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Your search keyword '"Xiao, Xuemei"' showing total 157 results
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157 results on '"Xiao, Xuemei"'

7. Connectivity Reliability of Compound Rail Transit Network: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration in China

Author

Xiao, Xuemei, Wang, Yanhui, Zhou, Zehong, Zhang, Chenchen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yadav, Sanjay, editor, Kumar, Rahul, editor, Zainuddin, Hidayat, editor, and Deng, Lvxiang, editor

Published
2023
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18. A Missing Traffic Data Imputation Method Based on a Diffusion Convolutional Neural Network–Generative Adversarial Network.

Author

Zhang, Chenchen, Zhou, Lei, Xiao, Xuemei, and Xu, Dongwei

Subjects
MISSING data (Statistics), INTELLIGENT transportation systems, VEHICLE detectors, GRAPH algorithms, EXPECTATION-maximization algorithms, MULTIPLE imputation (Statistics)
Abstract

Traffic state data are key to the proper operation of intelligent transportation systems (ITS). However, traffic detectors often receive environmental factors that cause missing values in the collected traffic state data. Therefore, aiming at the above problem, a method for imputing missing traffic state data based on a Diffusion Convolutional Neural Network–Generative Adversarial Network (DCNN-GAN) is proposed in this paper. The proposed method uses a graph embedding algorithm to construct a road network structure based on spatial correlation instead of the original road network structure; through the use of a GAN for confrontation training, it is possible to generate missing traffic state data based on the known data of the road network. In the generator, the spatiotemporal features of the reconstructed road network are extracted by the DCNN to realize the imputation. Two real traffic datasets were used to verify the effectiveness of this method, with the results of the proposed model proving better than those of the other models used for comparison. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Discovery of Graphene‐Water Membrane Structure: Toward High‐Quality Graphene Process

Author

Okmi, Aisha, primary, Xiao, Xuemei, additional, Zhang, Yue, additional, He, Rui, additional, Olunloyo, Olugbenga, additional, Harris, Sumner B., additional, Jabegu, Tara, additional, Li, Ningxin, additional, Maraba, Diren, additional, Sherif, Yasmeen, additional, Dyck, Ondrej, additional, Vlassiouk, Ivan, additional, Xiao, Kai, additional, Dong, Pei, additional, Xu, Baoxing, additional, and Lei, Sidong, additional

Published
2022
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35. Exogenous Application of 5-Aminolevulinic Acid Promotes Coloration and Improves the Quality of Tomato Fruit by Regulating Carotenoid Metabolism

Author

Wang, Junwen, primary, Zhang, Jing, additional, Li, Jing, additional, Dawuda, Mohammed Mujitaba, additional, Ali, Basharat, additional, Wu, Yue, additional, Yu, Jihua, additional, Tang, Zhongqi, additional, Lyu, Jian, additional, Xiao, Xuemei, additional, Hu, Linli, additional, and Xie, Jianming, additional

Published
2021
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47. 5-Aminolevulinic Acid Improves Morphogenesis and Na+ Subcellular Distribution in the Apical Cells of Cucumis sativus L. Under Salinity Stress.

Author

Wu, Yue, Liu, Na, Hu, Linli, Liao, Weibiao, Tang, Zhongqi, Xiao, Xuemei, Lyu, Jian, Xie, Jianming, Calderón-Urrea, Alejandro, and Yu, Jihua

Subjects
CUCUMBERS, SALINITY, SOIL salinity, PLANT regulators, PLANT growth, MORPHOGENESIS
Abstract

Soil salinity causes damage to plants and a reduction in output. A natural plant growth regulator, 5-aminolevulinic acid (ALA), has been shown to promote plant growth under abiotic stress conditions. In the present study, we assessed the effects of exogenously applied ALA (25 mg L−1) on the root architecture and Na+ distribution of cucumber (Cucumis sativus L.) seedlings under moderate NaCl stress (50 mmol L−1). The results showed that exogenous ALA improved root length, root volume, root surface area, and cell activity in the root tips, which were inhibited under salt stress. In addition, although salinity stress increased the subcellular Na+ contents, such as those of the cell wall, nucleus, plastid, and mitochondria, ALA treatment reduced these Na+ contents, except the soluble fraction. Molecular biological analysis revealed that ALA application upregulated both the SOS1 and HA3 transcriptional and translational levels, which suggested that the excretion of Na+ into the cytoplasm cloud was promoted by exogenous ALA. Meanwhile, exogenously applied ALA also upregulated the gene and protein expression of NHX1 and VHA-A under salinity stress, which suggested that the compartmentalization of Na+ to the vacuole was enhanced. Overall, exogenous ALA mitigated the damage caused by NaCl in cucumber by enhancing Na+ redistribution and increasing the cytoactivity of root cells. [ABSTRACT FROM AUTHOR]

Published
2021
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