As real-world decision-making increasingly involves complex and vague information under uncertain circumstances, the q-rung orthopair probabilistic hesitant fuzzy set (q-ROPHFS) has emerged as a significant generalization of conventional fuzzy sets, offering enhanced capability in managing uncertainty. Meanwhile, the combined compromise solution (CoCoSo) method, which synthesizes multiple decision-making strategies from a compromise perspective, is recognized as a powerful technique in multi-attribute decision-making (MADM). In this context, this paper develops a novel MADM framework that integrates the CoCoSo method with q-ROPHFSs. To begin with, by extending the Frank t-norm and t-conorm to q-ROPHFSs, a series of q-ROPHF Frank aggregation operators are proposed, including several weighted forms of q-ROPHF Frank average and geometric operators. Their desirable properties are also systematically examined. Subsequently, subjective weights derived from the modified best–worst method (BWM) and objective weights obtained via the entropy weight method are combined to construct a BWM–entropy-based integrated weighting model within the q-ROPHFS context. Based on the proposed aggregation operators and the combined weighting model, an integrated BWM–entropy–CoCoSo decision-making framework is developed to support MADM under q-ROPHFS environments. Finally, the applicability and practicality of the proposed method are demonstrated through a case study on renewable energy project selection. Its reliability and robustness are further validated through sensitivity analysis of key parameters. In addition, the advantages of the integrated MADM framework are confirmed via a comparative analysis with existing approaches.

Given that current research on multi-criteria group decision-making (MCGDM) methods primarily focuses on integrating deep learning algorithms, such as graph neural networks, there has been relatively little exploration of unsupervised learning algorithms. This research investigates this matter by introducing the concept of the Gaussian mixture model (GMM) in the context of group decision-making. As an unsupervised learning approach, GMM effectively captures the latent relationships among alternatives through cluster analysis and optimizes decision-making information using the expectation-maximization (EM) algorithm, thereby enhancing the construction of decision matrices and the ranking of alternatives. First, the EM algorithm within GMM is employed to process the raw information in group decision-making, resulting in an updated decision matrix. Next, to address uncertainties and fuzzy information in decision-making, the MCGDM problem is mapped into a Pythagorean fuzzy environment, where an innovative entropy measure is proposed to calculate the criteria weights using the entropy weight method. Additionally, a novel distance measure is developed and incorporated into the grey relational analysis (GRA)-TOPSIS approach, enabling comprehensive evaluation by reflecting the mutual relationships among alternatives and their closeness to the ideal option. Finally, an empirical evaluation of "zero-waste city" development demonstrates the practical applicability and effectiveness of the proposed approach.

Evaluating urban low-carbon development is essential for advancing China's dual-carbon strategy, yet existing approaches face challenges in handling ambiguity, indicator correlation, and decision bias. This study develops an integrated multicriteria evaluation framework under Fermatean fuzzy (FF) environments. The framework incorporates an improved FF bidirectional projection method, a modified criteria importance through intercriteria correlation weight allocation mechanism, and regret theory to mitigate similarity misjudgment, information distortion, and bounded rationality. An empirical study of five Chinese cities verifies the effectiveness of the proposed model. Moreover, the sensitivity analysis confirms the robustness of the method under different parameter settings, and the comparative analysis further demonstrates the superiority of the proposed approach. The findings indicate that the framework yields stable and discriminative evaluation results, offering methodological advancements and practical insights for assessing urban low-carbon transitions.

As an emerging logistics mode, overseas warehouses can greatly reduce the impact of epidemics on cross-border trade logistics. To this end, this study proposes a multi-criteria decision-making framework for overseas warehouse site evaluation based on improved evidence theory and a cloud model. First, a novel evaluation index system for overseas warehouse site assessment is constructed from both qualitative and quantitative perspectives. Considering that traditional evidence theory can only rank evaluation objects, this study combines the cloud model and the technique for order preference by similarity to ideal solution to provide basic belief allocation for evidence theory. Moreover, game theory is used to optimize the integrated weights based on the dynamic weight coefficient of evidence theory and the improved criteria importance through the intercriteria correlation method. Finally, a practical example is presented to demonstrate the proposed method, and several suggestions are offered to improve overseas warehouses from the perspective of enterprises and the government.

With the rapid expansion of graduate education, the traditional evaluation model centered on academic output has become inadequate for meeting the demands of the new era. The aim of this study is to establish a scientific quality evaluation system to ensure the effectiveness of cultivating high-level talent. Firstly, a system encompassed five primary indicators, namely mentor-student relationship, school assets, personal learning state, academic achievements, and mental state, is established. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to ascertain the relative importance of each indicator. Empirical evaluation is conducted using a sample of 163 valid questionnaires from five universities in Zhejiang Province. The findings of the research indicate that the overall sense of gain in graduate students’ academic and life experiences is at a “slightly above average” level. Relatively favorable evaluations are recorded for guiding relationships, school assets, and personal learning state. Conversely, relatively poor evaluations are recorded for academic achievements and mental state. The index system outlined in this article has been demonstrated to exhibit a certain degree of scientific rigor and practicality. It provides a theoretical basis and practical tools for the evaluation of the quality of graduate education, as well as for the optimization of related policies.

Multi-attribute decision-making (MADM) has expanded rapidly in engineering, management, and public policy, yet a systematic quantitative overview of its global evolution, collaboration patterns, and research frontiers remains lacking. To address this gap, this study conducts a bibliometric analysis of 5208 MADM-related publications indexed in the Web of Science (WOS) core collection (1978–2024), using CiteSpace and Origin to examine: temporal development, international and institutional collaboration, and the shifting intellectual base and research hotspots. The result reveals the following findings: (1) The development of the field exhibits a three-phase characteristic (foundation, explosive growth, and subsequent deepening), with the trend highly fitting the Boltzmann function, indicating continued growth in the future; (2) International collaboration is widespread but loosely connected, with China leading in publication output, while research institutions are dispersed with insufficient coordination; (3) The dominant disciplines are computer science and artificial intelligence, with a gradual expansion into multiple fields such as regional and urban planning, architecture, and marine engineering; (4) Co-authorship and co-citation analyses identify influential authors, journals, and seminal papers, and reveal a hotspot shift from basic methodological optimization (e.g., AHP, fuzzy sets) to advanced decision models and aggregation operators, and more recently to applications in renewable energy, geographic information systems, and industrial development. This research provides a quantitative basis for understanding the development patterns and frontier directions of international MADM.

The existing approaches for maintenance strategy selection cannot usually account for the intrinsic complexities, uncertainties, and bipolarities that are prevalent in critical engineering systems. This highlights the necessity of novel multi-attribute decision-making (MADM) models that can deal with ambiguity, imprecision, and conflicting objectives effectively. The research on bipolar complex fuzzy sets (BCFSs) and the construction of specialized aggregation operators (AOs) are the steps that can serve as a solid mathematical foundation to blend and integrate expert knowledge and complex systems to obtain more rational and well-balanced decisions concerning the maintenance of complex assets. To solve the hierarchical decision-making in the case of bipolar complex fuzzy (BCF) information, several prioritized AOs are formulated, such as BCF prioritized weighted averaging (BCFPWA), BCF prioritized ordered weighted averaging (BCFPOWA), BCF prioritized hybrid averaging (BCFPHA), BCF prioritized weighted geometric (BCFPWG), BCF prioritized ordered weighted geometric (BCFPOWG), and BCF prioritized hybrid geometric (BCFPHG) operators. The prioritized weighted operators represent the predetermined importance relationships between the criteria, the prioritized ordered weighted operators represent the various decision attitudes by reordering mechanisms, and the prioritized hybrid operators combine both the importance and attitudinal features, which provide more flexibility in complex maintenance decision settings. After that, we devise the notion of MADM within BCFS by employing these AOs, and then we analyze a case study, “maintenance strategy selection for engineering systems”. Finally, we carry out the comparative study of the deduced theory with a few existing theories to demonstrate the authenticity and capability of the developed theories.

As an emerging e-commerce model, livestream e-commerce (LEC) has presented novel opportunities for the advancement of the retail industry. While retailers reap the benefits by this platform, they also encounter challenges associated with platform risks. This paper aims to propose a novel hybrid model to address the risks inherent in livestream e-commerce platforms (LECPs) evaluation problems. Firstly, the paper makes use of q-Rung orthopair fuzzy (q-ROF) trust network (TN) to handle the intricate and uncertain evaluation environment of LECPs and utilizes combinative distance-based assessment (CODAS) to rank the alternative platforms. Secondly, considering that experts have different levels of familiarity with different indicator, a q-ROF-TN matrix between experts is established for each indicator to ascertain their respective weights, which adds robustness to the evaluation process. Thirdly, the dual feedback mechanism is designed to enhance group consensus that attempts to ensure that the evaluation process is collaborative and considers diverse expert opinions, leading to more reliable outcomes. Finally, a set of evaluation indicators is constructed covering various aspects like capital flow management capability and public domain traffic support, and the level of risk of the four LECPs is evaluated using the proposed method. Furthermore, the reliability and effectiveness of the proposed evaluation model are validated through sensitivity analysis and comparative study.

