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If you have formulas or use procedures that rely on comparing text, be careful that your text doesn t contain additional space characters. Adding an extra space character is particularly common when data has been imported from another source. Excel automatically removes trailing spaces from values that you enter, but trailing spaces in text entries are not deleted. It s impossible to tell, just by looking at a cell, if text contains one or more trailing space characters.



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This code adds a shift to green text for any bold passages in the applicable text. Within a style attribute, the content that the style changes is already implied by the usage, so no tag names and curly braces are allowed. You can t do the following, for example:

To use the sample code for this chapter, download chapter27.zip from the Web site. Follow the instructions later in this chapter to create a Flex project for use with BlazeDS and install the project components to the client and server. n





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If desired, your design may include a single control panel or a series of controls play, stop, fast-forward, rewind, and so on all affecting your audio. The two key RealAudio parameters are controls and console. The controls parameter sets the type of control being inserted; the console parameter indicates which embedded audio file is being played. To embed a RealAudio file with separate controls, you must follow a two-phase procedure. First, embed the actual audio file or, if the source is dynamic, a link to a data source. Follow these steps:

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These stages can take place iteratively, being a dif cult and time-consuming process In this context it would be very useful to have a tool acting as the knowledge engineer, ie it should guide the experts through a sequence of questions and it should automatically translate the answers into Bayesian models This tool will be called the knowledge acquisition tool (KAT) Nevertheless, it can be extremely complex for KAT to build any BN model In order to make it simpler, some assumptions can be made, for example, regarding the complexity of the BN structure In that case, only questions concerning some basic relations, variables, parameters and utilities are required Besides, if it is assumed that the Bayesian model is any of the previously explained, ie na ve or noisy-OR model, only a limited subset of probabilities is needed For the simple case shown in Figure 13.

=CHAR(149) =CHAR(150) =CHAR(151) =CHAR(152) =CHAR(153) =CHAR(154) s =CHAR(155) =CHAR(156) =CHAR(158) z =CHAR(159) =CHAR(160) =CHAR(161) =CHAR(162)

25, KAT would ask about the causes (interference, coverage problem), symptoms related to each cause (interference level, HO failure, RXLevel) and conditions related to each cause (frequency reuse, cell density) It would also inquire about the different states of each variable and some probabilities, eg probability of each symptom given the related causes On the basis of the answers to the questions it would automatically build a model like the one shown in Figure 1325, a na ve model or a noisy-OR model When dealing with TS experts, it has been realised that even specifying probabilities for a given structure is quite dif cult and that different experts can provide completely different values This can lead to inaccurate results Normally, operators have system databases containing the history of most variables in the network, which can be used to train the BN (learning from data).

rotate a to right 3 b i t s s h i f t a t o r i g h t 3 b i t s and insert 0 ( l o g i c s h i f t )

2. Create an event handler function that will be called when the event is dispatched. The function should receive a single event argument typed as ResultEvent and return void:

This means that it is possible to train the BN on the basis of previous problem cases and the probabilities can then be obtained from those previous experiences instead of being elicited from experts Furthermore, probabilities can be updated every time there is a new case This presents the additional value that the system can adapt to different environments, eg probabilities can be different for a rural and urban area In conclusion, learning from data is an important feature when using BNs But not only learning parameters, learning the structure of the BN is also possible (although more complex) Therefore, it is not necessary to make simpli cations such as done in the na ve model, but the structure is the one that best matches the existing data, and also it adapts to changes in the TS domain..

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