Following the 2019 coronavirus disease (COVID-19) outbreak in China, undergraduate students may experience psychological changes. During emergency circumstances, social support is an important factor influencing the mental health condition among undergraduate students in Shaanxi province. This study aims to find the factors associated with mental health symptoms of depression, anxiety, and stress among undergraduate students in Shaanxi province during the COVID-19 pandemic in China. A cross-sectional study was conducted from Feb 23 to Mar 7, 2020. A total of 1278 undergraduate students from the universities located in Shaanxi province participated in this study. The mental health symptoms were measured by 12-item Perceived Social Support Scale (PSSS) and Depression Anxiety Stress Scale (DASS-21) instruments. This survey showed that females receive more social support compared to males (t = -5.046, P<0.001); males have higher-level depression symptoms (t = 5.624, P<0.001); males have higher-level anxiety symptoms (t = 6.332, P<0.001), males have higher-level stress symptoms (t = 5.58, P<0.001). This study also found participants who have low social support was negatively correlated with mental health symptoms. In Conclusion, Males and low social support were associated with having the higher level of depression, anxiety, and stress symptoms among undergraduate students in Shaanxi province during the COVID-19 pandemic in China. Therefore, it is suggested that people should supply more social support for undergraduate students in Shaanxi province during COVID-19 pandemic.

A piezoelectric crystal biosensor method has been established for detection of listeria monocytogenes (LM) by gold nanoparticles (GNPs) signal amplification. Based on specific recognition of single-stranded DNA (ssDNA) hybridization, a sandwich format of ssDNA probes=complimentary ssDNA probes modified by GNPs=ssDNA of LM was fabricated. This novel strategy allows the detection limit of LM as low as 10 cfu=mL. The biosensor showed friendly biocompatibility, good specificity, acceptable stability, which provided a potential alternative tool for detection of LM in real samples. Furthermore, the developed method could be easily extended to other gene analysis schemes.

Stock market has gradually become an absolutely necessary part of financial market in China. The trend analysis and forecasting of stock prices become key topics in investment and security, which have great theoretical significance and application value. In this paper, the wavelet modulus maxima method is proposed for the abnormal detection of the stock market. The abnormal points detected by wavelet modulus maxima are replaced by the new interpolation points which will be used as an important index of Kalman algorithm to predict stock. The experimental results show that the proposed method can predict the stock data with higher credibility than Kalman algorithm. Therefore, the proposed method can reduce the investment risk and plays an important role in the economic development and financial building.

The applications of flow chemistry (continuous flow reactions) in the synthesis of pharmaceuticals and fine chemicals require more advanced optimization algorithms to guide laboratory-scale and industry-scale optimization. Although several Bayesian Optimization (BO) frameworks have been developed, they are rarely equipped with state-of-the-art noise-handling acquisition functions and have not been benchmarked by multiple real-world continuous flow kinetic models. In this study, we developed FlowBO for flow chemistry, equipped with the recently-developed MOO algorithm qNEHVI that can better handle experimental noise and make parallel recommendations. Also, five kinetic models built from experimental results, including four series reactions, were used as benchmarks for FlowBO and two other recognized BO frameworks. The results show that FlowBO outperforms in all four series reaction cases with optimization results >99.9% for conversion and selectivity. At the same time, FlowBO offers a range of optimum advantages with a wide choice of temperature, residence time, and reactant concentration, facilitating process optimization for subsequent steps (i.e. separation).

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