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Why is this a best practice Because the database server can perform this evaluation many times faster than ColdFusion Server can. This same technique can be used for a number of different evaluations and not just with CASE expressions. Take a close look at your existing applications to see whether you can find places where you can put this useful technique to work and measure the performance metrics before and after. You re likely to be pleasantly surprised!

With limit state design, the relation between uncertainties in loads and resistances can be more directly related to the probabilities of failure than they can with ASD This has led to new directions in North American and European building codes In the 1970s and 1980s, the eld of structural engineering was dramatically changed by the rapid development of structural reliability theory (Melchers 1987), and the resulting incorporation of probabilistic concepts in structural codes (AASHTO 1994; Ellingwood et al 1980)1 This was most notable in the development and implementation of load and resistance factor design (LRFD) as the basis for new code development in the 1980s and 1990s (Ravindra and Galambos 1978).

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After Ben Forta announced the new Query of Queries (QoQ) feature in ColdFusion 5 at the 2000 Developer s Conference, the crowd went absolutely wild! What a truly cool feature and how useful it was going to be! The problem is that Query of Queries is way too easily misused and even abused. We first need to explain the difference between what happens inside ColdFusion Server and what happens inside your database server as each handles a query. Your database server is a highly optimized data manipulation machine designed to do one thing really well and very fast: handle data. ColdFusion Server is an interpretation engine for parsing and processing a wide range of CFML requests via HTTP. In Version 5, ColdFusion added an internal library of database routines that enable ColdFusion query objects to be treated as ODBC table objects and that also enable certain SQL statements to be executed against them. Because the query objects look and act just as tables do to ColdFusion, they can be queried just as tables can by using the same methods (at least in theory). With ColdFusion MX, this library was enhanced slightly, but the underlying QoQ mechanism is still the same.

There is evidence suggesting that the ability to accurately recognize faces relies on an innate cortical face module. The existence of discrete regions of the cerebral cortex specialized for face perception was investigated by Kanwisher, McDermott, and Chun [4]. Face perception was de ned to include any higher-level visual processing of faces ranging from the detection of a face as a face to the extraction of information relative to identity, gaze, mood, or sex. A region of the fusiform gyrus that responded differentially to passive face viewing compared to passive object viewing was found and has been subsequently described as the fusiform face area (FFA). This region did not simply respond to animal or human images or body parts but to faces in particular. Additionally, this region generalizes to respond to images of faces taken from a different viewpoint with considerably different low-level features from the original set of face images. In contrast to the notion that humans are hardwired for face perception, it has been suggested that face recognition is a natural consequence of extensive experience

Now think about the internal differences for just a moment. On one side, you have a small library of routines that expose query objects as tables and enable basic row and column manipulation. No indexes to greatly speed queries, no sophisticated low-level data-handling algorithms just the most basic capabilities. On the other side, you have a screamer of an optimized data-manipulation machine that can do dataset gymnastics. QoQ becomes truly useful whenever you can t do everything that you need to do in the database itself. You see an example of this type of situation in the following section. QoQ is also useful if the original database query is very complicated and resource intensive, executes over a very large dataset in the database, produces a relatively small result set, and subsequent queries are subsets or mutations of the result set. An example of this situation is shown in the section, Getting multi-dimensional results, later in this chapter.

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