Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data.

Accurate PM2.5 concentrations predicting is critical for public health and wellness as well as pollution control. However, traditional methods are difficult to accurately predict PM2.5. An adaptive model coupled with artificial neural network (ANN) and wavelet analysis (WANN) is utilized to predict daily PM2.5 concentrations with remote sensing and surface observation data. The four evaluation metrics, namely Pearson correlation coefficient (R), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), are utilized to evaluate the performances of the artificial neural network (ANN) and WANN methods. From the predicting results, The WANN model has a higher R (R = 0.9990) during the testing period compared with R (R = 0.6844) based on the ANN model. Similarly, the WANN model has a lower MAPE (3.6988%), RMSE (1.0145 µg/m3), MAE (1.3864 µg/m3), compared with MAPE (80.0086%), RMSE (16.5838 µg/m3), MAE (12.2420 µg/m3) of the ANN. In addition, comparing the outcomes of the proposed WANN method with the ANN method, it was observed that the error during the training and verification period has decreased significantly. Furthermore, the statistical methods are used to analyze WANN and ANN, showing that WANN has higher training accuracy and better stability. Therefore, it is feasible to establish WANN to predict PM2.5 concentrations (1 day in advance) by using remote sensing and surface observation data.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors.

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China.

Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenarios.

Climate change affects air quality and people's health. Therefore, accurate prediction of future climate change is of great significance for human beings to better adapt and mitigate climate change. Using the projection simulation dataset of the CMIP6 multi-model ensemble, the future climate change in the Sahara region under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) is analyzed. The results show that annual and seasonal average surface air temperature in the Sahara region will continue to rise throughout the twenty-first century relative to the baseline period 1995-2014 if greenhouse gas (GHG) concentrations continue increasing. Under the four SSPs scenarios, the warming in the Sahara region will be more pronounced than in the whole world through the twenty-first century. The annual maximum temperature (TX), the annual minimum temperature (TN), the annual count of days with maximum temperature above 35 °C (TX 35), and the annual count of days with maximum temperature above 40 °C (TX 40) in the Sahara region will continue to increase until the end of the twenty-first century under the four scenarios. The results of climate change prediction can provide scientific reference for climate policy-making.

Percolation thresholds of randomly rotating patchy particles on Archimedean lattices.

We study the percolation of randomly rotating patchy particles on 11 Archimedean lattices in two dimensions. Each vertex of the lattice is occupied by a particle, and in each model the patch size and number are monodisperse. When there are more than one patches on the surface of a particle, they are symmetrically decorated. As the proportion χ of the particle surface covered by the patches increases, the clusters connected by the patches grow and the system percolates at the threshold χ_{c}. We combine Monte Carlo simulations and the critical polynomial method to give precise estimates of χ_{c} for disks with one to six patches and spheres with one to two patches on the 11 lattices. For one-patch particles, we find that the order of χ_{c} values for particles on different lattices is the same as that of threshold values p_{c} for site percolation on these lattices, which implies that χ_{c} for one-patch particles mainly depends on the geometry of lattices. For particles with more patches, symmetry becomes very important in determining χ_{c}. With the estimates of χ_{c} for disks with one to six patches, using analyses related to symmetry, we are able to give precise values of χ_{c} for disks with an arbitrary number of patches on all 11 lattices. The following rules are found for patchy disks on each of these lattices: (1) as the number of patches n increases, values of χ_{c} repeat in a periodic way, with the period n_{0} determined by the symmetry of the lattice; (2) when mod(n,n_{0})=0, the minimum threshold value χ_{min} appears, and the model is equivalent to site percolation with χ_{min}=p_{c}; and (3) disks with mod(n,n_{0})=m and n_{0}-m (m

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence.

