Background: Coronavirus disease (COVID-19) has become pandemic. The knowledge, attitudes, and practices (KAP) of the public play a major role in the prevention and control of infectious diseases. The objective of the present study was to evaluate the KAP of the Chinese public and to assess potential influencing factors related to practices.Methods: A cross-sectional online survey was conducted in China in February 2020 via a self-designed questionnaire comprising 33 questions assessing knowledge, attitudes, and practices.Results: For the 2,136 respondents from 30 provinces or municipalities in China, the accurate response rate for the knowledge section was 72.7–99.5%, and the average was 91.2%. In the attitude section, the percentage of positive attitudes (“strongly agree” and “agree”) ranged from 94.7–99.7%, and the average value was 98.0%. The good practices (“always” and “often”) results ranged from 76.1–99.5%, and the average value was 96.8%. The independent samples t-test revealed that gender and ethnic differences had no effect on knowledge, attitude or behaviour (P > 0.05). However, knowledge was associated with age (t = 4.842, p < 0.001), marital status (t=-5.323, p < 0.001), education level (t = 8.441, p < 0.001), occupation (t=-10.858, p < 0.001), and place of residence (t = 7.929, p < 0.001). Similarly, attitude was associated with marital status (t=-2.383, p = 0.017), education level (t = 2.106, p = 0.035), occupation (t=-4.834, p < 0.001), and place of residence (t = 4.242, p < 0.001). The results of multiple linear regression analysis showed that the factors influencing practices were knowledge (t=-3.281, p = 0.001), attitude (t = 18.756, p < 0.001), occupation (t=-3.860, p < 0.001), education level (t = 3.136, p = 0.002), and place of residence (t = 3.257, p = 0.001).Conclusions: The Chinese public exhibited a good level of knowledge of COVID-19, a positive attitude, and high adherence to good practices. COVID-19-related knowledge, attitudes and practices were affected by age, marital status, education level, occupation, and place of residence to varying degrees. In addition, practices were affected by knowledge and attitudes toward COVID-19.

Abstract. Complex function teaching essentially focuses on theoretical studying rather than practical application with respect to the properties of theoretical and abstract complex function course. This paper proposes a combination of mathematical modeling with complex function teaching, discusses a promising method of enhancing complex function teaching.

Adaptive gradient algorithm and its variants, such as RMSProp, Adam, AMSGrad, etc., have been widely used in deep learning. Although these algorithms are faster in the early phase of training, their generalization performance is often not as good as stochastic gradient descent (SGD). Hence, a trade-off method of transforming Adam to SGD after a certain iteration to gain the merits of both algorithms is theoretically and practically significant. To that end, a decreasing scaling transition scheme to achieve a smooth and stable transition from Adam to SGD, which is called DSTAdam. The convergence of the proposed DSTAdam is also proved in an online convex setting. Finally, the effectiveness of the DSTAdam is verified on the different datasets. The implementation is available at: https://github.com/kunzeng/DSTAdam.

Abstract. With the rapid development of urbanization, the improvement of living standard and the conversion of lifestyle, municipal household garbage treatment is becoming a challenging issue. The hierarchical analytical method is used in the present paper to establish a classification model on the waste reduction, so that each factor in the waste reduction classification is quantitatively analyzed, and the weight is determined by the three-demarcation method; On this basis, the key measures for the reduction and classification work are determined and put forward, and the effect of the waste reduction and classification in the next 5 years is predicted by taking Shenzhen city as an example.