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The main reductions of place/transition Petri nets have already been presented in 3. We now present their extensions to high level nets. 8.4.1. Principles of extension to high level nets Several kinds of reductions have been de ned for colored nets [COL 86, GEN 91, HAD 91]. We discuss these last ones because the author de nes a general extension method allowing a colored reduction to be obtained, starting from an ordinary one, and successfully applies his method to several classical reductions. This method relies on the three following principles: 1) Stay close to the ordinary application conditions; this means: a) Do not add structural constraints to the ordinary de nition. b) Only add functional conditions needed for preserving the equivalence between the original net and the reduced one. 2) Only consider extensions which allow a usable reduced net to be obtained. a) Do not enlarge the color domain of transitions. b) Only compose color functions or their inverses to de ne new color functions. 3) Always keep the same set of preserved properties. A clean methodology is to study the suf cient conditions such that a colored reduction may be considered as a preliminary unfolding followed by a sequence of classical reductions and concluded by a reverse folding. Therefore we study how to directly recognize the application conditions in a colored net and apply the transformation rules corresponding to a sequence of reductions of the unfolded net as shown in Figure 8.16.

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Differential Ability Scales, Second Edition (DAS-II), Early Years Level General Conceptual Ability Special Non-verbal Composite Verbal Cluster Verbal Comprehension Naming Vocabulary Non-verbal Reasoning Cluster Picture Similarities Matrices Spatial Cluster Pattern Construction Copying Neuropsychological Assessment, Second Edition (NEPSY-2) Tower (NEPSY) Phonological Processing Sentence Repetition Comprehension of Instructions Word Generation Arrows Peabody Picture Vocabulary Test, Fourth Edition (PPVT-IV) Expressive One Word Picture Vocabulary Test (EOWVT) Kaufman Assessment Battery for Children, Second Edition (KABC-2) Gestalt Closure Developmental Test of Visual-Motor Integration, Fifth Edition (DTVMI-V) Visual Motor Integration Taps /10 sec. Finger Tapping Right (dominant) Hand Left (nondominant) Hand Children s Memory Scale (CMS) Attention/Concentration Index Numbers Sequences Picture Locations (supplemental) Verbal Immediate Index Stories Visual Immediate Index Dot Locations Verbal Delayed Index Stories Visual Delayed Index Dot Locations Delayed Recognition Stories 16.0 15.3

NJ (n = 493)

The question Is the climb worth the view is applicable to ABM. That is, by building a more detailed and slightly more accurate ABM model, will the answer to your question be better answered Avoid the creeping-elegance syndrome. Larger models introduce maintenance issues. ABM rapid prototyping accelerates a team s acceptance of ABM by quickly giving them a vision of what their completed ABM system will look like once scaled to its proper level of detail and how they will use the new ABM information.

Here are 10 tips to help you make optimal use of your analytics data as you execute on Web site changes with a Web site testing tool: 1. Involve users early in the design process. Put together a panel of users from your target audience and ask them to provide feedback on new designs before you get to the process of coding and deploying the design. You may be able to eliminate subpar changes early, save time, and get fresh new perspectives. 2. Educate designers on your goals. When you re discussing improvements with your design team, use the sharing functionality in Google Analytics or walk through the analytics reports

Next, we suggest comparing the two estimates of beta the regression coef cient of (Van 500 minus TB3) and the two estimates of the R2 If these two estimates differ too much, we can assume the presence of serious numerical accuracy problems needing further investigation by software tools not readily available in Excel For our data, if the extra nonzero intercept is included, R2 = 0948, and the estimate of b denoted by b = 10145, with the Student s t statistic 2304 The estimate of the intercept is 47734 with the t statistic of 14392, which is statistically insigni cant Since one of the claims of CAPM is that the intercept will be zero, if the data yield an insigni cant intercept, this supports CAPM If the intercept (constant term) is forced to be zero, R2 = 0945 and b = 1.

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