In order to diagnose the root causes of stampede accident in scenic spots under uncertainty, this paper proposes an interval type-2 intuitionistic fuzzy Bayesian network (BN) method, which includes four steps. First, interval type-2 trapezoidal intuitionistic fuzzy numbers are used to express experts’ cognitive judgments about probability information of root nodes. Second, fuzzy possibility-probability transformation functions are extended to obtain the prior probability of root nodes. Third, BN deductive reasoning is conducted to obtain the occurrence probability of the top event. Last, BN abductive reasoning is performed to conduct BN probability updating analysis and important analysis. The proposed approach was implemented in an empirical study of the Mount Hua scenic spot in China. The results showed the overcrowding of tourists, poor management of tourist diversion, and tourists’ psychological panic are the main root causes. These findings can offer insightful suggestions for alleviating tourist stampede risk in the Mount Hua scenic spot.
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.
The purpose of this study is to develop an intelligent fuzzy model for operational risk assessment in ice and snow sports towns under conditions of climatic uncertainty. A hybrid fuzzy analytic hierarchy process-fuzzy inference system (FAHP-FIS) is a system that uses ERA5-Land reanalysis data and combines it with other various environmental, infrastructural, managerial, and economic indicators to create an interpretable operational risk index (ORI). The fuzzy model is found to be helpful in the nonlinear correlation of variables and has significant predictive power when tested on historical disruption data of Chinese snow-sport towns. The framework demonstrates the potential of fuzzy systems in risk assessment, resilience planning, and adaptive decision support. It serves as a scalable and transparent tool for managing climate-intensive tourism processes and promoting the sustainable development of winter sports destinations.
College English teaching quality evaluation is pivotal amid global engagement. It helps to identify instructional shortcomings, optimize curricula and teaching methods, and strengthen quality assurance. By addressing core issues, it enhances students’ practical skills, aligns teaching with societal demands, and enables universities to cultivate globally competent talent, thereby advancing educational quality and international exchange. College English teaching quality evaluation can be regarded as a multi-attribute group decision-making (MAGDM) problem. To address this specific MAGDM scenario, the Interactive Multi-Criteria Decision Making (TODIM) method was selected as the core analytical tool. Given the inherent uncertainty in assessing teaching quality, intuitionistic fuzzy sets (IFSs) were adopted to represent ambiguous decision-related information throughout the evaluation process. This paper develops an intuitionistic fuzzy TODIM (IF-TODIM) approach, tailored to solve MAGDM problems within the IFS framework. To verify the effectiveness of the IF-TODIM method, a practical case study focused on college English teaching quality evaluation was designed and implemented.
The teaching quality evaluation of university music programs constitutes multi-attribute group decision-making because it involves synthesizing judgments across diverse assessment dimensions from multiple experts. Probabilistic linguistic term sets have emerged as a robust tool for capturing the uncertainty and subjectivity in such evaluations, effectively quantifying ambiguous expert opinions. Recent advancements in the multi-attribute group decision-making method have seen the application of the exponential tomada de decisao interativa multicriterio (ExpTODIM) method, which offers unique advantages in handling complex trade-offs and decision-makers’ psychological tendencies. (This Portuguese term means interactive multicriteria decision-making in English.) To address the specific demands of music program teaching quality evaluation under uncertain conditions, the authors of this study developed a novel probabilistic linguistic ExpTODIM approach tailored to integrate probabilistic linguistic term sets with the ExpTODIM framework. The authors present a numerical study focused on the teaching quality evaluation of university music programs. This practical application validates the feasibility and effectiveness of the proposed probabilistic linguistic ExpTODIM method.
With the continuous emergence of new teaching philosophies and models in higher education, the original evaluation system has become insufficient to meet the current demands of university physical education (PE) instruction. This limitation has weakened the system’s ability to accurately assess and guide teaching practices, resulting in a situation in which PE teaching quality evaluation lags behind pedagogical development and remains reactive rather than proactive. University PE teaching quality evaluation is regarded as a multi-attribute decision-making issue. This paper aims to develop a multi-attribute decision-making approach based on an interval-valued intuitionistic fuzzy combined compromise solution method combined with a cosine similarity measure under interval-valued intuitionistic fuzzy. Attribute weights are determined using the criteria importance through intercriteria correlation method. Finally, a numerical example of university PE teaching quality evaluation is provided, and comparative analyses are conducted to demonstrate the advantages of the proposed interval-valued intuitionistic fuzzy combined compromise solution method.
