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423 results on '"Yang, Huiyu"'

1. LESnets (Large-Eddy Simulation nets): Physics-informed neural operator for large-eddy simulation of turbulence

Author

Zhao, Sunan, Li, Zhijie, Fan, Boyu, Wang, Yunpeng, Yang, Huiyu, and Wang, Jianchun

Subjects
Physics - Fluid Dynamics
Abstract

Acquisition of large datasets for three-dimensional (3D) partial differential equations are usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great potential in a variety of partial differential equations. In this work, we employ physics-informed neural operator, encoding the large-eddy simulation (LES) equations directly into the neural operator for simulating three-dimensional incompressible turbulent flows. We develop the LESnets (Large-Eddy Simulation nets) by adding large-eddy simulation equations to two different data-driven models, including Fourier neural operator (FNO) and implicit Fourier neural operator (IFNO) without using label data. Notably, by leveraging only PDE constraints to learn the spatio-temporal dynamics problem, LESnets retains the computational efficiency of data-driven approaches while obviating the necessity for data. Meanwhile, using large-eddy simulation equations as PDE constraints makes it possible to efficiently predict complex turbulence at coarse grids. We investigate the performance of the LESnets with two standard three-dimensional turbulent flows: decaying homogeneous isotropic turbulence and temporally evolving turbulent mixing layer. In the numerical experiments, the LESnets model shows a similar or even better accuracy as compared to traditional large-eddy simulation and data-driven models of FNO and IFNO. Moreover, the well-trained LESnets is significantly faster than traditional LES, and has a similar efficiency as the data-driven FNO and IFNO models. Thus, physics-informed neural operators have a strong potential for 3D nonlinear engineering applications.

Published
2024

2. Prediction of three-dimensional chemically reacting compressible turbulence based on implicit U-Net enhanced Fourier neural operator

Author

Zhang, Zhiyao, Li, Zhijie, Wang, Yunpeng, Yang, Huiyu, Peng, Wenhui, Teng, Jian, and Wang, Jianchun

Subjects
Physics - Fluid Dynamics
Abstract

The accurate and fast prediction of long-term dynamics of turbulence presents a significant challenge for both traditional numerical simulations and machine learning methods. In recent years, the emergence of neural operators has provided a promising approach to address this issue. The implicit U-Net enhanced Fourier neural operator (IU-FNO) has successfully demonstrated long-term stable predictions for three-dimensional incompressible turbulence. In this study, we extend this method to the three-dimensional chemically reacting compressible turbulence. Numerical results show that the IU-FNO model predicts flow dynamics significantly faster than the traditional dynamic Smagorinsky model (DSM) used in large eddy simulation (LES). In terms of prediction accuracy, the IU-FNO framework outperforms the traditional DSM in predicting the energy spectra of velocity, temperature, and density, the probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of temperature. Therefore, the IU-FNO represents a highly promising approach for predicting chemically reacting compressible turbulence.

Published
2024

3. A Priori Error Estimation of Physics-Informed Neural Networks Solving Allen--Cahn and Cahn--Hilliard Equations

Author

Zhang, Guangtao, Lin, Jiani, Zhai, Qijia, Yang, Huiyu, Chen, Xujun, Zheng, Xiaoning, and Leong, Ieng Tak

Subjects
Mathematics - Numerical Analysis, Mathematics - Optimization and Control
Abstract

This paper aims to analyze errors in the implementation of the Physics-Informed Neural Network (PINN) for solving the Allen--Cahn (AC) and Cahn--Hilliard (CH) partial differential equations (PDEs). The accuracy of PINN is still challenged when dealing with strongly non-linear and higher-order time-varying PDEs. To address this issue, we introduce a stable and bounded self-adaptive weighting scheme known as Residuals-RAE, which ensures fair training and effectively captures the solution. By incorporating this new training loss function, we conduct numerical experiments on 1D and 2D AC and CH systems to validate our theoretical findings. Our theoretical analysis demonstrates that feedforward neural networks with two hidden layers and tanh activation function effectively bound the PINN approximation errors for the solution field, temporal derivative, and nonlinear term of the AC and CH equations by the training loss and number of collocation points.

Published
2024

7. Parameter Identification for Partial Differential Equations with Spatiotemporal Varying Coefficients

Author

Zhang, Guangtao, Duan, Yiting, Pan, Guanyu, Chen, Qijing, Yang, Huiyu, and Zhang, Zhikun

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract

To comprehend complex systems with multiple states, it is imperative to reveal the identity of these states by system outputs. Nevertheless, the mathematical models describing these systems often exhibit nonlinearity so that render the resolution of the parameter inverse problem from the observed spatiotemporal data a challenging endeavor. Starting from the observed data obtained from such systems, we propose a novel framework that facilitates the investigation of parameter identification for multi-state systems governed by spatiotemporal varying parametric partial differential equations. Our framework consists of two integral components: a constrained self-adaptive physics-informed neural network, encompassing a sub-network, as our methodology for parameter identification, and a finite mixture model approach to detect regions of probable parameter variations. Through our scheme, we can precisely ascertain the unknown varying parameters of the complex multi-state system, thereby accomplishing the inversion of the varying parameters. Furthermore, we have showcased the efficacy of our framework on two numerical cases: the 1D Burgers' equation with time-varying parameters and the 2D wave equation with a space-varying parameter.

