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The whole vision of Web services was conceived with the notion of interoperability between different applications running on disparate systems. As the plumbing of the Web services was being figured out, a major development also was taking place that supported the idea of making disparate security systems interoperate with each other. Two main standards, Security Services Markup Language (S2ML) and Authentication Markup Language (AuthML), were committed toward making different security systems talk to each other meaningfully the latter was mainly focused on making security systems exchange authentication-related information among themselves. SAML was the result of merging these two parallel efforts into a single technology. This merging took place when both of these specifications were submitted to the Organization for the Advancement of Structured Information Standards (OASIS) by respective organizations: S2ML from Netegrity and AuthML from Securant Technologies. In order to refer to the SAML specification page hosted by the OASIS Security Services Technical Committee, visit the OASIS Web site at www.oasis-open.org/ committees/security/. As of this book s writing, the SAML specification set has already reached the committee specification level. SAML is a technology resulting from the increased trend toward sharing information among different organizations. Although the base technologies behind Web services facilitate this trend of inter-organization distributed computing, there had been no standard way of sharing information

One of HOMER s most powerful features is its ability to do sensitivity analyses on hourly data sets such as the primary electric load or the solar, wind, hydro, or biomass resource. HOMER s use of scaling variables enables such sensitivity analyses. Each hourly data set comprises 8760 values that have a certain average value. But each hourly data set also has a corresponding scaling variable that the modeler can use to scale the entire data set up or down. For example, a user may specify hourly primary load data with an annual average of 120 kWh/day, then specify 100, 150, and 200 kWh/day for the primary load scaling variable. In the course

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Now, you are ready to restart the servers. Remember to start Tomcat first and then start Apache. This will give Tomcat the opportunity to generate the configuration information that Apache needs. After the servers are running, you should be able to hit the Tomcat examples applications. Note that you will not get the Tomcat start page as before unless you changed Apache s document root to be the same as Tomcat s. This is so because there can only be one root context. Open your browser and enter the URL:

When Excel evaluates this formula, it first evaluates the formula named InterestRate (which exists only in memory, not in a cell). It then multiplies the result of this named formula by 1.05 and displays the result. This cell formula, of course, is equivalent to the following formula, which uses the actual cell reference instead of the name:

We will discuss the reason for using the benchmark weight rather than the fund weight in the section on the interaction effect, but in this example it is not relevant which weight we use, because the fund and index segment weights are the same. Exhibit 18.3 shows the calculation of the security selection effect for our sample fund. Here we earned a higher return than the benchmark in Japan because of our overweight in the better performing stock during the period. We take the return differential and weight it by the proportionate weight of Japan within the index in order to derive the value added due to security selection equal to 0.40%. EXHIBIT 18.3

Long-Term High-Grade Corporate Bond Index Government bonds use an approximate maturity of 20 years The commodity portion is represented by a return on the CRB Index plus 90 percent of the return on Treasury Bills (since a CRB Index futures position only requires a 10 percent margin deposit) Table 121 summarizes some of the results Over the entire 30-year period, US stocks were the best overall performer (142841) whereas the CRB Index came in second (117526) In the two periods beginning in 1965 and 1970 to 1988, the CRB Index was the best performer (97470 and 78797, respectively), while US stocks took second place (76678 and 65069, respectively) Those two periods include the inflationary 1970s when commodities experienced enormous bull markets In the fifteen years since 1975, stocks regained first place (55569) while corporate bonds took second place (33823).

=PMT(0.75%,48,-230000,0,1)

The CRB Index slipped to third place (33647) Since 1980, corporate bonds turned in the best returns (30058), with stocks and government bonds just about even in second place The CRB Index, reflecting the low inflation environment of the 1980s, slipped to last During the final period, from 1985 to 1988, stocks were again the best place to be, with bonds second Commodities turned in the worst performance in the final four years Although financial assets (bonds and stocks) were clearly the favored investments during the 1980s, commodities outperformed bonds by a wide margin over the entire 30-year span and were the best performers of the three classes during the most recent 20- and 25-year spans The rotating leadership suggests that each asset class has "its day in the sun," and argues against taking too short a view of the relative performance between the three sectors.

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