China’s substantial rural demographic presents both a challenge and an opportunity for national health advancement. Enhancing physical fitness within rural communities requires particular attention to older residents’ access to sports services. A thorough examination of current service provision revealed opportunities for systematic improvement through coordinated policy development, sustainable funding models, and targeted community engagement. A feasibility evaluation of renovating old residential areas from a low-carbon perspective involves multiple-attribute group decision-making. In this research, the authors addressed such challenges through methodological innovations in spherical fuzzy sets processing. The spherical fuzzy number Evaluation based on Distance from Average Solution (EDAS) technique was constructed to solve this kind of evaluation problem. For objective criterion weighting, they adapted the Method based on the Removal Effects of Criteria (MEREC) procedure to spherical fuzzy sets. Validation through a feasibility evaluation of the renovation of an old residential area confirmed the framework’s practical effectiveness, and comparative assessments with established methods substantiated its advantages across various application contexts.
The comprehensive advancement of rural revitalization relies heavily on robust support from modern financial services. This research focused on evaluating the quality of artificial intelligence-driven digital financial services in boosting rural revitalization. By analyzing data from smart credit systems and digital advisory platforms, the study aimed to measure effectiveness, identify gaps, and optimize strategies for sustainable and inclusive rural development through technology. The artificial intelligence-driven digital financial service in promoting rural revitalization quality evaluation was viewed as the multiple-attribute group decision-making issue. This study adapted the EDAS technique to operate within single-valued neutrosophic sets environments, creating a practical framework for handling such complex evaluations. The criteria importance through intercriteria correlation method was built to get the attribute’s weight. The computational procedures were systematically outlined, and the model was validated through detailed case study. The proposed method's effectiveness was further verified by comparative analysis with existing approaches.
The authors explored an intermodal routing problem for high-value goods, aiming to minimize the total costs, including those associated with transportation, storage, delays, damages, and carbon tax. To avoid transportation interruption when using the planned route in practice, they formulated the network capacity information uncertainty using triangular fuzzy numbers. A fuzzy nonlinear optimization model was built to address the problem. Under the goods owners’ cautious attitude toward transportation organization, they used chance-constrained programming with a necessity measure to make the proposed model crisp and further conducted model linearization to enable the problem to be easily solved. A numerical case study shows that improving the confidence level to enhance the reliability increases the total costs of intermodal transportation, as well as further verifies that the carbon tax policy with a high carbon tax rate can achieve green intermodal transportation. It also indicates the advantages of considering intermodal transportation, damage costs, and a soft time window in routing high-value goods.
The intrinsic needs of universities clarify the internal logic of their cultural development. As a distinct form of social culture, university culture takes shape gradually through long-term educational practice and is embedded within the profound cultural heritage of institutions. Each university cultivates its own unique cultural traditions and characteristics over the course of its historical development, forming the very foundation of its existence. In China, university culture represents the soul of higher education institutions. Evaluating the quality of university culture constitutes a multi-attribute decision making (MADM) challenge. Contemporary approaches utilize techniques like Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy to address such MADM problems. In this study, Z-numbers were applied to represent uncertain information. This study developed an integrated Z-number TOPSIS (ZN-TOPSIS) framework to handle MADM within a Z-number environment. To demonstrate the proposed approach's practicality, a numerical example for the evaluation of the quality of university culture construction is presented to show the ZN-TOPSIS approach.
Decision-making in martial arts involves making quick transitions between offense and defense in a state of uncertainty. This paper proposes a fuzzy reasoning system to model such decisions and incorporates kinematic features, such as distance, stance, velocity, and guard openness, into interpretable fuzzy rules. The system was implemented using the Taekwondo Unit Technique Human Action Dataset and the Karate Multimodal Motion Capture dataset. Mamdani inference was employed, along with centroid defuzzification. Experimental results demonstrate competitive accuracy (89.6% on Taekwondo Unit Technique Human Action Dataset, 91.5% on Karate sequences) and low latency, as well as better interpretability than support vector machines, random forests, and deep neural networks. Sensitivity and ablation experiments show that distance and guard openness are relevant parameters, and cross-dataset testing shows that the system can be applied to other martial arts styles, with possible applications in coaching, robotics, and intelligent training systems.
