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17. Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Li, Liming, Wang, Chen, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Barnard, Maxim, Bennett, Derrick, Boxall, Ruth, Chan, Kahung, Chen, Yiping, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fry, Hannah, Gilbert, Simon, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Lam, Hubert, Lin, Kuang, Liu, James, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozaricki, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Stevens, Becky, Turnbull, Iain, Wang, Baihan, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Xia, Qingmei, Liu, Chao, Pei, Pei, Sun, Dianjianyi, Yu, Canqing, Chen, Naying, Liu, Duo, Tang, Zhenzhu, Chen, Ningyu, Jiang, Qilian, Lan, Jian, Li, Mingqiang, Liu, Yun, Meng, Fanwen, Meng, Jinhuai, Pan, Rong, Qin, Yulu, Wang, Ping, Wang, Sisi, Wei, Liuping, Zhou, Liyuan, Dong, Caixia, Ge, Pengfei, Ren, Xiaolan, Li, Zhongxiao, Mao, Enke, Wang, Tao, Zhang, Hui, Zhang, Xi, Chen, Jinyan, Hu, Ximin, Wang, Xiaohuan, Guo, Zhendong, Li, Huimei, Li, Yilei, Weng, Min, Wu, Shukuan, Yan, Shichun, Zou, Mingyuan, Zhou, Xue, Guo, Ziyan, Kang, Quan, Li, Yanjie, Yu, Bo, Xu, Qinai, Chang, Liang, Fan, Lei, Feng, Shixian, Zhang, Ding, Zhou, Gang, Gao, Yulian, He, Tianyou, He, Pan, Hu, Chen, Sun, Huarong, Zhang, Xukui, Chen, Biyun, Fu, Zhongxi, Huang, Yuelong, Liu, Huilin, Xu, Qiaohua, Yin, Li, Long, Huajun, Xu, Xin, Zhang, Hao, Zhang, Libo, Su, Jian, Tao, Ran, Wu, Ming, Yang, Jie, Zhou, Jinyi, Zhou, Yonglin, Hu, Yihe, Hua, Yujie, Jin, Jianrong, Liu, Fang, Liu, Jingchao, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Jun, Cheng, Liang, Du, Ranran, Gao, Ruqin, Li, Feifei, Li, Shanpeng, Liu, Yongmei, Ning, Feng, Pang, Zengchang, Sun, Xiaohui, Tian, Xiaocao, Wang, Shaojie, Zhai, Yaoming, Zhang, Hua, Hou, Wei, Lv, Silu, Wang, Junzheng, Chen, Xiaofang, Wu, Xianping, Zhang, Ningmei, Chang, Xiaoyu, Li, Jianguo, Liu, Jiaqiu, Luo, Guojin, Sun, Qiang, Zhong, Xunfu, Gong, Weiwei, Hu, Ruying, Wang, Hao, Wang, Meng, Yu, Min, Chen, Lingli, Gu, Qijun, Pan, Dongxia, Wang, Chunmei, Xie, Kaixu, Zhang, Xiaoyi, Chen, Hongyuan, Liu, Liyang, Gou, Haiyan, Wang, Xun, Ding, Jing, Zhang, Ning, Mao, Yueshi, Zhou, Shanshan, Jin, Lirong, Cheng, Xin, Lu, Yun, Chen, Li, Hao, Zilong, Xing, Xiaona, Wang, Lei, Ju, Naixin, Mao, Yiting, Li, Shuya, Du, Peng, Wang, Deren, Sun, Xiaojia, You, Shihao, Wang, Weizhi, Zhu, Yanmei, Li, Xiaojiu, Dong, Yi, Levy, Muriel, Buckell, John, Wu, Nina, Yip, Winnie, and Mihaylova, Borislava

Published
2024
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25. Research on the Innovation Path of Teaching Methods of Civics Classes in Colleges and Universities Based on K-means Cluster Analysis

Author

Huang Aiwa, Li Ni, and Chen Jinyan

Subjects
feature selection optimization, k-means clustering, discriminant function, predictive model, civics teaching innovation, 22a10, Mathematics, QA1-939
Abstract