This study introduced a multi-criteria comprehensive evaluation method for assessing employer brands using online reviews, intuitionistic fuzzy technique for order preference by similarity to ideal solution, latent Dirichlet allocation, and Kano models. Initially, the latent Dirichlet allocation technique was applied to conduct topic mining of online employer brand reviews to construct a multi-index evaluation system. The weights of indexes were determined by analyzing the attributes of each indicator in the Kano model’s requirement perception evaluation table and querying the corresponding Kano category factors. Simultaneously, by calculating the intuitive fuzzy values of the positive, neutral, and negative emotions of the employer brand, the emotional tendencies in online comments were then quantified. Subsequently, the technique for order preference by similarity to ideal solution method was employed to calculate the comprehensive score of the employer brands. Finally, the scientific validity and rationality of the method were verified through empirical and comparative analysis.

This research presents a successful implementation of a Project-Based Learning (PBL) methodology to foster the acquisition of Transversal Competencies (TCs) and incorporate Sustainable Development Goals (SDGs) into higher education curricula. This was carried out in the framework of an Educational Innovation and Improvement Project (EIIP) carried out at the Universitat Politècnica de València (UPV) in Spain during the academic years that span from 2023 to 2025. Specifically, it was conducted within the “Dynamics of Mechanical Systems” course of the “Mechatronic Engineering” master’s program of the at the Higher School of Aerospace Engineering and Industrial Design (HSAEID), which involves an average of 40 students. Quantitative and qualitative techniques were used to collect evidence regarding the successful implementation of the PBL methodology. The EIIP project offers practical insights and strategies for efficiently integrating global competencies and sustainability into technical education. In this sense, the positive outcomes indicate that PBL not only enhances technical competencies and motivation but also cultivates critical thinking, collaboration, problem-solving abilities, acquisition of learning goals, and preparing students to address complex global challenges of the labor market and. However, ongoing evaluation and continuous improvement processes, supported by robust feedback mechanisms, are crucial for sustaining and enhancing these educational innovations.

BACKGROUND:The economic development of central business districts (CBDs) is a key driver of urban economic growth and enhances regional competitiveness. However, existing research predominantly focuses on macro-level analyses, lacking systematic and quantitative indicator systems for detailed evaluation of CBD economic development performance, while studies on urban office buildings or specific CBD components often neglect the broader context of the entire district. OBJECTIVES:This study aims to propose a multi-criteria evaluation system to effectively evaluate the economic development performance of CBDs, and provide theoretical references for a more comprehensive evaluation of the sustainable CBD growth in other regions. METHODS:Firstly, the analytic hierarchy process (AHP) and entropy method are proposed to determine combination weights for each indicator. The VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is then introduced to rank the economic development performance of CBDs. Finally, a numerical example of Zhejiang province is carried out to validate the results by comparing its rankings with existing methods. RESULTS AND CONCLUSION:The multi-criteria evaluation of regional CBD economic development showed that the top two cities remained consistent across subjective, objective, and combined weights methods, while discrepancies in rankings for other cities were balanced by the combined weights approach. Meanwhile, minor variations in rankings highlighted the unique advantages of each method. These findings validate the effectiveness of combining subjective and objective methods for assessments. Future research should enhance data comprehensiveness and reliability through fieldwork to further improve the scientific validity of such evaluations.

The purpose of this study is to effectively establish an evaluation framework for green development in the tourism industry. A novel evaluation index system for green tourism development based on “society-economy-environment” that incorporates environmental protection values is constructed firstly. Subsequently, a comprehensive assessment framework was established using a combination of entropy weight and Complex Proportional Assessment (COPRAS) methods. Taking Zhejiang Province as an example, the numerical results show that between 2013 and 2023, the level of green development in Zhejiang Province’s tourism industry followed an “N” pattern of “rise-fall-rise,” with overall performance improving. However, the four specific dimensions of development exhibit characteristics of “strong economy, weak ecology, and a need to strengthen investment and environmental awareness.” Finally, several valuable suggestions are proposed, such as strengthening crisis management and optimizing investment factors, to promote the improvement of green development levels.

