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6.2.7 Modeling Modules and Their Interfaces The Uni ed Modeling Language (UML) [5] is a visual language for the modeling of business processes, software applications, and system architectures. Used as a standard by the Objects Management Group [17], UML is particularly suited for object-oriented design. UML provides a standard visual notation to describe a system s blueprints, including high-level concepts such as business processes and system functions, as well as more speci c items such as classes and objects. UML diagrams can be divided into two types: structure diagrams and behavior diagrams. Structure diagrams depict the static organization of the elements and include component, class, and object diagrams. Behavior diagrams describe the ow of activities through dynamic actions of the system and include use-case, activity, collaboration, and sequence diagrams (Figure 6.2). UML should be applied in concert with other modeling/design best practices, starting with mapping use cases to modules, continuing on to de ning interfaces, and then to de ning application logic.

9.1. Introduction One of the main interests of Petri nets is to combine qualitative analysis (i.e. property veri cation) and quantitative analysis (i.e. performance evaluation) [FLO 78, MOL 81, REI 98a, REI 98b]. In comparison, concurrency models such as process algebra [HIL 96] have only recently been extended with stochastic features, and although rst results are promising, there is still more research to be done on performance evaluation of stochastic Petri nets. Similarly, the usual models for performance evaluation, such as queueing networks [KLE 75], do not include synchronization mechanisms and adding them using ad hoc constructions [FDI 86, DAL 97] does not achieve the generality and the simplicity of concurrency modeling with Petri nets. Stochastic Petri nets were introduced in a pragmatic way at the end of the 1970s, in order to bene t from Markov chain evaluation methods. This approach leads to immediate results but conceals the semantic features underlying the de nition of stochastic Petri nets and cannot be easily generalized to different probability distributions. So in the three chapters devoted to stochastic Petri nets, we proceed as follows. First we tackle the semantic level, i.e. the level of the stochastic processes, and we study their properties with the aim of designing analysis methods. Here we have chosen to emphasize the principles which characterize every method. So, we omit programming features related to numerical computations. Indeed, these features are not speci c to stochastic Petri nets and are covered by a number of excellent books [STE 94, BOL 98]. Then we present models (and extensions) of stochastic

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The problem is that the accounting records hold only the actual cost cw;b .x; y/. Evaluating cw .x/ or cb .y/ requires creative thinking. Could we use cw;b .x; 0/ instead of cw .x/ The answer is probably not. The factory was built to produce the products simultaneously, and many design decisions were taken to optimize the joint production. This implies that cw;b .x; 0/ is greater than cw .x/. Moreover, calculations of cw;b .x; 0/ from the accounting records may be very inaccurate. This is because the only quantity that is related to producing wooden souvenirs and is easy to compute from accounting records is cw;b .x; y/ c.B/. However, cw;b .x; y/ c.B/ is greater than cw;b .x; 0/, because it includes all the common cost (electricity and labour) as if both souvenirs were produced in quantities x; y (since we ve only subtracted the directly attributable cost). Using cw;b .x; y/ c.B/ as a proxy for cw .x/ in (7.14) will lead to an underestimate of the incremental cost of bronze souvenirs and to an overestimate of the stand-alone cost of wooden souvenirs.

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361 Minorities Survey Questionnaire, 337 348 Mo cell line, 193 194 Model Ethical Protocol, 69 Modern pharmacogenetics, 141 142. See also Pharmacogenetics Mohrenweiser, Harvey W., 29 Molecular biological techniques, development of, 29 Molecular pro les, 144 Molecular signatures, 146 Monogenic diseases, genetic testing for, 236 Moore v. Regents of the University of California, 189, 192 193 Morbidity, drug-related, 234 Mortality, 244, 272 drug-related, 234 Motulsky, Arno G., 137 Mouse genome, 41 MSD720 gene, 293 Multidrug resistance (MDR)-1 gene, 171 Multifaceted platforms, expansion to, 125 Multiple organisms, genomic sequences from, 41 42 Multiracial people, 268 Muscular dystrophy, 142 Mutations drug handling and, 174 175 in different genes, 174 175 Mycobacterium leprae, 148 Mycobacterium tuberculosis, 147 148 N-acetyltransferase (NAT2), 140 National Center for Biotechnology Information (NCBI), 34 National Center for Toxicogenomics (NCT), 43, 146, 147 National Health Council focus group study, 4 National Human Genome Research Institute, 305 National Institute for Clinical Excellence (NICE), 240 National Institute of Environmental Health Sciences (NIEHS), 43

Blind combination of multiple generators is not guaranteed to yield good results Another approach to combining generators, originated by Collings (1987), is more straightforward Collings compounds generators by maintaining a pool of k separate generators of different types and intermixing the results A separate generator is used to generate a number i from [1, k], and the ith generator is used to provide the next number in the sequence This can greatly extend the period and can overcome serious distributional aws in individual generators If the periods of each generator used in the pool are p, the period of the combined generator is roughly p 2 All of the PRNGs discussed are designed to generate uniformly distributed random numbers Sampling of random numbers from other distributions is usually done by applying a transformation to a uniformly distributed PRNG.

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