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| 1 | A joint call for actions to advance taxonomy in China显示文摘Taxonomy plays an important role in understanding the origin, evolution, and ecological functionality of biodiversity. There are large number of unknown species yet to be described by taxonomists, which together with their ecosystem services cannot be effectively protected prior to description. Despite this, taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents. Further, the impact factordriven evaluation systems in China exacerbate this downward trend, so alternative evaluation metrics are urgently necessary. When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation. In light of these challenges, all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China. | Chaodong Zhu Arong Luo Ming Bai Michael COrr Zhonge Hou Siqin Ge Jun Chen Yibo Hu Xuming Zhou Gexia Qiao Hongzhi Kong Limin Lu Xiaohua Jin Lei Cai Xinli Wei Ruilin Zhao Wei Miao Qingfeng Wang Zhongli Sha Qiang Lin Meng Qu Jianping Jiang Jiatang Li Jing Che Xuelong Jiang Xiaoyong Chen Lianming Gao Zongxin Ren Chunlei Xiang Shixiao Luo Donghui Wu Dong Liu Yanqiong Peng Tao Su Chenyang Cai Tianqi Zhu Wanzhi Cai Xingyue Liu Hu Li Huaijun Xue Zhen Ye Xuexin Chen Pu Tang Shujun Wei Hong Pang Qiang Xie Feng Zhang Feng Zhang Xianjin Peng Aibing Zhang Taiping Gao Changfa Zhou Chen Shao Libin Ma Zhaoming Wei Yunxia Luan Ziwei Yin Wu Dai Cong Wei Xiaolei Huang Jingxian Liu Xiangsheng Chen Tianci Yi Zhisheng Zhang Zhulidezi Aishan Qin Li Hongying Hu | 2022 | Zoological Systematics2022，47，3： | 1 |
| 2 | Ex- perimental Study on the Compression Properties and Failure Mechanism of 3D Integrated Woven Spacer Composites 显示文摘 | Zhao Chuangqi Li Diansen Ge Tianqi | 2004 | Composite Structures2004，65，： | 1 |
| 3 | Experimental study on the compression properties and failure mechanism of 3D integrated woven spacer composites显示文摘 | Zhao Chuangqi Li Diansen Ge Tianqi | 2014 | Materials and Design2014，56，6： | 1 |
| 4 | Isolation and Identification of Pseudo Seven-Coordinate Ru(Ⅲ)Intermediate Completing the Catalytic Cycle of Ru-bda Type of Water Oxidation Catalysts显示文摘Isolation of RuⅢ-bda(17-electron specie)complex with an aqua ligand(2-electron donor)is challenging due to violation of the 18-electron rule.Although considerable efforts have been dedicated to mechanistic studies of water oxidation by the Ru-bda family,the structure and initial formation of the Ru^(Ⅲ)-bda aqua complex are still controversial.Herein,we challenge this often overlooked step by designing a pocketshape Ru-based complex 1.The computational studies showed that 1 possesses the crucial hydrophobicity at the Ru^(V)(O)state as well as similar probability of access of terminal O to solvent water molecules when compared with classic Ru-bda catalysts.Through characterization of single-crystal structures at the Ru^(Ⅱ) and Ru^(Ⅲ) states,a pseudo seven-coordinate“ready-togo”aqua ligand with Ru^(Ⅲ)…O distance of 3.62A was observed.This aqua ligand was also found to be part of a formed hydrogen-bonding network,providing a good indication of how the Ru^(Ⅲ)-OH_(2)complex is formed. | Tianqi Liu Ge Li Nannan Shen Linqin Wang Brian J.J.Timmer Shengyang Zhou Biaobiao Zhang Alexander Kravchenko Bo Xu Marten S.G.Ahlquist Licheng Sun | 2022 | CCS Chemistry2022，4，7： | 1 |
| 5 | Monaural speech enhancement combining deep neural network and convex optimization显示文摘The accuracy of noise estimation directly affects the quality of speech enhancement algorithm.To improve the noise suppression effect of current speech enhancement algorithm when noise is estimated and effectively solve the unconstrained optimization problem,a time-frequency mask algorithm based on DNN combined with convex optimization is proposed for monaural speech enhancement.Firstly,the power spectra of noisy speech is extracted as the input of DNN.Secondly,the inter-channel correlation factor between noise and noisy speech is taken as the training target of DNN.Next,the objective function of convex