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The comparison showed a fairly good agreement with less computational cost than a typical numerical modelling, obtaining a computational demand decrease of 55%.
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1H Δδbind was fitted using the best 1 1 binding model, obtaining a KD of 49 μM (Supplementary Figure S6d).
We tested three models, obtaining a model with good fit to the data.
However, due to complicated nonlinear programming models, obtaining a global optimal solution is difficult.
The behavior of the linear kernel was very similar to our model, obtaining a maximum peak on precision (0.913).
Based on the model obtained a feedforward as well as a feedback controller were designed.
Based on the ESOM models obtained, a global PID controller is designed and its robustness is examined.
The model obtains a good fit to the available population data and is fit for risk assessment purposes.
Taking advantage of the models obtained, a study of seasickness prediction has been done, and some interesting results have been obtained.
This means that the model obtains a zero coupon yield curve but one that is estimated without reference to an input yield curve.
Our classification model obtains a sensitivity (SE) of 89.28%, specificity (SP) of 93.75% and an area under the curve (AUC) of 0.986.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com