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https://www.selleckchem.com/products/abt-199.html Directly discharging low-concentration rare-earth wastewater not only wastes rare-earth resources but also pollutes the environment. In this study, the biosorption behavior of Serratia marcescens for Eu(III) was studied with emphasis on the optimization of adsorption conditions, adsorption kinetics, and adsorption isotherm. It was shown that the maximum adsorption capacity of Serratia marcescens reached 115.36 mg·g-1 under an optimal condition, indicating the good adsorption capability of Serratia marcescens for Eu(III). The adsorption kinetics and adsorption isotherm analysis showed that the adsorption process conforms to the pseudo-second-order kinetic model and Langmuir adsorption isotherm, indicating that the adsorption of Eu(III) by Serratia marcescens is a monolayer chemical adsorption process. In addition, the adsorption mechanism was investigated by using characterizations of zeta potential, scanning electron microscope-energy dispersive spectrometer (SEM-EDS), Fourier transform infrared (FT-IR), and X-ray photoelectron spectroscopy (XPS) analyses. It was revealed that the adsorption of Eu(III) by S. marcescens is a combination of electrostatic attraction, ions exchange and coordination. These findings indicate that S. marcescens can be used as a potential biosorbent to recover rare earth elements from rare earth wastewater.Bronchopneumonia is the most common infectious disease in children, and it seriously endangers children's health. In this paper, a deep neural network combining long short-term memory (LSTM) layers and fully connected layers was proposed to predict the prevalence of bronchopneumonia in children in Chengdu based on environmental factors and previous prevalence rates. The mean square error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (R) were used to detect the performance of the deep learning model. The values of MSE, MAE, and R in the test dataset are 0.0051, 0.053

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