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As an important major public health emergency, COVID-19 broke out more than two years. At present, China has entered the post-epidemic era. However, it is still necessary to study the medical health resource allocation in public health emergencies. Therefore, the evaluation of medical health resources allocation is important. Firstly, we use two kinds of linguistic preference orderings (LPOs) to represent experts' opinions when evaluating the medical health resources allocation in public health emergencies. Then, a novel ORESTE method with LPOs is developed to solve multiple criteria decision-making (MCDM) problems. Additionally, we apply the proposed ORESTE method to solve a practical MCDM problem involving the medical health resources allocation in public health emergencies. Finally, some comparative analyses among the proposed ORESTE method and some existing methods under a double hierarchy linguistic environment are set up, and some discussions are summarized to show the validity and applicability of the proposed novel ORESTE method. [ABSTRACT FROM AUTHOR]

In the consensus reaching process (CRP) permitting negotiation, the efficiency of negotiation is affected by the order of negotiation with decision makers (DMs), the time, and the number of moderators. In this paper, the sorted negotiation against DMs considering efficiency and time is initiated into consensus decision making, which can improve the speed and effectiveness of consensus. Based on the opinion dynamics (opinion evolution), uniform and normal distributions are used to describe the uncertainty of DMs' opinions and negotiation time, the opinion order efficiency and cost coefficient are coined, and the cost-constrained optimal efficiency sorted negotiation model and the optimal efficiency sorted negotiation model involving multiple moderators and time constraints are respectively constructed. The optimal solution of the chance-constrained model is obtained in the context of China's urban demolition negotiation using an improved genetic algorithm, and an optimum set of influential individuals based on opinion similarity is introduced so that assessment criteria for validating the reasonableness of the sorting sequence are determined. Sorted consensus negotiation combined with complex scenarios such as different representation formats of opinions, characteristics of DMs, other solving algorithms, Bayesian dynamics, etc. can be included in future works. [ABSTRACT FROM AUTHOR]

This study aims to present a multiattribute decision-making (MADM) and clustering method to explore the ranking, classifications and evolution mechanisms of the research fronts in the Web of Science Essential Science Indicators (ESI) database. First, bibliometrics are used to reveal the characteristics of the 57 ESI research fronts with more than 40 ESI highly cited papers (ESI-HCPs) for each research front. Second, the eight representative indicators are discovered to get answers to the following two questions: (i) Who publishes the ESI-HCPs that form a research front? and (ii) Where citations to these ESI-HCPs come from on a research front? Next, we investigate the ranking and clusters among the 57 ESI research fronts using the MADM and k -means clustering method and uncover the evolution process of the research fronts in different clusters based on the representative indicators. We also compare the performances of different countries in these research fronts and find that the USA and China are the leading countries in most research fronts. However, the two countries behave differently with regard to the rankings, the classifications and the evolution. [ABSTRACT FROM AUTHOR]

Co-patents are outcomes of R&D collaboration, which has been proven with higher-quality. Does this mean that high-quality patents should also extend their advantage to the technology market? Based on the transaction cost theory, we use the China National Intellectual Property Administration (CNIPA) database and logit model to explore the effect of co-ownership on firms' patent commercialization and the factors of co-patents that affect their commercialization. Our findings illustrate that co-ownership has a negative impact on patent commercialization. In addition, the co-owner's nature, country, and co-patent's industry influence the commercialization of co-patents. Firstly, a company and a university or research institution's co-owned co-patents are less likely to be commercialization than a company and a company co-owned co-patents. Secondly, multi-countries co-owned co-patents are less likely to be commercialization than a single-country co-owned co-patents. Thirdly, co-patents in high technology (high-tech) industries are less likely to be commercialization than co-patents in non-high-tech industries. This paper supports policymakers in implementing policies to promote the co-patents' commercialization. Meanwhile, our paper suggests that to pursue the economic value of the R&D collaborative intellectual property fruits, R&D collaborative intellectual property fruits are not be encouraged to be applied as the co-patents. [ABSTRACT FROM AUTHOR]

Allocating initial water rights in a river basin is not only a significant problem but also an essential research topic. It plays a crucial role in promoting the construction of a system of water rights and water-saving society as well as alleviating the contradiction between water supply and demand. Thus, the effective initial allocation of water rights has theoretical and practical significance for enhancing water rights management in China. First, this paper analyzes the impact of the strictest water resources management system on the establishment of the main criteria in the initial water rights allocation index system of a basin, including social, economic and environmental benefit criteria. Then, a new fuzzy comparison-based method, namely the fuzzy best-worst method (FBWM), is proposed to determine the weights of criteria and a procedure for group decision-making by FBWM is put forward. Finally, a case study about the initial water rights allocation in the Huaihe River Basin is illustrated to show how the developed FBWM works for real-world applications. Sensitivity analysis and comparative analysis with the existing methods are also provided to show the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The tension brought about by sickbeds is a common and intractable issue in public hospitals in China due to the large population. Assigning the order of hospitalization of patients is difficult because of complex patient information such as disease type, emergency degree, and severity. It is critical to rank the patients taking full account of various factors. However, most of the evaluation criteria for hospitalization are qualitative, and the classical ranking method cannot derive the detailed relations between patients based on these criteria. Motivated by this, a comprehensive multiple criteria decision making method named the intuitionistic multiplicative ORESTE (organísation, rangement et Synthèse dedonnées relarionnelles, in French) was proposed to handle the problem. The subjective and objective weights of criteria were considered in the proposed method. To do so, first, considering the vagueness of human perceptions towards the alternatives, an intuitionistic multiplicative preference relation model is applied to represent the experts’ preferences over the pairwise alternatives with respect to the predetermined criteria. Then, a correlation coefficient-based weight determining method is developed to derive the objective weights of criteria. This method can overcome the biased results caused by highly-related criteria. Afterwards, we improved the general ranking method, ORESTE, by introducing a new score function which considers both the subjective and objective weights of criteria. An intuitionistic multiplicative ORESTE method was then developed and further highlighted by a case study concerning the patients’ prioritization.