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375 results on '"Yang, Qingxin"'

8. Distinguished biological adaptation architecture aggravated population differentiation of Tibeto-Burman-speaking people

Author

Sun, Yuntao, Wang, Mengge, Sun, Qiuxia, Liu, Yan, Duan, Shuhan, Wang, Zhiyong, Zhou, Yunyu, Zhong, Jun, Huang, Yuguo, Huang, Xinyu, Yang, Qingxin, Li, Xiangping, Su, Haoran, Cai, Yan, Jiang, Xiucheng, Chen, Jing, Yan, Jiangwei, Nie, Shengjie, Hu, Liping, Yang, Junbao, Tang, Renkuan, Wang, Chuan-Chao, Liu, Chao, Deng, Xiaohui, Yun, Libing, and He, Guanglin

Published
2024
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26. CO2 Hydrogenation to Hydrocarbons over Fe‐Based Catalysts: Status and Recent Developments.

Author

Krausser, Laura, Yang, Qingxin, and Kondratenko, Evgenii V.

Subjects
*RENEWABLE energy sources, *CATALYSTS, *MANUFACTURING processes, *CHEMICAL industry, *HYDROCARBONS
Abstract

To control anthropogenic CO2 emissions worldwide, it is necessary not only to align the chemical industry and energy sector with renewable resources but also to implement large‐scale utilization of CO2 as a feedstock. The Fe‐catalyzed CO2‐modified Fischer‐Tropsch Synthesis (CO2‐FTS) is one of the most promising options for efficient CO2 utilization, as it can be used to synthesize desired higher hydrocarbons (C2+), including lower olefins (C2=‐C4=), the main building blocks of the chemical industry, and long‐chain hydrocarbons (C5+), which can be used as fuels. To optimize catalyst and process design for the purpose of developing an economically viable industrial process, the reaction mechanism and the factors controlling product selectivity need to be fully understood. This article discusses the current state‐of‐the‐art in catalyst design and approaches for making effective progress in addressing these challenges. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Multiple Human Population Movements and Cultural Dispersal Events Shaped the Landscape of Chinese Paternal Heritage.

Author

Wang, Mengge, Huang, Yuguo, Liu, Kaijun, Wang, Zhiyong, Zhang, Menghan, Yuan, Haibing, Duan, Shuhan, Wei, Lanhai, Yao, Hongbing, Sun, Qiuxia, Zhong, Jie, Tang, Renkuan, Chen, Jing, Sun, Yuntao, Li, Xiangping, Su, Haoran, Yang, Qingxin, Hu, Liping, Yun, Libing, and Yang, Junbao

Subjects
CHINESE people, TECHNOLOGICAL innovations, CULTURAL movements, EAST Asians, FOSSIL DNA
Abstract

Large-scale genomic projects and ancient DNA innovations have ushered in a new paradigm for exploring human evolutionary history. However, the genetic legacy of spatiotemporally diverse ancient Eurasians within Chinese paternal lineages remains unresolved. Here, we report an integrated Y-chromosome genomic database encompassing 15,563 individuals from both modern and ancient Eurasians, including 919 newly reported individuals, to investigate the Chinese paternal genomic diversity. The high-resolution, time-stamped phylogeny reveals multiple diversification events and extensive expansions in the early and middle Neolithic. We identify four major ancient population movements, each associated with technological innovations that have shaped the Chinese paternal landscape. First, the expansion of early East Asians and millet farmers from the Yellow River Basin predominantly carrying O2/D subclades significantly influenced the formation of the Sino-Tibetan people and facilitated the permanent settlement of the Tibetan Plateau. Second, the dispersal of rice farmers from the Yangtze River Valley carrying O1 and certain O2 sublineages reshapes the genetic makeup of southern Han Chinese, as well as the Tai-Kadai, Austronesian, Hmong-Mien, and Austroasiatic people. Third, the Neolithic Siberian Q/C paternal lineages originated and proliferated among hunter-gatherers on the Mongolian Plateau and the Amur River Basin, leaving a significant imprint on the gene pools of northern China. Fourth, the J/G/R paternal lineages derived from western Eurasia, which were initially spread by Yamnaya-related steppe pastoralists, maintain their presence primarily in northwestern China. Overall, our research provides comprehensive genetic evidence elucidating the significant impact of interactions with culturally distinct ancient Eurasians on the patterns of paternal diversity in modern Chinese populations. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response

