url: =dblp: Shuiming Zhong\nShuiming Zhong, Xiaoxiang Lu, Meng Li, Chengguang Liu, Yong Cheng, Victor S. Sheng:\nAn Adaptive Construction Test Method Based on Geometric Calculation for Linearly Separable Problems. ICCCS (6) 2018: 392-405\nYan Xu, Jing Yang, Shuiming Zhong:\nAn online supervised learning method based on gradient descent for spiking neurons. Neural Networks 93: 7-20 (2017)\nZhan Shi, Xiaoqin Zeng, Tingting Zhang, Song Huang, Zekun Qi, Hui Li, Bin Hu, Yi Yao, Shuiming Zhong:\nBidirectional transformation between BPMN and BPEL with graph grammar. Comput. Electr. Eng. 51: 304-319 (2016)\nYu Xue, Shuiming Zhong, Tinghuai Ma , Jie Cao:\nA Hybrid Evolutionary Algorithm for Numerical Optimization Problem. Intelligent Automation & Soft Computing 21(4): 473-490 (2015)\nAn ensemble algorithm with self-adaptive learning techniques for high-dimensional numerical optimization. Appl. Math. Comput. 231: 329-346 (2014)\nLihong Huang, Xiaoqin Zeng, Shuiming Zhong, Lixin Han:\nSensitivity study of Binary Feedforward Neural Networks. Neurocomputing 136: 268-280 (2014)\nYu Xue, Xiangmao Chang, Shuiming Zhong, Yi Zhuang:\nAn efficient energy hole alleviating algorithm for wireless sensor networks. IEEE Trans. Consumer Electronics 60(3): 347-355 (2014)\nJing Yang, Xiaoqin Zeng, Shuiming Zhong:\nComputation of multilayer perceptron sensitivity to input perturbation. Neurocomputing 99: 390-398 (2013)\nYan Xu, Xiaoqin Zeng, Shuiming Zhong:\nA New Supervised Learning Algorithm for Spiking Neurons. Neural Computation 25(6): 1472-1511 (2013)\nJing Yang, Xiaoqin Zeng, Shuiming Zhong, Shengli Wu:\nEffective Neural Network Ensemble Approach for Improving Generalization Performance. IEEE Trans. Neural Netw. Learning Syst. 24(6): 878-887 (2013)\nShuiming Zhong, Xiaoqin Zeng, Shengli Wu, Lixin Han:\nSensitivity-Based Adaptive Learning Rules for Binary Feedforward Neural Networks. IEEE Trans. Neural Netw. Learning Syst. 23(3): 480-491 (2012)\nShuiming Zhong, Xiaoqin Zeng, Huiyi Liu, Yan Xu:\nApproximate computation of Madaline sensitivity based on discrete stochastic technique. SCIENCE CHINA Information Sciences 53(12): 2399-2414 (2010)\nXianming Chen, Xiaoqin Zeng, Rong Chu, Shuiming Zhong:\nA quantified sensitivity measure of Radial Basis Function Neural Networks to input variation. IJCNN 2010: 1-6\nXiaoqin Zeng, Jing Shao, Yingfeng Wang, Shuiming Zhong:\nA sensitivity-based approach for pruning architecture of Madalines. Neural Computing and Applications 18(8): 957-965 (2009)\nYanjun Liu, Xiaoqin Zeng, Shuiming Zhong, Shengli Wu:\nA Sensitivity-Based Training Algorithm with Architecture Adjusting for Madalines. SMC 2009: 4586-4591\nA Novel Ensemble Approach for Improving Generalization Ability of Neural Networks. IDEAL 2008: 164-171\nYanjun Liu 0004\n[j12] [j5] [j2]\nYu Xue 0003\n[j12] [j6] [j4]\n[j11] [j8] [j6] [j5] [j4] [j3] [j2] [c3] [j1] [c2] [c1]\nYi Zhuang 0002


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url: =Engineering of a near-infrared fluorescent probe for real-time simultaneous visualization of intracellular hypoxia and induced mitophagy - Chemical Science (RSC Publishing)\nEngineering of a near-infrared fluorescent probe for real-time simultaneous visualization of intracellular hypoxia and induced mitophagy†\nYongchao Liu,a Lili Teng,a Lanlan Chen,a Hongchang Ma,a Hong-Wen Liu a and Xiao-Bing Zhang *a\nChem. Sci., 2018,9, 5347-5353\nEngineering of a near-infrared fluorescent probe for real-time simultaneous visualization of intracellular hypoxia and induced mitophagy\nY. Liu, L. Teng, L. Chen, H. Ma, H. Liu and X. Zhang, Chem. Sci., 2018, 9, 5347


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