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43 results on '"Zhang, Boyang"'

25. A Robust Protocol for Decellularized Human Lung Bioink Generation Amenable to 2D and 3D Lung Cell Culture

Author

Dabaghi, Mohammadhossein, primary, Saraei, Neda, additional, Carpio, Mabel Barreiro, additional, Nanduri, Vibudha, additional, Ungureanu, Julia, additional, Babi, Mouhanad, additional, Chandiramohan, Abiram, additional, Noble, Alexander, additional, Revill, Spencer D., additional, Zhang, Boyang, additional, Ask, Kjetil, additional, Kolb, Martin, additional, Shargall, Yaron, additional, Moran-Mirabal, Jose, additional, and Hirota, Jeremy Alexander, additional

Published
2021
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27. CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

Author

Chong Dongfeng, Wanzhang Wang, Zhang Boyang, and Hong Ji

Subjects
panicle moisture content (pmc), Physics and Astronomy (miscellaneous), Logarithm, Correlation coefficient, Mean squared error, wheat moisture content (wmc), cars algorithm, General Mathematics, 010103 numerical & computational mathematics, 01 natural sciences, Approximation error, wheat, Partial least squares regression, Computer Science (miscellaneous), Range (statistics), 0101 mathematics, Mathematics, lcsh:Mathematics, spectral detection, 0402 animal and dairy science, Univariate, Regression analysis, 04 agricultural and veterinary sciences, lcsh:QA1-939, 040201 dairy & animal science, Chemistry (miscellaneous), Algorithm
Abstract

To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350&ndash, 2500 nm, bands of 728&ndash, 907 nm, 1407&ndash, 1809 nm, and 1940&ndash, 2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

Published
2020

38. Preferable Adsorption of Nitrogen and Phosphorus from Agricultural Wastewater Using Thermally Modified Zeolite–Diatomite Composite Adsorbent

Author

Zheng Xiaotong, Yucheng An, Wang Xiaoling, Songmin Li, Zhang Boyang, and Liu Yuyang

Subjects
sem mapping, lcsh:Hydraulic engineering, Materials science, Scanning electron microscope, Geography, Planning and Development, Composite number, chemistry.chemical_element, 02 engineering and technology, 010501 environmental sciences, Aquatic Science, 01 natural sciences, Biochemistry, grey image recognition, lcsh:Water supply for domestic and industrial purposes, Adsorption, diatomite, lcsh:TC1-978, composite adsorbent, Fourier transform infrared spectroscopy, 0105 earth and related environmental sciences, Water Science and Technology, Pollutant, lcsh:TD201-500, Phosphorus, 021001 nanoscience & nanotechnology, Nitrogen, gradient recognition, Chemical engineering, chemistry, Wastewater, thermally modified zeolite, 0210 nano-technology
Abstract

Nitrogen and phosphorus adsorbents are widely used to mitigate agricultural non-point source pollution. However, research on adsorbents mainly involves studying chemical adsorption properties, and analyzes of the effects of adsorbent on pollutant removal has not considered the surface morphology of the adsorbent or the surface distribution of pollutants. In this study, we focus on the surface morphology of the adsorbent and the surface distribution of contaminants while examining chemical adsorption properties. The crystal composition of the adsorbent was evaluated by x-ray diffraction (XRD) characterization. Kinetic adsorption data and adsorption isotherms demonstrated that thermally modified zeolite exhibits better nitrogen adsorption. The optimal removal of nitrogen and phosphorus by thermally modified zeolite and diatomite occurred at a 3:2 ratio, reaching a removal rate of 92.07% and 84.61%, respectively. The potential adsorption mechanism of a composite adsorbent for nitrogen and phosphorus capture was investigated by Fourier transform infrared spectroscopy. Scanning electron microscopy mapping, grey image recognition, and gradient recognition confirmed a relationship between the surface morphology of the adsorbent and the distribution of surface pollutants. The larger the surface of the gradient, the more uneven it is, the more nitrogen and phosphorus sites are adsorbed on the surface, and the more nitrogen and phosphorus are adsorbed. These results suggest that thermally modified zeolite/diatomite can serve as a promising adsorbent for nitrogen and phosphorus removal in practical applications.

Published
2019

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