Table1_The Impact of Entrepreneurship Perceptions on Entrepreneurial Intention During the COVID-19 Pandemic.pdf

The global pandemic of COVID-19 is a challenge for entrepreneurship education in universities and various organizations. Although positive responses to overcome the challenges of COVID-19 are being made, entrepreneurship strategies and policies might not meet students’ requirements. In order to enrich education management research, the main aim of this study is to provide a conceptual model and examine the relationship between perceptions, perceived positive attitudes on entrepreneurship education, and entrepreneurial intention (EI) during the COVID-19 crisis. The model is tested by using data from universities that are located in Shanghai, P.R. China. The study reveals that 1) perceived social norms and perceived self-efficacy positively influence perceived positive attitudes in entrepreneurship education; 2) there is no relationship between perceived entrepreneurial barriers and perceived positive attitudes in entrepreneurship education; 3) perceived positive attitudes in entrepreneurship education positively influence EI. The findings contribute to university and government policies on the development of entrepreneurial education. The framework of this study provides insight into the influential factors of entrepreneurship education that contribute to theoretical studies in the COVID-19 pandemic.</p

Table_1_The Effect of Psychological Capital and Role Conflict on the Academic Entrepreneurial Intents of Chinese Teachers in Higher Education: A Study Based on the Theory of Planned Behavior.xlsx

Because academic entrepreneurship is an innovation driving force in China’s economy, teachers are key knowledge creators in the process of entrepreneurship. Therefore, it is particularly important to give attention to the individual psychological mechanism factors at play in the process of teachers in higher education academic entrepreneurship. The purpose of this study is to identify individual psychological capital and role conflict issues among university teachers in China. To accomplish this aim, we investigated the emergence of positive academic entrepreneurial intents, continued through the process of academic entrepreneurship, and clarified the impact of psychological capital and role conflict on entrepreneurial intent. Based on the theory of planned behavior, we constructed a research model from the perspective of entrepreneurial intent prior to entrepreneurial action. We established a cohort of teachers in 17 higher education institutions (N = 525) in southern China, with psychological capital and role conflict as the prior independent variables and the teachers’ academic entrepreneurial intent as the dependent variable. Using quantitative analysis, SPSS 22.0, and AMOS 23.0, we conducted reliability and validity tests, correlation analysis, and structural equation models on the collected data. We reached the following conclusions: (1) psychological capital has a positive effect on attitudes toward academic entrepreneurship; (2) psychological capital has a positive effect on perceived behavioral control; (3) role conflict has a negative effect on perceived behavioral control; (4) academic entrepreneurial attitudes have a positive effect on academic entrepreneurial intent; (5) perceived behavioral control has a positive effect on academic entrepreneurial intent; (6) subjective norms have a positive effect on academic entrepreneurial intent. We also provide some suggestions about academic entrepreneurship for university administrators.</p

Summary of ARIMA, SVR and simple statistical model fitting.

Summary of ARIMA, SVR and simple statistical model fitting.</p

Additional file 1: of Relationship between facet tropism and facet joint degeneration in the sub-axial cervical spine

The original data of the gender, age, cervicl level, grading of the facet degeneration and facet orientations. (XLSX 448 kb

Number of bookings per zip code.

*The zip codes are not all listed in the graph because of space limitation. *The zip codes are not continuous, but are discrete values.</p

Travel demand and distance analysis for free-floating car sharing based on deep learning method - Fig 8

Top-left (LSTM with “relu” activation, 3 layers and 50 hidden nodes). Top-right (LSTM with “tanh” activation, 3 layers and 50 hidden nodes). Bottom-left (Simple RNN) Bottom-right (LSTM with “relu” activation, 3 layers with 20 hidden nodes).</p

SVR with ‘linear’ and ‘rbf’ kernel prediction results.

SVR with ‘linear’ and ‘rbf’ kernel prediction results.</p

Number of bookings for unique vehicle IDs.

*The vehicle IDs are not all listed in the graph because of space limitation.</p

Travel demand and distance analysis for free-floating car sharing based on deep learning method - Fig 10

Top-left (LSTM with “relu” activation, 3 layers and 50 hidden nodes). Top-right (Simple RNN). Bottom-left (LSTM with “relu” activation, 3 layers and 30 hidden nodes). Bottom-right (LSTM with “tanh” activation, 3 layers with 50 hidden nodes).</p

Simple recurrent neural network.

Simple recurrent neural network.</p