•美食中的生物技术-核心是“发酵”(Fermentation)•发酵的本质是微生物将原料中的分子进行转化，通常是将糖转化为酸、气或酒精，从而创造出具有独特风味和营养价值的产物。•在发酵过程中，微生物还会产生独特的代谢产物，例如有机酸和维生素等，增添了产物的风味和营养

3、(10分/150分)已知系统的状态转移矩阵为 3e--e-23e--2e-2 中(t)=+4e-2e2+2e请求出()、A 解:根据状态转移矩阵的运算性质有:

氮是人们所知道的最丰富的、处于游离态的元素。它占大气体 积的78.1%[即占78.3%(原子百分数)或7.5%(质量百分数)], 业上每年从大气中生产数百万吨的氮。一切有生命的物质来说 呈化合状态的氮是必不可少的,在蛋白质的组成中,按质量计氮平 均约占15%目前许多国家用工业法大规模固氮,将其用于农业肥 料和其他化学制品的生产

真理是什么呢？亚里士多德认为逻辑与它有关。他的讲义合集《工具论》（O rg a n o n，可 追溯到公元前4世纪）是最早的关于逻辑的详细著作。对于古希腊人而言，逻辑是追寻真理的 过程中用于分析语言的一种手段，因此它被认为是一种哲学。亚里士多德的逻辑学的基础是 三段论。最有名的三段论（它并非是在亚里士多德的著作中发现的）是： (所有的人都是要死的; 苏格拉底是人; 所以,苏格拉底是要死的。)

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

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

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

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

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

Carbonyl compounds are a broad group of compounds that includes aldehydes, ketones,carboxylic acids, and esters