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Current ontologies can vary greatly in a number of different ways, from content to structure and implementation: Content Type. Content-de ning terms can be typed and de ned at different levels of precision, from abstract (such as concepts) to speci c (or itemized) object detail, within the same conceptual domain. Description Level. This classi cation re ects the formalization span of models from simple descriptive lexicons and controlled vocabularies, to the more complex models where properties can de ne new concepts and relationships between terms. Conceptual Scope. Some models are restricted to more general, upper-level relationships, essentially free of domain-speci c relationships. Upper ontologies are used to bootstrap or graft more speci c ones that target very speci c domains in great detail. These differently-scoped models can work together, complementing each other for particular applications. Instantiation. Each model can draw its own distinction between the classical terminology concept and some point where it deals with instances of objects that manifest the terminology (that is, the model s assertional component). In practical implementation, the chosen instantiation line is where a knowledge database of speci c items can be extracted and maintained as a separate entity. Speci cation Language. Although ontologies can be built using ordinary programming languages, it is more common to use languages specially designed to support ontology construction or description logic. Some examples are given later in this chapter. As this list suggests, all ontologies are not created equal. Rather, each is constructed according to particular design goals and developer demands. It is fortunate therefore that the underlying intent of Web ontologies is for interoperability. If managed in the appropriate languages (XML-based), applications can translate between them, or use several at once to complement each other.

Due to the high cost of face-to-face communications, we ve found other ways to communicate with our customers, and there has always been a relationship between the cost and the value We have the telephone, which is a very effective tool While not nearly as valuable as face-to-face contact, it is still a tremendous tool for certain applications The telephone has.

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increase in baseline oxygen consumption (Fig. 13.4). Physiologic dead space during pregnancy remains unchanged (Pivarnik et al. 1992; Prowse & Gaensler 1965; Sady et al. 1989). Changes in pulmonary function tests are noted during pregnancy (Fig. 13.5). Because of the increased resting oxygen requirements during pregnancy and the increased work of breathing caused by the pressure of the enlarging uterus on the diaphragm, there is decreased oxygen availability for the performance of aerobic exercise. Thus, both subjective workload and maximum exercise performance are decreased (Artal et al. 1986; Clapp 1990). During treadmill exercise in pregnancy, arteriovenous oxygen difference has been shown to be decreased (Pivarnik et al. 1990). However, in some t women there appear to be no associated changes in maximum aerobic power or acid base balance during exercise in pregnancy compared to nonpregnant controls (Pivarnik et al. 1992; Romen et al. 1991; Sady et al. 1989).

It is easy to show that [ChLa74] the ratio of server utilizations is given by:

Hong Kong Baptist University, Hong Kong, P.R. China Faculty of Technical Sciences, University of Novi Sad, Serbia 3 School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada K1N 6N5

Basagni et al. (2008) addressed single-sink relocation by modeling it as a mixed integer linear programming (MILP) problem. The sensory eld is partitioned into a two-dimensional grid. The set S of grid points, called sink sites, are the candidate locations that the sink may visit. The sink moves step by step, each step to a site within a prede ned range dMAX ; it has to stay at a site for at least a prede ned tmin number of time units. When the sink is traveling, it is considered not reachable, and thus sensors hold their data and do not transmit. The holding time can be tuned by adjusting the granularity of grid division and the value of dMAX . Network-wide ooding is used by the sink to notify sensors of its location and reachability. Sink site set S and maximum step-moving distance dMAX de ne a graph. The MILP problem is to determine the initial sink site, sink relocation path, and

= 0.776, = 1.125,

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