In many decision-making situations, we are not restricted to two kinds of aspects, such as membership degree or nonmembership degree, and sometimes we need to include the abstinence degree (AD). However, many fuzzy set theories fail to cover issues, such as an intuitionistic fuzzy soft set, Pythagorean fuzzy soft set and q-rung orthopair fuzzy soft set. All the above notions can only consider membership degree and a nonmembership degree in their structures. The spherical fuzzy soft set compensates for these drawbacks in its structure. Moreover, the Dombi t-norm and Dombi t-conorm are the fundamental apparatuses to generalize the basic operational laws of sum and product. Therefore, in this article, based on the dominant features of spherical fuzzy soft sets and valuable features of the Dombi t-norm and Dombi t-conorm, we initially developed the basic Dombi operational laws for spherical fuzzy soft numbers. Moreover, based on these newly developed operational laws, we introduced aggregation operators called spherical fuzzy soft Dombi average (weighted, ordered weighted, hybrid) aggregation operators. We discussed the basic properties of these aggregation operators. Additionally, we have developed a multiple criteria decision making (MCDM) approach using an explanatory example via our approach to show its effective utilization. Furthermore, a comparative study of our approach shows the superiority of our introduced notions.
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http://dx.doi.org/10.1016/j.heliyon.2023.e16816 | DOI Listing |
Sci Rep
January 2025
School of Philosophy and Public Management, Henan University, 475001, Kaifeng, China.
Privacy fatigue caused by privacy data disclose and the complexity of privacy control has become an important factor influencing people's privacy decision-making behavior. At present, academia mainly studies privacy fatigue as a key determinant to explain the privacy paradox problem, but there is insufficient attention to its influencing factors and specific pathway of occurrence. Exploring the antecedents of privacy fatigue is of great significance for alleviating users' subjective privacy detachment and promoting privacy protection.
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January 2025
Institute of Sustainable Construction, Vilnius Gediminas Technical University, Vilnius, Lithuania.
Subjective weighting methods are widely employed to determine criteria weights in multi-criteria decision-making (MCDM) environment. Inputs from decision-makers, including opinions, assessments, assumptions, evaluations, interpretations, expectations, and judgments, are primarily relied upon in these methods. Significant challenges are faced due to two primary factors: the inherent uncertainty in inputs and the process of pairwise comparisons.
View Article and Find Full Text PDFHeliyon
February 2024
Centre of Mathematics, Universidade do Minho, Braga, Portugal.
Decision-making in real-world scenarios faces uncertainty. Fuzzy theory has been a means to represent such uncertainty. In this study, we propose an approach that incorporates bipolarity into multi-criteria decision-making processes applied to digital marketing.
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December 2024
Departamento de Matemática Aplicada, Instituto de Matemática, Estatística e Computação Científica (IMECC), Universidade Estadual de Campinas, Campinas, Brazil.
Species delimitation in hard corals remains controversial even after 250+ years of taxonomy. Confusing taxonomy in Scleractinia is not the result of sloppy work: clear boundaries are hard to draw because most diagnostic characters are quantitative and subjected to considerable morphological plasticity. In this study, we argue that taxonomists may actually be able to visually discriminate among morphospecies, but fail to translate their visual perception into accurate species descriptions.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Mathematics, University of Management and Technology, Lahore, 54000, Pakistan.
Selecting the best power source that is legal, affordable, environmentally friendly, and able to ensure long-term viability is a difficult but vital task. Existing frameworks based on traditional fuzzy and soft sets are unable to adequately capture the complexity of the optimal energy system selection (ESS). These decision models may also be complex, especially when rough data and integrity need to be taken into account.
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