939 resultados para parameter driven model


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Optimal sampling times are found for a study in which one of the primary purposes is to develop a model of the pharmacokinetics of itraconazole in patients with cystic fibrosis for both capsule and solution doses. The optimal design is expected to produce reliable estimates of population parameters for two different structural PK models. Data collected at these sampling times are also expected to provide the researchers with sufficient information to reasonably discriminate between the two competing structural models.

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Over the past years, the paradigm of component-based software engineering has been established in the construction of complex mission-critical systems. Due to this trend, there is a practical need for techniques that evaluate critical properties (such as safety, reliability, availability or performance) of these systems. In this paper, we review several high-level techniques for the evaluation of safety properties for component-based systems and we propose a new evaluation model (State Event Fault Trees) that extends safety analysis towards a lower abstraction level. This model possesses a state-event semantics and strong encapsulation, which is especially useful for the evaluation of component-based software systems. Finally, we compare the techniques and give suggestions for their combined usage

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Model transformations are an integral part of model-driven development. Incremental updates are a key execution scenario for transformations in model-based systems, and are especially important for the evolution of such systems. This paper presents a strategy for the incremental maintenance of declarative, rule-based transformation executions. The strategy involves recording dependencies of the transformation execution on information from source models and from the transformation definition. Changes to the source models or the transformation itself can then be directly mapped to their effects on transformation execution, allowing changes to target models to be computed efficiently. This particular approach has many benefits. It supports changes to both source models and transformation definitions, it can be applied to incomplete transformation executions, and a priori knowledge of volatility can be used to further increase the efficiency of change propagation.