Cerebral infarction is a severe hypoxic ischemic necrosis with accelerated neuronal cell apoptosis in the brain. As a monoclonal antibody against interleukin 6, tocilizumab (TCZ) is widely used in immune diseases, whose function in cerebral infarction has not been studied. This study aims to reveal the role of TCZ in regulating neuronal cell apoptosis in cerebral infarction. The cerebral infarction rat model was constructed by middle cerebral artery occlusion and treated with TCZ. Cell apoptosis in hippocampus and cortex of the brain was examined with TUNEL method. Rat neuronal cells cultured in oxygen-glucose deprivation (OGD) conditions and treated with TCZ were used to compare cell viability and apoptosis. Apoptosis-related factors including B-cell lymphoma extra large (Bcl-xL) and Caspase 3, as well as the phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in brain cortex were analyzed from the protein level. Results indicated that TCZ treatment could significantly prevent the promoted cell apoptosis caused by cerebral infarction or OGD (P < 0.05 or P < 0.01). In brain cortex of the rat model, TCZ up-regulated Bcl-xL and down-regulated Caspase 3, consistent with the inhibited cell apoptosis. It also promoted tyrosine 705 phosphorylation of STAT3, which might be the potential regulatory mechanism of TCZ in neuronal cells. This study provided evidence for the protective role of TCZ against neuronal cell apoptosis in cerebral infarction. Based on these fundamental data, TCZ is a promising option for treating cerebral infarction, but further investigations on related mechanisms are still necessary.

To achieve efficient and accurate remaining life prediction and effectively express the uncertainty of prediction results, this paper proposes a remaining life prediction method based on fuzzy evaluation-Gaussian process regression (FE-GPR). First, the prediction of the remaining useful life (RUL) is affected by unknown variables, such as the environment, and it is difficult to achieve accurate predictions. It is necessary to effectively express the uncertainty of such prediction results. In this paper, we have put forward a RUL prediction method based on GPR, which can realize the RUL prediction with a confidence interval. Second, combined with the characteristics of the GPR method, an observation data preprocessing method based on fuzzy evaluation is proposed. The initial fuzzy evaluation method is established based on expert knowledge. Then, the classification nodes are optimized by the gravitational search algorithm (GSA) and historical data. This method, which uses fuzzy logic combined with expert knowledge, can avoid over-fitting in the case of limited data, and effectively improves the prediction accuracy of the GPR model. Finally, we use NASA PCoE. lithium battery data for a case study. The results show that the FE-GPR method achieves a more accurate RUL prediction and effectively reflects the uncertainty of the prediction results.

No abstract

Following the development of globalization, various industries not only in their own country, but also in the whole world, the relationship between peers is closer, the scale of technology exchange, experience exchange, research results exchange is gradually expanding, the gap of applied English talents is increasing, and more demands are put forward for the training and teaching of specialized English skilled personnel. Under the vigorous expansion situation of MOOC and SPOC, this paper introduces the concepts and processes of MOOC and SPOC, and discusses the relationship between them and their respective characteristics and advantages. The author finds that it is this post-MOOC era that provides a rich approach and a new platform for ESP teaching experiments in colleges and universities.

No abstract

In the field of high-end manufacturing, it is valuable to study few-shot health condition estimation. Although transfer learning and other methods have effectively improved the ability of few-shot learning, they still cannot solve the lack of prior knowledge. In this paper, by combining data enhancement, knowledge reasoning, and transfer learning, a generative knowledge-based transfer learning model is proposed to achieve few-shot health condition estimation. First, with the effectiveness of data enhancement on machine learning, a novel batch monotonic generative adversarial network (BM-GAN) is designed for few-shot health condition data generation, which can solve the problem of insufficient data and generate simulated training data. Second, a generative knowledge-based transfer learning model is proposed with the performance advantages of the belief rule base (BRB) method on few-shot learning, which combines expert knowledge and simulated training data to obtain a generalized BRB model and then fine-tunes the generalized model with real data to obtain a dedicated BRB model. Third, through uniform sampling of NASA lithium battery data and simulating few-shot conditions, the generative transfer-belief rule base (GT-BRB) method proposed in this paper is verified to be feasible for few-shot health condition estimation and improves the estimation accuracy of the BRB method by approximately 17.3%.

This paper presents a new scheme for the online solution of a networked multi-agent pursuit–evasion game based on an online adaptive dynamic programming method. As a multi-agent in the game can form an Internet of Things (IoT) system, by incorporating the relative distance and the control energy as the performance index, the expression of the policies when the agents reach the Nash equilibrium is obtained and proved by the minmax principle. By constructing a Lyapunov function, the capture conditions of the game are obtained and discussed. In order to enable each agent to obtain the policy for reaching the Nash equilibrium in real time, the online adaptive dynamic programming method is used to solve the game problem. Furthermore, the parameters of the neural network are fitted by value function approximation, which avoids the difficulties of solving the Hamilton-Jacobi–Isaacs equation, and the numerical solution of the Nash equilibrium is obtained. Simulation results depict the feasibility of the proposed method for use on multi-agent pursuit–evasion games.

No abstract