This paper can be downloaded without charge from the Social Science Research Network electronic library at: http: //papers.ssrn. com/paper. taf?abstract id=10037 Bernard Black Reinier Kraakman' In this Article, Professors Black and Kraakman develop a \self-enforcing\ approach

一、什么是计算机网络 计算机网络（Computer Network)是将处于 不同地理位置上的数台计算机系统通过通信设备 和传输介质相互物理连接，并在网络软件的控制 下，能实现在网络上进行通信和资源共享的系统 称为计算机网络

97.1 Computer and Communications Security Physical Security Cryptology Software Security Hardware Security Network Security Personnel Security 97.2 Fundamentals of Cryptography Central Paradigms Pseudorandomness Zero-Knowledge

Computer networks are geographically distributed collections of communication links and switching proces￾sors, the purpose of which is to transport data between computers, workstations, and terminals. In general the elements of a computer network must follow compatible rules of operation together to function effectively. These rules of operation are known as protocols

94.1 Databases Database Abstraction • Data Models • Relational Databases • Hierarchical Databases • Network Databases • Architecture of a DBMS • Data Integrity and Security • Emerging Trends 94.2 Rule-Based Expert Systems

Wet milling of cereal grains differs fundament￾ally from dry milling in being a maceration process in which physical and chemical changes occur in the nature of the basic constituents - starch, protein and cell wall material - in order to bring about a complete dissociation of the endosperm cell contents with the release of the starch granules from the protein network

基于人工神经网络( Artificial Neural Network)的控制(ann-based Control)简称神经控制(Neural Control)。神经网络是由大量人工神经元(处理单元)按照一定的拓扑结构相互连接而成的一种具有并行计 算能力的网络系统。它是在现代神经生物学和认识科学对人类信息处理研究的基础上提出来的,具有很强 的自适应和学习能力、非线性映射能力、鲁棒性和容错能力

内分泌系统是重要的调节系统，以体 液调节的方式，对机体新陈代谢、生长发 育和繁殖等起着重要的调节作用。 内分泌系统受到神经系统和免疫系统 的影响，三者相互作用，共同组成一个网 络，即神经-内分泌-免疫网络(Neuro￾endocrin-immune network)

Find the four Y parameters of the network shown in Fig

In this chapter we continue our introduction to circuit analysis by studying periodic functions in both the time and frequency domains. Any periodic function may be represented as the sum of an infinite number of sine and cosine functions which are harmonically related. The response of the linear network to the general periodic forcing function may be obtained by superposing the partial responses