Author

Lin, Jing-wen, Tang, Chao, Wei, Han-cheng, Du, Baowen, Chen, Chuan, Wang, Minjin, Zhou, Yongzhao, Yu, Ming-xia, Cheng, Lu, Kuivanen, Suvi, Ogando, Natacha S., Levanov, Lev, Zhao, Yuancun, Li, Chang-ling, Zhou, Ran, Li, Zhidan, Zhang, Yiming, Sun, Ke, Wang, Chengdi, Chen, Li, Xiao, Xia, Zheng, Xiuran, Chen, Sha-sha, Zhou, Zhen, Yang, Ruirui, Zhang, Dan, Xu, Mengying, Song, Junwei, Wang, Danrui, Li, Yupeng, Lei, ShiKun, Zeng, Wanqin, Yang, Qingxin, He, Ping, Zhang, Yaoyao, Zhou, Lifang, Cao, Ling, Luo, Feng, Liu, Huayi, Wang, Liping, Ye, Fei, Zhang, Ming, Li, Mengjiao, Fan, Wei, Li, Xinqiong, Li, Kaiju, Ke, Bowen, Xu, Jiannan, Yang, Huiping, He, Shusen, Pan, Ming, Yan, Yichen, Zha, Yi, Jiang, Lingyu, Yu, Changxiu, Liu, Yingfen, Xu, Zhiyong, Li, Qingfeng, Jiang, Yongmei, Sun, Jiufeng, Hong, Wei, Wei, Hongping, Lu, Guangwen, Vapalahti, Olli, Luo, Yunzi, Wei, Yuquan, Connor, Thomas, Tan, Wenjie, Snijder, Eric J., Smura, Teemu, Li, Weimin, Geng, Jia, Ying, Binwu, and Chen, Lu

Published
2021
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32. Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network.

Author

Mou, Jianhui, Yang, Qingxin, Tang, Yi, Liu, Yuhui, Li, Junjie, and Yu, Chengcheng

Subjects
REMAINING useful life, LITHIUM-ion batteries, CONVOLUTIONAL neural networks, BATTERY management systems, IONIC conductivity, FEATURE extraction
Abstract

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods. [ABSTRACT FROM AUTHOR]

Published
2024
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33. A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China.

Author

Yang, Qingxin, Luo, Li, Lin, Zhangpeng, Wen, Wei, Zeng, Wenbo, and Deng, Hong

Subjects
*MEDICAL malpractice, *MACHINE learning, *PREDICTION models, *RANDOM forest algorithms, *IDENTIFICATION, *DATABASES
Abstract

Purpose: To explore the feasibility and validity of machine learning models in determining causality in medical malpractice cases and to try to increase the scientificity and reliability of identification opinions. Methods: We collected 13,245 written judgments from PKULAW.COM, a public database. 963 cases were included after the initial screening. 21 medical and ten patient factors were selected as characteristic variables by summarising previous literature and cases. Random Forest, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) were used to establish prediction models of causality for the two data sets, respectively. Finally, the optimal model is obtained by hyperparameter tuning of the six models. Results: We built three real data set models and three virtual data set models by three algorithms, and their confusion matrices differed. XGBoost performed best in the real data set, with a model accuracy of 66%. In the virtual data set, the performance of XGBoost and LightGBM was basically the same, and the model accuracy rate was 80%. The overall accuracy of external verification was 72.7%. Conclusions: The optimal model of this study is expected to predict the causality accurately. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Temperature-dependent hysteresis model based on temporal convolutional network.

Author

Zhang, Hao, Yang, Qingxin, Zhang, Changgeng, Li, Yongjian, and Chen, Yifan

Subjects
*SPONTANEOUS magnetization, *HYSTERESIS, *TEMPERATURE effect
Abstract

The variation of temperature modifies the magnetic behavior of ferromagnetic cores which may affect the performance of electrical equipment. Therefore, it is imperative to develop a temperature-dependent hysteresis model to precisely calculate electromagnetic characteristics of electrical equipment. In this paper, a Temporal Convolutional Network (TCN) in combination with the Play operator is developed. The proposed model incorporates the temperature-dependent spontaneous magnetization intensity as the model input to introduce the temperature effect. To enhance the accuracy of model training outcomes, the Bayesian optimization approach for automatically selecting network model parameters is provided. The results show that the proposed model can accurately predict the hysteresis characteristics of materials under varying temperature and frequency conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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