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The proposed DHHFLOWLAD is used to design a recommendation system, which aims to provide the most appropriate treatment to the patient under a double hierarchy hesitant fuzzy linguistic environment.

Based on the ordered weighted distance measure and logarithmic aggregation, we first propose a double hierarchy hesitant fuzzy linguistic ordered weighted logarithmic averaging distance (DHHFLOWLAD) measure in this paper.

A case study is presented to illustrate the practicability and efficiency of the proposed approach. The results show that the recommendation system can prioritize TCM treatment plans effectively. Moreover, it can cope with pattern recognition problems efficiently under uncertain information environments.

An expert system is proposed to combat COVID-19 that is an emerging infectious disease causing disruptions globally. Traditional Chinese medicine (TCM) has been proved to relieve symptoms, improve the cure rate, and reduce the death rate in clinical cases of COVID-19.

The purpose of this article is to conduct a main path analysis of 627 articles on the theme of Pythagorean fuzzy sets (PFSs) in the Web of Science (WoS) from 2013 to 2020, to provide a conclusive and comprehensive analysis for researchers in this field, and to provide a study on preliminary understanding of PFSs.

The research topic of Pythagorean fuzzy fields, through keyword extraction and describing the changes in characteristic themes over the past eight years, are firstly examined. Main path analysis, including local and global main paths and key route paths, is then used to reveal the most influential relationships between papers and to explore the trajectory and structure of knowledge transmission.

The application of Pythagorean fuzzy theory to the field of decision-making has been popular, and combinations of the traditional Pythagorean fuzzy decision-making method with other fuzzy sets have attracted widespread attention in recent years. In addition, over the past eight years, research interest has shifted to different types of PFSs, such as interval-valued PFSs.

This paper implicates to investigate the growth in certain trends in the literature and to explore the main paths of knowledge dissemination in the domain of PFSs in recent years.

This paper aims to identify the topics in which researchers are currently interested, to help scholars to keep abreast of the latest research on PFSs.

The purpose of this paper is to propose an approach for predicting the movements of Chinese medicinal material price indexes using news based on text mining.

A research framework and three major methods, namely, domain dictionary construction, market convergence time calculation and dimensionality reduction integrating semantic analysis, are proposed for the approach. The proposed approach is applied in practice for predicting the price index movements of the top ten Chinese medicinal materials that receive the greatest media attention.

A set of experiments performed herein show that a predictive relationship exists between the news and the commodity market and that each of the three major methods improves the forecasting performance.

Because the field of Chinese medicinal materials lacks a corpus that can be used for sentiment analysis, the accuracy of a trained automatic sentiment classifier is lower than obtained by a manual method, which can cause the calculated convergence result to be inaccurate, thus affecting the final prediction model. The manual method of having people label news decreases the proposed method’s aspects of being intelligent and automatic.

Using the method proposed herein to predict the trends in Chinese medicinal materials prices helps farmers arrange a reasonable planting plan to pursue their best interests.

The method proposed herein to predict the trends in the prices of Chinese medicinal materials is conducive to the government arranging planned drug availabilities in order to avoid disasters in which herbs are looted.

The produced prediction result is meaningful in supporting farmers and investors to make better decisions in growing and trading Chinese medicinal material, which leads to financial returns on investments and the avoidance of severe losses.

In seaport industries, vessel arrival delay is inevitable because of numerous factors, e.g. weather, delay due to the previous stop, etc. The period of delay can be as short at 15 min of as long as a few days. This causes disruption to the planned sea operation operations, and more importantly, to the resources utilization. In traditional berth allocation and quay crane assignment problems (BA-QCA), the risk of vessel arrival delay has not been considered. Accordingly, the purpose of this paper is to employ a proactive planning approach by taking into consideration the vessel arrival delay into the optimization of BA-QCA problems.

In the existing BA-QCA problems, vessel arrival time is usually deterministic. In order to capture the uncertainties of arrival delay, this paper models the arrival time as a probability distribution function. Moreover, this paper proposes to model the delay risk by using the period between the expected arrival time and the expected waiting time of a vessel. Lastly, the authors propose a new modified genetic algorithm and a new quay crane assignment heuristic to maximize the schedule reliability of BA-QCA.

A number of numerical experiments are conducted. First of all, the optimization quality of the proposed algorithm is compared with the traditional genetic algorithm for verifying the correctness of the optimization approach. Then, the impact of vessel arrival delay is tested in different scenarios. The results demonstrate that the impact of vessel arrival delay can be minimized, especially in the situations of high vessel to potential berth ratio.

The proposed vessel arrival modeling approach and the BA and QCA approach can increase the operations efficiency of seaports. These approaches can increase the resource utilization by reducing the effect of vessel arrival delay. In other words, this can improve the throughput of seaport terminals.

This paper proposes to minimize the delay risk based on the conditional probability of the vessel completion time based on the previous vessel at the assigned berth. This modeling approach is new in literature.

The purpose of this paper is to examine influence strategies in organization-stakeholder relationships, by examining the National Basketball Association (NBA) and Anschutz Entertainment Group’s (AEG) involvement in the operations of MasterCard Center in Beijing, China.

Using Frooman’s model of stakeholder influence strategies, a case study of AEG and the NBA China was undertaken, relying on archival sources and interviews with key stakeholders.

The study produced two major findings. First, Bloomage employed different influence strategies to press the NBA and AEG to further reduce their involvement in the MasterCard Center’s operations. Second, Bloomage used cultural differences to justify the need to reduce its reliance on the NBA and AEG.

The study adds to the literature on stakeholder theory and sport organizations by examining organizations establishing themselves in foreign markets, and the influence strategies employed by key local stakeholders.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

In real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

The purpose of this study is to propose an analytical framework for generating main path analysis (MPA) and demonstrate the process involved in identifying, analyzing the MPA on a citation network and empirically testing in the research field chromosome anomalies (CA).

The proposed methodological structure involves five phases of the process. Search path method is used to measure the weights of each citation link from a source vertex to a sink vertex. The key route local main path and global main path are generated to identify the knowledge diffusion trajectories and validated by cross-referencing with existing literature, co-citation analysis and centrality measures of social network analysis.

The empirical validation of this framework within CA research demonstrates its potential for tracing knowledge diffusion and technological development trajectories over three decades. This approach elucidates two major intellectual knowledge flows. The first key-route main path identified the primary diagnostic protocols. The second key-route main path revealed that cancer or carcinogenesis is identified as one of the mainstream of CA.

The limitations of the data and coverage period restrict the scope of this study. MPA was applied exclusively to the most influential sub network and disregarded other sub networks. MPA identified the seminal papers that provided a historical development in diagnostic protocol and their interconnectedness of disorders and diseases. This helps the researchers to develop targeted therapies and interventions, especially in cancer treatment.

Exploiting MPA on CA research provides valuable insights to stakeholders in developing evidence-based public health policies. This is crucial for preventing the birth of children with birth defects or genetic diseases, promoting public health and reducing the socioeconomic burden on a country through enhanced surveillance and prevention efforts.

The study suggests that in addition to traditional scientometrics measures, MPA can be used to trace the evolution of knowledge and technological advancements. It also highlights the role of social network analysis measures in extracting main paths.