Published
2023

14. Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model

Author

Shen, Ying, Yang, Huiyu, and Lin, Lin

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Sound, Quantitative Biology - Quantitative Methods
Abstract

Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants' interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method. The source code and EATD-Corpus are available at https://github.com/speechandlanguageprocessing/ICASSP2022-Depression.

Published
2022

16. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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32. Annual Research Progress of Glioma in China in 2021

Author

CHEN Wenlin, WANG Yaning, XING Hao, LIANG Tingyu, SHI Yixin, WANG Hai, YANG Huiyu, LIU Qianshu, LI Junlin, GUO Xiaopeng, WANG Yu, and MA Wenbin

Subjects
glioma, magnetic resonance imaging, temozolomide, targeted therapy, immune microenvironment, Medicine
Abstract

Glioma, the most prevalent primary malignant tumor of the central nervous system, has a high degree of malignancy and poor prognosis for patients. At present, the researches of glioma mainly focus on the investigation of the mechanism of tumor occurrence and the discovery of new therapeutic methods and agents. In-depth researches have also been conducted on the optimization of molecular pathological typing of glioma, improvement of diagnostic imaging techniques and formulation of comprehensive treatment guidelines. In this review, we summarize the achievements and important progress made by Chinese medical scientists in the field of glioma in 2021, and propose possible future research directions with the aim of providing reference for clinical research.

Published
2022
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35. Superhydrophobic PVDF/SiO2 composite films with a hierarchical structure for highly stabilized radiative cooling.

Author

Tan, Zhengfei, Yang, Huiyu, Cheng, Xiaohua, Yu, Guowen, Liu, Hai, Zhang, Bingqing, and Gong, Chunli

Subjects
*ULTRAVIOLET radiation, *COOLING, *FRICTION, *SOLVENTS, *WEATHERING
Abstract

PVDF/SiO2 composite films with a hierarchical structure were prepared by water bath solvent exchange and they realized the integration of self-cleaning and radiative cooling. The high scattering properties of SiO2 effectively enhanced the radiative cooling performance of the films, and the weathering stability of the composite films was evaluated by UV radiation treatment and friction resistance analysis. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Terahertz spectroscopy study of oridonin and ponicidin in the anticancer Chinese herbal medicine Rabdosia rubescens.

Author

Gao, Yang, Peng, Zhuang, Yang, Huiyu, Zhang, Xinrui, Zhao, Yuhan, Hou, Zeyu, Su, Bo, Li, Kai, and Zhang, Cunlin

Subjects
TERAHERTZ time-domain spectroscopy, MOLECULAR vibration, MOLECULAR structure, MOLECULAR spectra, HERBAL medicine, TERAHERTZ technology
Abstract

Rabdosia rubescens, a Chinese herbal medicine with anticancer properties, contains two active ingredients: oridonin and ponicidin. Both compounds exhibit antitumor effects by inducing tumor cell apoptosis and autophagy and inhibiting tumor cell proliferation. To further explore the differences in molecular structure and pharmacological properties between the two substances, this study employs Terahertz Time-Domain Spectroscopy (THz-TDS) to investigate the spectral characteristics of oridonin and ponicidin in the frequency range of 0.1 to 2.3 THz. The crystal structures of the two substances are simulated using Materials Studio software and Density Functional Theory (DFT), yielding their spectra and molecular vibration modes, which elucidate the mechanism underlying the peak generation. The consistency between experimental and simulation results confirms the reliability of the experimental findings. Thus, THz spectroscopy can effectively distinguish between these two substances. Finally, a THz detection is performed on Rabdosia rubescens capsules purchased from the market, revealing the presence of absorption peaks for both substances in their absorption spectra. This provides a new approach for detecting active ingredients in Chinese herbal medicines. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Renin and (pro)renin receptors induce vascular smooth muscle cell proliferation and neointimal hyperplasia by activating oxidative stress and inflammation.

Author

Zhang, Nana, Song, Xiaosu, Bian, Yunfei, Bai, Rui, Yang, Huiyu, Wang, Gang, Li, Hong, and Xiao, Chuanshi

Subjects
PROLIFERATING cell nuclear antigen, PRORENIN receptor, VASCULAR smooth muscle, PLATELET-derived growth factor, SPRAGUE Dawley rats, LOSARTAN
Abstract