The innovative design of teaching methods of Civics classes in colleges and universities is an effective measure to help the teaching of Civics classes in colleges and universities. In this paper, the significance of innovative Civics teaching methods is addressed, and a novel path for teaching Civics courses in colleges and universities is proposed. Through feature selection, the K-Means clustering algorithm is optimized, and after optimal selection of the initial clustering center, balanced discriminant function, and combing of the optimization process, the model is applied to the actual innovation of Civics and Political Science teaching, quantitatively assessing the behavioral characteristics of the students and constructing the prediction model to achieve dynamic monitoring of students’ performance. The empirical teaching analysis method is aimed at quantitatively assessing the path proposed in this paper. The clustering results show that consistent with the preset, the model divides students’ learning behavioral characteristics into four categories with clear boundaries and good clustering effect, and most of the student’s grades are concentrated around 60-80 points, according to which the teachers can adjust their teaching strategies in time to improve the teaching effect. Through the control experiment, we know that for the students who have gone through the innovative Civics teaching mode, the mean value of Civics achievement is 71.594, which is 19.329 points higher than the students who have studied in traditional Civics teaching, which is confirmed by the P-value in the t-test of independence which is less than 0.05. It indicates that the innovative teaching method for civics has a significant effect on improving students’ civics achievement.

Published
2024
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30. Tobacco smoking and risks of more than 470 diseases in China: a prospective cohort study

Author

Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Burgess, Sushila, Chan, Peter Ka Hung, Chen, Yiping, Chen, Zhengming, Clarke, Johnathan, Clarke, Robert, Du, Huaidong, Mohamed, Ahmed Edris, Fry, Hannah, Gilbert, Simon, Hill, Mike, Im, Becky Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Lam, Hubert, Lin, Kuang, Mazidi, Mohsen, Millwood, Iona Y, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Sherliker, Paul, Stevens, Rebecca, Turnbull, Iain, Walters, Robin, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Guo, Yu, Han, Xiao, Hou, Can, Xia, Qingmei, Liu, Chao, Lv, Jun, Pei, Pei, Yu, Canqing, Chen, Junshi, Collins, Rory, Li, Liming, Wang, Chen, Peto, Richard, Chen, Naying, Liu, Duo, Tang, Zhenzhu, Chen, Ningyu, Jiang, Qilian, Lan, Jian, Li, Mingqiang, Liu, Yun, Meng, Fanwen, Meng, Jinhuai, Pan, Rong, Qin, Yulu, Wang, Ping, Wang, Sisi, Wei, Liuping, Zhou, Liyuan, Dong, Caixia, Ge, Pengfei, Ren, Xiaolan, Li, Zhongxiao, Mao, Enke, Wang, Tao, Zhang, Hui, Zhang, Xi, Chen, Jinyan, Hu, Ximin, Wang, Xiaohuan, Guo, Zhendong, Li, Huimei, Li, Yilei, Weng, Min, Wu, Shukuan, Yan, Shichun, Zou, Mingyuan, Zhou, Xue, Guo, Ziyan, Kang, Quan, Li, Yanjie, Yu, Bo, Xu, Qinai, Chang, Liang, Fan, Lei, Feng, Shixian, Zhang, Ding, Zhou, Gang, Gao, Yulian, He, Tianyou, He, Pan, Hu, Chen, Sun, Huarong, Zhang, Xukui, Chen, Biyun, Fu, Zhongxi, Huang, Yuelong, Liu, Huilin, Xu, Qiaohua, Yin, Li, Long, Huajun, Xu, Xin, Zhang, Hao, Zhang, Libo, Su, Jian, Tao, Ran, Wu, Ming, Yang, Jie, Zhou, Jinyi, Zhou, Yonglin, Hu, Yihe, Hua, Yujie, Jin, Jianrong, Liu, Fang, Liu, Jingchao, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Jun, Cheng, Liang, Du, Ranran, Gao, Ruqin, Li, Feifei, Li, Shanpeng, Liu, Yongmei, Ning, Feng, Pang, Zengchang, Sun, Xiaohui, Tian, Xiaocao, Wang, Shaojie, Zhai, Yaoming, Zhang, Hua, Hou, Wei, Lv, Silu, Wang, Junzheng, Chen, Xiaofang, Wu, Xianping, Zhang, Ningmei, Zhou, Weiwei, Li, Jianguo, Liu, Jiaqiu, Luo, Guojin, Sun, Qiang, Zhong, Xunfu, Gong, Weiwei, Hu, Ruying, Wang, Hao, Wang, Meng, Yu, Min, Chen, Lingli, Gu, Qijun, Pan, Dongxia, Wang, Chunmei, Xie, Kaixu, Zhang, Xiaoyi, Chan, Ka Hung, and Xiao, Dan

Published
2022
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37. Accuracy of Zygomatic Implant Placement Using Task‐Autonomous Robotic System or Dynamic Navigation: An In Vitro Study.