Environmental, climatic, and resource pressures have driven China to create a green low-carbon circular developing economic system (GLCDES). However, scientifically evaluating its effectiveness is hindered by three critical gaps: fragmented assessments of the green, low-carbon, and circular dimensions; a reliance on biased weighting methods; and inadequate dynamic spatiotemporal analysis. To tackle these challenges, this study proposes a hybrid multi-criteria evaluation framework. We construct a systematic GLCDES index system with five criteria layers: development support, low-carbon development, green development, circular development, and economic–social development. A game theory-based combination weighting method integrates subjective AHP and CRITIC weights to balance policy priorities and empirical patterns. The VIKOR method enhanced by grey relational analysis evaluates provincial performance by optimizing group utility and individual regret. Using panel data from 30 Chinese provinces (2000–2021), this study reveals a significant improvement in China’s overall GLCDES levels, although regional imbalances persist. Spatiotemporal analysis confirms dynamic convergence and spatial agglomeration effects. Simultaneous equation models identify environmental regulation as a negative feedback mechanism that promotes green financing and technological innovation. This research offers a unified assessment framework, addresses methodological biases, and validates the “regulation–finance–innovation” transmission path, providing key policy implications.

Consumption-led economic growth is crucial for enhancing economic resilience, improving social welfare, and fostering endogenous drivers for innovative development. The purpose of this study is to develop a novel multi-criteria evaluation framework to assess the level of consumption-led economic development in the Yangtze River Delta urban agglomeration. Initially, an evaluation system for consumption-led economic development is constructed across five dimensions: economic autonomy, demand structure, consumption level, consumption structure, and consumption environment. The evaluation framework based on the Vertical and Horizontal Scatter Degree and Entropy Method (VHSD-EM), is then applied to analyze the development level of consumption-led patterns in the core cities of the Yangtze River Delta in China from 2015 to 2021. The empirical results reveal significant disparities in consumption-led economic development among the cities. In 2021, Shanghai achieved the highest comprehensive score (7.83), followed by Hangzhou, Suzhou, Hefei, Ningbo, and Nanjing. The average score for the region was 7.37, suggesting that the Yangtze River Delta urban agglomeration is transitioning toward a consumption-led growth model, with some cities exhibiting characteristics of a high-mass consumption stage. However, the development stages vary across cities, reflecting differences in economic structure and policy focus. Finally, several recommendations are suggested based on the numerical analysis.

Amidst the escalating complexity and ambiguity inherent in real-world decision-making within uncertain environments, the Fermatean probabilistic hesitant fuzzy set (FPHFS) emerges as a significant extension of the Fermatean fuzzy set (FFS), gaining momentum in the realm of multi-attribute decision-making (MADM). This paper proposes a novel MADM approach that integrates the distance measure and cross-entropy of FPHFSs with the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method. This approach is designed to concurrently account for the objective weighting of attributes, the stochastic nature of evaluation data, and the psychological preferences of decision-maker (DM). Initially, leveraging the definitions of distance measures and entropy for FPHFSs, various forms of distance and cross-entropy measures are proposed. Subsequently, by integrating the proposed generalized Fermatean probabilistic hesitant fuzzy cross-entropy with the maximum deviation method, two models for determining the objective weights of attributes are developed. To intuitively capture and process DMs' preference information, a linear preference function within the decision-making process is constructed using the introduced generalized Fermatean probabilistic hesitant fuzzy distance. Ultimately, the preference function-based Fermatean probabilistic hesitant fuzzy VIKOR (FPHF-VIKOR) approach is established. By applying it to a numerical example of supply chain finance risk assessment, the decision-making procedure of the method is thoroughly demonstrated. The excellent performance of the distance and crossentropy measures within this model is examined through parameter sensitivity analysis. Additionally, a realworld application concerning financing enterprise selection highlights the practicality and scalability of the proposed method. Comparative analysis with existing MADM methods and VIKOR variants further confirms the scientific feasibility and effectiveness of our approach.

Subsea tunnels with sightseeing function (STSF) are valuable for integrating transportation across different locations, particularly in cross-sea regions. Given their technical complexity, significant capital commitment, and substantial social impact, site evaluation of STSF has become a critical priority. In this study, a dynamic complex measurement alternative and ranking according to the compromise solution (MARCOS) framework is proposed for STSF site evaluation in a Pythagorean hesitant fuzzy environment. A novel score function for a Pythagorean hesitant fuzzy set is defined to handle an incomparable problem. Moreover, a growth coefficient matrix is constructed to reflect the internal development potential of decision objects, considering changes in expert and criteria weights during the dynamic decision-making process. An STSF evaluation system is designed to satisfy stability, sociality, and economic developments. An empirical study is conducted in Zhejiang Province, China, to verify the robustness and effectiveness of the proposed evaluation framework by comparing its results with other methods.

