fuzzy analytical hierarchy process methodology, identifies the top three potential impacts as the reduction of CO2 emissions, monetary savings, and the diminishment of air pollution.

The maturation of the Industry 4.0 concept has brought numerous benefits to human society. However, it is not without its challenges, including neglect of worker welfare, vulnerability of global supply chains, and environmental degradation. To enhance the adaptability of the Industry 4.0 concept, Industry 5.0 has been developed. As of now, the practical implementation of Industry 5.0 has not yet been fully realized. This paper presents a novel conceptual framwork to analyze and evaluate the complex interrelationships of development factors in Industrial 5.0. Through extensive literature review and prolonged interviews with experts, three critical dimensions and their 18 key factors for the development of Industry 5.0 have been identified. Herein, a combination of Fermatean Fuzzy sets (FFs) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) has been employed to discern the interrelationships among these factors, and an Influential Network Relationship Map (INRM) has been constructed to aid decision-makers in formulating improvement strategies. The results indicate that “Sustainable Development” is the most influential dimension, with factors “Renewable Energy,” “Data-Driven Analysis Technologies,” and “Distributed Control” emerging as the most significant factors within their respective dimensions.