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Pythagorean fuzzy set (PFS) is an effective approach for handling uncertainty in practical applications, whereas how to measure the uncertainty degree of PFS is still an open issue. In this paper, we propose a novel entropy measure of PFS by taking into account both Pythagorean fuzziness entropy in terms of the membership and non-membership degrees, and Pythagorean hesitation entropy in terms of hesitation degree. To be specific, we firstly propose a new cross-entropy measure of PFS to assess the difference of uncertainty between PFSs. By measuring the cross-entropy with the crisp set, we then propose a new entropy measure of PFS, which mathematically proves the satisfaction requirements of the axioms defined for the entropy of PFS. Based on that, we propose weighted cross-entropy and entropy measures which can be used for different applications. Furthermore, we illustrate the superiority of the proposed entropy measures of PFS by the comparisons with existing entropy measures of PFS. In addition, we propose an entropy-based multi-criteria decision-making (MCDM) method, which intends to minimize the total uncertainty amount of decision matrix to make the deciding result more convincing. As a result, we conduct a sensitivity analysis of the proposed weighted entropy measure in the MCDM method, and a company investment case study is attached to illustrate the effectiveness of the proposed MCDM method. The experimental result shows that the MCDM method based on the newly defined weighted entropy measure is stable, even in the setting of the variation of parameter. [ABSTRACT FROM AUTHOR]

Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors' interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method. [ABSTRACT FROM AUTHOR]

The belief interval-valued soft set (BIVSS) combines soft set theory and belief interval value (Dempster–Shafer theory). In this study, we propose a generalized belief interval-valued soft set (GBIVSS) approach and explore the associated properties of this approach in decision-making applications. Using the score function, the scoring function and similarity measure used to compare the relationships between GBIVSS are proposed. Then, we applied the GBIVSS to deal with multi-attribute decision making (MADM) problems. Furthermore, we used a case study of car purchase to illustrate the rationality of the proposed approach. In addition, we compare the effectiveness and advantages of our proposed approach and other existing models, which show superior performance in our proposed approach. GBIVSS provides a solution for multi-attribute problems. [ABSTRACT FROM AUTHOR]

Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents' behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking. [ABSTRACT FROM AUTHOR]

Background: Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases. Methods: We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence of delta (1-3.5 Hz), theta (4-7.5 Hz), alpha (8-12.5 Hz), and beta (13-30 Hz) bands were calculated in the frontal, central, temporal, parietal, and occipital regions. Results: Fifty patients with episodic migraine (1-5 headache days/month) and 20 HCs completed the study. Patients were classified into inter-ictal, pre-ictal, ictal, and post-ictal phases ( n = 22, 12, 8, 8, respectively), using 36-h criteria. Compared to HCs, inter-ictal and ictal patients, but not pre- or post-ictal patients, had lower EEG power and coherence, except for a higher effective connectivity in fronto-occipital network in inter-ictal patients ( p < .05). Compared to data obtained from the inter-ictal group, EEG power and coherence were increased in the pre-ictal group, with the exception of a lower effective connectivity in fronto-occipital network ( p < .05). Inter-ictal and ictal patients had decreased EEG power and coherence relative to HCs, which were 'normalized' in the pre-ictal or post-ictal groups. Conclusion: Resting-state EEG power density and effective connectivity differ between migraine phases and provide an insight into the complex neurophysiology of migraine. [ABSTRACT FROM AUTHOR]

We describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5–10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities. Design Type(s) behavioral data analysis objective • source-based data analysis objective • stimulus or stress design Measurement Type(s) brain activity measurement Technology Type(s) electroencephalography Factor Type(s) Sample Characteristic(s) Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format) [ABSTRACT FROM AUTHOR]