§6.1 反馈的基本概念及判断方法 §6.2 负反馈放大电路的四种基本组态 §6.3 负反馈放大电路的方框图及一般表达式 §6.4深度负反馈放大电路放大倍数的分析 §6.5 负反馈对放大电路性能的影响

一.前言 空间句法的由来 空间句法的字面含义 空间句法的特质 二.解读空间句法 空间句法的相关理论 空间句法的技术手段 三.空间句法的应用 基于宏观层面——城市规划的应用 基于微观层面——建筑设计的应用 四.结语 空间句法的学术意义 空间句法的定位

1.《中医学综合实验》的主要目的是培养研究生独立地、综合运用多 课种实验方法开展中医实验研究的能力。课程设计以充分调动研究生的学习 目热情,培养独立研究的基本能力,及其综合能力,并在教学形式上给予保 的证。在教学中要求研究生综合所学中、西医学知识、现代研究技术,独立 要自主地开展包括选题、课题设计、研究准备、实验实施、实验数据分析, 求至撰写实验报告等系统的实践,使研究生在实践中去检验所学的知识,暴 露其知识面的欠缺

4-1:(1)()ZnZn2+cd2+cd(+)(2)(-)(C)Fe2+,Fe3+AgAg(+) 4-2根据标准电极电势数据分析,只能选Fe 4-4:(1)E=0.0113V,Pb(+);(2)E=0.0477V

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

4.1 Define data mining as an enabling technology for business analytics 4.2 Understand the objectives and benefits of data mining 4.3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4.5 Learn different methods and algorithms of data mining 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining

3.1 Understand the basic definitions and concepts of data warehousing 3.2 Understand data warehousing architectures 3.3 Describe the processes used in developing and managing data warehouses 3.4 Explain data warehousing operations 3.5 Explain the role of data warehouses in decision support