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您的检索式：作者名="Wanping Cai"
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| 1 | Magnetism variation of the compressed antiferromagnetic topological insulator EuSn_(2)As_(2)显示文摘We report a comprehensive high-pressure study,up to 21.1 GPa,on the antiferromagnetic topological insulator EuSn_(2)As_(2) achieved through synchrotron X-ray diffraction,Raman scattering,electrical resistance,magnetic resistance,and Hall transport measurements in combination with first-principles calculations.The Néel temperatures determined from resistance are increased from(24±1)to(77±8)K under pressure,which is a result of enhanced magnetic exchange couplings between Eu^(2+) ions yielded by our first-principles calculations.The negative magnetoresistance of EuSn_(2)As_(2) persists to higher temperatures accordingly.However,the enhancement of the observed Néel temperatures deviates from the calculations above 10.0 GPa.In addition,the magnitude of the magnetoresistance,Hall coefficients,and charge carrier densities show abrupt changes between 6.9 and 10.0 GPa.The abrupt changes likely originate from a pressure-induced valence change of Eu ions from a divalent state to a divalent and trivalent mixed state or are related to the structural transition revealed by Raman scattering measurements.Our results provide insight into magnetism variation in EuSn_(2)As_(2) and similar antiferromagnetic topological insulators under pressure. | Hualei Sun Cuiqun Chen Yusheng Hou Weiliang Wang Yu Gong Mengwu Huo Lisi Li Jia Yu Wanping Cai Naitian Liu Ruqian Wu Dao-Xin Yao Meng Wang | 2021 | Science China(Physics,Mechanics & Astronomy)2021，64，11： | 0 |
| 2 | Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis显示文摘Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA. | LIANG GuiZhao CHEN ZeCong YANG ShanBin MEI Hu ZHOU Yuan YANG Li ZHOU Peng YANG ShengXi SHU Mao LIAO ChunYang WU ShiRong LI GenRong HE Liu GAO JianKun Gan MengYu LI DeJing CHEN GuoPing WANG GuiXue LONG Sha JING JuHua ZHENG XiaoLin ZENG Hui ZHANG QiaoXia ZHANG MengJun YANG Qi TIAN FeiFei TONG JianBo WANG JiaoNa LIU YongHong LI Bo QIU LiangJia CAI ShaoXi ZHAO Na YANG Yan SU XiaLi SONG Jian CHEN MeiXia ZHANG XueJiao SUN JiaYing LI JingWei CHEN GuoHua CHEN Gang DENG Jie PENG ChuanYou ZHU WanPing XU LuoNan WU YuQuan LIAO LiMin LI Zhi LI Jun LU DaJun SU QinLiang HUANG ZhengHu ZHOU Ping LI ZhiLiang | 2008 | Science China Chemistry2008，51，2： | 0 |
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