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The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

In real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

Melting universality, quantification and relative computability into a meta‐synthesis, pansystems theory develops an investigation on W‐fuzziness and 0*‐fuzziness connected with generalized conceptions such as derivative, equation, variational principle and OR. The purpose of this paper is to unify various mathematical structures, fuzziness categories, definitions of systems are unified within a general framework.

The paper includes topics: pansystems approach to fuzzy systems and relations, pansystems variational principle and Zadeh's extension principle, pansystems clustering and its fuzzy embodiment, pansystems topology and approximation to fuzziness, relative unification of fuzziness and roughness.

Zadeh extension principle about fuzziness transmission can be considered as a specific model of pansystems extremum principle, and so the more modes can be developed. Based on them a further investigation is present on pansystems clustering, which is a W‐fuzzy clustering, an extension or sublation of traditional one and fuzzy one.

Pansystems clustering embodies mutuality of many logoi of different subbraches with classification‐styled OR, including related interpromotions of the principles among knowledge rediscovery, data mining, mathematical reasoning and the investigations of fuzzy systems. W‐fuzziness and 0*‐fuzziness realize a relative unification for many logoi and principles.

Single-valued neutrosophic sets (SVNSs) are efficient models to address the complexity issues potentially with three components, namely indeterminacy, truthness and falsity. Taking advantage of SVNSs, this paper introduces some new aggregation operators (AOs) for information fusion of single-valued neutrosophic numbers (SVNNs) to meet multi-criteria group decision-making (MCGDM) challenges.

Einstein operators are well-known AOs for smooth approximation, and prioritized operators are suitable to take advantage of prioritized relationships among multiple criteria. Motivated by the features of these operators, new hybrid aggregation operators are proposed named as “single-valued neutrosophic Einstein prioritized weighted average (SVNEPWA) operator” and “single-valued neutrosophic Einstein prioritized weighted geometric (SVNEPWG) operators.” These hybrid aggregation operators are more efficient and reliable for information aggregation.

A robust approach for MCGDM problems is developed to take advantage of newly developed hybrid operators. The effectiveness of the proposed MCGDM method is demonstrated by numerical examples. Moreover, a comparative analysis and authenticity analysis of the suggested MCGDM approach with existing approaches are offered to examine the practicality, validity and superiority of the proposed operators.

The study reveals that by choosing a suitable AO as per the choice of the expert, it will provide a wide range of compromise solutions for the decision-maker.

Although fuzzy logic is being extensively used in electronics and mathematical sciences, it has found little or no application in the social sciences, especially criminology. As a mathematical system, fuzzy logic generalizes the Boolean logic and can be a very useful tool for the social sciences where concepts and terms involve shades of meanings. Outlines the essential mathematics behind this approach and develops a technique that could be useful in building offender profiles from fuzzy descriptions provided by witnesses. Also suggests several other possible areas of applications of this mathematical system.

The purpose of this paper is to show which models, uncertain or certain, simple or complicated, are more suitable when they are faced with incomplete information and inaccurate data.

The characteristics of fuzzy mathematics, grey system theory, rough set theory and the basic characteristics of incomplete information and inaccurate data in uncertain systems are analysed.

The similarities and differences among fuzzy mathematics, grey system theory, rough set theory and probability statistics are compared. The principle of simplicity of scientific theories, methods, and models are discussed.

It is suggested that the tendency to concentrate on a complicated model isn't always necessary when faced with the condition of incomplete information and inaccurate data.

The paper shows that a more satisfied result can be obtained with an uncertain model than with a meticulous model on a certain situation.

To define some new kinds of fuzzy maps related with c‐zero dimensionality fuzzy, and various notions of fuzzy connectedness (due to Fatteh and Bassan, and Ajmal and Kohli, respectively), and obtain several properties about them.

The paper shows several properties of some new fuzzy maps.

The paper shows new results on fuzzy maps and various notios of fuzzy connectedness.

Clearly, this paper is devoted to fuzzy topological spaces.

The main applications are in the mathematical field.

The paper shows original results on fuzzy topology.

D.Çoker constructed the fundamental theory of intuitionistic fuzzy topological spaces. The purpose of this paper is to introduce a new concept of compactness and a definition of paracompactness for intuitionistic fuzzy topological spaces, and obtain several preservation properties.

Two new covering properties in intuitionistic fuzzy topological spaces are defined and studied.

Relations on these new properties and covering properties on fuzzy topology in the Chang's sense are obtained.

Clearly, this paper is devoted to intuitionistic fuzzy topological spaces.

The main applications are in the mathematical field.

The paper shows original results on fuzzy topology.

Grey set is the important foundation of the grey mathematics and grey system theory, and the possibility function is the way of expressing grey set. This paper aims to establish the method of determining the possibility function of grey set and discusses its extended applications.

First, the grey kernel and the grey support set of grey set are defined, and the properties of grey kernel are analyzed. Second, according to the decomposition theorem of grey set, a method of determining the possibility function of grey set is put forward in this paper, which is called the method of increasing information and taking maximum and minimum (IITMM), and then it is further simplified as the method of increasing information and taking maximum (IITM), and an simple example is given to illustrate the calculation procedure. Finally, the grey information cluster method (GICM) based on IITM is proposed and applied to the ecological and geographical environment analysis of pine caterpillar.

The results show that the grey kernel of grey set still has grey uncertainty; the method of IITM has simple calculation and strict mathematical basis, and it can synthesize the information of the research object and accords with the principle of using minimum information; the GICM and the fuzzy cluster method have the same classification effect.

The researches show that method of IITM can be used not only to determine the possibility function of the grey set effectively, but also be used for the evaluation and cluster analysis of connotative objects. The classification result of the GICM presented in this paper is more precise than that of the fuzzy cluster method.

The paper succeeds in realizing both the IITM method for determining the possibility function of grey set and the GICM based on IITM for the connotative objects.

The purpose of this paper is to obtain an axiomatic characterization of Lowen's fuzzy compactness.

The paper shows a characterization of Lowen's fuzzy compactness.

The paper shows new results on fuzzy compactness.

Clearly, this paper is devoted to fuzzy topological spaces.

The main applications are in the mathematical field.

The paper presents original results on fuzzy topology.