The longitudinal research of type-2 fuzzy sets domain: From conceptual structure and knowledge diffusion perspectives

A data-driven group emergency decision-making method based on interval-valued intuitionistic hesitant fuzzy sets and its application in COVID-19 pandemic

The outbreak of Corona Virus Disease 2019 (COVID-19) makes people more concerned about the validity and timeliness of emergency decision making. When an emergency occurs, it is difficult for decision makers (DMs) to give accurate assessment information in the early stage due to the urgency of time, the incompleteness of information, and the limitations of DMs’ cognition and knowledge. Hence, we use interval-valued intuitionistic hesitant fuzzy sets rather than exact numbers to better characterize the fuzziness and uncertainty of emergencies. In addition, the Internet has become a major platform for the public to express their opinions or concerns, so we can collect the user-generated content on social media to help DMs determine appropriate emergency decision-making criteria which are the premise and basis of scientific decisions. However, there is likely to be some correlation between the obtained criteria. To this end, we first extend the Bonferroni mean (BM) operator to the interval-valued intuitionistic hesitant fuzzy environment, and propose three interval-valued intuitionistic hesitant fuzzy BM operators to capture the interrelation of fuzzy input variables, including an interval-valued intuitionistic hesitant fuzzy BM operator, a simplified interval-valued intuitionistic hesitant fuzzy BM operator, and a simplified interval-valued intuitionistic hesitant fuzzy weighted BM (SIVIHFWBM) operator. Then, a new group emergency decision-making method based on the SIVIHFWBM operator and social media data is proposed, and the specific steps of ranking all emergency plans are put forward. Moreover, our method is applied to evaluate emergency plans for the prevention and control of COVID-19. Finally, the effectiveness and feasibility of the method are verified by the sensitivity analysis, validity test, and comparative analysis.

Detecting the research trends and evolution of energy resilience: a bibliometric analysis

The concept of resilience is widely used in many specific fields, such as energy. Energy resilience is receiving increasing scientific attention. In the long-term sustainable development of society, energy and resilience policies are the key strategies to achieve international development goals. This paper analyzes the existing energy resilience literature and presents the research hot spots and trends. After extracting 4887 articles from the Science Citation Index Expanded and Social Sciences Citation Index databases, this paper takes the literature data during 1985-2021 through topic search using the keywords of energy resilience and applies bibliometrics to study the development traits of the field. The primary research purposes are to discover the knowledge framework of energy resilience, together with a review of integral development trends, elementary publication characteristics, and an analysis of related leading journals. Aside from analyzing the number of publications, citations, and cooperation types, this study identifies the most prolific researchers from the country-level, institution-level, and author-level in this area, thus providing a longitudinal overview of the dynamic research evolution of energy resilience and the knowledge spread locus in this domain.

A University Teachers’ Teaching Performance Evaluation Method Based on Type-II Fuzzy Sets

This paper proposes a university teachers’ teaching performance evaluation method based on type-II fuzzy sets (T2 FSs), which solves the problems of fuzziness, complexity and uncertainty in teaching performance evaluation. Firstly, the evaluation indicator system is constructed from the aspects of teaching attitude, teaching contents, teaching professionalism, teaching methods and teaching effects. Then, T2 FSs theory and the perceptual computing method are introduced to model subjective judgments and capture uncertainties, effectively handling higher levels of uncertainty in the evaluation process. Furthermore, the linguistic weighted average operator is applied as the computing with words engine to aggregate scores and weights of indicators, which effectively integrates the uncertain information in the input data into the final evaluation conclusion and guarantees the accuracy of the evaluation results. Finally, the effectiveness of the method of this study is evaluated by simulation experiments. The computational results demonstrate that it can capture more uncertain and complex information, and is more accurate and reliable than the type-I fuzzy sets method.