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Four types of porcine epidemic diarrhea virus (PEDV) variants with a large deletion in the spike protein were detected, together with the original US PEDV, from pig fecal and oral fluid samples collected during 2016-2017 in the US. Two of the variants are similar to those identified in Japan: one contains a 194-aa deletion, the same as PEDV variant TTR-2/JPN/2014, while the other contains a 204-aa deletion, the same as PEDV variant JKa-292/CS1de204. Two new S1 NTD-del PEDV variants were found: one contains a 201-aa deletion located at residues 30-230 and the other contains a 202-aa deletion located at residues 24-225 of the S protein. This is the first report on coinfection of S1 NTD-del PEDV variants and the original US PEDV strain in US pigs, indicating that PEDV continues to evolve in pigs and might be responsible for disease pattern changes.

It has been more than 30 years since the French pioneered the research of the apple harvesting robot, and with the joint efforts of scholars all around the world, a variety of prototypes have been developed. However, the existing apple harvesting robot prototype is still in the experimental research stage because of its low harvesting efficiency. With the help of information technology, the related research has ushered in a milestone development, and it is full of opportunities and challenges for apple harvesting robotic researchers. In this article, it briefly introduced the development history, structure, and composition of apple harvesting robots and the operation process, which makes readers have a clear understanding of apple harvesting robot and its harvesting principle. Then systematically summarizing the research results of apple harvesting robots both at domestic and at foreign, we carried out in following three aspects: rapid and accurate recognition and positioning of target fruit, all-weather operation mode, and application of intelligent computing theory in apple harvesting robots, and it analyzes the research progress of apple harvesting robot in detail. The results show that improving the harvesting efficiency is the key and hot spot for the research on apple harvesting robots. Under the impetus of information technology, how to achieve fast and accurate recognition of the fruits of multienvironment and multiple information, obtain reasonable path planning, and further optimization of control strategies are all important research directions.

Summary This article investigates the issue of adaptive finite‐time tracking control for a category of output‐constrained nonlinear systems in a non‐strict‐feedback form. First, by utilizing the structural characteristics of radial basis function neural networks (RBF NNs), a backstepping design method is extended from strict‐feedback systems to a kind of more general systems, and NNs are employed to approximate unknown nonlinear functions. In addition, the system output is constrained to the specified region by applying the barrier Lyapunov function (BLF) technique. Furthermore, the finite‐time stability of the system is proved by employing the Bhat and Bernstein theorem. As a result, an adaptive finite‐time tracking control scheme for the output‐constrained nonlinear systems with non‐strict‐feedback structure is proposed by applying RBF NNs, BLF, finite‐time stability theory, and adaptive backstepping technique. It is demonstrated the finite‐time stability of the system, the prescribed convergence of the system output and tracking error, the boundedness of adaptive parameters and state variables. Finally, a simulation example is implemented to illustrate the effectiveness of the presented neural control scheme.

Infectious Bursal Disease Virus (IBDV) has haunted the poultry industry with severe, prolonged immunosuppression of chickens when infected at an early age and can easily lead to other secondary infections. Understanding the pathogenic mechanisms could lead to effective prevention and control of Infectious Bursal Disease (IBD). Evidence suggests that the N-terminal domain of polymerase in segment B plays an important role, but it is not clear which part or residual is crucial for the pathogenicity. Using a reverse genetics technique, a molecular clone (rNN1172) of the parental vvIBDV strain NN1172 was generated, and its pathogenicity was found to be the same as the parental virus. Then, three recombinant chimeric viruses were rescued based on the rNN1172 and substituted with the counterparts in the N-terminal domain of the attenuated vaccine strain B87: the rNN1172-B87VP1a (substituting the full region of the 1–167 aa residuals), the rNN1172-B87VP1a∆4 (substituting the region of the 5–167 aa residuals), and the rNN1172-VP1∆4 (one single aa residual substitution V4I), to better explore the role of the N-terminal domain of polymerase on the viral pathogenicity. Interestingly, all these substitutions played different roles in the viral pathogenicity: the mortality of the rNN1172-B87VP1a-challenged chickens was significantly reduced from 30% to 0%. No obvious lesion was found in the histopathological examination, and the lowest viral genome copy number was also detected in the bursa when compared to the parental and two other recombinant viruses. The mortalities caused by rNN1172-B87VP1a∆4 and rNN1172-B87VP1∆4, respectively, were all reduced to 10% and had a delayed onset of death. Our results also revealed that the pathogenicity of the IBDV was consistent with the viral replication efficiency in vivo (bursae). This study demonstrated that the full region of the N-terminal of polymerase plays an important role in viral replication and pathogenicity, but the substitutions of its partial region or a single residual do not completely lead to the virus attenuation to Three-Yellow chickens, although that significantly reduces its pathogenicity.

To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.

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