The outbreak of coronavirus disease 2019 (COVID-19) has seriously affected the environment, ecology, economy, society, and human health. With the global epidemic dynamics becoming more and more serious, the prediction and analysis of the confirmed cases and deaths of COVID-19 has become an important task. We develop an artificial neural network (ANN) for modeling of the confirmed cases and deaths of COVID-19. The confirmed cases and deaths data are collected from January 20 to November 11, 2020 by the World Health Organization (WHO). By introducing root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE), statistical indicators of the prediction model are verified and evaluated. The size of training and test confirmed cases and death base employed in the model is optimized. The best simulating performance with RMSE, R, and MAE is realized using the 7 past days' cases as input variables in the training and test dataset. And the estimated R are 0.9948 and 0.9683, respectively. Compared with different algorithms, experimental simulation shows that trainbr algorithm has better performance than other algorithms in reproducing the amount of the confirmed cases and deaths. This study shows that the ANN model is suitable for predicting the confirmed cases and deaths of COVID-19 in the future. Using the ANN model, we also predict the confirmed cases and deaths of COVID-19 from June 5, 2020 to November 11, 2020. During the predicting period, the R, RMSE, and MAE for new infected confirmed cases of COVID-19 are 0.9848, 17,554, and 12,229, respectively; the R, RMSE, and MAE for new confirmed deaths of COVID-19 are 0.8593, 631.8, and 463.7, respectively. The predicted confirmed cases and deaths of COVID-19 are very close to the actual confirmed cases and deaths. The results show that continuous and strict control measures should be taken to prevent the further spread of the epidemic.

Organoid culture system for patient-derived lung metastatic osteosarcoma.

Osteosarcoma (OS) is the most common primary bone malignancy with high rates of recurrence and metastasis. OS often spreads to lungs, an optimized model for studying lung metastatic OS cells may help develop potential therapies for patients with lung metastasis. Here we firstly report an organoid culture system for lung metastatic OS tissues. We provided a fully described formula that was required for establishing lung metastatic OS organoids (OSOs). Using this protocol, the lung OSOs were able to be maintained and serially propagated for at least six months; the OSOs can also be generated from cryopreserved patient samples without damaging the morphology. The patient-derived lung OSOs retained the cellular morphology and expression of OS markers (Vimentin and Sox9) that recapitulate the histological features of the human OS. The microenvironment of primary lung metastatic OSOs preserved a similar T cell distribution with the human lung OS lesions; this provided a possible condition to explore how OS cells may react to immunotherapy. OSOs established from this protocol can be further utilized for studying various aspects of OS biology (e.g., tumorigenesis and drug screen/discovery) for precision medicine.

Epidemiological analysis of primary liver cancer in the early 21st century in Guangxi province of China.

BACKGROUND AND OBJECTIVE: In Guangxi province, from 1970s to 1990s, the mortality of primary liver cancer (PLC) ranked the first among a variety of malignant tumors. Investigating the epidemiological characteristics of PLC is very important for developing reasonable and effective treatment strategy, allocating health resources rationally, and evaluating the quality of PLC prevention and control. This study was to analyze the mortality and epidemiological characteristics of PLC in Guangxi province between 2004 and 2005. METHODS: Multi stage stratified cluster random sampling method was used to select 9 counties (cities or urban areas) as sample points. The residents' death causes between 2004 and 2005 were analyzed, and the epidemiological characteristics of PLC were investigated. RESULTS: In the period of 2004-2005, the crude mortality of PLC was 34.39/100,000 in Guangxi province population (55.30/100,000 in men and 13.21/100,000 in women). The national population standardized mortality in 1964 was 22.17/100,000. The man to woman ratio of mortality was 4.19:1. PLC ranked as the first death cause among a variety of malignant tumors, and PLC related death accounted for 30.70% of all tumor related death cases. Age specific mortality of PLC was increased with age, rising significantly from 30 year old (from 25 year old in men and from 40 year old in women), and reached a peak at 75 year old. CONCLUSIONS: The mortality of PLC shows a decreasing trend in Guangxi province in the early 21st century, and the starting age of PLC death peak postpones about 10 years than that in 1990s. It shows that the comprehensive prevention and control measures of PLC implemented in Guangxi province are fruitful. However, the PLC mortality in Guangxi province is still significantly higher than the national average level, and it still ranks as the first death cause in a variety of malignant tumors in Guangxi province. PLC mainly occurs in middle aged and elderly people. The prevention and treatment research of PLC still has a long way to go.