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How to identify the critical success factors (CSFs) of public health emergencies (PHEs) is of great practical significance to carry out a scientific and effective risk assessment. The purpose of this paper is to address this issue.

In this paper, the authors propose a new approach to identify the CSFs by hesitant fuzzy linguistic set and a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. First, a larger group of experts are clustered into three groups according to similarity degree. Then, the weight of each cluster is determined by the maximum consensus method, and the overall direct influence matrix is obtained by clustering with hesitant fuzzy linguistic weighted geometric (HFLWG) operators. Finally, the overall direct influence matrix is transformed into the crisp direct impact matrix by the score function, and 11 CSFs of PHEs are identified by using the extended DEMATEL method.

In addition, an example of PHEs shows that the approach has good identification applicability. The approach can be used to solve the problems of fuzziness and subjectivity in linguistic assessments, and it can be applied to identify the customer service framework with the linguistic assessments process in emergency management.

This paper extends the above DEMATEL method to study in the hesitant fuzzy linguistic context. This proposed hybrid approach has a wider application in the high-risk area where disasters frequently occur.

This paper aims to propose a two-stage vibration isolation system for large airborne equipment to isolate aircraft vibration load.

First, the vibration isolation law of the discrete model of large airborne equipment under different damping ratios, stiffness ratios and mass ratios is analyzed, which guides the establishment of a three-dimensional solid model of large airborne equipment. Subsequently, the vibration isolation transfer efficiency is analyzed based on the three-dimensional model of the airborne equipment, and the angular and linear vibration responses of the two-stage vibration isolation system under different frequencies are studied.

Finally, studies have shown that the steady-state angular vibration at the non-resonant frequency changes little. In contrast, the maximum angular vibration at the resonance peak reaches 0.0033 rad, at least 20 times the response at the non-resonant frequency. The linear vibration at the resonant frequency is at least 2.14 times the response at the non-resonant frequency. Obviously, the amplification factor of linear vibration is less than that of angular vibration, and angular vibration has the most significant effect on the internal vibration of airborne equipment.

The two-stage vibration isolation equipment designed in this paper has a positive guiding significance for the vibration isolation design of large airborne equipment.

Improvement and optimization design of a two-stage vibration isolation system proposed in this paper are conducted to ensure the device of electronic work effective.

The proposed two-stage vibration isolation system of airborne equipment is optimized and parameterized based on multi-objective genetic algorithm.

The results show that compared with initial two-stage vibration isolation system, the angular vibration of the two-stage vibration isolation system becomes 3.55 × 10^-4 rad, which decreases by 89%. The linear isolation effect is improved by at least 67.7%.

The optimized two-stage vibration isolation system effectively improves the vibration reduction effect, the resonance peak is obviously improved and the reliability of the mounting bracket and the shock absorber is highly improved, which provides an analysis method for two-stage airborne equipment isolation design under complex dynamic environment.

This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

The visible time window (VTW) calculation of satellites to ground targets is significant for Earth observation satellites' operation management and control. With the improvement of satellite maneuvering capability and the complexity of on-orbit observation tasks, the traditional VTW calculation methods can no longer meet the demands of satellite operation management and control due to a large amount of calculation and low efficiency. The purpose of this study is to propose a fast VTW calculation method based on map segmentation named map segmentation method (MSM), to improve the calculation efficiency, and further solve this problem.

The main feature of the MSM method is to segment the map and subsatellite trajectories and traverse the subsatellite points within a specific range around the target, significantly reducing the search space and the amount of computation and improving computational efficiency.

Numerical simulations for two satellite orbits are implemented to verify the feasibility of the proposed VTW calculation method, and the traditional traversal method (TM) is also performed for comparative analysis. The results show that the proposed method can obtain the same VTW, using less calculation time than the TM. The computational efficiency is significantly improved, especially for many tasks. The calculation time of observing 500 targets is saved by more than 70%, indicating a broad application prospect.

This paper proposes an original VTW calculation method based on map segmentation to improve the calculation efficiency. The simulation scenarios are designed to verify the accuracy and effectiveness of the proposed method, and the observation targets are randomly distributed on the map. For comparative analysis, the TM is also performed under the same simulation conditions.

This study aims to investigate the effect of social media use on healthcare workers’ psychological safety and task performance and the moderating role of perceived respect from patients during public health crises.

To test the proposed moderated mediation model, a survey was conducted in 12 Chinese medical institutions. A total of 637 valid questionnaires were collected for data analysis.

The results revealed that psychological safety mediated the relationships between task-related social media (TSM) use and social-related social media (SSM) use and task performance. In addition, perceived respect from patients moderated the relationship between TSM use and psychological safety, as well as the indirect relationship between TSM use and task performance through psychological safety.

This study sheds new light on understanding how different types of social media use influence task performance in the context of public health crises. Furthermore, this study considers the interactions of healthcare workers with colleagues and patients and examines the potential synergistic effects of these interactions on healthcare workers’ psychological state and task performance.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

Course credentialing essentially involves the implementation of digital badges. A number of studies have recently been conducted that clarify this philosophy and promote their adoption in learning programmes. The research presented in this chapter contributes to the body of knowledge on learning and teaching regarding the comparative analyses of learning outcomes, multidimensional credentialing and digital badges relevant to the pedagogy of construction management. The aim of this research was to determine the maturity of multidimensional credentialing within its online pedagogy in particular. A review of literature pertaining to course multi-disciplining, accreditation, credentialing and digital badges had been conducted. Comparative analyses were performed to allocate learning outcomes of the tertiary institution and two accrediting bodies onto each unit in the online delivery mode of a Construction Management Bachelor Degree course. These analyses had yielded three matrices that could assist decision-making on how to assess learning in each unit. The results revealed that almost all units in this course had implemented at least one tertiary institution and accrediting body learning outcome.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.