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Development assurance level (DAL) is the measurement of the rigor of development assurance tasks performed to functions or items. The DAL assignment is an important process of aircraft system development that can make the reliability and safety of the system stay at acceptable levels. This paper aims to propose an optimization approach for the DAL assignments to minimize the development cost of aircraft systems.

The mathematical model for the DAL assignment optimization has been developed on the basis of the given expressions of objective function and constraints. In addition, a hybrid algorithm model synthesizing the advantages of genetic algorithm (GA) and Tabu search (TS) has been proposed to solve the optimization problem of the DAL assignment.

The results of the case study show that the proposed hybrid algorithm is more efficient and effective than the exhaustive method as well as the pure GA.

The proposed approach can be applied in the development of aircraft systems, and it has great significance in minimizing the development cost as well as keeping the system reliability and safety at an acceptable level.

The constrained optimization method has been applied in the DAL assignments, the corresponding mathematical model has been built and a hybrid evolutionary algorithm has been proposed to solve the optimization problem.

In today's complex and rapidly changing business environment, cross-boundary growth is increasingly critical to the survival or even success of organizations. The purpose of this study is to examine the forming mechanism of firm’s cross-boundary growth by integrating the two important antecedent factors of performance pressure and managerial discretion into a united framework and theoretically analyze the direct role of performance pressure on firm’s cross-boundary growth as well as reveal the moderating role of managerial discretion. Also, the authors select listed manufacturing companies in China as samples to empirically test the research hypotheses.

The authors design a multiple regression model to perform empirical analysis by using a panel of 4,002 year-observations in 1,334 listed manufacturing companies between 2013 and 2016. The sample data sources mainly come from the Wind Database, which is mainland China's leading financial database and software services provider. The hypotheses proposed are tested by adopting a panel data set of the listed manufacturing companies of China.

Empirical results show that performance pressure has a positive effect on the cross-industry growth and cross-domestic regional growth but a negative effect on the cross-international regional growth, and managerial discretion has a different moderating effect. Specifically, capital intensity strengthens the positive effect of performance pressure on cross-industry growth but weakens the negative effect of performance pressure on cross-international regional growth. State ownership enhances the positive effect of performance pressure on cross-domestic regional growth but decreases the negative effect of performance pressure on cross-international regional growth. CEO duality increases the negative impact of performance pressure on cross-international regional growth.

This study provides several implications for top executives, including how to dialectically consider the double-edged effect of performance pressure on cross-boundary growth of firms, create an appropriate environments of managerial discretion and design the types of cross-boundary growth strategies that top executives can follow in the volatility, uncertainty, complexity and ambiguity era.

Although the relevant literature highlights the importance of performance pressure, it has not been related to the cross-boundary growth of firms. This paper makes an incremental contribution to the literature on the forming mechanisms of firm’s cross-boundary growth by providing an important perspective of performance pressure to firm growth determinants and taking into account the moderating role of managerial discretion.

The purpose of this paper is to investigate the superior relationship between blood lipid- and cardiovascular disease (CVD)-related hematological parameters using superior grey relational analysis (GRA).

A total of 294 individuals who underwent simultaneous routine blood examination and blood lipid examination in the Physical Examination Center of the First Affiliated Hospital of Shantou University Medical College were included in this study. Superior GRA was performed to find out the superior factor in CVD-related hematological parameters and blood lipids. CVD-related hematological parameters included red blood cell distribution width, white cell count, and platelet count, platelet distribution width, mean platelet volume, as well as platelet crit. The indicators of blood lipids analyzed here consist of low-density lipoprotein, high-density lipoprotein, triglyceride and total cholesterol.

The results showed that all the grey relational degree of hematological parameters and blood lipids were over 0.8; the superior factor in hematological parameters was PLT, whereas TC was the superior factor in blood lipids.

Findings of this study suggested that hematological parameters are closely related to blood lipids and a potential role for hematological parameters in the prediction of dyslipidemia, which need further study; TC has the greatest influence on hematological parameters, whereas TG displays a minimal impact.

To the authors’ best knowledge, it was the first study to analyze the relationship between various CVD-related hematological parameters and blood lipids via superior GRA.

The purpose of this paper is to re-examine the effectiveness of price limits on stock volatilities in China over a more recent time period spanning from 2007 to 2012. The motivation stems from the fact that very high stock market volatilities are observed in China and we are sceptical of the volatility mitigating effect claimed by advocates of price limits.

The effectiveness of price limits on volatilities is examined using an event study methodology and within an expanded framework of volatility-volume relationships. The sample stocks include the 300 component stocks of the CSI300 Index.

Both event study and regression analysis suggest that price limits exaggerate market volatilities by causing volatility spillovers. The destabilising effect is much more pronounced for small firm stocks and when the market falls. In addition to the informational source of volatilities (represented by volume), price limits create another non-trivial frictional source of volatilities in China’s stock market.

This research is the first to re-examine the price limit effect in China’s stock market in an expanded framework of volatility-volume relationships. It identifies price limits, in addition to information, as another non-trivial frictional source of volatilities. The findings derived from a recent sample period confirm the conventional view of inefficiency of price limits raised by Fama (1989) and provide evidence in support of the pervasive trend of stock market deregulations.

In the information commons (IC) space of library, it is very important to recognize the emotional state of users for better playing the role of IC. In view of this point, this paper aims to discuss the human expression of user emotion.

An emotional state recognition method based on body posture change under video monitoring is proposed. In this method, two parameters are proposed to represent the emotional state of users. Finally, the distribution of users’ overall emotional state is recognized.

