IBM Platform LSF

这是安装MATLAB Parallel Server与IBM Platform LSF的并行计算工具箱插件的详细说明。这些示例文件利用通用调度程序接口，允许用户通过IBM Platform LSF和其他兼容调度程序提交作业到MATLAB Parallel Server。安装完成后，您需要执行进一步的设置以准备调度程序的使用。详细的安装步骤请参阅自述文件。更多关于通用调度程序接口的信息，请访问：https://www.mathworks.com/help/matlab-parallel-server/configure-using-the-generic-scheduler-interface.html

此工具帮助您有效读取LSF格式文件（GWENSTECK示波器内部数据格式），利用Matlab进行开发。

Win10平台编译的Hadoop，解压后直接可用，可在本地模拟Hadoop集群环境。

Check results

探讨了扩展卡尔曼滤波、最小二乘滤波和粒子滤波的基本原理，并提供了相应的Matlab仿真案例。这些方法在信号处理和状态估计中发挥着重要作用。

MRiLab项目正在迁移到GitHub，最新版本可从MRiLab官网获取。MRiLab是一个数值MRI模拟包，模拟MR信号形成、k空间采集和MR图像重建。它提供了多个专用工具箱，用于分析射频脉冲、设计MR序列、配置多个发射和接收线圈、研究磁场属性和评估实时成像技术。结合这些工具箱，主要MRiLab仿真平台可用于定制虚拟MR实验，支持新技术和应用的原型设计与测试。如果您发现MRiLab对科学成果有帮助，请引用以下论文：基于广义多池交换组织模型的快速逼真MRI模拟，IEEE医学影像交易，2016，doi: 10.1109/TMI.2016.2620961。

The atmospheric reanalysis products encompass four-dimensional gridded information of atmospheric variables such as temperature, pressure, humidity, wind speed, and direction over a historical period. These products can be widely applied in areas such as climate change, weapon system design, and other fields. In response to the data formats and application characteristics of reanalysis products, the general framework of the reanalysis product application platform adopts the classic MVC three-layer model, based on third-party software development and integration for decoding, statistical analysis, visualization, and standardized I/O interfaces. Previously, ASCII gridded data required users to export data in GRIB format, which involved time-consuming and cumbersome stitching software. The hybrid data management of points and surfaces enables efficient management of PB-level reanalysis products, with features such as high scalability and low latency. The point-surface hybrid management of field units and point units can meet the data retrieval needs of different temporal and spatial scales.

Oracle 12c GI Installation Guide, an English manual, provides a step-by-step guide on installing Oracle 12c Grid Infrastructure (GI) on a Linux platform. This guide covers the prerequisites, installation process, and post-installation configuration for Oracle 12c GI.

后台管理系统数据库platform.sql解析 该数据库文件支撑着后台管理系统的四大基础功能模块： 字典模块: 类似于生活中的字典，为系统各模块提供字典数据。包含字典类型和基础字典两部分，基础字典是字典类型的具体内容。 日志模块: 记录系统的登录日志和操作日志，为分析人员登录情况和系统运行情况提供数据支撑。 系统模块: 涵盖系统属性、系统监控和系统配置。系统属性实时显示系统软硬件信息；系统监控实时显示系统接口访问和数据操作情况；系统配置用于设定系统公共参数，是唯一需要数据存储和使用的部分。 权限模块: 由人员、职位、角色和权限四部分组成，共同构建系统的权限管理体系。

Big data technology has become a hot research topic in the field of education, focusing on analyzing large amounts of educational data collected to improve teaching methods and enhance education quality. Among educational big data, learning analytics is particularly important, as it helps teachers understand students' learning progress and implement personalized teaching, thus promoting teaching reform. In higher education, the application of big data-based learning analytics technology can monitor students' learning processes. By analyzing students' behavioral patterns during the learning process, teachers can gain a more intuitive understanding of each student's performance. This technology provides a series of insights such as 'who is learning', 'what is being learned', and 'how well students are learning', which is crucial for ensuring educational quality. Data collection is the first step in big data learning analytics, which involves utilizing various technical means to gather data from different sources. In the context of online education, the primary source of data is students' online behavior during the learning process. This data includes but is not limited to, video viewing patterns, discussion board participation scores, assignment scores, exam results, and forum interaction scores. These data need to be collected using appropriate tools such as web crawlers written in Python or by calling data through API interfaces. Once the data is collected, the next step is data preprocessing. This stage involves cleaning the data, removing unreliable data points like test accounts and extreme outliers. The goal of preprocessing is to ensure the accuracy of subsequent analysis, structure the data for easy storage, and prepare it for analysis. Data analysis is the core part of learning analytics and primarily includes statistical analysis and visualization, clustering analysis, predictive analytics, association rule mining, and text mining. These methods help teachers gain deeper insights into students' behavioral patterns, learning habits, and performance trends. Statistical analysis and visualization transform data into charts and graphs for intuitive representation of students' learning progress. Clustering analysis groups students by learning habits or grades, while predictive analytics forecasts students' future performance based on historical data. Association rule mining focuses on identifying relationships between students' behaviors, and text mining analyzes content from discussion boards to understand students' learning attitudes and thought processes. The application and development of big data in education holds great potential. With the rapid growth of global data, educational big data is gradually becoming a field of focus both domestically and internationally, offering significant value in education. In practical projects, the application of learning analytics has already shown results. For example, a research project mentioned in the article uses the 'C Programming 1' course on a MOOC platform to analyze students' learning behavior data combined with performance data to help teachers better understand students' progress and offer reasonable teaching suggestions. The application of big data in education, particularly in learning analytics on MOOC platforms, is becoming a key driver of educational reform.