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338 results on '"Sun, Jiaojiao"'

15. Exploiting Multi-Dec Net for Detecting Traffic Congestion in the Surveillance System

Author

Li, Jie, Sun, Jiaojiao, Wang, Jun, Zhuo, Yedi, Yinli Jin, A. N. D., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Wang, Wuhong, editor, Wu, Jianping, editor, Jiang, Xiaobei, editor, Li, Ruimin, editor, and Zhang, Haodong, editor

Published
2023
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23. An exhaustive pre-clinical study of a circRNA drug candidate (cmRNA1210) encoding IL-12sc for anti-tumor therapeutics.

Author

Wang, Shuhang, primary, Yang, Jiali, additional, Wei, Lixiang, additional, Tan, Yiling, additional, Sun, Jiaojiao, additional, Wu, Fan, additional, Huang, Lingkun, additional, Ma, Peiwen, additional, Zhou, Jiawei, additional, Zhao, Guo, additional, Wang, Yuning, additional, Tang, Qiyu, additional, Zuo, Chijian, additional, and Li, Ning, additional

Published
2024
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24. Traffic Congestion Detection Based on the Image Classification with CNN

Author

Zhuo, Yedi, Wang, Ping, Sun, Jiaojiao, Jin, Yinli, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Yu, Xiang, editor

Published
2022
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38. Construction and Validation of a Risk Warning Model of Depression in Patients with Pulmonary Nodule

Author

Sun, Jiaojiao, Zhou, Yunxu, and Wang, Zhiqiang

Subjects
Depression, Mental -- Care and treatment -- Risk factors, Hospital patients -- Care and treatment, Low density lipoproteins -- Social aspects, Health, Psychology and mental health
Abstract

Background. To establish a depression risk warning model for patients with pulmonary nodules and to provide a theoretical basis for medical staff to identify high-risk patients early and quickly and take timely intervention measures. Methods. A total of 535 hospitalized patients with pulmonary nodules were selected, and the relevant data were analyzed by single-factor analysis. Binary logistic regression analysis was used to determine the independent risk factors for depression in patients with pulmonary nodules and to establish a risk warning model. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were used to evaluate the goodness of fit and prediction effect of the model, and the cross-validation method was used to verify the efficacy of the model. Results. The prevalence of depression in patients with pulmonary nodules was 47.29%. Univariate analysis showed that CRP, albumin, creatinine, phosphorus, calcium, triglyceride, cholesterol, low-density lipoprotein, high-density lipoprotein, β 2-microglobulin, objective support, social support, and education level were related to depression in patients with pulmonary nodules (P, Author(s): Jiaojiao Sun [1]; Yunxu Zhou [1]; Zhiqiang Wang (corresponding author) [2] 1. Introduction Nowadays, with the advancement of people's living standards, the continuous improvement of health awareness, and the [...]

Published
2023
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