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Numerical examples using simulated noise polluted data of 2D truss and frame models confirm that the proposed method is able to successfully update structural models even in the presence of mass modeling errors.
Through a series of simple examples using simulated neural responses, we illustrate how stimulus asymmetries can bias reverse correlation RF estimates (even for uncorrelated stimuli) and demonstrate how explicit correction for spherical asymmetries can remove this bias.
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The identification procedure is demonstrated for pure batch operation with a worked example using simulated noisy data from an extended Van de Vusse reaction network consisting of five species and four elementary reactions [Van de Vusse, J.G., 1964. Plug-flow type reactor versus tank reactor. Chemical Engineering Science 19, 994 997].
We discuss issues of cellular subtype identification in this novel, general model framework, and provide a detailed example using simulated data.
The efficiency and robustness of the proposed method are demonstrated through two illustrative examples, one using simulated continuous stirred tank reactor (CSTR) and the other using Tennessee Eastman process problem (TEP) data.
For example, Beaumont & Balding [ 10], using simulated datasets, found that as many as 50% of true directionally selected biallelic codominant loci (selection coefficient 0.05) were mis-classified as being neutral by Fdist, a proportion that might be even higher with dominant markers.
We compare two of the newer methods using simulated data and real data from SAS online examples.
We investigate these effects using simulated data and illustrate our results with a practical example in nutrition epidemiology.
As a comprehensive example, a probabilistic model for the dynamical behaviour of a slender beam is inverted using simulated data.
Using simulated 3GPP Rel.
In the example, we use simulated data generated using the marker genotypes for the mice data set.
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CEO of Professional Science Editing for Scientists @ prosciediting.com