Epidemic threshold of a COVID-19 model with gaussian white noise and semi-Markov switching.

In this study, we investigate the COVID-19 propagation dynamics using a stochastic SIQR model with Gaussian white noise and semi-Markovian switching, focusing on the impacts of Gaussian white noise and semi-Markovian switching on the propagation dynamics of COVID-19. It is suggested that the fate of COVID-19 is entirely determined by the basic reproduction number R0, under mild extra conditions. By making sensitivity analysis on R0, we found that the effect of quarantine rate on R0 was more significant compared to transmission rate. Our results demonstrate that: (i) The presence of Gaussian white noise, while reducing the basic reproduction number R0 of COVID-19, also poses more challenges for the prediction and control of COVID-19 propagation. (ii) The conditional holding time distribution has a significant effect on the kinetics of COVID-19. (iii) The semi-Markov switching and Gaussian white noise can support irregular recurrence of COVID-19 outbreaks.

How does green credit reduce carbon emissions? Dynamic spatial interactions and regional disparities.

Green credit is an important green financial policy tool to promote green development. However, research is needed to explore how green credit reduces carbon emissions, especially with respect to its dynamic spatial interactions and regional disparities. Based on a theoretical analysis, this paper empirically tests the carbon emission reduction effect of green credit and its three mechanisms by combining a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, dynamic spatial Durbin model (SDM), and the mediation model, including their dynamic spatial interactions and regional disparities. The study concludes that green credit can reduce carbon emission intensity based on strong spatio-temporal interactions in China. Green credit mainly reduces carbon emission intensity through scale and technology mechanisms with different spatio-temporal interactions. The tertiary industry in China does not currently have completely clean production; as such, the upgrading of the industrial structure as stimulated by green credit in the long term cannot yet effectively reduce carbon emissions. In addition, the carbon emission reduction effect of green credit and its three mechanisms have different levels of performance and dynamic spatial interactions in different regions of China. Finally, targeted policy recommendations are proposed to apply green credit to effectively reduce the carbon emission intensity.

Comparative Study of Physical Education Teaching in Middle Schools at Home and Abroad Using Clustering Algorithm.

Physical education in middle school is very important for teenagers, so it is also crucial to understand the differences between PET (Physical Education Teaching) systems in middle schools at home and abroad. The frontier and hotspot of PET research in middle schools at home and abroad are examined in this paper using citation analysis, information visualization, and cluster analysis, as well as CiteSpace software. The findings show that PET method research in China is qualitative, whereas PET method research in middle schools around the world is quantitative evaluation and empirical research. Domestic research hotspots focus on classroom instructional design, whereas foreign countries focus on load identification theory's application in instructional design. Frontier research in the United States is dispersed and covers a wide range of topics, whereas research in other countries focuses on cognitive load theory. The classification time of this improved algorithm is reduced by 190.97 seconds when compared to the traditional KNN algorithm, and the total time is increased by more than 50%. According to the findings, nonsports or nonsports influencing factors should be given more consideration in the study of adolescent physical fitness decline in China.

[Estimation of soil's heavy metal concentrations (As, Cd and Zn) in Wansheng mining area with geochemistry and field spectroscopy].

In the present paper, Chongqing Wansheng mining area was selected as the study area, and the ASD FieldSpec Pro III portable spectroradiometer (350 to 2,500 nm) was used as the spectral measurement instrument to collect the reflectance spectra of 171 soil samples and 123 soil samples in March and in August 2012 respectively. In order to create the retrieval model to retrieve soil heavy metal concentration, 40 among the 123 soil samples in August 2012 were collected to do chemical analysis. Then, the heavy metals' concentration and the reflectance of the 40 soil samples were analyzed together. The results show that the ratio of the reflectance at 2,320 and 1,755 nm in the nearinfrared range, the ratio of the reflectance on 2,260 and 2,210 nm in the nearinfrared range, and the ratio of the reflectance at 480 and 1,920 nm in the visible and near infrared range has a significant correlation with heavy metal concentration of As, Cd and Zn respectively, so the spectral absorption feature parameters (SAFP) for retrieving soil heavy metal concentration of As, Cd and Zn from soil reflectance was created. Thus, the soil heavy metal concentration of As, Cd and Zn of the soil samples can be retrieved with the reflectance spectra. Then the distributions of the soil heavy metal concentration of As, Cd and Zn were obtained with the interpolation method in study area in March and in August 2012 respectively.

Hydrothermal synthesis, crystal structure, conductivity, and thermal decomposition of [Cu(4,4'-bipy)(H2O)(Mo3O10)].H2O.

The hydrothermal reaction of (NH(4))(6)Mo(7)O(24).4H(2)O, CuCl(2).2H(2)O, and 4,4'-bipyridine yields bipyridine-ligated copper-trimolybdate monohydrate [Cu(4,4'-bipy)(H(2)O)(Mo(3)O(10))].H(2)O in the monoclinic system with space group of C(2/c) and cell parameters of a = 15.335(2) A, b = 15.535(2) A, c = 15.106(2) A, beta = 101.162(2) degrees, V = 3530.7(9) A(3), and Z = 8. Its structure consists of one-dimensional infinite ([Mo3O10]2-)( infinity ) chains linked through [Cu2(H2O)2(4,4'-bipy)] units. The Mo-O chain contains distorted [MoO(6)] octahedra connected through corner-sharing oxygen atoms into infinite chains along the c direction and each chain is located in the channel formed by four adjacent crossing chains of [Cu(4,4'-bipy)(H2O)](n)(2n+). The crystal shows weak conductivity through Mo-O chain along the c direction and insulating property along either a or b direction. Furthermore, a crystalline bimetallic oxide, CuMo3O10, forms when the title compound undergoes thermal treatment in N(2) atmosphere after the complete removal of the ligands.