采用FLAC3D软件对淹井前后、加固前后采场溜井的稳定状况进行模拟计算,验证了所采取加固技术的可靠性和合理性.该模拟计算对类似矿山采场溜井的加固和恢复具有重要的参考价值

提出了感应钢包炉的等效电路以及感应钢包炉的漏磁场、空载磁场和负载磁场的数学模型,利用有限元法作了计算,进而给出了等效电路参数及工作特性的算法

第一节 表型组学概述 第二节 表型组的测量技术 第三节 表型组的数据分析 第四节 细胞与类器官表型组学 第五节 人类表型组 第六节 动植物表型组 第七节 未来展望

一、函数逼近:用比较简单的函数代替复杂的函数 二、误差为最小，即距离为最小（不同的度量意义）

项目一 物流统计工作 项目二 物流调查技能与数据分析 项目三 物流市场供需统计分析 项目四 物流仓储统计分析 项目五 物流运输统计分析 项目六 物流配送统计分析 项目七 海关统计分析

2.1 Matlab简介 MatlabCleve的首创者 Moler博士在数值分析,特 别是在是指线性代数的领域中很有影响。 Matlab(Matrix Laboratory,即矩阵实验室) Matlab于1984年推出了正式版本。后来 Moler组建 了一个名为 MathWorks的软件开发公司( http: //www. mathworks. com)专门扩展并改进 Matlab. 1998年推出5.3版。2000年11月6日推出最新版本 Matlab 6. 0

8.1 Explore some of the emerging technologies that may impact analytics, business intelligence (BI), and decision support 8.2 Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem 8.3 Describe the current and future use of cloud computing in business analytics 8.4 Describe how geospatial and location-based analytics are assisting organizations 8.5 Describe the organizational impacts of analytics applications 8.6 List and describe the major ethical and legal issues of analytics implementation 8.7 Identify key characteristics of a successful data science professional

7.1 Learn what Big Data is and how it is changing the world of analytics 7.2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for Big Data analytics 7.4 Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies 7.6 Become familiar with select Big Data platforms and services 7.7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics

6.1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6.4 Describe how spreadsheets can be used for analytical modeling and solutions 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6.8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation

5.1 Describe text mining and understand the need for text mining 5.2 Differentiate among text analytics, text mining, and data mining 5.3 Understand the different application areas for text mining 5.4 Know the process of carrying out a text mining project 5.5 Appreciate the different methods to introduce structure to text-based data 5.6 Describe sentiment analysis 5.7 Develop familiarity with popular applications of sentiment analysis 5.8 Learn the common methods for sentiment analysis 5.9 Become familiar with speech analytics as it relates to sentiment analysis