Interval Consistency Repairing Method for Double Hierarchy Hesitant Fuzzy Linguistic Preference Relation and Application in the Diagnosis of Lung Cancer

Natural language is more in line with the real thoughts of people than crisp numbers considering that qualitative language information is more consistent with the expression habits of experts. Double hierarchy hesitant fuzzy linguistic preference relation (DHHFLPR) can be used to express complex linguistic preference information accurately because the pairwise comparison methods are more accurate than non-pairwise methods. Consistency reflects the rationalization of a preference relation and can be used to judge whether a preference relation is self-contradictory or not. In this paper, an interval consistency index of DHHFLPR is developed, which is consisted by the consistency indices of all double hierarchy linguistic preference relations associated with the DHHFLPR. Additionally, an average consistency index of DHHFLPR is given by calculating the average value of the consistency indices of all double hierarchy linguistic preference relations. Moreover, we develop a consistency checking and repairing method for DHHFLPR. Finally, we apply the proposed method into a practical group decision-making problem that is to identify the most critical factors in developing lung cancer, and some comparative analyses involving the connections and differences among the proposed consistency indices are analysed

A survey on energy justice: a critical review of the literature

The increasing scarcity of resources and the escalating complexity of reality make the fairness ensuring in energy activities even more difficult. In this context, energy justice, as an emerging cross-field, tries to provide solutions based on practical problems. In the face of the surge of energy justice publications, it is necessary to review them in time, so that we can comprehend the significant achievements and the research directions worthy of further exploration. With the help of visualization tools, this paper conducts a comprehensive quantitative analysis of 1,910 energy justice publications. Based on the results, we reach the following main conclusions: (1) The energy justice publications have only emerged rapidly in recent years; (2) The research hotspots are closely related to the renewable energy transition; (3) The distribution of prominent contributors in this field is relatively concentrated. The main contribution of this study is to comprehensively display the essential characteristics of the literature in this field, such as the evolutions of research themes and the performances of research contributors in different dimensions, so as to provide readers with an effective way to understand the knowledge structure in this field, and help related researchers rationally examine the existing results

The risk assessment of construction project investment based on prospect theory with linguistic preference orderings

Multiple experts decision-making (MEDM) can be regarded as a situation where a group of experts are invited to provide their opinions by evaluating the given alternatives, and then select the optimal alternative(s). As a useful linguistic expression model, linguistic preference orderings (LPOs) were established in which the order of alternatives and the relationships between two adjacent alternatives are fused well. Considering that prospect theory has the superiority in depicting risk attitudes (risk seeking for losses and risk aversion for gains) during the uncertain decision-making process, this paper develops a consensus model based on prospect theory to deal with MEDM problems with LPOs. Firstly, each LPO provided by expert is transformed into the responding DHLPR with complete consistency. Then, the reference point of expert is determined and the prospect preference matrix is established. Moreover, we can obtain the overall prospect consensus degree for a MEDM problem by calculating the similarity degree between individual and collective prospect preference matrix. Furthermore, a consensus improvement method is developed to complete the consensus reaching process. Finally, we apply the proposed method to deal with a practical MEDM problem involving the construction project investment, and make some comparative analyses with existing methods.National Natural Science Foundation of China (NSFC) 71771155China Postdoctoral Science Foundation 2020M680151Sichuan Postdoctoral Science special FoundationSichuan University Postdoctoral Interdisciplinary Innovation Startup FoundationFundamental Research Funds for the Central Universities YJ202015European Union (EU) TIN2016-75850-RSichuan Province System Science and Enterprise Development Research Center Xq20B0

Managing consensus by multi-stage optimization models with linguistic preference orderings and double hierarchy linguistic preferences

Preference ordering structures are useful and popular tools to represent experts’ preferences in the decision making process. In the existing preference orderings, they lack the research on the precise relationship between any two adjacent alternatives in the preference orderings, and the decision making methods are unreasonable. To overcome these issues, this paper establishes a novel concept of linguistic preference ordering (LPO) in which the ordering of alternatives and the relationships between two adjacent alternatives should be fused well, and develops two transformation models to transform each LPO into the corresponding double hierarchy linguistic preference relation with complete consistency. Additionally, to fully respect the experts’ expression habits and provide more refined solutions to experts, this paper establishes a multi-stage consensus optimization model by considering the suggested preferences represented in both the continuous scale and the discrete scale, and develops a multi-stage interactive consensus reaching algorithm to deal with multi-expert decision making problem with LPOs. Furthermore, some numerical examples are presented to illustrate the developed methods and models. Finally, some comparative analyses between the proposed methods and models and some existing methods have been made to show the advantages of the proposed methods and models. First published online 24 February 202

