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A model then demonstrates how these facts can emerge in tandem.
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The authors first define their model, and then demonstrate that this model matches a number of data sets from experiments examining phase precession.
The applicability of the model is then demonstrated by an illustrative example.
The current preliminary implementation of the model is then demonstrated for a discrete example.
The effectiveness of this model is then demonstrated by an illustrative example.
Applicability and validity of the model is then demonstrated using field data and results.
The efficacy of the model was then demonstrated by using Google Drive, Dropbox, and OneDrive [57 60].
Possible modifications to an existing predictor model are then demonstrated, and different estimates are discussed for one actual building.
Using this model, we then demonstrate how a number of external interventions in the structure and/or organization of market interactions (occurring before trade, after trade, or during negotiations themselves) can profoundly alter the nature of these dispositions.
The capability of the model is then demonstrated by simulations of Nabarro diffusional creep and the Kirkendall effect, both showing excellent agreement with classical descriptions of dislocation climb plasticity and interdiffusion.
The predictive capability of the (simplified) ANN model is then demonstrated by considering another, independent dataset, not used during the ANN training, and comparing the evolution of the heat load to the LHe bath computed by the ANNs with that obtained from the (detailed) 4C model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com