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QoS requirements and real-time policies, as installable/con gurable units supported by the CIAO component framework [61]. The Model Driven Architecture (MDA) [17] adopted by the OMG is a software development paradigm that applies domain-speci c modeling languages systematically to engineer DRE systems. MDA tools are being combined with QoS-enabled component middleware to address multiple QoS requirements of DRE systems in real time [59]. RTSJ middleware research. RTSJ middleware is an emerging eld of study. Researchers are focusing at RTSJ implementations, benchmarking efforts, and program compositional techniques. The TimeSys corporation has developed the of cial RTSJ Reference Implementation (RI) [58], which is a fully compliant implementation of Java that implements all the mandatory features in the RTSJ. TimeSys has also released the commercial version, JTime, which is an integrated real-time JVM for embedded systems. In addition to supporting a real-time JVM, JTime also provides an ahead-of-time compilation model that can enhance RTSJ performance considerably. The jRate [5] project is an open-source RTSJ-based real-time Java implementation developed at Washington University, St Louis. jRate extends the open-source GCJ runtime system [14] to provide an ahead-of-time compiled platform for RTSJ. The Real-Time Java for Embedded Systems (RTJES) program [23] is working to mature and demonstrate real-time Java technology. A key objective of the RTJES program is to assess important real-time capabilities of real-time Java technology via a comprehensive benchmarking effort. This effort is examining the applicability of Real-time Java within the context of real-time embedded system requirements derived from Boeing s Bold Stroke avionics mission computing architecture [55]. Researchers at Washington University in St Louis are investigating automatic mechanisms [7] that enable existing Java programs to become storage-aware RTSJ programs. Their work centers on validating RTSJ storage rules using program traces and introducing storage mechanisms automatically and reversibly into Java code.

18:5-1

For longitudinal data subject to attrition, the resulting data have a monotone missingdata pattern for the repeated measures. Let (X1, . . . XP, Y1, Y2) denote a (P + 2)-dimensional vector of variables with X 5 (X1, . . . XP) fully observed covariates and Y1,Y2 with missing values in a monotone pattern. We can divide the dataset into three parts based on the missing-data pattern. The first part contains subjects with both Y1 and Y2 present, denoted as P00; the second part contains cases with Y1 present and Y2 missing, denoted as P01; the third part contains cases with both Y1 and Y2 missing, denoted as P11. We can then define the propensity score for Y1,

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In many to many relationships, the child beans are assigned a new Collection for the parents. As you can see in Figure 17 3, the new collection contains the old list of parents, plus the new parents. The psuedo code for the EJB container takes a little more work now. For each child bean, the container must grab the list of current parents, add a child, and then reset the collection. (Remember that each child bean can potentially have a different set of parents, meaning that you must treat each one individually.)

Summary The three theoretical perspectives discussed here make clear that the relationship between lying and nonverbal behaviour is complex Zuckerman et al s (1981a) assumptions that liars may show signs of emotions and cognitive load seem straightforward However, those assumptions may lead to opposite behaviours For example, arousal typically leads to an increase in eye blinks whereas cognitive load typically leads to a decrease in eye blinks; and the emotional approach predicts an increase in certain movements (signs of nervous behaviour), whereas the cognitive load approach predicts a decrease in movements during deception as a result of neglecting the use of body language DePaulo, Lindsay et al s (2003) self-presentation perspective stresses that experiencing emotions or cognitive effort is not the exclusive domain of liars Truth tellers may also experience these and, as a result, may also display nonverbal cues associated with emotion or cognitive load Zuckerman et al.

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s (1981a) attempted behavioural control prediction is not straightforward because the behaviours shown by deceptive senders, as a result of this deliberate control, will depend on not only their perceptions of what constitutes a credible nonverbal display but also their acting skills in performing this display However, some general predictions can be made on the basis of the attempted behavioural control approach that contradict what can be expected on the basis of the emotional and cognitive effort approaches The emotional and cognitive effort approaches predict an increase in speech hesitations and speech errors due to nervousness and cognitive load respectively, whereas the attempted control approach predicts that liars will attempt to suppress most of those speech disturbances, and will therefore sound atypically smoothly.

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