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African swine fever (ASF) is a highly contact infectious disease caused by the African swine fever virus (ASFV). The extremely complex structure and infection mechanism make it difficult to control the spread of ASFV and develop the vaccine. The ASFV genome is huge with many antigenic genes. Among them, CP204L (p30), CP530R (pp62), E183L (p54), B646L (p72), and EP402R (CD2v) are involved in the process of the virus cycle, with strong immunogenicity and the ability to induce the body to produce neutralizing antibodies. In this study, the recombinant virus rBartha-K61-pASFV that expresses the above ASFV antigen genes was constructed by Red/ET recombineering technology using pseudorabies virus (PRV) vaccine strain Bartha-K61. Western blot analysis showed that the ASFV antigen gene was expressed and the recombinant virus showed good genetic stability and proliferation characteristics in 15 continuous generations on porcine kidney (PK15) cells. The results of immunoassay of piglets and mice showed that rBartha-K61-pASFV had good immunogenicity and could induce higher antibody levels in the body. Therefore, PRV was a promising viral vector for expressing the ASFV antigen gene, and all the experiments in this study laid a foundation for the further development of a new viral vector vaccine of ASFV.

In order to efficiently evaluate the operating status of photovoltaic (PV) arrays, a design of fast current-voltage (I-V) characteristic tester is proposed in this paper. The tester uses the dynamic capacitor charging method to quickly sweep I-V characteristic curves. Then, a new artificial bee colony and Nelder-Mead simplex (ABC-NMS) hybrid algorithm is used to extract parameters of the single diode model of PV arrays. The experimental results show that the tester can quickly measure high-quality I-V curves of PV array and achieve accurate model parameters extraction. In addition, the root mean square error (RMSE) of curve fitting can be used to effectively indicate the partial shading condition.

Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are carried out on the both laboratory PV array and the PV model to acquire abundant simulated and experimental fault data samples. The optimized KELM is then applied to train the fault diagnosis model using the data samples. Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance.