Entrepreneurship of college students has always been a hot topic in families, schools and society. Massive studies aim to explore entrepreneurial behavior. However, under the condition of the 10% success rate of student entrepreneurship, the adverse impact of COVID-19 and the changed circumstance of domestic entrepreneurship, this exploration aims to study the factors that influence college students’ entrepreneurial behavior choices under the epidemic. First, through the retrieval of relevant literature and theoretical study, the variable factors that affect behavior choices are (...) sorted and summarized. It is assumed that the factors that affect behavior choices are entrepreneurial motivation, entrepreneurial ability, willingness to behave, and entrepreneurial environment. Second, a questionnaire is designed to investigate the choice of entrepreneurial behavior for students who are starting a business or going to start a business. The standard effect values of the survey results are calculated by using structural equation modeling. The results reveal that the effect values of the nine hypothetical results are all in line with the prediction, which prove a positive impact of the four variable factors on the choice of entrepreneurial behavior. The experimental parameters set are as follows. The standardized effect value of Hypothesis 1 is 0.216; that of Hypothesis 2 is 0.221; that of Hypothesis 3 is 0.284; that of Hypothesis 4 is 0.329; that of Hypothesis 5 is 0.247; that of Hypothesis 6 is 0.339; that of Hypothesis 7 is 0.357; that of Hypothesis 8 is 0.336; that of Hypothesis 9 is 0.485. Besides, the entrepreneurial environment has the greatest impact on behavior choice. Therefore, it is believed that the government, society, schools need to strengthen the correct guidance of entrepreneurial students and create a good entrepreneurial environment to cope with economic changes under the epidemic. (shrink)

Acoustic emission technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension (...) was developed. By means of the principle component analysis for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage. (shrink)

This study aims to investigate the role of Chinese English as a foreign language teachers’ aggression and burnout in their professional success. To accomplish this, 362 EFL teachers were invited to respond to three valid measures of the variables. Performing Spearman’s rho correlation tests, negative and significant correlations were found between teacher burnout, teacher aggression, and teacher professional success. Moreover, as the results of regression analyses indicated, both teacher aggression and teacher burnout were found to be the negative predictors of (...) EFL teachers’ professional success. It implies that the higher the amount of teacher aggression and teacher burnout, the less professionally successful a teacher would be. The implications of the results are finally discussed. (shrink)

As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography. Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network is designed to improve the accuracy and optimize the model with applications in sEMG signal (...) movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods. (shrink)

The network-based cooperative information spreading is a widely existing phenomenon in the real world. For instance, the spreading of disease outbreak news and disease prevention information often coexist and interact with each other on the Internet. Promoting the cooperative spreading of information in network-based systems is a subject of great importance in both theoretical and practical perspectives. However, very limited attention has been paid to this specific research area so far. In this study, we propose an effective approach for identifying (...) the influential latent edges which, if added to the original network, can promote the cooperative susceptible-infected-recovered dynamics. To be specific, we first obtain the probabilities of each nodes being in different node states by the message-passing approach. Then, based on the state probabilities of nodes obtained, we come up with an indicator, which incorporates both the information of network topology and the co-SIR dynamics, to measure the influence of each latent edge in promoting the co-SIR dynamics. Thus, the most influential latent edges can be located after ranking all the latent edges according to their quantified influence. We verify the rationality and superiority of the proposed indicator in identifying the influential latent edges of both synthetic and real-world networks by extensive numerical simulations. This study provides an effective approach to identify the influential latent edges for promoting the network-based co-SIR information spreading model and offers inspirations for further research on intervening the cooperative spreading dynamics from the perspective of performing network structural perturbations. (shrink)