The intestinal immune system is crucial for protection from pathogenic infection and maintenance of mucosal homeostasis. We studied the intestinal immune microenvironment in a serovar Typhimurium intestinal infection mouse model. Intestinal lamina propria macrophages are the main effector cells in innate resistance to intracellular microbial pathogens. We found that Typhimurium infection augmented Tim-3 expression on intestinal lamina propria CD4 T cells and enhanced galectin-9 expression on F4/80 CD11b macrophages. Moreover, CD4 T cells promoted the activation and bactericidal activity of intestinal F4/80 CD11b macrophages via the Tim-3/galectin-9 interaction during Typhimurium infection. Blocking the Tim-3/galectin-9 interaction with α-lactose significantly attenuated the bactericidal activity of intracellular Typhimurium by macrophages. Furthermore, the Tim-3/galectin-9 interaction promoted the formation and activation of inflammasomes, which led to caspase-1 cleavage and interleukin 1β (IL-1β) secretion. The secretion of active IL-1β further improved bactericidal activity of macrophages and galectin-9 expression on macrophages. These results demonstrated the critical role of the cross talk between CD4 T cells and macrophages, particularly the Tim-3/galectin-9 interaction, in antimicrobial immunity and the control of intestinal pathogenic infections.

This study presents a combined model based on the exploratory factor analysis (EFA) and the least square support vector machine (LSSVM) to predict the contamination degree of insulator surface. Firstly, EFA method is utilised to reduce numerous influence factor variables of the insulator contamination into a few factor variables, which could decrease the complexity of the model. Then, regarding the above factor variables as new input variables, LSSVM model is established to predict the insulator contamination degree. In order to obtain the optimal predictive value, the non‐dominated sorting genetic algorithm II is applied on the optimization of LSSVM model parameters. The proposed EFA‐LSSVM combined model is compared with the models of LSSVM, back propagation neural network, and multiple linear regression on the model performance. Results indicate that the EFA‐LSSVM combined model in this study effectively overcomes the shortcomings of the other three models mentioned above in computational time, prediction accuracy and generalization ability. Finally, the feasibility of the proposed model in predicting contamination degree of insulator surface is verified by adopting the radar map of the evaluation indexes of model performance.

Composite insulators, which use high temperature vulcanized (HTV) silicone rubber as shed material, are widely applied in transmission lines. Ice accumulation on their surfaces may inflict flashover accident and even massive power outage. In this study, a superhydrophobic (SH) coating on HTV silicone rubber was fabricated by the sol–gel process combined with the plasma jet treatment method. It was found that the as-prepared SH coating exhibited prominent superhydrophobicity and an excellent self-cleaning property with a water contact angle of 160.15°, a contact angle hysteresis of 0.60°, a sliding angle of 1.8°, and a surface free energy of 0.1421 mN/m. The anti-icing behavior of water droplets on the as-prepared SH coating surface was investigated at a low temperature of −30 °C and compared with that of the HTV silicone rubber surface. The results indicated that the freezing time on the SH coating was postponed obviously and was as long as 150 s. The SH coating surface exhibited about 5.6 times delay in freezing at −30 °C compared with the HTV silicone rubber surface. Furthermore, heterogeneous nucleation theory and heat transfer theory were introduced to explain the difference in freezing time between the as-prepared SH coating and HTV silicone rubber. It could be concluded that the SH coating had a large nucleation free energy barrier and a low heat transfer rate between the droplet and the surface and, thus, was able to effectively delay the freezing time.

A combined model based on improved information entropy and vague support vector machine (IVSVM) is introduced into transformer fault diagnosis using dissolved gas analysis in oil (DGA). The improved information entropy method is used to obtain the weights of each gas and to weight the raw data, and the processed training data and the corresponding fault types are inputted into the vague support vector machine (VSVM) model to obtain classifiers. Firstly, the training data are weighted by the improved information entropy method to discretise the original data from the mixed state for subsequent classifier training. Then, the vague set divides the events into true, false and unknown factors, which can optimise the sub-interface of SVM and improve the accuracy of the boundary point classification. Finally, fault data from the literature and actual collections are selected for training and testing. By comparing with the widely used ratio method and artificial intelligence method, it can be concluded that the method described herein can effectively improve the accuracy of fault diagnosis. The result shows that this method has better applicability when facing actual fault type classification with higher data similarity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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