Circular economy and fuzzy set theory: a bibliometric and systematic review based on Industry 4.0 technologies perspective

The Circular Economy (CE) is receiving more attention, especially in Industry 4.0 (I4.0). In the face of several ambiguous and uncertain information, fuzzy techniques based on Fuzzy Set Theory (FST) are essential for developing CE strategies. This paper uses bibliometric methods to analyze the characteristics of the authors, nations/regions, institutions of the literature of FST and CE, and the collaborations relations between them, and then summarize the literature on fuzzy techniques in the CE and identify the specific role that FST can play in each stage of CE, its primary effects on the CE’s pre-preparation stage, design and production stage, and recycling and reuse stage. Meanwhile, the paper explores the advantages of I4.0 technologies for CE and analyzes the research on the role of fuzzy techniques based on FST for CE and I4.0 technologies. Last but not least, this paper is concluded by summarizing the knowledge gained from the bibliometric and content analyses of the literature and suggesting further research directions of investigation. This research will draw attention to FST’s contribution and encourage its advancement in CE and I4.0 technologies

Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors

Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions. First published online 19 November 202

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

An overview of Big Data in Healthcare: multiple angle analyses

Big data have been in use since the 1990s, which usually include some complex data sets whose sizes are beyond the ability of commonly used software to handle within a reasonable period of time. In recent years, big data analytics by providing personalized medicine and regulation analysis, providing clinical risk intervention and forecast analysis, reducing waste and nursing patients with external and internal variability, standardization of medical terminology and patient registration, and fragmentation of the solution, help to improve health care. This paper provides an overview of the contents of big data healthcare. We summarize some kinds of medical big data, including the electronic health records, the medical image data, the healthcare system big data, the health Internet of Things and healthcare informatics, the remote medical monitoring big data, the biomedical big data, and other sources of big data. Furthermore, we discuss some methods for handling different kinds of medical big data. Additionally, we analyze the privacy of medical big data and summarize some methods and technologies to protect privacy. Aiming at some special cases, we list some other analyses and methods for them. Most importantly, we discuss the potential challenges and future research directions related to big data healthcare

Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment

Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by multisensors with a consensus. In this paper, we first transform the track association problem to multiattribute group decision making (MAGDM), and describe the MAGDM with nested probabilistic-numerical linguistic term sets (NPNLTSs). Then, a consensus model with NPNLTSs is constructed which has two key processes. One is a consensus checking process, and the other is a consensus modifying process. Based on which, a track association algorithm with automatic modification is put forward based on the consensus model. After that, the solution of a case study in practice is given to obtain the corresponding track by the proposed method, and it provides technical support for the track association problems. Finally, we make comparisons with other methods from three aspects, and the results show that the proposed method is effective, feasible, and applicable. Moreover, some discussions about the situation where there is only one echo point at a time are provided, and we give a discriminant analysis method

A bibliometric analysis of carbon neutrality: Research hotspots and future directions

Global attention has shifted in recent years to climate change and global warming. The international community has set the objective of carbon neutrality to address the climate crisis. Carbon neutrality has drawn significant attention as a crucial step in the fight against climate change, with individual nations having established their carbon neutrality targets. This paper aims to use bibliometric analysis to investigate research hotspots and trends in carbon neutrality research, and accesses the literature through the Web of Science (WoS) core database and undertakes an in-depth examination of 909 publications linked to carbon neutrality around the world using Vosviewer and Bibliometrix software. According to the findings, the number of carbon neutrality publications has increased dramatically in recent years. There are also notable differences in carbon neutrality research across countries and regions. China and the US are the primary drivers and leaders of carbon neutrality research, and developing countries have relatively little carbon neutrality research. Research has concentrated on carbon neutrality’s practical, technical, policy, and economic aspects, as well as renewable energy sources, carbon conversion technologies, and carbon capture and storage technologies are also research hotspots. The paper also outlines opportunities for the advancement of carbon neutrality research in the future, including how it might be further integrated with Artificial intelligence (AI) and the metaverse, and how to attack the difficulties and uncertainties faced by the post-epidemic rebound. This study aids in understanding the current state of the field of carbon neutrality research and can be used to guide future studies