PurposeThe main aim of this paper is to establish a reasonable and scientific evaluation index system to assess the high quality and full employment (HQaFE).Design/methodology/approachThis paper uses a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria framework to evaluate the quality and quantity of employment, wherein the integrated weights of attributes are determined by the combined the Criteria Importance Through Inter-criteria Correlation (CRITIC) and entropy approaches.FindingsFirstly, the gap in the Yangtze River Delta in employment quality is narrowing year by year; secondly, employment skills as well as employment supply and demand are the primary indicators that determine the HQaFE; finally, the evaluation scores are clearly hierarchical, in the order of Shanghai, Jiangsu, Zhejiang and Anhui.Originality/valueA scientific and reasonable evaluation index system is constructed. A novel CRITIC-entropy-TOPSIS evaluation is proposed to make the results more objective. Some policy recommendations that can promote the achievement of HQaFE are proposed.

This research is carried out within the framework of a two-year Educational Innovation and Improvement Project (EIIP) to be implemented at the Universitat Politècnica de València (UPV) during the academic years 2023-2024 and 2024-2025. The project aims to design and apply a Project-Based Learning methodology (PBL) for a proper acquisition of Transversal Competences (TCs) and integration of the Sustainable Development Goals (SDGs) in a mechanical engineering subject that is taught in the first year of the master’s degree in Mechatronic Engineering from the School of Design Engineering. Based on an assessment by the teaching staff of the current situation and the aspects to be improved, this article explains how the PBL methodology will be applied and performs an analysis through surveys and personal interviews with the students of their level of knowledge regarding the TCs and the SDGs that are worked on in the subject at the beginning of the academic year. The results show the need to continue working in the project and implementing active teaching techniques, such as PBL methodologies, to achieve a more attractive and motivating subject, closer to the labour market and a sustainable society.

The contemporary university has evolved into a multifaceted institution, serving not only as a hub for research, professional training, and knowledge dissemination, but also as a pivotal component of economic machinery and societal advancement, especially within the context of globalization and intense competition. These institutions are now expected to significantly contribute to the socio-economic development of their surroundings, striving for excellence and competing within an increasingly interconnected global framework. To meet these heightened expectations, universities have undergone profound transformations over recent decades. This period of change is particularly notable within the Spanish context, mirroring the broader societal shifts since the onset of the democratic transition in 1975 through to the present day in 2024. This era has necessitated substantial reforms in university governance, equipping these institutions with the requisite tools to navigate and address emerging challenges effectively. A pivotal moment in this transformative journey was the enactment of the University Reform Law of 1983. Grounded in constitutional principles, this law marked a significant milestone in updating and revitalizing university education in Spain. After this, the Organic Law on Universities of 2001 further aligned the Spanish university framework with the broader European educational landscape, reinforcing the integration and competitiveness of Spanish institutions on a continental scale. However, to comprehensively grasp the magnitude and implications of these changes, it is essential to extend our analysis beyond the immediate past and explore the historical trajectory of Spanish universities over previous centuries. By examining this extensive historical context, we can better understand the foundational elements that have shaped the current state of higher education. This article aims to meticulously analyze the evolution of the Spanish university system, delineating the chronological progress, identifying persistent challenges, and engaging in a critical discourse on potential solutions. Through this examination, the article seeks to provide a nuanced understanding of the interplay between historical legacies and contemporary reforms, offering insights that are essential for shaping the future trajectory of higher education in Spain.

As the digital economy rapidly evolves and enterprise digital transformation progresses further, specialization, refinement, differential, and innovation (SRDI) enterprises are facing new requirements and challenges. High levels of digital maturity are critical for the success of enterprises. Therefore, effectively assessing the digital maturity of SRDI enterprises is a problem that must be addressed. This study develops a multi-criteria decision-making model to evaluate the digital maturity of SRDI enterprises in complex situations. First, an integrated weighting model that combines the best–worst method with the entropy model is presented. Second, by combining the traditional evaluation based on the distance from the average solution with a single-valued neutrosophic set, a novel multi-criteria decision-making framework including an integrated weights approach is constructed, and its main computing steps are described in detail. Third, a case study of the proposed method in the digital maturity of SRDI enterprises level evaluation is explored based on the constructed evaluation index system. Finally, a numerical example of SRDI enterprises in five cities of China is designed. The assessment methodology and conclusion of the development level of SRDI enterprises for digital maturity proposed in this study provide a theoretical reference and practical inspiration for public administration departments and related enterprises.