Introduction: Renin and prorenin promote the proliferation of vascular smooth muscle cells (VSMCs) through the (pro)renin receptor, or (P)RR, to promote restenosis occurrence. This study aimed to explore whether prorenin promoted the proliferation of VSMCs in a (P)RR-mediated Ang II-independent manner. Methods : Losartan and PD123319 were used to block the interaction between (P)RR and angiotensin in vitro. Cells were treated with renin, platelet-derived growth factor (PDGF), or RNAi-(P)RR, either jointly or individually. Cell proliferation was measured via Cell Counting Kit-8 (CCK-8) and flow cytometry methods; moreover, real-time polymerase chain reaction (RT–PCR) and Western blot (WB) assays were used to detect the expression of cyclin D1, proliferating cell nuclear antigen (PCNA), (P)RR, NOX1, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling proteins. Immunofluorescence staining was conducted to measure the expression of (P)RR, and the levels of renin, PDGF-BB, inflammatory factors, and oxidative stress were determined by using enzyme-linked immunosorbent assay (ELISA). Moreover, a balloon catheter was used to enlarge the carotid artery of the Sprague Dawley rats. PRO20 was applied to identify angiotensin II (Ang II). The hematoxylin and eosin, RT–PCR, and WB results validated the cell assay results. Results : Renin promoted the proliferation of rat VSMCs by enhancing cell viability and cell cycle protein expression when Ang II was blocked, but silencing (P)RR inhibited this effect. Furthermore, renin enhanced NOX1-mediated oxidative stress and inflammation by activating the extracellular signal-regulated kinase 1/2 (ERK1/2)-AKT pathway in vitro. Similarly, the inhibition of (P)RR resulted in the opposite phenomenon. Importantly, the inhibition of (P)RR inhibited neointimal hyperplasia in vivo after common carotid artery injury by restraining NOX1-mediated oxidative stress through the downregulation of the ERK1/2-AKT pathway. The animal study confirmed these findings. Conclusion : Renin and (P)RR induced VSMC proliferation and neointimal hyperplasia by activating oxidative stress, inflammation, and the ERK1/2-AKT pathway in an Ang II-independent manner. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Patient-derived 3D bioprinting pan-cancer drug screening platform for personalized medicine.

Author

Yang, Huiyu, primary, Zhang, Jiangang, additional, Shan, Ying, additional, Wang, Yanan, additional, Cao, Yaning, additional, Sun, Yuning, additional, Jin, Ying, additional, Pan, Lingya, additional, Ke, Chunhai, additional, Cui, Wei, additional, Yang, Mian, additional, Zhou, Bo, additional, Jia, Ziqi, additional, Cao, Heng, additional, Wang, Yu, additional, Ma, Wenbin, additional, Xu, Xiao, additional, Zhao, Hai-Tao, additional, Yang, Huayu, additional, and Mao, Yilei, additional

Published
2024
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46. Chinese EFL Learners' Phonetics Learning Guided by Visuospatial Cues through the Medium of Mobile Phones

Author

Yang, Huiyu

Abstract

The relevant studies using a cross sectional view of speech organs supplemented with visuospatial cues and verbal text to explore EFL learners' learning effectiveness and behavior through mobile devices when learning English phonetics are scarce. This study was attempted to investigate whether the presence of visuospatial cues can benefit EFL learners with different levels of prior knowledge in learning English phonetics through mobile devices. The present study investigated the interaction between the experimental condition and the learners' prior knowledge on their task performances and cognitive load ratings. Fifty-six English as a foreign language (EFL) learners recruited from two sections of a linguistics course participated in the experiment. First, their background knowledge concerning English phonetics was evaluated to determine their prior knowledge level. Then, they were randomly assigned into two experimental conditions--picture-plus-text and picture-plus-text-plus-cueing. After the experimental treatment, the participants were administered retention and transfer tests as well as cognitive load measurement. Experimental treatment and prior knowledge were the independent variables, while retention test, transfer test, study time, and number of clicks were the dependent variables. The results of the present study emphasized the importance of visuospatial cues on inducing deep cognitive processing as indicated by the learners' test performance and study patterns.

Published
2019
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48. The Shear‐Accelerated II–I Phase Transition of Isotactic Poly(1‐Butene).

Author

Jin, Rui, Xin, Rui, Zhang, Xinyan, Li, Yunpeng, Yang, Huiyu, Yan, Shouke, and Sun, Xiaoli

Subjects
PHASE transitions, ISOTHERMAL temperature, SHEARING force, CRYSTALLIZATION, QUANTITATIVE research
Abstract

The II–I phase transition of isotactic poly(1‐butene) (iPBu) leads to improved mechanical performance. However, this will take several weeks and increase storage and processing costs. In this work, shear forces are introduced into the supercooled iPBu melt, and the effects of isothermal crystallization temperature (Tc) and shear temperature (Tshear) on crystallization and phase transition are explored. Shear‐induced transcrystalline morphology of Form II with a significantly shortened crystallization induction period can be observed at relatively high Tc (105 °C). Besides, the shear‐induced Form II can transit to Form I faster than the unsheared one. In addition, the phase transition rate increases as the Tshear decreases, with the fastest rate occurring at Tshear of 120 °C. The half transition time (t1/2) is measured as 6.3 h when Tc = 105 °C, Tshear = 120 °C, which is much shorter than the 20.7 h required for unsheared samples. The accelerated phase transition of iPBu can be attributed to the stretching of molecular chains, resulting from shear treatment. This study provides a quantitative analysis of the influence of the shear treatment and the Tshear on the II–I phase transition rate. It also presents a cost‐effective and straightforward approach for expediting the phase transition process. [ABSTRACT FROM AUTHOR]

Published
2024
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