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Your search keyword '"Zheng, Xiaogang"' showing total 279 results
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279 results on '"Zheng, Xiaogang"'

33. Mg–Fe Layered Double Hydroxides/Polyacrylonitrile Nanofibers for Solar-Light Induced Peroxymonosulfate Elimination of Tetracycline Hydrochloride.

Author

Peng, Hao, Ye, Beilei, Luo, Meiying, and Zheng, Xiaogang

Subjects
LAYERED double hydroxides, POLYACRYLONITRILES, NANOFIBERS, PEROXYMONOSULFATE, TETRACYCLINE, TETRACYCLINES
Abstract

The photo-induced peroxymonosulfate (photo-PMS) reaction is a promising route to eliminate antibiotics from waste water. To achieve excellent photo-PMS activity in Mg–Fe layered double hydroxides (LDHs) for tetracycline hydrochloride (TCH) degradation under simulative solar-light irradiation, Mg–Fe LDHs-loaded polyacrylonitrile (Mg–Fe/PAN) nanofibers were in-situ prepared via the hydrothermal route. For comparison to the photocatalysis and photo-PMS process, the Mg–Fe/PAN-assisted photo-PMS process exhibited a better elimination activity for TCH elimination. In addition, the photo-PMS activities of Mg–Fe/PAN composites were greatly affected by Mg–Fe LDHs content, TCH concentration, pH, and inorganic salts. Among these Mg–Fe/PAN composites, the optimal MgFe2/PAN with a Mg/Fe molar ratio of 1:2 and a nominal Mg–Fe LDHs content of 2.0 wt. % removed 81.31% TCH solution of 80 mg L−1 TCH within 120 min. This enhanced photo-PMS capacity of MgFe2/PAN was ascribed to the abundant active sites formed by functional groups and oxygen defects for efficient TCH species adsorption and photon capturing, and the tight interface between Mg–Fe LDHs nanoparticles and PAN nanofibers for the rapid separation and transfer of photoinduced e−/h+ pairs. SO4•− and •O2− radicals were vital for the MgFe2/PAN-assisted photo-PMS reaction. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning.

Author

Zhong, Xin, Du, Fujia, Hu, Yi, Hou, Xu, Zhu, Zonghong, Zheng, Xiaogang, Huang, Kang, Ren, Zhimin, and Hou, Yonghui

Subjects
AUTOMATIC classification, IMAGE recognition (Computer vision), MACHINE learning, DENSITY of stars, INSPECTION & review, DAYLIGHT, GEOGRAPHIC names, IMAGE registration
Abstract

Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. Visual inspection is currently the primary approach for analyzing cloud distribution at ground-based astronomical sites. However, the main disadvantages of manual observation methods are human subjectivity, heavy workloads, and poor real-time performance. Therefore, a real-time automatic cloud image classification method is desperately needed. This paper presents a novel cloud identification method named the PSO+XGBoost model, which combines eXtreme Gradient Boosting (XGBoost) with particle-swarm optimization (PSO). The entire cloud image is divided into 37 sub-regions to identify the distribution of the clouds more precisely. Nineteen features, including the sky background, star density, lighting conditions, and subregion grayscale values, are extracted. The experimental results have shown that the overall classification accuracy is 96.91%, and our model can outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements. [ABSTRACT FROM AUTHOR]

Published
2024
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