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The default property value in the file includes the Cloudscape driver. You should not remove the Cloudscape information from here if you are using other databases, as the reference implementation uses the database internally and relies on the existence of this driver. To complete the data source setup, you need to provide the JNDI reference information so you can relate the drivers to the JNDI access name you use. Inside your various EJBs, servlets and JSPs, you define a JNDI name that is your driver. As part of your standard security, the application needs to know a user name, a password, and which virtual database the JDNI name refers to. All of this information is supplied through the properties that start with jdbcDataSource. The syntax of this property follows the same styles as for the basic driver setup. After the first part of the property name, you need to supply a number. This number should match the number you used for the driver. So, based on the preceding example, jdbcDataSource.0 supplies all the information for the Oracle driver, and jdbcDataSource.1 is used by the DB2 driver. This time you need to specify the name that the driver is registered as for JNDI name and also the JDBC URL for connecting to the database. This is what an Oracle driver definition looks like:

The best-known examples of a combination of solvolysis followed by desolvation to produce a polymeric material come from phosphorus chemistry. Phosphorus(V) chloride can be partially hydrolyzed by water to HOP(O)Cl2 followed by dehydrohalogenation to [PO2 Cl]n . Alternatively, phosphorus(V) chloride can undergo partial ammonolysis with ammonium chloride followed by dehydrohalogenation to a phosphazene. These reactions are shown in Eqs. 2.51 and 2.52. nPCl5 C 2nH2 O nPCl5 C nNH4 Cl ! n HOP(O)Cl2 C3n HCl [PNCl2 ]n C 4nHCl ! [PO2 Cl]n Cn HCl ! 2.52 2.51

Hard-coded location identifiers generally occur in applications that are built from prototypes or built quickly. Perhaps a JSP developer needs to access a database and so includes the connection information on the page, as shown below.

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Table 3.2 presents the backtested portfolio results from December 31, 1992, to December 31, 2002, for this system. Assuming $200,000.00 under management, the portfolio would have enjoyed an 8.48 percent average annualized return on investment over the 10year backtested period while enduring a 19.98 percent maximum drawdown. Although these results are somewhat encouraging, traders employing this system must be willing to endure 61.18 percent losing trades, 10 consecutive losses, and lengthy intervals (almost two years) prior to achievement of new equity peaks. It is interesting to notice how the low correlations of assets within our portfolio improved overall performance of this system. Diversification is probably among the most underemphasized benefits of system trading. A brief glance through the totals column shows that the portfolio s worst drawdown was only around 16 percent greater than the worst componentbased drawdown. Moreover, because the portfolio s total net profits were additive and the worst drawdown was not, the profit to maximum drawdown ratio enjoyed a significant improvement when compared to almost every asset within our portfolio.

(a) Show that is the covariance of a stationary centered Gaussian process {X(t) : t R} (the Ornstein Uhlenbeck process) (b) Compute the spectral density of the process. (c) Study the H lder properties of the paths. o (d) Show that the process is Markovian, that is, if t1 < t2 < < tk , k a positive integer k 2, then P X(tk ) B X(t1 ) = x1 , . . . , X(tk 1 ) = xk 1 = P X(tk ) B X(tk 1 ) = xk 1 for any Borel set B and any choice of x1 , . . . , xk 1 . (The conditional probability is to be interpreted in the sense of Gaussian regression.)

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