Civil engineering construction projects are an important part of urban construction. Various risks inevitably exist in these projects. Owners often choose supervisors to monitor contractors, and discrepancies arise between supervisors and contractors regarding risk assessments; how to balance their opinions and identify the most critical risks from a common perspective becomes a critical issue. To address this, this paper proposes a novel hesitant fuzzy dual-agent collaborative technique for order preference by similarity to ideal solution (TOPSIS) model. It identifies the most critical risks from the collaborative perspective, providing support for collaborative management. The main innovations include: (a) a dual-agent collaborative mechanism between contractors and supervisors; (b) a novel definition of hesitancy that captures uncertainty and is integrated into an improved distance formula; and (c) a TOPSIS-based computational model that converts linguistic evaluations into numerical results without information loss, enabling precise risk ranking and identification of the most critical risks.
The behavior of sports event spectators is influenced by various factors, and the relationship between each influencing factor is vague. The traditional analysis method for audience behavior in sports events cannot effectively handle the non-linear relationship of audience behavior and is challenging to address the ambiguity and uncertainty. Fuzzy set theory was combined with neural networks better to handle the nonlinear relationships and complexity of audience behavior. A large amount of audience behavior data was collected and preprocessed to ensure the quality of the data. Through fuzzy set theory, the data was fuzzified, and a series of rules was established to create a fuzzy rule library. Data analysis was conducted using fuzzy reasoning, and ambiguity resolution was successfully achieved. By combining fuzzy set theory with backpropagation neural networks, a fuzzy neural network (FNN) was employed to predict audience satisfaction with the event. The experimental results showed that the average accuracy of FNN in predicting audience satisfaction was 96.4%. Based on fuzzy set theory and combined with backpropagation neural networks, a comprehensive analysis of audience behavior in sports events can be conducted to accurately predict audience satisfaction.
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.
Before the official release of the Chinese college entrance examination enrollment plan compilation, provincial education administrative departments can scientifically estimate the total planned enrollment and make a pre-allocation, which helps to analyze dynamic trends and to promptly identify potential problems. This study constructs an interval bankruptcy model for college entrance examination planned enrollment allocation and proposes an interval constrained equal loss rule based on Moore subtraction, under the assumptions that the total quantity of planned enrollment is represented as an interval value and the planned enrollment quotas declared by individual colleges and universities are defined as exact values. The case analysis results show that the proposed allocation method has significant advantages of simple calculation and strong practicality. Meanwhile, it avoids negative numbers in interval subtraction, greatly reduces the uncertainty of intervals, and provides a rational and effective solution for addressing practical problems in the allocation of planned enrollment.
Tourism marketing is vital for a low-carbon environmental system and societal well-being, especially with the rapid development of China's tourism industry. For businesses, understanding tourism marketing theory, utilizing effective marketing tools, and staying abreast of market trends are crucial for sustained success. This study focuses on evaluating tourism marketing performance, examining the application of data analysis techniques in this domain. Using three core variables—promotion of urban tourism, destination reputation, and tourist revisit rate prediction using a system dynamics model—this study demonstrates that the fuzzy neural inference system excels in predicting these variables, achieving high accuracy with an optimal coefficient of determination. This highlights the significant advantages of data analysis in assessing tourism marketing performance and provides robust methodological support for future studies.
How to evaluate the citizens' sense of gain (CSG) is an essential problem in the new smart city. To solve the problem, this paper investigates a group decision making (GDM) method with probabilistic linguistic preference relations (PLPRs). First, the additive consistency of PLPR is defined by the linguistic hybrid weighted average (LHWA) operator. The LHWA-based linguistic preference relation is extracted from a PLPR to derive the priority weights. For the GDM problem in the linguistic environment, combining the linguistic evaluations and their probability of different citizens, i.e, decision makers (DMs), the PLPRs of DMs' subsets (DMSes) are formed to obtain the corresponding priority weights. The similarity divergences and the proximity divergences of DMSes are introduced to derive DMSes' weights. Using the relative entropy, the comprehensive priority weights are generated for ranking alternatives. Thus, a GDM method with PLPRs is proposed and validated.
Online reviews are an important influencing factor in consumer purchasing decisions, and consumers' diverse cultural backgrounds affect the expression of online reviews as well as decision-making process. This study proposes an integrated ranking method through online reviews. Specifically, the study constructs a fraudulent review detection model to detect fraudulent reviews. Then introduces the Hofstede's national cultural framework and cultural distance theory to propose a method for calculating the weighted sentiment values of reviews, and interval-valued Pythagorean fuzzy sets are used for representation. Finally, the ranking function is improved by introducing a weight factor to aggregate the ratings and reviews, and obtain the hotel ranking. To validate the proposed method, a case study is conducted using real hotel reviews. The results show that this method provides a suitable and differentiated ranking of alternatives for consumers in specific countries. This paper also offers insights for online review websites and hotel managers.
