African swine fever (ASF) is a highly contagious, acute, febrile disease caused by the African swine fever virus (ASFV), with morbidity and mortality rates approaching 100% in domestic and wild swine, resulting in massive economic losses to the pig industry worldwide. This study aimed to express the p30, p54, and p72 proteins encoded by ASFV in vitro using the Lactobacillus lactis (L. lactis) expression system. Here, six new functional recombinant L. lactis were constructed, and the expression of the p30 protein, p54 protein, p72 protein, p30-LTB (heat-labile enterotoxin B, LTB) fusion protein, p54-LTB fusion protein, and the p72-LTB fusion protein was successfully detected by Western blot analysis. Following oral immunization of rabbits with recombinant L. lactis, serum IgG, intestinal mucosal sIgA, cytokines (IL-4 and INF-γ), and splenocyte viability were higher than in the control group via ELISA. Notably, without the LTB adjuvant group, humoral and Th1 cellular immunity were promoted, whereas, with the LTB adjuvant group, local mucosal immunity, humoral immunity, and Th2 cellular immunity were promoted, providing new insights into the design and development of an ASFV subunit vaccine.

In order to improve the recognition accuracy of graphene pressure sensors, a graphene/silicon pressure sensor array is studied based on backpropagation (BP) neural network. The principle of the pressure sensor array and the workflow of BP neural network are introduced. The 100 groups of training samples ranging from 0 to 1000 kPa are studied based on levenberg-marquardt (L-M) optimisation algorithm, and a multiple BP (M-BP) neural network is designed to improve the recognition accuracy of the pressure sensor array. The training recognition accuracy and test recognition accuracy of M-BP neural network are about 99.9%. This work plays an important role in the application of graphene pressure sensors in more fields, especially in the solutions for weak pressure detection in artificial intelligence and human-computer interaction.

It is widely accepted that graded buffer layer between the p-layer and i-layer increase the efficiency of amorphous silicon solar cells. The open-circuit voltage (Voc), short current density (Jsc) and fill factor (FF) of the thin film solar cell are obviously increased. In the present study, hydrogenated amorphous silicon (a-Si:H) thin film solar cells have been fabricated by 27.12 MHz plasma enhanced chemical vapor deposition (PECVD). We discussed the three conditions at the p/i interface without buffer layer, buffer layer and graded buffer layer of thin film solar cells by TCAD software. The influences of the performance of the solar cell with the different buffer layer are investigated. The cell with graded buffer layer has higher efficiency compared with the cells without buffer layer and buffer layer. The graded buffer layer enhances the conversion efficiency of the solar cell by improving Vocand FF. It could be attributed to a reduction of interface recombination rate near the junction. The best performance of conversion efficiency (η)=8.57% (Voc=0.81 V, Jsc=15.46 mA/cm2, FF=68%) of the amorphous silicon thin film solar cell was achieved.

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