Abstract With the increasing requirements of the financial market, the approval standards for listing on the stock market have become higher, leading to many private enterprises being excluded. As a result, a method called “backdoor listing” has become popular in the financial market for entry. To demonstrate their strength, many companies choose to sign performance commitments before listing, with all employees committing to performance, and... With the increasing requirements of the financial market, the approval standards for listing on the stock market have become higher, leading to many private enterprises being excluded. As a result, a method called “backdoor listing” has become popular in the financial market for entry. To demonstrate their strength, many companies choose to sign performance commitments before listing, with all employees committing to performance, and then report the final performance commitment to relevant authorities for approval. However, this approach has exposed several issues, primarily arising from the misalignment between performance commitments and final corporate earnings. Therefore, it is essential to evaluate the performance commitments made by employees scientifically, avoid corporate earnings management, and thereby mitigate the risk of “performance plunge” and “performance cliff” after listing. This study collects large data using data mining and applies cluster analysis to divide performance commitments. Finally, the relationship between employee performance commitments and corporate earnings management impact is identified based on the decision tree model algorithm. The results indicate that unreasonable performance commitments are one of the main reasons for improper corporate earnings management. Using the decision tree model algorithm can make correct decisions to reduce the risks caused by employee performance commitments. The study takes the employee performance commitments and corporate earnings management under the state of mergers and acquisitions as an example to elaborate on this conclusion using the algorithm. Read More Read Less

Abstract The legalization and structure construction of multi-modal green industry development is a crucial issue in the current context of environmental protection and sustainable development. In this context, this paper explores how to promote the development of green industries by establishing sound legal and institutional systems. Measures such as formulating relevant regulations, strengthening environmental supervision, promoting environmental certification, enhancing talent cultivation, and advancing green procurement... The legalization and structure construction of multi-modal green industry development is a crucial issue in the current context of environmental protection and sustainable development. In this context, this paper explores how to promote the development of green industries by establishing sound legal and institutional systems. Measures such as formulating relevant regulations, strengthening environmental supervision, promoting environmental certification, enhancing talent cultivation, and advancing green procurement are proposed. By improving relevant policies and regulations and strengthening the regulation of various green industries, this approach effectively incentivizes their healthy, stable, and efficient operation, thus achieving dual sustainability in the economic, social, and environmental domains. Additionally, it contributes to enhancing China’s competitiveness and influence in the global environmental protection field. Read More Read Less

Abstract This paper explores the application of artificial intelligence neural network technology in evaluating university education. Reviewing the latest domestic and foreign research progress shows that traditional evaluation methods suffer from subjectivity and lack of objective data support. The basic principles and successful application cases of neural network technology are elaborated to lay the foundation for its application in evaluating the effectiveness of education in... This paper explores the application of artificial intelligence neural network technology in evaluating university education. Reviewing the latest domestic and foreign research progress shows that traditional evaluation methods suffer from subjectivity and lack of objective data support. The basic principles and successful application cases of neural network technology are elaborated to lay the foundation for its application in evaluating the effectiveness of education in universities. The paper provides a detailed introduction to the application of technologies such as deep learning, natural language processing, and data mining, as well as how to accurately assess the effectiveness of education in universities by constructing models. An experimental design is presented, and the use of data validates the feasibility and effectiveness of the proposed method. This approach is expected to promote education development and improve the quality and level of education in universities. Read More Read Less

Abstract By applying deep learning, we can better understand knee joint injuries that may occur in ball sports. This method can help us prevent these injuries more effectively and provide better protection for our athletes. This study developed a deep learning technique based on multiple modalities to identify common, major, and meniscus tear injuries, achieving rapid and accurate diagnosis. Additionally, it effectively categorizes the affected... By applying deep learning, we can better understand knee joint injuries that may occur in ball sports. This method can help us prevent these injuries more effectively and provide better protection for our athletes. This study developed a deep learning technique based on multiple modalities to identify common, major, and meniscus tear injuries, achieving rapid and accurate diagnosis. Additionally, it effectively categorizes the affected organs into different lesions based on their shape, size, and function. Experimental results demonstrated that the model accurately reflects the changes in the ROC curve, with an AUC change rate of 97.89% in normal anterior cruciate ligament tear or meniscus tear situations, significantly outperforming other methods and providing high accuracy. Read More Read Less