It is found that the change of human posture reflects the emotional state of users to a certain extent. The spatial frequency of the user’s average body position change and per capita body position change can reflect the spatial distribution of individual and body position change, respectively.

The method in this paper can effectively overcome the inaccuracy of manual identification of video monitoring images, especially in the case of a large number of users and effectively help the construction of university library IC space and provide a basis for the setting of environmental parameters.

As cross-cultural mergers and acquisitions (M&A) have learning effects on organisations, assessing their impacts on corporate performance is crucial. This study aims to explore the impact of inter-firm cultural differences on long-term post-M&A stock market performance.

The authors select domestic M&A transactions of Chinese listed companies during 2010–2021 as the sample. Then, the authors use the partial least squares structural equation model (PLS-SEM) to construct the latent variable of cultural differences in four dimensions to explore long-term stock market performance.

Cultural differences first positively and then negatively impact post-M&A performance. Three transmissions mechanisms are identified: investor sentiment, takeover premiums and information disclosure quality. Further analysis reveals that acquirer stock performance improves with higher analyst coverage and non-local shareholders but worsens if there are business affiliations between the acquirer and target firms.

This study can help optimise information disclosure systems in M&A transactions for regulatory authorities and aid investors’ understanding of post-M&A performance changes. Furthermore, it can improve acquirers’ understanding of the risks and opportunities in cross-cultural M&A, thereby facilitating the adaptation of management practices to the im-pacts of cultural differences.

By integrating the theories of resource dependence and transaction costs, this study examines the reversal effect of cultural differences between merging companies on post-M&A performance. The authors use a PLS-SEM to empirically analyse the main effects and reveal three transmission mechanisms.

As an important form of the e-commerce industry, online group buying is under the spotlight from with two sides: cheaper price but longer waiting time. The purpose of this paper is to adequately investigate the interaction between saving and waiting time of group buying comprehensively.

To fill the research gap, the authors elaborate a dual-channel supply chain (SC) with regular retail (individual buying) and group-buying channel, and formulate the demand based on the consumer utility with the positive effect of saving money and the negative effect of wasting time.

The authors find that power structure only changes the optimal prices, instead of the waiting time. The selling price mainly influences consumer demands, instead of the price discount of group buying. The SC profits are only positive to the channel preference, and it is the decisive parameter of consumers' choice. The price sensitivity lays a more remarkable impact on the SC compared to the time sensitivity. Above all, the price is the main factor of group buying, instead of time.

These results underscore the improvement for the dual-channel SC of group buying, providing managerial insights for the group-buying industry.

This paper aims to theoretically investigate a knowledge management model from the combined perspective of knowledge acquisition and knowledge application and its effect on organizational performance.

This study reviews prior research on knowledge acquisition and knowledge application, puts forward the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application” and more importantly proposes an integrated model by combining these two dimensions. Four case examples of enterprises are subsequently described and analyzed to illustrate the sources of knowledge acquisition, the objects of knowledge application and their influences on organizational performance.

Four knowledge management modes and their impacts are confirmed in this study. Specifically, the organization of the turbojet engine mode (high extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve good performance. The pipeline mode (high extensiveness of knowledge acquisition and low concentration of knowledge application) is the second, which has limited influence on good organizational performance. Organizations with the flashlight mode (low extensiveness of knowledge acquisition and high concentration of knowledge application) can achieve limited performance under the appropriate environment. The candle mode (low extensiveness of knowledge acquisition and low concentration of knowledge application) is the worst, performance of which is poor due to the break of the knowledge chain.

This paper holds that organizations should actively use the turbojet engine mode, adopt the pipeline mode and the flashlight mode cautiously, and avoid falling into the candle mode.

To the best of the authors’ knowledge, this study is among the first to propose the concepts of “the extensiveness of knowledge acquisition” and “the concentration of knowledge application,” and provides a combined model for analyzing differences in organizational performance from the perspective of knowledge.

The online Question and Answer community is full of a large number of science and technology topics, the discussion and dissemination of which play an important role in promoting the popularization of new technologies and cultivating public enthusiasm for science. However, the spread of false information and rumors weakens the community's positive effect, making the community more difficult for people to obtain useful information on such topics. Research on the influencing factors and governance of the spread of false information on science and technology topics has become the key to the spread of popular science.

Therefore, this paper uses the Elaboration Likelihood Model as the theoretical framework to examine the role of the factors influencing the spread of false information on science and technology topics in Zhihu community on the information persuasion and the impact on public behavior attitude from the core path and the edge path. This paper compiles a crawler program to capture 12,893 response information under the “Metaverse” topic in Zhihu community as an empirical sample and uses text mining and conducts visual correlation analysis to explore the key factors affecting the persuasive transmission path of information on science and technology topics.

The research finds that the content specialization, content consistency and content coherence of science and technology topics affect personal judgment from the aspect of information content through the core path and have a positive correlation with information persuasion; the number of comments, the length of the text and the publishing authors' influence from the edge image characteristics through the edge path are positively correlated with the information persuasion. Then, from the perspective of topic platform, government and topic participants, this paper puts forward a general plan to improve the information persuasion of science and technology topics so as to deal with false information.

Compared with the small data set of the traditional questionnaire survey, the research based on community empirical big data is more reliable. The model takes into account the attitude and behavior of users and is more suitable for the research on the transmission path of scientific and technological information in the internet era. This research provides a direction for analyzing the text characteristics and development trends of information in the field of science and technology and is conducive to promoting the optimization of the network information environment and building a good ecology, with the spread of rumors about science and technology topics curbed and the governance of false information strengthened.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.