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This article attempts to discover whether the National People's Congress (NPC), China's law-making body, is becoming more specialized and thereby losing its rubber-stamp image. The exploration of the composition of the NPC's permanent committees demonstrates that specialization is indeed a discernible trend. More and more social elites have been co-opted onto them. The empirical results further attest to the usefulness of the information efficiency theory. Unfortunately, specialization has yet to give the Chinese legislature more autonomy. Only 30% of committee members are privileged to serve more than one five-year term. The influence of the Party-state core and the lack of membership stability have put additional restraints on the NPC's autonomy. (J Contemp China/GIGA)

SIMPLE SUMMARY: Visual identification of cattle in a realistic farming environment is helpful for real-time cattle monitoring. Based on continuous cattle detection, identification, and behavior recognition, it is possible to utilize cameras on farms within company or government networks to provide the services of production supervision, early disease detection, and animal science research for precision livestock farming. However, cattle identification in the wild is still a difficult problem due to the high similarities of different identities and the variances of the same identity as posture or perspective changes. Our proposed method based on deep convolutional neural networks and deep metric learning provides a promising approach for cattle identification and paves the way toward continuous monitoring of cattle in a nearly natural state. ABSTRACT: Visual identification of cattle in the wild provides an essential way for real-time cattle monitoring applicable to precision livestock farming. Chinese Simmental exhibit a yellow or brown coat with individually characteristic white stripes or spots, which makes a biometric identifier for identification possible. This work employed the observable biometric characteristics to perform cattle identification with an image from any viewpoint. We propose multi-center agent loss to jointly supervise the learning of DCNNs by SoftMax with multiple centers and the agent triplet. We reformulated SoftMax with multiple centers to reduce intra-class variance by offering more centers for feature clustering. Then, we utilized the agent triplet, which consisted of the features and the agents, to enforce separation among different classes. As there are no datasets for the identification of cattle with multi-view images, we created CNSID100, consisting of 11,635 images from 100 Chinese Simmental identities. Our proposed loss was comprehensively compared with several well-known losses on CNSID100 and OpenCows2020 and analyzed in an engineering application in the farming environment. It ...

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.