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The high specific strength makes magnesium alloys have a wide range of applications in aerospace, military, automotive, marine and construction industries. However, its poor corrosion resistance and weldability have limited its development and application. Friction stir welding (FSW) can effectively avoid the defects of fusion welding. However, the microstructure, mechanical properties and corrosion behavior of FSW joints in magnesium alloys vary among different regions. The purpose of this paper is to review the corrosion of magnesium alloy FSW joints, and to summarize the protection technology of welded joints.

The corrosion of magnesium alloy FSW joints includes electrochemical corrosion and stress corrosion. This paper summarizes corrosion protection techniques for magnesium alloys FSW joints, focusing on composition, microstructure changes and surface treatment methods.

Currently, this research is mainly focused on enhancing the corrosion resistance of magnesium alloy FSW joints by changing compositions, structural modifications and surface coating technologies. Refinement of the grains can be achieved by adjusting welding process parameters, which in turn minimizes the effects of the second phase on the alloy’s corrosion resistance.

This paper presents a comprehensive review on the corrosion and protection of magnesium alloys FSW joints, covering the latest research advancements and practical applications. It aims to equip researchers with a better insight into the field and inspire new studies on this topic.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.