Author

Chen, Jinyan, Tao, Baoxin, Yu, Xinbo, Wu, Yiqun, and Wang, Feng

Subjects
*DYNAMICAL systems, *DENTAL implants, *STATISTICS, *MAXILLA, *EDENTULOUS mouth
Abstract

ABSTRACT Objectives Materials and Methods Results Conclusions To evaluate and compare the accuracy of task‐autonomous robot‐assisted implant surgery (RAIS) and dynamic computer‐assisted implant surgery (dCAIS) for zygomatic implant placement.Ten atrophic edentulous maxilla models requiring zygomatic implant (ZI) placement were randomly divided into the RAIS and dCAIS groups. Osteotomies and implant placement were performed under the guidance of a task‐autonomous robotic system or dynamic navigation system. A total of 20 ZIs were analyzed. The angular, coronal, lateral coronal, coronal depth, apical, lateral apical, and apical depth deviations were measured and analyzed between the two groups. The primary outcome parameters were the angular deviations between the planned and the placed ZIs. Data was subjected to descriptive and comparative statistical analysis. The significance of inter‐group differences for continuous variables was assessed with Student's two‐sample t‐tests, Welch two‐sample t‐tests, and Mann–Whitney U tests according to the distribution normality and variance homogeneity.ZI placement deviations were compared between the RAIS and dCAIS groups, showing a mean angular deviation of 0.92 ± 0.40° versus 2.03 ± 0.53° (p < 0.001), a mean (±SD) coronal deviation of 0.48 ± 0.25 mm versus 1.29 ± 0.46 mm (p < 0.001), and a mean apical deviation of 0.88 ± 0.28 mm versus 1.96 ± 0.46 mm (p < 0.001).For computer‐guided ZI placement, task‐autonomous RAIS was superior to dCAIS in terms of accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Exploring the Learning Curve of Dental Implant Placement Using a Task‐Autonomous Robotic System Among Young Dentists From Different Specialties—A Pilot Module Study.

Author

Zhuang, Minjie, Chen, Jinyan, Tao, Baoxin, Gul, Meisha, Wang, Feng, and Wu, Yiqun

Subjects
*LEARNING curve, *ARTIFICIAL implants, *DENTAL implants, *OPERATIVE surgery, *SURGICAL robots
Abstract

ABSTRACT Background Purpose Methods and Materials Results Conclusions The learning curve effect of dynamic computer‐assisted implant surgery (D‐CAIS) was observed among inexperienced novice surgeons. The learning curves can provide valuable information for novice surgeons and valid comparisons between new and conventional techniques. Recently, robotic computer‐assisted implant surgery (R‐CAIS) has shown promise as a novel dental implant surgical technique for both partially and edentulous patients. However, its learning curve remains unknown.The aim of this study was to explore the learning curve of dental implant placement surgery with a task‐autonomous robotic system among young dentists with different specialties.Four young dentists (mean age: 25.3 ± 1.5 years at the beginning of their first attempt) with equal representation of males and females and with different specialties participated in this study. None of the participants had prior experience in R‐CAIS. Each operator placed eight implants over eight attempts using a semi‐active task‐autonomous robotic system. Among the eight implants, four were straight lateral incisor implants, and four were 30°‐tilted premolar implants. The implants were placed in each dental quadrant of the maxillary and mandibular jaw modules. The operation time was recorded. Coronal, apical, and angular deviations between the planned and actual sites of implant placement were measured by merging preoperative and postoperative cone‐beam computed tomography (CBCT) scans. The data were analyzed with repeated‐measures ANOVA (α = 0.05).The mean time for implant placement was associated with the number of attempts (p < 0.01). The time taken for the second attempt was significantly shorter than that of the first attempt (33.26 vs. 30.47 min; p < 0.001) then it plateaued. Three‐dimensional (3D) angular (p = 0.31), coronal deviation (p = 0.26), and apical deviation (p = 0.06) did not differ significantly among attempts. The mean values and standard deviations of 3D coronal deviation, 3D apical deviation, and 3D angular deviation were 0.71 ± 0.31 mm, 0.72 ± 0.30 mm, and 0.94 ± 0.58°, respectively. Neither the position of the jaw (p > 0.59) nor the tilt angle of the implant (straight or 30°‐tilted, p > 0.85) was related to implant placement accuracy.Dentists quickly learned the basic workflow of R‐CAIS and thus facilitated the clinicians in the mastery of implant placement on edentulous jaw modules, leading to a comparable operating speed and high precision. Moreover, the accuracy of placement of straight and tilted implants in both the maxilla and mandible with R‐CAIS was satisfactory. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Enhancing depression detection: A multimodal approach with text extension and content fusion.

