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Every EJB needs to have an entry in a deployment descriptor. This is the first EJB in our package, so we will create a complete deployment descriptor here and then edit it as we move forward. We will build this file up from fragments so we can explain things as we go along. The deployment descriptor for an EJB is named ejb-jar.xml and must appear in the META-INF directory of the .jar file that contains the EJB. We will create and edit the file in the /etc directory and put it into the META-INF directory when we build the .jar file. The opening lines of the file are shown in the following code:

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We might discuss the situation with the manager and decide to exclude that period from the analysis when we better understand the reasons for the performance during that period Or alternatively, we could decide that that period is more representative of the manager s expected performance over a particular market cycle and overweight that period in our analysis of performance It also might be interesting to look more closely at the recent performance of Fund C..

You now see a blank Form Design View window, as shown in Figure 15-3. The form is bound to the query Pets and Owners as shown in the Property window on the screen. This means that the fields from the query are available for use in the form design, and they appear in the Field List window. Data from the query will be displayed when the form is viewed or printed.

Dynamics(const Parameter& tting, double a, double sigma, double b, double eta, double rho) : ShortRateDynamics(Handle<DiffusionProcess>(new OrnsteinUhlenbeckProcess(a, sigma)), Handle<DiffusionProcess>(new OrnsteinUhlenbeckProcess(b, eta)), rho), tting_( tting) {} virtual Rate shortRate(Time t, double x, double y) const { return tting_(t) + x + y; } private: Parameter tting_; }; /********************************************************************************** Analytical term-structure tting parameter varphi(t). varphi(t) is analytically de ned by varphi(t) = f(t) + 0.5((sigma(1-exp(-at)))/{a})^2 + 0.5({eta(1-exp(-bt))}(b))^2 + rho(sigma(1-exp(-at)))(a)/(eta(1-exp(-bt)))(b), where f(t) is the instantaneous forward rate at t. **********************************************************************************/ class G2::FittingParameter : public TermStructureFittingParameter { private: class Impl : public Parameter::Impl { public: Impl(const RelinkableHandle<TermStructure>& termStructure, double a, double sigma, double b, double eta, double rho) : termStructure_(termStructure), a_(a), sigma_(sigma), b_(b), eta_(eta), rho_(rho) {}

4. From the output, look for an indication that the new disk was found. For example, if it s a second IDE hard disk, you should see hdb: in the output. For a second SCSI drive, you should see sdb: instead. (The hd and sd drive letters are incremented as they are found by the kernel.) Be sure you identify the correct disk, or you will erase all the data from disks you probably want to keep! 5. Use the fdisk command to create partitions on the new disk. For example, if you are formatting the second IDE disk (hdb), you can type the following:

0.5 126.7

Similar to intellectual property, each of the parties should review the provisions in the IT outsourcing contract to determine its rights with respect to key equipment and facilities. If the agreement were to terminate, what are the party s ongoing rights Several issues to consider are as follows:

Most functions that you develop with VBA return a single value. It s possible, however, to write a function that returns multiple values in an array.

diminishing returns obtained as the number of branches increases. In addition, for L = 1 total channel inversion suffers a large capacity penalty relative to truncated channel inversion. However, as the number of combining branches L increases, the effect of fading is progressively reduced, and this penalty diminishes remarkably. In particular, as L increases, we see that truncated channel inversion yields a small increase in capacity over total channel inversion, and this small increase in capacity diminishes as the average SNR and/or the number of combined branches L increase. The corresponding outage probability (15.20) for the optimum adaptation and truncated channel inversion (with optimum cutoff o ) policies with MRC are shown in Fig. 15.7.

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You can create multi-dimensional analyses that far surpass the limitations of traditional Crosstab queries. You can interactively change your analysis without re-writing your query. You can dynamically sort, filter, group, and add custom calculations with a few clicks of the mouse. You have drill-down capabilities that enable you to collapse and expand analytical details without writing code. You can perform more of your analysis in Access instead of spending time exporting raw data back and forth to Excel.