Molecular structure and properties of click hydrogels with controlled dangling end defect

In this study, controlled amount of dangling ends is introduced to the two series of poly(ethylene glycol)‐based hydrogel networks with three and four crosslinking functionality by using click chemistry. The structure of the gels with regulated defect percentage is confirmed by comparing the results of low‐field NMR characterization and Monte Carlo simulation. The mechanical properties of these gels were characterized by tensile stress–strain behaviors of the gels, and the results are analyzed by Gent model and Mooney–Rivlin model. The shear modulus of the swollen gels is found to be dependent on the functionality of the network, and decreases with the defect percentage. Furthermore, the value of shear modulus well obeys the Phantom model for all the gels with varied percentage of the defects. The maximum extension ratio, obtained from the fitting of Gent model, is also found to be dependent on the functionality of the network, and does not change with the defect percentage, except at very high defect percentage. The value of the maximum extension ratio is between that predicted from Phantom model and the Affine model. This indicates that at the large deformation, the fluctuation of the crosslinking points is suppressed for some extend but still exists. Polymer volume fractions at various defect percentages obtained from prediction of Flory–Rehner model are found to be in well agreement with the swelling experiment. All these results indicate that click chemistry is a powerful method to regulate the network structure and mechanical properties of the gels. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016 , 54, 1227–1236     

Polymer networks functionalized with low-valent phosphorus cations

In this work, we show that polymer networks composed of tertiary alkyl phosphines can be cleanly functionalized with phosphino-phosphonium or triphosphenium cations. Methods for functionalizing the polymers range from halide abstraction of commercially available reagents, to ligand exchange from simple to make reported compounds, and finally, macromolecular ligand design guided by observations made at the molecular level to accommodate the formation of kinetically favored triphosphenium cation functionalized networks. The synthesis, comprehensive characterization, and comparison of the new polymers to molecular analogues is outlined. It is shown the addition of the low valent phosphorus centers to the polymer network has the effect of tuning material physical properties.     

基于级联卷积神经网络的前列腺磁共振图像分类

针对深度学习训练成本高，以及基于磁共振图像的前列腺癌临床诊断需要大量医学常识且极为耗时的问题，本文提出了一种基于级联卷积神经网络（Convolutional Neural Network，CNN）和磁共振图像的前列腺癌（Prostate Cancer，PCa）自动分类诊断方法，该网络以Faster-RCNN作为前网络，对前列腺区域进行提取分割，用于排除前列腺附近组织器官的干扰；以基于ResNet改进的网络结构CNN40_bottleneck作为后网络，用于对前列腺区域病变进行分类.后网络由瓶颈结构串联组成，其中使用批量标准化（Batch Normalization，BN）、全局平均池化（Global Average Pooling，GAP）进行优化.实验结果证明，本文方法对前列腺癌诊断结果较好，而且缩减了训练时间和参数量，有效降低了训练成本.     

Improved neural networks based on genetic algorithm for pulse recognition

Pulse diagnosis is an important part of Chinese medicine and has played an important role in the development of Chinese medical science. However, the pulse is traditionally determined by cutting it off, which leads to a lack of objective standard pulse identification methods and affects their accuracy and feasibility. This research has studied and discussed the processing and identification of four kinds of pulse: normal pulse, wiry pulse, smooth pulse, and thready pulse. Four frequency-domain characteristics of the pulse wave and six kinds of wavelet scale energy characteristic information were extracted, and a three-layer BP (backprocessing) neural network was established. The LM (Levenberg–Marquard) algorithm and a genetic algorithm were used to improve the BP neural network, to train on and predict experimental samples, and to obtain classification accuracies of 90% and 95% respectively. Moreover, improved BP neural network based on a genetic algorithm has shown highly superior performance in terms of convergence speed and low error rate.     

Quantum control based on machine learning in an open quantum system

Designing robust control schemes in n-level open quantum system is significant for quantum computation. Here, we investigate two quantum control strategies based on supervised machine learning to suppress the quantum noise in an open quantum system. One is controlling state distance and the other is governing the average of a Hermitian operator. In this process, the dynamics of the system is mapped to a neural network where the control fields correspond to the weights. Besides, the system is transformed into the coherence Bloch space without using superoperator thus the complications are reduced largely. As an example, the two control protocols are demonstrated in a two-level and four-level systems, respectively. By applying these examples, the results show that the state of the system transfers to the target state and the average of a Hermitian operator to its minimum value in a given time despite disturbed by various types of noise.     

基于U-Net的盐体识别方法

卷积神经网络在计算机视觉领域取得重大突破，利用其强大的图像处理能力，将地下沉积盐体的识别问题转化为图像语义分割问题，应用深度卷积神经网络实现盐体地震图像的像素级语义分割.本文在U-Net基础上，增加网络深度并同时引入批归一化和Dropout处理，使得神经网络模型具有更高的可信度和更强的泛化能力.通过实验发现，在卷积层之后引入批归一化处理，并在池化层和叠加层之后引入Dropout可以稳定提升模型对盐体图像的分割性能.     

新组合模型在光电功率预测中的应用

为提高光伏预测要求的精准性,文章提出一种新算法将神经网络和ARMA算法改进组合,构成NEW ARMA-BP模型算法.以某30兆瓦的光伏电站采集的输出功率为输入样本,基于ARMA和BP神经网络算法在Matlab环境下依次搭建了相应的预测模型,预估光伏短期输出量.采用"误差正态检验图"判断基于两种不同算法的误差水平,依据两种单模型预测误差,运用所提出的新方法计算权值并获得新的预测值.基于Matlab的仿真结论验证了组合预测在光伏输出预测领域的适用性.     

Observation of chimera patterns in a network of symmetric chaotic finance systems

The economic and financial systems consist of many nonlinear factors that make them behave as the complex systems. Recently many chaotic finance systems have been proposed to study the complex dynamics of finance as a noticeable problem in economics. In fact, the intricate structure between financial institutions can be obtained by using a network of financial systems. Therefore, in this paper, we consider a ring network of coupled symmetric chaotic finance systems, and investigate its behavior by varying the coupling parameters. The results show that the coupling strength and range have significant effects on the behavior of the coupled systems, and various patterns such as the chimera and multi-chimera states are observed. Furthermore, changing the parameters' values, remarkably influences on the oscillators attractors. When several synchronous clusters are formed, the attractors of the synchronized oscillators are symmetric, but different from the single oscillator attractor.     

Applications of Markov spectra for the weighted partition network by the substitution rule

The weighted self-similar network is introduced in an iterative way. In order to understand the topological properties of the self-similar network, we have done a lot of research in this field.Firstly, according to the symmetry feature of the self-similar network, we deduce the recursive relationship of its eigenvalues at two successive generations of the transition-weighted matrix.Then, we obtain eigenvalues of the Laplacian matrix from these two successive generations.Finally, we calculate an accurate expression for the eigentime identity and Kirchhoff index from the spectrum of the Laplacian matrix.