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High Entropy Alloys (HEA) are a new type of metal material with broad application prospects in the high-end equipment manufacturing industry.Due to the unique phase structure and “four major effects” of high entropy alloys， which endow them with excellent mechanical properties such as strength，hardness，toughness and wear resistance，high entropy alloys have become a research hotspot in the field of materials.Corrosion is a common and unavoidable form of failure in metallic materials，and high entropy alloys are widely used in industrial fields due to their excellent corrosion resistance.Thus， it is necessary to focus on studying the corrosion resistance performance， corrosion resistance mechanism and laws of high entropy alloys for better developing high-performance high entropy alloys.This article firstly briefly described the definition and classification of high entropy alloys， then summarized the preparation methods of high entropy alloys， and provided a detailed overview of the research status of high entropy alloy corrosion resistance.Different preparation processes and process parameters had a direct impact on the microstructure of high entropy alloys， and high entropy alloy components had broad design space.Therefore， by adjusting the preparation process parameters and component composition， the corrosion resistance of high entropy alloys could be further optimized.At last，the research topics and ideas for future high entropy alloys were discussed.

AIM: To detect the expression levels of long non-coding RNA(lncRNA)X-inactive specific transcript(XIST)and silencing information regulatory factor 2 associated enzyme 1(SIRT1)in serum of patients with type 2 diabetes mellitus(T2DM), and to explore their correlation with diabetic retinopathy(DR)and their diagnostic value. METHODS: Prospective study. A total of 214 patients with T2DM admitted to our hospital from January 2022 to February 2023 were selected as the research subjects. Based on whether retinopathy occurred, they were divided into 126 cases(126 eyes)in the non-DR group and 88 cases(88 eyes)in the DR group. An additional 130 healthy individuals who underwent a physical examination during the same period were selected as the control group. The serum levels of lncRNA XIST and SIRT1 in the three groups were measured and compared. The relationship between lncRNA XIST and SIRT1 expression with DR was analyzed using Pearson's method. The receiver operating characteristic(ROC)curve was used to evaluate the predictive value of serum lncRNA XIST, SIRT1, and their combination for DR. Multivariate Logistic regression analysis was performed to investigate the factors affecting the occurrence of DR in T2DM patients.RESULTS: Compared with the control group, the levels of serum lncRNA XIST and SIRT1 in the non-DR group and DR group were successively decreased(all P

This paper adopts a nonlinear support vector machine to categorize the information on students’ ideology and politics in colleges and universities, and completes the construction of a dynamic early warning mechanism for ideology and politics education. Firstly, it builds the early warning system, establishes the students’ civic and political information file, designs the user information module of the system, then sets the index system according to the opinions of the civic and political teachers in colleges and universities, and designs the civic and political dynamics early warning module based on this. Following the completion of the design, four groups of tests were conducted. Colleges A and B were used to implement the system for individual testing, followed by early warning testing using data from multiple colleges simultaneously. Finally, the most significant indicators were tested using group condition analysis. The various tests show that the system response speed is 322ms, which is lower than the industry requirement of 1s. Among the three metrics tested, the system accuracy of this paper is the highest, at 87.2%, 88.1%, and 80.7%, respectively. The X5 scores of 87.56% of the students in each university were generally low, in the range of 1 to 2, indicating that the students were not clear about the importance of the Civics program, but the X12 scores of 94.52% of the students were high, indicating that they believed that the Civics program should be retained. Obtaining a high level of Civics education is made possible by achieving a mission responsibility consistency score of 0.865.

This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.