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Not only instance models, but also entire modelling languages are subject to change.
Signal detection theory, for instance, models the detectors and cues in more detail.
In synthetic biology, for instance, models are used for the design of circuits to reliably perform specialized tasks.
This new language is supported by a graphical modeling tool which can visualize different instance models for domain problems.
We use realistic network sizes (up to 100 mesh nodes) to perform a thorough comparative experimental study on these three instance models with different key-parameter settings.
However, it only focuses on inference ontologies, i.e. ontologies that enable reasoning, for instance, models expressed in the Web Ontology Language (OWL).
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Sony Ericsson, for instance, modeled its sleek T280 on its $300 T650 phone.
In first instance modeling was based on the lumping of the components and the reaction network.
We quantify the impact of these measures on the ifcOWL ontology and instance model size.
We test our algorithms in computational experiments using randomly generated instances along realistic data, as well as a realistic instance modeling the German long-distance network.
Our research strategy is to build a general model for complex systems first and then generate the supply chain instance model from it.
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