Comparison of requirements for clinical study of SARS-CoV-2 antigen test kits in China, the USA and Europe.

Aims: This study was designed to analyze the requirements for clinical trials of SARS-CoV-2 antigen testing to explore the rationality and scientific rigor of clinical trials. Methods: The guidelines for the listing of SARS-CoV-2 antigen tests were compared and the requirements for clinical trials were analyzed to find similarities and differences between China, the USA and Europe. Results: The requirements for clinical trials of SARS-CoV-2 antigen tests in China, the USA and Europe were consistent in terms of methods. However, differences were found in the requirements for protocol design. Conclusion: The differences in clinical trial requirements stem from regulations and the actual conditions across regions, but all clinical trials are designed to obtain valid clinical performance of products.

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.

How Is Alumni Giving Affected by Satisfactory Campus Experience? Analysis of an Industry-Research-Oriented University in China

Alumni giving is an emerging funding source for industry-research-oriented universities in China, which show unstable and limited growth compared to their elite counterparts despite providing their alumni a satisfactory campus experience. Identifying the mechanisms whereby campus experience satisfaction results in steady alumni donation is essential for providing guidance on effective alumni relations practice in the Chinese context. Using structural equation modeling, this quantitative study surveyed 238 alumni of an industry-research-oriented university in China to explore the relationships among campus experience satisfaction, faculty-alumni contacts, trust in foundation, and alumni-giving intention. The results indicate that campus experience satisfaction is a significant predictor of the other three, while also indirectly affecting alumni donation willingness, which includes faculty-alumni contact and trust in foundation. It was also revealed that trust in foundation could be enhanced by strengthening contact between faculty and alumni. Theoretically, this study identifies and reveals the key determinants of increased alumni giving and their interactive mechanisms in the Chinese higher education ecosystem. For sustainability, suggestions for optimizing alumni relation practices are provided to university administrators and policymakers to advance higher education’s contribution to social and economic development.