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The C O RBA object model is statically typed This means that all attributes, parameters and operation return values have a static type, defined using C O R B A I D L The rationale for this static typing is that it enables type safety to be checked in those situations when strongly typed programming languages are used for client and server object implementations: The types identified in the interfaces are then mapped to static types in the programming language representations of client and server objects In the case of Java, Ada-95, C and C , the programming language compilers actually validate that the interfaces have been respected by client and server objects Object types in the C O RBA object model have a unique name The name is introduced in IDL using the keyword i n t e r f a c e .

Figure 5-12 Demonstration of diffraction with a grating. An optical bench may be used to hold optical elements at fixed locations. A collimated bright white light source and a heatcut filter are used to illuminate a diffraction grating covered by a closely apposed aperture mask containing a 2 3 mm hole or slit. If a slit is used instead of a hole, it should be perpendicular to the rulings in the grating.

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The interface then contains operation, attribute, type and exception definitions that belong to the object type In addition to object types, the C O RBA object model defines a number of atomic types These are represented in IDL as boolean, char, short, long, float, string More complex types can be constructed from object and atomic types using type construction mechanisms These constructed types can be given new names using the t y p e d e f construct The type constructors are declared in IDL using the keywords s e q u e n c e , s t r u c t , a r r a y and u n i o n Instances of these complex types are complex values that do not have their own identity and cannot have references.

The rationale of this decision is that the object model should support designers in passing complex values from a client to a server object or vice versa If sequences and arrays were treated as objects, they would be passed by reference and the values would need to be transferred with additional object requests..

The growth in the use of video-conferencing has been steady, rather than spectacular. It is not clear why this has been the case. Nevertheless, it is beginning to attract a signi cant number of enthusiasts and these will

The predictive density fZ (z|D) is estimated by averaging the approximate overall waiting time density f Z (z| ) over the posterior samples 1 , . . . , m : 1 f Z (z|D) = m

The new cognitive-behavioural therapy based on this theory is delivered on an individual basis and addresses weight loss, the maintenance of weight lost, body image, weight goals and, originally, primary goals (Cooper & Fairburn, in press). These primary goals are de ned as the objectives which patients hope to achieve as a result of weight loss and include improving self-con dence, increasing one s social life, etc. Equally distinct and original is the emphasis given to the long-term maintenance of weight rather than continuing and inde nite weight loss (Cooper & Fairburn, in press).

Fig. 7.6 Diagram to show the abnormal investigations in patients with multiple sclerosis. None is speci c. MR scanning is used. CSF abnormalities are found, especially the presence of oligoclonal bands in the CSF globulin. AEP, auditory evoked potential from ear to temporal cortex; CMCV, central motor conduction velocity from motor cortex to limbs; SSEP, somatosensory evoked potential from limbs to sensory cortex; VEP, visual evoked potential from eye to occipital cortex.

6.6 CODE FOR transform.c The code below contains transform.c for the examples of Sections 6.3 and 6.4. Modifying this code for a new analysis is straightforward. The optimization program currently implemented requires each parameter being optimized to exist on the real line. However, most of the parameters encountered in practice have a restricted range, such as (0, ) or (a, b). This code makes the necessary transformation to each parameter prior to evaluating the log-posterior distribution or log-likelihood function. For any parameter such that 0 < < + , we transform to e in transform.c. If is bounded so that a < < b, we transform as follows: a + be . 1 + e

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