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Number of results: 11
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Abstract

Senecavirus A (SVA) the only member of the Senecavirus genus within the Picornaviridae family, is an emerging pathogen causing swine idiopathic vesicular disease and epidemic transient neonatal losses. Here, SVA strain (CH-HNKZ-2017) was isolated from a swine farm exhibiting vesicular disease in Henan Province of Central China. A phylogenetic analysis based on complete genome sequence indicated that CH-HNKZ-2017 was closely related to US-15-40381IA, indica- ting that a new SVA isolate had emerged in China.
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Abstract

In order to predict the distribution of shrinkage porosity in steel ingot efficiently and accurately, a criterion R√L and a method to obtain its threshold value were proposed. The criterion R√L was derived based on the solidification characteristics of steel ingot and pressure gradient in the mushy zone, in which the physical properties, the thermal parameters, the structure of the mushy zone and the secondary dendrite arm spacing were all taken into consideration. The threshold value of the criterion R√L was obtained with combination of numerical simulation of ingot solidification and total solidification shrinkage rate. Prediction of the shrinkage porosity in a 5.5 ton ingot of 2Cr13 steel with criterion R√L>0.21 m・℃1/2・s -3/2 agreed well with the results of experimental sectioning. Based on this criterion, optimization of the ingot was carried out by decreasing the height-to-diameter ratio and increasing the taper, which successfully eliminated the centreline porosity and further proved the applicability of this criterion.
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Abstract

Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.
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