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22: JMS Step 4. In this step you retrieve another administered object the topic. This example assumes you have previously placed a topic object into the JNDI name space as described in the section "Administered Objects." You can use the same context you created in Step 1. Look up the topic that has been bound to the name "testTopic", cast it to a Topic object, and assign it to the handle topic. You can perform this step at any time before the next step. It is not dependent on any of the first three steps (apart from the setting up of the initial context). Step 5. Once you have the Topic object, you are ready to create a TopicPublisher object that you will use to send messages to the topic. The session has a method called createTopicPublisher that takes a Topic object as a parameter. It creates the sender and connects it to the specified topic. Step 6. This is where you create the message to be sent. In this case you are creating an instance of TextMessage. Use the session to create the message object by calling the createTextMessage method. If you want to send other types of messages, you can call one of the other methods on the session object (createObjectMessage, createStreamMessage, and so on). Step 7. Finally, you can send the message to the destination topic. You do this by invoking the send method of the TopicPublisher, passing as an argument the message object to be sent.

label mappings are of little practical value. So, you are left with a requirement for a label distribution protocol.

17:10-5

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Listeners can choose to receive only a subset the events announcedby regisof tering a "pattern" with the event manager,Only events that match the pattern are so that forwardedtothelistener.Thearchitecturecanbeappliedrecursively listeners also announce events to another set of listeners. In this way, an event dissemination "tree" can be constructed. An hportant property of this architecture is that the announcer is insulated from knowledge of how many listeners there are and from which listeners are affected by a particular event. Listeners do not have to be processes; they may simply be objects in which amethodisinvoked as aresult of an event. This i i c ~ ~ i the ~echanism sometimes called i ~ ~ ~ ~ v oi c ~ sinceo ~ announcer does not is invoke listener methods explicitly. Listener methods are invoked implicitly as a result of an event announcement.

EXHIBIT 11.1 (Continued)

Breakfast Raspberry Muffin ( 7) 1 oz (30 g) low-fat cheese 1 orange Coffee Lunch 2 Pita with Curried Turkey ( 14)

positions are always on the road. Furthermore, there is an indication in Figure 7.5 that the terrain is best represented in the larger resolution 10 m DTM, particularly in the area of steeper slopes (i.e. in the rst half of the data collection route). In addition, in the case of the 50 m DTM, the biquintic and bicubic are slightly better, by about 0.23 m, than the bilinear in terms of RMSE. However, in the case of the 10 m DTM, the bilinear is conversely better (about 0.03 m) than the biquintic and bicubic. So, there is no signi cant difference (less than 0.24 m) in height error between the three interpolation algorithms when interpolating from the same resolution DTM. As expected, the RMSE of RAW (22.5 m) is the worst compared with RRF. Table 7.6 shows the position errors provided by RRF and those provided by RAW. It is very clear from Table 7.6 and Figure 7.6 that RRF provides a more

away from the ridge, driving the lithosphere laterally in the same direction by viscous drag on its base, and nally descended back into the deep mantle at the ocean trenches, assisting the subduction of the lithosphere. This possible mechanism will be discussed more fully in Section 12.7.

Above the effect of the nite size pupil function P(x,y) was neglected. Letting x01 Mx1 y01 Equation (9.4-7) can be written as

public void ejbCreate() throws CreateException, EJBException { ... } public void ejbCreate(int id) throws CreateException, EJBException { ... } public void ejbCreate(String name, String addr, String email, String country, String phone) { throws CreateException, EJBException ... }

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(P 5 0.82). These differences suggest that interactions between treatment and baseline covariates are included when estimating the propensity scores. We assume MAR in our analysis, and estimate the propensity scores by a logistic regression, with the inclusive criterion of retaining all variables with P values less than 0.20. The MAR assumption is supported by the fact that we have rich baseline information for characterizing the respondents and nonrespondents, although it would be advisable to conduct a sensitivity analysis to assess the impact of violations from MAR, for example treating dropouts as treatment failures. We apply the PSPP methods to the data to derive the BMI reduction at 3, 6 and 12 months. For BMI reduction at 3 months, we fit two stratified PSPP models: ! 2 P ^ , where Y is ^ Ic sc Y1 (1) Stratified PSPP method with null g function, denoted as 1 c 1 the estimated 3-month propensity score. (2) Stratified PSPP method with baseline covariates in the g function, denoted ! 2 P ^ g baseline covariates . Ic sc Y1 as

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