Faults locating of power distribution systems based on successive PSO-GA algorithm.

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information.

Dendritic cell-targeted recombinantLactobacilli induce DC activation and elicit specific immune responses against G57 genotype of avian H9N2 influenza virus infection.

H9N2 avian influenza viruses are of significance in poultry and public health for the past two decades. Vaccination plays an important role in preventing the infection in domestic poultry. Current H9N2 vaccines have not yet offered ideal protection and eliminated shedding of G57 genotype viruses responsible for H9N2 outbreaks during 2010-2013. Targeted vaccination is a promising strategy to improve vaccine effectiveness. Such a vaccine strategy can be achieved if it is targeted to dendritic cells (DCs) that directly elicit mucosal and adaptive immune responses against microbe challenge. For this purpose, we develop a DC-targeted mucosal vaccine for the oral delivery of the HA protein fused to a DCpep by using Lactobacillus plantarum as an antigen delivery system against G57 virus infection. It showed that Lactobacillus plantarum expressing HA-DCpep confers efficient protection against G57 H9N2 infection, due to have the potential to activate DCs by the TLR-induced NF-κB pathway, to promote DC migration by the CCR7-CCL19/CCL21 axis, thereby enhancing the presentation of immunogen to T and B lymphocytes, resulting in skewing T cells polarization towards Th1, Th2 and Treg cells and evoking more efficient mucosal and adaptive immunity responses. The presented oral mucosal vaccine strategy illustrates the feasibility and efficacy of antigen targeting to DCs through genetic fusion of vaccines to DC-targeting peptides and aids in the design and selection of indications that could be used with this oral vaccine platform against influenza.