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Tourist destinations are introducing Mobility as a Service (MaaS) packages to satisfy tourists' needs and reduce the use of private or rental cars. The main objectives of this paper are: (1) to explore the extent to which the latent variable tourists' awareness of climate change (TACC) depends on a set of covariates with a particular focus on certain variables related to the MaaS concept; and (2) to analyse the elasticity of the latent variable (TACC), analysing the indicators that form the TACC latent variable as well as some specific MaaS-related segments. A Fuzzy Hybrid Analysis approach is applied to the information matrix provided by a survey administered to 1218 respondents in Gran Canaria and Tenerife. The results confirm statistically significant differences in the latent variable for a group of covariates with particular relevance for those more inclined to use bike-sharing systems at the destination. The results also show that the TACC index is less elastic regarding items in which environmental problems are significant today, and that it is essential to promote policies that reduce greenhouse gas emissions. Still, the responses vary widely depending on whether respondents are regular bike riders. The study offers valuable insights for destination marketing and management professionals. By understanding the connections between MaaS, climate change awareness and tourist behaviour, destinations can develop more sustainable tourism strategies that appeal to environmentally conscious travellers.

National identity studies diverge on several issues, such as the number of factors and their respective items’ adscription. Multi-Group Confirmatory Factor Analysis (MGCFA) is the standard method applied to cross-national datasets. Differences between groups can be the result of measurement artefacts. We argue that these problems can be better addressed by an alternative approach that builds a synthetic indicator named Relative National Identity Synthetic Indicator (RNISI), based on a Fuzzy Hybrid Analysis (FHA). The study aims to shed some light on the study of the latent variable national identity by comparing two methodologies: the classic method most often used (MGCFA) and the Fuzzy-Hybrid Approach, which, to our knowledge, has not been previously applied. This empirical study was based on a dataset from across ten countries using two waves (2003 and 2013) of the International Social Survey Programme (ISSP). The FHA results were compared with those obtained by two MGCFA models in which national identity was built as a second-order construct that depends on the ethnic, ancestry and civic first-order latent variables. The comparison lets us conclude that FHA can be considered a valid tool to measure the national identity by groups, and to provide additional information in form of elasticity figures. These figures can be employed to analyse the indicator’s sensitivity by group and for each of the items included in the national identity construct.

The study analyzes national identity using the International Social Survey Program (ISSP) database for the waves of 2003 and 2013. First, the Exploratory Factor Analysis (EFA) and the Multigroup Confirmatory Factor Analysis (MGCFA) are used to find the dimensions of the items included in the national identity module. Second, the civic and ethnic dimensions are analyzed through both a fuzzy clustering analysis and an extended apostle model to classify citizens’ national identity as the following: (1) post nationalists; (2) ethnic oriented; (3) civic-oriented; (4) credentialists. Third, the fuzzy eco-extended apostle model is applied to analyze 16 different national identity categories, for which the four pure mentioned categories are further studied. Fourth, the effects of some social characteristics, such as country-year, political orientation-year, and age-year, on the respective pure national Identity categories are studied using two distinct approaches, namely, contingency tables and conditional probability ratios. Results show that citizens tend to be more pure-credentialist than any other category and that social characteristics play a determinant role in explaining each category of citizens’ national identity.

Complex dynamical systems are represented by fuzzy hybrid differential equations (FHDEs), which describe systems that have mixed discrete and continuous behaviours with uncertainty. These equations are indispensable for control engineering, biology, and economic forecasting, as they model real-world phenomena. Nevertheless, it is intrinsically challenging to solve FHDEs because the dynamics, discontinuities, and uncertainties in parameters and conditions are all nonlinear and fuzzy. Traditional numerical methods, such as the Runge–Kutta-Fehlberg (RKF5) method, Finite Difference Methods (FDM), and spectral approaches, generally fail to provide accurate, stable, and efficient solutions, especially in problems with discontinuities, sharp transitions, or the propagation of uncertainty. The primary objective of this paper is to introduce a novel scheme called the AMWWG, which effectively solves FHDEs. AMWG method combines the advantages of wavelet-based multi-resolution analysis with those of the Galerkin projection technique. The method utilises local error estimates to refine the solution domain, adapting to the solution characteristics: fine refinement is used in areas of steep gradients, discontinuities, and fuzzy transitions, while coarse refinement is used everywhere else. The selective refinement approach enables the method to utilise minimal computational efforts where they are most needed, resulting in a significant order of magnitude reduction in computational cost with no loss in solution accuracy. Numerical experiments are performed, yielding extensive results reported on several benchmark FHDEs, which are corroborated with results from known analytical solutions and other complex examples with high nonlinearity and discrete switching behaviours. It is demonstrated that the AMWG method is more accurate, has lower memory requirements, and is faster than traditional methods. Additionally, it demonstrates a superior ability to handle both fuzzy uncertainty and sharp transitions. Thus, the AMWG method is demonstrated to be a powerful, flexible, and scalable numerical tool for solving FHDEs, offering a high degree of flexibility and significant potential for application in large-scale scientific or engineering problems.

Segregation is an important step in health care waste management. If done incorrectly, the risk of preventable infections, toxic effects, and injuries to care and non-care staff, waste handlers, patients, visitors, and the community at large, is increased. It also increases the risk of environmental pollution and prevents recyclable waste from being recovered. Despite its importance, it is acknowledged that poor waste segregation occurs in most health care organizations. This study therefore intends to produce, for the first time, a classification of failure modes related to segregation in the Nuclear Medicine Department of a health care organization. This will be done using Failure Mode and Effects Analysis (FMEA), by combining an intuitionistic fuzzy hybrid weighted Euclidean distance operator, and the multicriteria method Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA). Subjective and objective weights of risk factors were considered simultaneously. The failure modes identified in the top three positions are: improper storage of waste (placing items in the wrong bins), improper labeling of containers, and bad waste management (inappropriate collection periods and bin set-up).

The public and political debate about immigration now play a big role in all European elections, and there is a trend increasing an anti-immigrant sentiment that receives important media attention. This work, based on the European Social Survey (ESS) round 9 data for 27 European countries, contributes to such debate by introducing a new method in the field, a Fuzzy-Hybrid Approach (FHA), that complements other methodological methods that have been used to measure citizens’ attitudes towards immigrants. The novel approach in the field provides a synthetic indicator that measures openness towards immigrants (OTISI). Then, we analyse the relationship that exists between some specific sociodemographic variables and the new index. Results show that country, political orientation, age, religion, economic situation, gender, birthplace, employment, education, universalism, and conformity are key drivers that explain different attitudes towards immigrants. Our findings concur with other previous studies showing that the results are robust and that the method can be applied in future social science studies.

The aim of this article is to investigate the approach to multiple attribute group decision making (MAGDM) with intuitionistic fuzzy information. We first introduce a deviation measure between two intuitionistic fuzzy numbers, and then utilize the intuitionistic fuzzy hybrid aggregation operator to aggregate all individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. Based on the deviation measure, we develop an optimization model by which a straightforward formula for deriving attribute weights can be obtained. Furthermore, based on the intuitionistic fuzzy weighted averaging operator and information theory, we utilize the score function and accuracy function to give an approach to ranking the given alternatives and then selecting the most desirable one(s). In addition, we extend the above results to MAGDM with interval-valued intuitionistic fuzzy information.

Solving Multiple Attribute Group Decision Making (MAGDM) problems has become one of the most important researches in recent days. In situations where the information or the data is of the form of an Intuitionistic Triangular Fuzzy Number (ITrFN) or Intuitionistic Trapezoidal Fuzzy Number (ITzFN), a new distance function is defined for ranking the alternatives in the decision making process. After processing the decision information through a sequence of arithmetic aggregation operators, namely, the Intuitionistic Triangular Fuzzy Weighted Arithmetic Averaging (ITrFWAA), Intuitionistic Triangular Fuzzy Ordered Weighted Averaging (ITrFOWA) operator and the Intuitionistic Triangular Fuzzy Hybrid Aggregation (ITrFHA) operator, the proposed distance function is utilized to rank the best alternative. A model is proposed to solve MAGDM problems using the developed distance formula defined for ITrFNs. Numerical illustration is provided and comparisons are made with some of the existing MAGDM models and ranking procedures.

In this paper, a novel type-2 fuzzy expert system for prediction the amount of reagents in desulfurization process of a steel industry in Canada is developed. In this model, the new interval type-2 fuzzy c-regression clustering algorithm for structure identification phase of Takagi–Sugeno (T–S) systems is presented. Gaussian Mixture Model is used to generate partition matrix in clustering algorithm. Then, an interval type-2 hybrid fuzzy system, which is the combination of Mamdani and Sugeno method, is proposed. The new hybrid inference system uses fuzzy disjunctive normal forms and fuzzy conjunctive normal forms for aggregation of antecedents. A statistical test, which uses least square method, is implemented in order to select variables. In order to validate our method, we develop three system modeling techniques and compare the results with our proposed interval type-2 fuzzy hybrid expert system. These techniques are multiple regression, type-1 fuzzy expert system, and interval type-2 fuzzy TSK expert system. For tuning parameters of the system, adaptive-network-based fuzzy inference system is used. Finally, neural network is utilized in order to reduce error of the system. The results show that our proposed method has less error and high accuracy.

This paper extends the technique for order preference by similarity to ideal solution (TOPSIS) for solving multi-attribute group decision making (MAGDM) problems under triangular intuitionistic fuzzy sets by using its correlation coefficient. In situations where the information or the data is of the form of triangular intuitionistic fuzzy numbers (TIFNs), some arithmetic aggregation operators have to be defined, namely the triangular intuitionistic fuzzy ordered weighted averaging (TIFOWA) operator and the triangular intuitionistic fuzzy hybrid aggregation (TIFHA) operator. An extended TOPSIS model is developed to solve the MAGDM problems using a new type of correlation coefficient defined for TIFNs based on the triangular intuitionistic fuzzy weighted arithmetic averaging (TIFWAA) operator and the TIFHA operator. With an illustration this proposed model of MAGDM with the correlation coefficient of TIFNs is compared with the other existing methods.