Author

Chen, Jinyan, Liu, Shuxian, Xu, Meijia, and Wang, Peicheng

Subjects
*OPTICAL character recognition, *PSYCHOLOGICAL tests, *MENTAL depression, *SOCIAL background, *EMOTIONS
Abstract

Background: With ubiquitous social media platforms, people express their thoughts and emotions, making social media data valuable for studying and detecting depression symptoms. Objective: First, we detect depression by leveraging textual, visual, and auxiliary features from the Weibo social media platform. Second, we aim to comprehend the reasons behind the model's results, particularly in medicine, where trust is crucial. Methods: To address challenges such as varying text lengths and abundant social media data, we employ a text extension technique to standardize text length, enhancing model robustness and semantic feature learning accuracy. We utilize tree‐long short‐term memory and bidirectional gate recurrent unit models to capture long‐term and short‐term dependencies in text data, respectively. To extract emotional features from images, the integration of optical character recognition (OCR) technology with an emotion lexicon is employed, addressing the limitations of OCR technology in accuracy when dealing with complex or blurred text. In addition, auxiliary features based on social behaviour are introduced. These modalities' output features are fed into an attention fusion network for effective depression indicators. Results: Extensive experiments validate our methodology, showing a precision of 0.987 and recall rate of 0.97 in depression detection tasks. Conclusions: By leveraging text, images, and auxiliary features from Weibo, we develop text picture sentiment auxiliary (TPSA), a novel depression detection model. we ascertained that the emotional features extracted from images and text play a pivotal role in depression detection, providing valuable insights for the detection and assessment of the psychological disorder. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Accuracy of immediate dental implant placement with task‐autonomous robotic system and navigation system: An in vitro study.

Author

Chen, Jinyan, Zhuang, Minjie, Tao, Baoxin, Wu, Yiqun, Ye, Lijuan, and Wang, Feng

Subjects
*DENTAL implants, *DYNAMICAL systems, *CLINICAL medicine, *ROBOTICS, *EDENTULOUS mouth
Abstract

Objectives: The aim of this study was to compare the accuracy of dental implant placement in a single tooth gap, including the postextraction site and healed site, using a task‐autonomous robotic system and a dynamic navigation system. Materials and Methods: Forty partially edentulous models requiring both immediate and conventional implant placement were randomly divided into a robotic system group and a navigation system group. The coronal, apical, and angular deviations of the implants were measured and assessed between the groups. Results: The deviations in immediate implant placement were compared between the robotic system and dynamic navigation system groups, showing a mean (±SD) coronal deviation of 0.86 ± 0.36 versus 0.70 ± 0.21 mm (p =.101), a mean apical deviation of 0.77 ± 0.34 versus 0.95 ± 0.38 mm (p =.127), and a mean angular deviation of 1.94 ± 0.66° versus 3.44 ± 1.38° (p <.001). At the healed site, significantly smaller coronal deviation (0.46 ± 0.29 vs. 0.70 ± 0.30 mm, p =.005), apical deviation (0.56 ± 0.30 vs. 0.85 ± 0.25 mm, p <.001), and angular deviation (1.36 ± 0.54 vs. 1.80 ± 0.70 mm, p =.034) were found in the robotic system group than in the dynamic navigation group. Conclusions: The position in both immediate and conventional implant placement was more precise with the task‐autonomous robotic system than with the dynamic navigation system. Its performance in actual clinical applications should be confirmed in further trials. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Density Functional Theory Studies on Tuning p‑Band Electronic Structures of TiS2‑Based Single-Atom Catalysts for Polysulfide Conversion in Lithium–Sulfur Batteries.

