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The purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.

Different from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.

A case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.

The casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.

Considering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.

(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

The capability of microparticle/objects patterning in the three-dimensional (3D) printing structure could improve its performance and functionalities. This paper aims to propose and evaluate a novel acoustic manipulation approach.

A novel method to accumulate the microparticles in the cylindrical tube during the 3D printing process is proposed by acoustically exciting the structural vibration of the cylindrical tube at a specific frequency, and subsequently, focusing the 50-μm polystyrene microparticles at the produced pressure node toward the center of the tube by the acoustic radiation force. To realize this solution, a piezoceramic plate was glued to the outside wall of a cylindrical glass tube with a tapered nozzle. The accumulation of microparticles in the tube and printing structure was monitored microscopically and the accumulation time and width were quantitatively evaluated. Furthermore, the application of such technology was also evaluated in the L929 and PC-12 cells suspended in the sodium alginate and gelatin methacryloyl.

The measured location of pressure and the excitation frequency of the cylindrical glass tube (172 kHz) agreed quite well with our numerical simulation (168 kHz). Acoustic excitation could effectively and consistently accumulate the microparticles. It is found that the accumulation time and width of microparticles in the tube increase with the concentration of sodium alginate and microparticles in the ink. As a result, the microparticles are concentrated mostly in the central part of the printing structure. In comparison to the conventional printing strategy, acoustic excitation could significantly reduce the width of accumulated microparticles in the printing structure (p < 0.05). In addition, the possibility of high harmonics (385 and 657 kHz) was also explored. L929 and PC-12 cells suspended in the hydrogel can also be accumulated successfully.

This paper proves that the proposed acoustic approach is able to increase the accuracy of printing capability at a low cost, easy configuration and low power output.

The purpose of this paper is to review systematically the research of grey relation analysis (GRA) models.

Three different approaches, the springboard to build a GRA model, the angle of view in modelling, and the dimension of objects, are analysed, respectively.

The GRA models developed from the models based on relation coefficients of each point in the sequences in early days to the generalized GRA models based on integral or overall perspective. It evolved from the GRA models which measure similarity based on nearness, into the models which consider similarity and nearness, respectively. The objects of the research advanced from the analysis of relationship among curves to that among curved surfaces, and further to the analysis of relationship in three‐dimensional space and even the relationship among super surfaces in n‐dimensional space.

The further research on GRA models is proposed. One is about the property of GRA model. An in‐depth knowledge about the properties of GRA model will help people to understand its function, applicable area and requirements for modelling. The other one is about the extension of research object system. The object to be analysed should be extended from the common sequence of real numbers to grey numbers, vectors, matrices, and even multi‐dimensional matrices, etc.

The purpose of this paper is to explore a new approach to solve the problem of positive and negative offset in the calculation process of integral elements, then propose a series of new grey relational degree model for cross sequences.

The definitions of cross sequences and area elements have been proposed at first. Then the concept of difference degree between sequences has been put forward. Based on the definition of difference degree between sequences, various modified grey relational degree models for cross sequences have been proposed to solve the measurement problem of cross sequence correlation relationships.

(1) The new definition of cross sequences; (2) The area element; (3) Various modified grey relational degree models for cross sequences based on the definition of difference degree between sequences.

The grey relational analysis model of cross sequences is a difficult problem in grey relational analysis. The new model proposed in this article can effectively avoid the calculation deviation of grey relational analysis model for cross sequences, and reasonably measure the correlation between cross sequences. The new model was used to analyse the food consumer price index in Shaanxi Province, clarifying the relationship between different types of food consumer price indices, some interesting results that are not completely consistent with general economic theory were obtained.

The new definition of cross sequences, the area element and various modified grey relational degree models for cross sequences were proposed.

The purpose of this paper is to construct some negative grey relational analysis models to measure the relationship between reverse sequences.

