Recycling solar-grade silicon from end-of-life photovoltaic modules by Al-Si solvent refining.

The development of the solar market has been fast in the past decades, and the number of photovoltaic module installations is large. The photovoltaic modules have a lifetime of about 25 years and need recovery after that. The aluminium-back surface field (Al-BSF) module is the first kind of large-scale installed module and will come to its end of life in the next few years. The recycling of silicon material in the Al-BSF module is investigated in this work. The components of the module are separated, and the silicon material in the module is collected and then purified by (aluminium-silicon) Al-Si solvent refining for reuse. It is found that Al-Si solvent refining removed key impurity elements, namely boron and phosphorus, in the collected silicon. Kinetics has a great effect on boron and phosphorus removal, and boron and phosphorus contents in purified silicon decrease with decreasing cooling rate. The boron and phosphorus contents in silicon are lowered to 0.28 and 0.03 ppmw, respectively, after two times of Al-Si solvent refining with the cooling rate of 5.55 * 10-4 K second-1, and it meets the requirement of solar-grade silicon.

Virus diversity, wildlife-domestic animal circulation and potential zoonotic viruses of small mammals, pangolins and zoo animals.

Wildlife is reservoir of emerging viruses. Here we identified 27 families of mammalian viruses from 1981 wild animals and 194 zoo animals collected from south China between 2015 and 2022, isolated and characterized the pathogenicity of eight viruses. Bats harbor high diversity of coronaviruses, picornaviruses and astroviruses, and a potentially novel genus of Bornaviridae. In addition to the reported SARSr-CoV-2 and HKU4-CoV-like viruses, picornavirus and respiroviruses also likely circulate between bats and pangolins. Pikas harbor a new clade of Embecovirus and a new genus of arenaviruses. Further, the potential cross-species transmission of RNA viruses (paramyxovirus and astrovirus) and DNA viruses (pseudorabies virus, porcine circovirus 2, porcine circovirus 3 and parvovirus) between wildlife and domestic animals was identified, complicating wildlife protection and the prevention and control of these diseases in domestic animals. This study provides a nuanced view of the frequency of host-jumping events, as well as assessments of zoonotic risk.

[Advances of miRNA-mediated regulatory roles in plant-microbe interaction].

There are many bidirectional communication and crosstalk between microbes and host plants. The plant-pathogen interaction directly affects the survival of host plants, while the interaction between plants and their probiotics benefits both. Plant miRNA responds quickly to pathogenic or beneficial microbes when they enter the plant tissues, while microbes also produce miRNA-like RNA (milRNA) to affect plant health. These means miRNA or milRNA is an important fast-responding molecular mediator in plant-microbe interactions, and these internal mechanisms have been better understood in recent years. This review summarized the regulatory roles of miRNA in plant-pathogens and plant-probiotics interaction. The regulatory role of miRNA in disease resistance of host plants during plant-pathogens interaction, and the regulatory role of miRNA in promoting host growth and development during plant-probiotics interaction, as well as the cross-kingdom regulatory role of milRNA in host plants, were discussed in-depth.

A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China.

In spite of the state-of-the-art performances of machine learning in the PM2.5 estimation, the high-value PM2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM2.5 with R2 of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM2.5 concentrations, the spatial evolution of PM2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM2.5 (PM2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM2.5 (PM2.5_D) were CO, AOD and NO2. This study not only provided an effective solution to the PM2.5 underestimation and AOD missing problems in the PM2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM2.5 and some spatiotemporal variables.

The relationship between social support and Internet addiction among Chinese college freshmen: A mediated moderation model.

Purpose: Internet addiction has become a worldwide mental health problem, and this problem is particularly prominent in China. Although current studies have shown that social support is closely related to Internet addiction, the mechanism of the relationship between the two is not clear at present. This study aimed to find out the influencing factors and the mechanism of Internet addiction among college freshmen, and to form scientific prevention and intervention plan on this basis. Method: This study adopts the cluster sampling method to select 322 college freshmen in a typical postsecondary school in Shandong Province, using Chinese Internet Addiction Scale (CIAS), Social Support Rating Scale (SSRS), and Network-related Maladaptive Cognition Scale (NRMCS) to investigate the relationship between social support, network-related maladaptive cognition, gender, and the degree of Internet addiction. Results: The findings of this study are as follows: (1) After controlling age and family location, social support had a significant negative predictive effect on Internet addiction; (2) Gender acted as a moderator between the relationship of social support and Internet addiction; and (3) Additionally, the moderating effect of gender was completely mediated by network-related maladaptive cognition. Conclusion: There is a mediated moderating effect between social support and Internet addiction. That is, gender plays a moderating role between social support and Internet addiction, and this moderating effect is mediated by network maladaptive cognition.

Facial image inpainting for big data using an effective attention mechanism and a convolutional neural network.

Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output image artifacts or fuzzy textures. Therefore, it is of practical significance to study how to effectively restore an incomplete facial image. In this study, we proposed a facial image inpainting method using a multistage generative adversarial network (GAN) and the global attention mechanism (GAM). For the overall network structure, we used the GAN as the main body, then we established skip connections to optimize the network structure, and used the encoder-decoder structure to better capture the semantic information of the missing part of a facial image. A local refinement network has been proposed to enhance the local restoration effect and to weaken the influence of unsatisfactory results. Moreover, GAM is added to the network to magnify the interactive features of the global dimension while reducing information dispersion, which is more suitable for restoring human facial information. Comparative experiments on CelebA and CelebA-HQ big datasets show that the proposed method generates realistic inpainting results in both regular and irregular masks and achieves peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as well as other evaluation indicators that illustrate the performance and efficiency of the proposed model.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis.

PM2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM2.5 from 2015 to 2018 in the YRDUA. Results showed that PM2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM2.5 and CO was the most consistent, and the correlation between PM2.5 and SO2/NO2 improved with the increase of time scale, while the correlation between PM2.5 and O3 was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m²) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m²) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.