A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

In the era of digital economy, the impact of innovation resources on high-quality economic growth has become increasingly prominent. There are many researches on the influencing factors of innovation performance. The purpose of this study is to explore the factors that affect the innovation performance of high-end manufacturing clusters in China based on the dual perspectives of digital economy and innovation network. A total of 194 valid questionnaires were collected. And structural equation modeling has been used to test the proposed research models and hypotheses. The results indicated that, the higher the centrality of the innovation network, the more the cluster enterprises can play the centrality advantage, which has a significant positive impact on the innovation performance of the cluster. Similarly, both the strength and density of innovation networks also impacted on cluster innovation performance, but to a lesser extent. We also found that the digital empowerment derived from the digital economy can get rid of the limitations caused by spatial distance and lead to the improvement of resource utilization, which plays a positive moderating role between innovation network and innovation capacity. Implications for digital economy and innovation networks to improve the quality of innovation performance are provided.

Employee direct involvement and indirect involvement have been identified as essential forms of an enterprise’s democratic management in the digital economy. Research on the complementary effects of direct and indirect involvement is still in a blank state in China, which limits the external validity and accumulation of employee participation theory. The present study aimed to investigate the complementary effects of employee direct involvement and indirect involvement on the firm’s financial performance. Although previous research suggests that the influence of employee direct or indirect involvement on corporate financial performance has been examined separately, it is unclear whether the association between employee direct involvement and indirect involvement is complementary or conflictual. Based on strategic human resource management theory, we semantically encode 2,680 corporate social responsibility reports and the annual reports of 268 state-owned listed enterprises published from 2014 to 2018 via content analysis method, and the economic effects of employee direct involvement and indirect involvement were concurrently measured. We use configuration theory to explore the complementary effects between employee direct involvement and indirect involvement. Our results reveal that (1) employee involvement in Chinese enterprises was unbalanced, (2) both employee direct involvement and indirect involvement were positively related to enterprise’s financial performance, and (3) there is a complementary effect between the two forms of employee involvement. Theoretical and practical implications of these findings are discussed.

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