The definition of reverse sequence has been given at first based on analysis of relative position and change trend of sequences. Then, several different negative grey relational analysis models, such as the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model have been put forward based on the corresponding common grey relational analysis models. The properties of the new models have been studied.

The negative grey relational analysis models proposed in this paper can solve the problem of relationship measurement of reverse sequences effectively. All the new negative grey relational degree satisfying the requirements of normalization and reversibility.

The proposed negative grey relational analysis models can be used to measure the relationship between reverse sequences. As a living example, the reverse incentive effect of winning Fields Medal on the research output of winners is measured based on the research output data of the medalists and the contenders using the proposed negative grey relational analysis model.

The definition of reverse sequence and the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model are first proposed in this paper.

Drawing from social capital theory and resource dependence theory, this paper aims to test the relationship between top management team (TMT) government social capital and firm’s innovation performance via firm’s network prestige, and the moderating effect of TMT academic social capital.

The authors collected data from the China Stock Market and Accounting Research Database as well as A-share listed firms’ annual reports, and finally generated a sample of 922 firms and 2,464 firm-years from 2008 to 2014. UCINET 6.0 was used to analyze the data.

The authors find that the government social capital of TMT is positively related to firms’ innovation performance and firms’ network prestige plays a mediating role in this relationship. In addition, TMT academic social capital can strengthen the links between TMT government social capital and innovation performance through firms’ network prestige.

This paper not only contributes to literatures on the mechanism in the relationship between government social capital and firms’ innovation, but also to literatures on the effectiveness of the heterogeneity of firm’s social capital.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

This article intends to explore how organizational learning affects innovation in public library services, and the role of public librarians in innovation in library services.

This study collected 375 valid questionnaires from 19 public libraries in Shanghai and Zhejiang based on organizational learning, organizational innovation and employee psychological empowerment theory. Additionally, SPSS and HLM are used to analyze the relationship among the three processes of organizational learning: knowledge acquisition, knowledge sharing and knowledge application, and public library service innovation.

Results show that organizational learning has a significant positive effect on the service innovation of public libraries. Knowledge acquisition and knowledge application in the process of organizational learning have a significant positive influence on the service innovation of public libraries, but the impact of knowledge sharing on service innovation is weak. Employee psychological empowerment has a negative regulating influence on knowledge sharing–public library service innovation, but no significant influence on knowledge application–public library service innovation and knowledge acquisition–public library service innovation.

This research explores the effectiveness of the theory of organizational learning in the field of public libraries and also confirms the role of librarians in the work of public libraries. Together, they promote the innovation of public libraries.

The purpose of this paper is automatic classification of TV series reviews based on generic categories.

What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated.

With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names.

The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work.

Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing.

One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.

Entrepreneurial process strongly relies on context. The previous entrepreneurship research in developed countries over-emphasizes on its economic impact, but ignores its social impact, which leads to the slow development of entrepreneurship theories. Transitioning China provides entrepreneurs with a typical environment where opportunities and constraints coexist, which is a new research area in the field of entrepreneurship study.

Based on the grounded theory approach, this paper generalizes a local entrepreneurial process model from a multiple case study.

The paper states that the interaction among entrepreneurs, opportunities and institutional context is the core of the process. To be specific, entrepreneurial process includes an inner and an outer mechanism. The inner mechanism is based on the relations among institutional constraints, entrepreneurs and opportunities: nascent entrepreneurs, forced by institutional constraints to start a business, undergo a psychological process with entrepreneurial angst, reflective learning and effectuation and finally create business opportunities. The outer mechanism is grounded in the relations among new ventures, institutional evolution and opportunity development: new ventures facilitate institutional evolution through institutional entrepreneurship strategies, which in turn supports the ventures in the sustainable development of opportunities.

This study illuminates the social and institutional impact of entrepreneurial behavior, which is gradually fading and forgotten in modern society. The findings of the study enrich the research on entrepreneurial process, entrepreneurial cognition and institutional entrepreneurship and also provide implications for entrepreneurs.