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Also, the present diversity of computer architectures cannot fit in one model; the experimentation on current computers is relevant and necessary to gain more precise prognostics about EA performance and robustness.
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Lastly, we note that to perform such extensive experimentation with animals in a laboratory would be extremely expensive and time-consuming, pointing to a beneficial role of large-scale system modeling in the experimentation process.
The estimated results compare well with the results of the model experimentation.
In the second three-year project phase (model experimentation) the model was deployed and its effects were observed.
By implementing this algorithm, the model experimentation can be performed as follows.
The substantial animal morbidity and mortality associated with the model makes experimentation inefficient and can lead to faulty data analysis.
However, the high rate of animal morbidity and mortality associated with the model makes experimentation inefficient and can lead to faulty data analysis.
The model reduces experimentation significantly; in principle, it requires only a single non-reacting experiment to obtain the macromixing parameter and a single reacting experiment to obtain the micromixing parameter.
Systems biology has contributed workflows for data-driven modeling and model-driven experimentation to the life sciences.
A similar approach is employed to calculate numeric weights on the edges of the FCM model in order to form a weight matrix W. This weight matrix gathers the suggested weights of all interconnections among the concepts of the FCM model and is fixed throughout the experimentation.
Uncertainties play important role in the experimentation and modeling of various engineering and science problems.
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