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TABLE 5.2 Summary of Predictive Distribution Results for Examples 5.4 and 5.9, the Cancer Flowgraph with General Waiting Time Distributions Percentile 0.25 0.50 0.75 0.90 0.95 0.99 Mean s.d. Rejection Sampling 17.66 28.16 54.46 114.21 174.05 355.47 42.13 59.92 SS I 17.79 28.54 56.03 117.90 179.69 365.53 43.02 60.29 SS II 18.32 29.58 62.40 141.05 221.66 476.06 48.06 81.44

This chapter starts with a brief discussion of how you can use a JSP page as a client to an enterprise bean. You'll take a look at the JSP page that contains the custom tag to locate an EJB home object as well as enterprise bean business method references in JSP scriptlets and expressions. You'll also see that the JSP page shown in this chapter is strikingly similar to the JSP page shown in Listing 10-8 in 10. The JSP pages that handle the logon and checks for a valid user/password combination is the same as the JSP pages in 10. For our purposes here, you will concentrate on the user option to display the client s transaction history, where you will see one approach in using beans together with JSPs to implement the transaction history function. Next, you'll see the code that constitutes the custom tag used in the JSP page. You'll also see the code for the tag library descriptor used to describe the tag. The code for the enterprise bean comes next. Here's where the code that implements the business methods resides. The business methods use very similar code to the methods of the AcctHistory bean shown in Listing 10-9 in 10. Included is a deployment descriptor that describes the properties of the enterprise bean to the EJB container. You may want to quickly glance at 10, The Make Money Brokerage Application, if you need a quick refresher on the application s functionality.

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This chapter presents several specialized Grids, which are described here to assist in explaining Grid network requirements. Although since the initial inception of Grid architecture many individual types have been defined, they tend to fall within several representative classes, as noted in the previous chapter. Reflecting their origin within the large-scale science community, one class of Grids, computational Grids, is optimized for computational performance. They are designed primarily as tools for computationally intensive problems. Another class of Grids, data Grids, consists of environments that address the particularly problem of managing and analyzing large-scale distributed data. Service Grids are those that have been designed to meet the specific requirements of specialized services within distributed environments. Many other types of Grids exist. The following section, which presents examples of specialized Grid environments, is followed by an overview of the network requirements inherent in general Grid environments.

2002 SURVEY OF CREDIT PORTFOLIO MANAGEMENT PRACTICES Do you attribute economic capital to individual units or individual transactions 80% of the respondents to the survey answered this question. All of the following responses are expressed as percentages of the number of respondents answering this question. Yes No If yes, Standard-deviation-based risk contribution measures Marginal (incremental) risk contribution measures Tail-Risk risk contribution measures Other 48% 24% 14% 14% 91% 9%

We now move on to how economists view the supply of goods and services. The key underlying concept is that supplying things is costly. As any business owner knows, you have to pay people to supply the things you want. Even more interesting, though, is the fact that the more you want them to supply, the higher their costs of supplying each additional unit. (In 10, we explain why this situation remains true.) Because production costs rise as you produce more output, when you want producers to make more and more, you have to pay them more and more. This fact implies that supply curves slope upward.

Grid architecture was designed to allow an expansive set of resources to be integrated into a single, cohesive environment. This resource integration can be accomplished in advance of use or it can be implemented dynamically. Traditionally, the integration of network resources into environments requiring real-time ad hoc changes has been a challenge because networks have not been designed for dynamic reconfiguration. However, new architecture and techniques are enabling communication services and network resources to be integrated with other Grid resources and continually changed dynamically. 1.4.2.7 Resource sharing

Too often, bioethicists exaggerate their capacity to bring longstanding ethical debates to ultimate closure. They mistakenly claim that morality is settled, orderly, and in a state of re ective equilibrium when this account tells only one part of a very complex story. In pluralistic societies, we nd both relatively stable social norms as well as con icting understandings of how we should live and die. Several of the dominant approaches in bioethics rely upon the notion of shared, common, intuitively obvious moral norms. However, many of the issues addressed by bioethicists reveal the existence of disagreements over what constitutes common sense moral reasoning. Disputes about ethical issues in medicine, healthcare and biotechnology often occur along cultural and religious fault lines. Such con icts emerge in clinical settings, the deliberations of advisory bodies and professional organizations, and in legislative arenas.87

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