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The purpose of this research is to investigate how generalisable the public service motivation (PSM) observed in Western society is to China and to examine the effects of public service motivation on job satisfaction.

Exploratory factor analysis and confirmatory factor analysis techniques are applied to survey data of 191 public servants in China to investigate the generalisability of Western PSM. Using hierarchical regression analysis, the paper examines the effects of the dimensions of PSM on job satisfaction.

The results show that the public service motivation observed in the West exists in China, but the generalisability of the construct is limited. Three of the four dimensions of public service motivation (attraction to public policy making, commitment to the public interest, and self‐sacrifice) exist in China, but the fourth dimension (compassion) is unconfirmed.

The paper is the first to examine the generalisability and instrumentality of PSM as observed in Western society to China. The results indicate that the public service motivation observed in the West also exists in China, but that the generalisability is limited. Public service motivation emerges from the results as a positively significant predictor of job satisfaction in the public sector of China. It enhances the applicability and meaningfulness of the concept of public service motivation across political and cultural environments.

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

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.

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.

Purpose: The public health burden caused by tobacco is heavy among first-generation Chinese immigrant men whose home country has significantly higher smoking rates than the United States. The current study is part of a larger effort to pilot an mHealth tobacco cessation intervention using MMS (graphic) mobile phone technologies to target East Asian immigrant populations. Grounded in the Extended Parallel Process Model (EPPM), our specific aims were to determine what message themes, level of graphic intensity, and types of efficacy information are most appropriate and useful for mHealth interventions targeting this population.

Methodology/Approach: A qualitative study utilizing a series of focus groups (k = 5) was conducted with male adult smokers who were born in China and currently reside in the United States. The primary aim of the focus groups was to solicit reactions to a series of preliminary messages developed by the research team. A secondary aim was to gauge receptivity to the use of MMS as a vehicle for smoking cessation intervention. Participants (n = 32) were recruited from local Chinese communities in a large Mid-Atlantic metropolitan area.

Findings: Opinions about different message strategies were mixed. However, participants tended to rate messages more positively when they focused on the impact of smoking on family and loved ones, particularly children. Messages with fear-arousing images were also perceived to be effective at low frequency of exposure, but there were concerns that they may backfire at high exposure. Awareness of and interest in Quitline were low, and concrete quitting tips were perceived as more effective. Participants reported a preference for receiving messages a few times a week, and an MMS message platform was generally preferred to WeChat (a Chinese social media platform).

Implications: Our results suggest that graphic MMS messaging holds promise as an effective intervention method for this population and that EPPM is an appropriate framework to develop, test, and analyze mHealth intervention messages. While messages that focused primarily on impact on children, health, and specific quitting tips were generally found to be more effective, a mix of different types of messages that address a wide range of issues may be most appropriate for this population.

Originality/Value: This study is the first to explore the utility of graphic text messaging as an intervention method to promote smoking cessation among male Chinese immigrants. Findings from the study provide important insights for future intervention work targeting this underserved population.

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.

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.

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.

With the widespread use and development of automobile, much attention has been paid to its security issues. So to ensure the driving safety, the automobile must be equipped with good braking performance. In the process of braking, the friction from friction pair causes continuous wear and tear of the surface of brake lining and increases the gap between break pairs, until the lining is not being used (Belhocinea et al., 2014); thus, it is very important to detect the lining wear rate.

This paper designed the automobile brake friction test wear rate detection system based on Labview.

Through the detect data, we find that the automobile brake lining wear rate detection system has higher detect accuracy, in the process of detection, the brake lining without the defects such as cracks and bulges, which shall effect the normal use, the lining has no remarkable scratch to disk friction surface, can completed meet the requirements of users.

The automobile brake friction test wear rate detecting system adopts the components of USB-9211 DAQ, optoNCDT1700 non-contract high accuracy displacement sensor, in addition the Labview software to complete the functions such as lining wear rate real time detection, data multichannel real time acquisition, display, and storage record, etc., and uses LabSQL to import the detecting data to Microsoft Access database, which can satisfy the demands of various customers. Moreover, the wear rate real time detection can reflect the lining’s wear regulation of different manufacturers and different material and provide a reliable basis for selecting the appropriate lining material and predicting the lining’s lifetime.