透辉石作为矽卡岩型尾矿中的重要组成部分,研究其火山灰反应活性对于该类型尾矿的综合利用具有重要意义,但是目前还未见到相关报道.以透辉石、天然石膏和氢氧化钙为原料制备净浆试块,研究了磨细透辉石的火山灰反应活性,并利用X射线衍射、扫描电镜、傅里叶红外光谱、差示扫描量热法和核磁共振对净浆试块的水化产物进行分析,为初步判断矽卡岩型尾矿是否具有火山灰反应活性提供重要依据.结果表明,磨细的透辉石净浆试块抗压强度在3、7和28 d龄期时分别为9.83、12.79和18.87 MPa,显示出磨细的透辉石具有火山灰反应活性.磨细透辉石的水化产物以C-S-H凝胶为主.核磁共振结果显示,随着水化反应的不断加深,处于Q2结构状态的硅原子比例有所减少,生成的C-S-H凝胶的铝/硅比低于原始结构的透辉石.随着养护龄期的增加,仅有少量石膏参与反应,Ca(OH)2会被大量消耗,水化产物逐渐增多.未参与反应的石膏颗粒起到填充作用,也有助于促进体系强度的持续增长

2.1 Understand the nature of data as it relates to business intelligence (BI) and analytics 2.2 Learn the methods used to make real-world data analytics ready 2.3 Describe statistical modeling and its relationship to business analytics 2.4 Learn about descriptive and inferential statistics 2.5 Define business reporting, and understand its historical evolution

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

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

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

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

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

采用水热法和还原氮化法合成了菊花状形貌的氮化钛（TiN）纳米材料，并将其与还原氧化石墨烯（rGO）水热复合制备了氮化钛–还原氧化石墨烯（TiN-rGO）复合材料。利用扫描电镜、X射线衍射、X射线光电子能谱等测试方法对材料的形貌和物相进行了表征和分析。结果表明，TiN-rGO复合材料很好地保持了TiN菊花状的三维结构和rGO透明褶皱的形貌，且层状的rGO均匀地包覆在了菊花状的TiN的周围。用TiN-rGO复合材料修饰玻碳电极（GCE）制得了TiN-rGO/GCE电化学传感器，用于测定人体中的生物小分子DA和UA。由于复合材料中TiN和rGO的协同效应，构建的电化学传感器表现出了优秀的电化学性能。检测结果表明：TiN-rGO/GCE传感器对DA和UA的检测限分别为0.11和0.12 μmol·L?1，线性范围分别为0.5～210 μmol·L?1和5～350 μmol·L?1，且具有良好的抗干扰性、重现性和稳定性，且成功应用于人体内真实样品的DA和UA检测

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

1.1 Understand the need for computerized support of managerial decision making 1.2 Recognize the evolution of such computerized support to the current state—analytics/data science 1.3 Describe the business intelligence (BI) methodology and concepts 1.4 Understand the various types of analytics, and see selected applications 1.5 Understand the analytics ecosystem to identify various key players and career opportunities