optimization is constructed by using the correlation factor obtained from DNN model.Finally,a new hybrid conjugate gradient method combined with convex optimization,is used for iterative processing on an initial mask.The final mask is used to obtain the enhanced speech.Compared with conventional methods,the simulation results show that under different background noise with low SNR,the obtained ratio mask makes the enhanced speech achieve better LSD,PESQ,STOI and segSNR indices,and improves the overall quality of speech and can effectively suppress noise. | ZHANG Xiaoyan ZHANG Tianqi GE Wanying BAI Yangliu | 2021 | Chinese Journal of Acoustics2021，40，3： | 0 |
| 6 | A case of diffuse large B-cell lymphoma with interstitial pneumonia显示文摘This report involves a 54-year-old female patient diagnosed with diffuse large B-cell lymphoma who developed interstitial pneumonia(IP)during treatment.The patient presented to the ward with enlarged lymph nodes in the neck and was treated with the standard regimen,which included rituximab,cyclophosphamide,doxorubicin liposomes,vincristine,and prednisone(R-CDOP regimen).After 3 cycles,the treatment was assessed as effective.However,following the 4th cycle,the patient experience shortness of breath after physical activity.A repeat lung computer tomography indicated IP,which completely recovered after receiving“full coverage”treatment.Subsequently,the patient underwent 2 cycles of cyclophosphamide,doxorubicin liposomes,vincristine,and prednisone(CDOP),followed by local radiotherapy.Currently,the patient is now being followed up with regular reviews. | Ge Song Changxi Zhou Shuxia Wang Tianqi Tao Weiping Guan Xuan Wu Ping Zhu Bo Yang Xuechun Lu | 2023 | Cancer Pathogenesis and Therapy2023，1，3： | 0 |
| 7 | Music separation method based on repeating structural model and sound source separation显示文摘In order to solve the problem of separation between singing and accompaniment in the musical signals,a music separation method based on repeating structural model and sound source separation is proposed.Firstly,the harmonic source and the percussive source of the music are separated by iterative method.Then the beat spectrum was introduced to analyze the energy spectrum matrix of the different sound sources.And the repeated periodic components of the harmonic source are retained to obtain accompaniment while the repeated periodic components of the percussive source are removed to separate the singing voice.On the MIR-1 K dataset,the separation experimental results of 1000 music fragments show that the proposed method has an advantage over other existing music separation methods. | ZHANG Tian ZHANG Tianqi GE Wanying YU Shengqi | 2020 | Chinese Journal of Acoustics2020，39，4： | 0 |
| 8 | Monaural noisy speech separation combining sparse non-negative matrix factorization and deep attractor network显示文摘The performance of the monaural speech separation method is limited when the speech mixture is disordered by background noise.To obtain the enhanced separated speech from the noisy mixture,a monaural noisy speech separation method combining sparse nonnegative matrix factorization(SNMF)and deep attractor network(DANet)is proposed.This method firstly decomposes the noisy mixture into coefficients of speech and noise respectively.Then the speech coefficient is projected to a high-dimensional embedding space and a DANet is trained to force the embeddings to move to different clusters.The attractor points are used to separate the speech coefficients by masking method,and finally the enhanced separated speeches are reconstructed by the speech basis and their corresponding coefficients.Experimental results in various background noise environments show that the proposed algorithm effectively suppress the noises without decreasing the quality of reconstructed speech by comparison with different baseline methods. | GE Wanying ZHANG Tianqi FAN Congcong ZHANG Tian | 2021 | Chinese Journal of Acoustics2021，40，2： | 0 |
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