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Drawing on job demands and resources theory and the challenge-hindrance stressor framework, this study aims to investigate the effect of team knowledge complexity on team information sharing and information searching and examine whether team learning goal orientation mediates these effects.

The authors conducted two studies. Study 1 used a field survey study conducted among 374 employees positioned in 68 new product teams. Study 2 used a three-wave online survey study conducted among 208 leaders to investigate the teams they managed.

The findings of the two studies reveal that team knowledge complexity has a positive direct effect on team information sharing and information searching. Furthermore, team learning goal orientation mediates these two relationships.

The findings indicate that team knowledge complexity is generally beneficial for the team information process. Therefore, instead of fearing an increase in the knowledge complexity of the projects, organizations should dare to present challenge demands to team members to enhance their engagement in information processing. Organizations could also pay attention to team member selection during team composition processes. For example, selecting team members with a high level of learning goal orientation is helpful in facilitating team information processing.

Although previous studies have found that knowledge complexity is beneficial for team output, less is known about how knowledge complexity influences team processes. This study clarifies the relationships between team knowledge complexity, information sharing and information searching and examines team learning goal orientation as a vital mediator.

According to the relevance of product quality and machine degradation state, a hybrid maintenance policy is designed. The paper aims to discuss this issue.

Product quality control and machine maintenance are considered simultaneously in this policy. Based on this policy, the economic model of x-bar control chart is proposed using statistical process control and renewal reward theory.

This model is solved by genetic algorithm and the experimental results validated its feasibility.

In this model, the four corresponding relationship, which is between product quality monitoring result and machine degradation state, is analyzed.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

The purpose of this paper is to explore the influence of organizational culture (OC) on talent management (TM) by a case study of a real estate company.

The method of case study is adopted in the present study.

The authors present four propositions. The first is OC has an effect on TM. The second is a new conceptual model of TM. The third is a 4-P pattern to identify and develop the talent. The fourth is to adopt both the spiritual and material satisfactions that retain the talent.

The primary limitation of this study is embedded in the case study method, which is not sufficient to represent the totality. The other limitation is that the issue of cohesion and team efficacy of talents is not considered. This study argues the relationship between OC and TM and expands the existing TM and OC theory. The effect of professional idealism is emphasized on in the process of TM. Talent can be retained firmly within the organization through the methods of rebuilding and strengthening OC.

A conceptual model of TM, 4-P pattern of evaluation and the operational mean to retain the talent is introduced.

China's marine economy occupies an important position within the national economy, and its contribution thereto is constantly improving. The overall operation of the marine economy shows positive developmental trends with potential for further growth. The purpose of this research is to analyse the prosperity of China's marine economy, reveal trends therein and forecast the likely turning point in its operation.

Based on the periodicity and fluctuation of China's marine economy development, China's marine economic prosperity indicator system is established from five perspectives. On this basis, China's marine economic operation prosperity index can be synthesised and calculated, then a dynamic factor model is constructed. Using the filtering method to calculate China's marine economic operational Stock–Watson index, Markov switching has been used to determine the trend to transition. Furthermore, China's current marine economic prosperity is evaluated through analysis of influencing factors and correlation analysis.

The analysis shows that, from 2017 to 2019, the operation of the marine economy is relatively stable, and the prosperity index supports this finding; meanwhile it also exposes problems in China's marine economy, such as an unbalanced industrial structure, low marine economic benefits and insufficient capacity for sustainable development.

Through the analysis of the prosperity of China's marine economy, the authors reveal the trends in China's marine economy and forecast its likely future turning point.

The purpose of this paper is to present a study of radial aircraft tire for safety assessment during various scenarios.

A detailed finite element (FE) model of aircraft tire was established based on the actual geometry of the target tire for numerical simulations. As the major component of this tire, rubber material usually presents a complicated mechanical behavior. To obtain the reliable hyperelastic properties of rubber, a series of material tests have been processed. Moreover, in order to validate the proposed model, the simulations results of inflation and static load scenarios were compared with the experimental results. Both of the control volume and corpuscular particle method methods were used in the numerical simulations of aircraft tire.

