Wind energy prediction is crucial for efficient management and dispatch of power systems, and accurate prediction can optimize the integration of renewable energy sources and can be a good solution for energy security. This study proposes a chaos-enhanced bi-directional LSTM model for wind power forecasting that incorporates chaotic signals from Lorentz attractors to enhance the feature representation of time series data and combines the powerful contextual information capturing capability of bi-directional LSTM. The goal of this research is to develop a model that outperforms traditional as well as current mainstream machine learning aspects of prediction methods. The method splices the generated slices of chaotic signals with the input sequences in the feature dimension and captures the forward and backward time dependencies using bi-directional LSTM, and finally feeds its output into the fully connected layer for final prediction. The experimental results show that the proposed model outperforms several current mainstream machine prediction models with a MAPE of only 0.5979% and an RMSE of 4.2, which demonstrates the effectiveness and superiority of the model in wind power prediction.

With the rapid development of machine learning, particularly deep learning, the automatic recognition and classification of brain tumors have become a significant focus of research in medical image analysis. However, due to the complex morphology and indistinct boundaries of brain tumors, traditional manual annotation methods are time-consuming and prone to errors, highlighting the need for more intelligent diagnostic tools. This study compares the effectiveness of applying AutoML and YOLO series models (YOLOv5 and YOLOv8) in brain tumor recognition, aiming to evaluate the performance of each model and provide insights for improving diagnostic accuracy and efficiency. The results indicate that YOLOv8 outperforms the other models in terms of precision, recall, and F1-score, making it highly suitable for high-precision applications. YOLOv5, with its lightweight design, offers advantages in inference speed. While EasyDL performs slightly lower in certain metrics, its high precision and ease of use make it a competitive option in specific scenarios.The main contribution of this paper lies in not only verifying the high feasibility of the emerging AutoML technology with weaker requirements on expert knowledge in the field of MRI image recognition for brain tumors, but also providing a detailed comparative analysis that can serve as a reference for medical practitioners to flexibly choose suitable model algorithms based on their cost, demand, etc. conditions.

班级寝室员工综合管理系统是采用Java+HTML+Javascript语言开发的一款JavaWeb应用，顾名思义是一套集班级管理、寝室管理与员工管理为一体的数据管理系统，通过用户在前端与系统直观的交互，使得数据的导入导出编辑等操作简单化，视觉直观化，从而降低对数据处理的复杂程度，提高工作效率。员工、班级与寝室信息的增删改查中，考虑到班级和寝室信息一般比较固定，无需做大的改动，故同时具备单条数据的增加、编辑与删除功能，其中编辑功能具备自动拉取数据库数据进行填充，方便编辑指定的信息。员工信息的增删改查中，还特意增加了批量删除和两种不同的批量导入的方法，一种是类似于单条数据添加，但是可以通过点击“增加更多”按钮一次导入多个用户信息，另一种是用户只需获得特定格式的数据文本文件，以每行为一条数据，快捷完成批量导入工作，极大的解决了大量数据导入复杂繁琐的难题，是当前版本重要的创新优势点。

本系统基于Spring Boot和Vue 3架构开发，前端采用Vue 3结合Vue Router和Element Plus，后端使用Spring Boot框架搭建，数据持久化层采用MyBatis进行高效数据操作。系统实现了用户与餐饮商家之间的便捷交互，提供实时菜单展示、订单管理、支付处理、多角色管理控制平台分离等功能。通过Vue 3的响应式特性和Element Plus的UI组件，提供流畅的用户体验；后端则通过Spring Boot的高效处理能力，确保系统稳定可靠，满足高并发需求。该订餐系统旨在提升用户体验并优化餐饮商家的运营效率。本系统的优势与创新体现在多个方面。首先，系统支持第三方OAuth登录，若针对个体餐饮场景，接入QQ和微信登录，提供微信无感登录功能，极大简化了用户登录流程，提高了用户体验；若针对企业团餐，支持企业内部账户登录，实现私域流量管理，进一步提升服务的个性化与效率。其次，系统基于云平台部署，用户可以按需下载和更新软件，及时获取功能拓展和系统更新，确保软件始终保持最佳状态，满足不断变化的市场需求。这些创新设计不仅提升了系统的灵活性和便捷性，也增强了用户粘性和系统的可扩展性。