Author

Chen, Jinyan, Zhong, Jinze, Liu, Siqi, Wang, Yuhan, Hou, Jianhua, and Duan, Qian

Abstract

Despite extensive investigation, the utilization of sulfur and the cycling stability of lithium–sulfur (Li–S) batteries are significantly impeded by the polysulfide shuttle effect and sluggish sulfur reaction kinetics. In this study, aimed at enhancing the performance of Li–S batteries, we focus on the implementation of single metal atom (Be, Mg, Ca, V, Nb, and Ta)-doped TiS2 monolayers as cathode catalysts. Our findings reveal that Be-TiS2, Mg-TiS2, and Ca-TiS2 exhibit superior polysulfide adsorption capabilities and lower values for the rate-determining step in terms of Gibbs free energy. Electronic structure analysis further elucidates that the enhanced anchoring and electrocatalytic activities stem from the upward displacement of the p-band center and the narrowing of the gap within the Δd-p-band, respectively. Moreover, Be-TiS2 and Ca-TiS2 monolayers facilitate the acceleration of the Li2S decomposition reaction and Li-ion migration on their surfaces. This investigation effectively advances our understanding of the role of TiS2 in the polysulfide conversion process and offers valuable insights into the design of cathodes in Li–S batteries. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Mortality and recurrent vascular events after first incident stroke: a 9-year community-based study of 0·5 million Chinese adults

Author

Chen, Yiping, Wright, Neil, Guo, Yu, Turnbull, Iain, Kartsonaki, Christiana, Yang, Ling, Bian, Zheng, Pei, Pei, Pan, Dongxia, Zhang, Yidan, Qin, Haiqiang, Wang, Yilong, Lv, Jun, Liu, Ming, Hao, Zilong, Wang, Yongjun, Yu, Canqing, Peto, Richard, Collins, Rory, Li, Liming, Clarke, Robert, Chen, Zhengming, Chen, Junshi, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Bragg, Fiona, Chang, Yumei, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Holmes, Michael, Kerosi, Rene, Lancaster, Garry, Lin, Kuang, McDonnell, John, Millwood, Iona, Nie, Qunhua, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sohoni, Rajani, Wang, Jenny, Wang, Lin, Yang, Xiaoming, Han, Xiao, Hou, Can, Jing, Biao, Liu, Chao, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Zeng, Mingyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Xu, Qinai, Kang, Quan, Guo, Ziyan, Wang, Dan, Hu, Ximin, Wang, Hongmei, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Fu, Zhifang, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Lan, Jian, Liu, Yun, Wei, Liuping, Chen, Ningyu, Wang, Ping, Meng, Fanwen, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Zhou, Weiwei, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Zhang, Xukui, Wu, Huifang, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixu, Chen, Lingli, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Long, Huajun, Li, Xianzhi, Zhang, Libo, and Qiu, Zhe

Published
2020
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50. Cooking fuels and risk of all-cause and cardiopulmonary mortality in urban China: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Boxall, Ruth, Chang, Yumei, Chen, Yiping, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Hill, Mike, Holmes, Michael, Iona, Andri, Kartsonaki, Christiana, Kerosi, Rene, Kong, Ling, Kurmi, Om, Lancaster, Garry, Lewington, Sarah, Lin, Kuang, McDonnell, John, Millwood, Iona, Nie, Qunhua, Radhakrishnan, Jayakrishnan, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sherliker, Paul, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Jenny, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Bian, Zheng, Han, Xiao, Hou, Can, Pei, Pei, Liu, Chao, Tan, Yunlong, Yu, Canqing, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Zeng, Mingyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Xu, Qinai, Kang, Quan, Guo, Ziyan, Wang, Dan, Hu, Ximin, Wang, Hongmei, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Fu, Zhifang, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Lan, Jian, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Wang, Ping, Meng, Fanwen, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Zhou, Weiwei, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Zhang, Xukui, Wu, Huifang, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixu, Chen, Lingli, Zhang, Yidan, Pan, Dongxia, Gu, Qijun, Huang, Yuelong, Chen, Biyun, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Long, Huajun, Li, Xianzhi, Zhang, Libo, Qiu, Zhe, Yu, Kuai, Qiu, Gaokun, Wang, Chaolong, Pan, An, Liang, Liming, Hu, Frank B, and Wu, Tangchun

Published
2020
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