The comparisons of the two methods exhibit close agreement with the experimental results. To assess the safety of aircraft tire during the landing scenario, the dynamic simulations were processed with different landing weights and vertical landing speeds. According to the relevant airworthiness regulations and technical documents, the tire pressure, deflection and load have been chosen as the safety criteria. Subsequently, the analysis, results and comments have been discussed in detail.

The validated FE model proposed in present study can be effectively used in tire modeling in static and dynamic problems, and also in the design process of aircraft tire.

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.

Relationship management evolves with dynamic and complex environments of megaprojects. However, studies on the longitudinal measurement of relationship management performance for each stakeholder in dynamic and complex project environments are lacking. The purpose of this research is to propose an NK-network evolution model to evaluate stakeholder performance on relationship management in the development of megaprojects.

The model input includes the stakeholder-associated issues and stakeholders' relational strategies, the co-effects of which determine the internal effects of relationship management in megaprojects. The model processing simulates the stakeholder performance of relationship management under the dynamic and complex nature of megaprojects. The NK model shows the dynamic stakeholder interactions on relationship management, whereas the network model presents the complex stakeholder structures of the relationships between stakeholders and relevant issues. The model output is the evolution graph to reveal the weak stakeholder performance on relationship management in the timeline of the project duration.

The research finding reveals that all stakeholders experience the plunge of stakeholder performance of relationship management at the decision-making moment of the planning stage. Construction, environmental and pressure groups may experience the hardship of relationship management at the start of the construction stage. The government is likely to suffer difficulties in relationship management in the late construction stage. Local industry groups would face challenges in relationship management in the middle of the construction stage and handover stage.

The research provides a useful approach to measuring weak moments of relationship management for each stakeholder in various project phases, considering the dynamic and complex environments of megaprojects. The proposed model extends the current knowledge body on how to make project stakeholder analysis by modelling dynamic and complex environments of megaprojects, with bridging the knowledge domains of evolution modeling techniques and network methods.

We challenge the discontinuity (also called incompatibility) hypothesis of generalized and particularistic trust, suggesting that the two types of trust are incompatible. This view is problematic because if so, it remains unclear, for instance, how communities scoring high in particularistic trust can ever develop further when transferring trust to spheres outside the community is not an option. In this research, we explore the potential permeability of different types of trust in an emerging market context using the case of China.

Using a purposeful sampling technique, we gathered data among Chinese professionals (n = 290) in the Jingjinji Metropolitan Region in Tianjin. We analyzed the data by performing structural equation modeling.

As we identify interdependencies between generalized and particularistic types of trust, our results speak in favor of the continuity hypothesis. We find that the more people trust other people from an outside group (out-group trust), the less they trust quasi-familiar others (in-group trust). Further, in-group trust increases once the environment urges people to engage in informal network (guanxi)-based transactions.

Advancing the common view of China being a typical low-trust society, in which distrust in strangers (outsiders) prevails, we find a recent trend of an increase in general trust, which might lead to increases of out-group and in-group trust alike. Contrary to the wide spread idea that guanxi is declining in the present day, we find guanxi to be persistent.

The purpose of this paper is to analyze the operational decisions of a manufacturer who produces multiple products and the government's selection of cap-and-trade and carbon tax regulations.

This paper explores the production decisions of a multi-product manufacturer under cap-and-trade and carbon tax regulations in a cap-dependent carbon trading price setting and compares carbon emission, the manufacturer's profits and social welfare under the two regulations. Game theory and extreme value theory are used to analyze our models.

First, the authors find that the optimal profit of the manufacturer (the optimal cap) increases and then decreases with the cap (the unit carbon emission of product). Second, if the environmental damage coefficient is moderate, the optimal cap of unit environmental damage coefficient is independent of the product carbon emission or other related product parameters. Ultimately, cap-and-trade regulation always generates more carbon emission than carbon tax regulation. And cap-and-trade regulation (carbon tax regulation) can generate more social welfare if the environmental damage coefficient is low (high), and the social welfare under the two regulations is equal to each other, or otherwise.

This paper contributes the prior literature by considering the inverse relationship of the allocated cap and the carbon trading price and discusses the social welfare under cap-and-trade and carbon tax regulations. Some important and new results are found, which can guide the government's implementation of the two regulations.