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We develop an interval observer working in closed loop, for the state variables estimation of systems whose modelling is uncertain, as it is generally the case for bioprocess models.
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A control design procedure for disturbance rejection, when the disturbance model is uncertain, is proposed.
It calculates two different intrinsic motivations from this model: one to explore where the model is uncertain, and one to acquire novel experiences that the model has not yet been trained on.
Using a probabilistic loads extrapolation approach and the first order reliability method, a large multi-megawatt wind turbine blade and tower structural reliability are assessed when the extreme turbulence model is uncertain.
When the uncertainty is extremely localized, one can expect gains in computational efficiency of two to three orders of magnitude while only modest gains of 2 3 times are observed when half the model is uncertain.
The effect that these patients would have had on the accuracy of the models is uncertain.
However, whether these results apply to policies for future funding models is uncertain.
As a result, the relationship between drug exposure (total and free) across species and models is uncertain.
Therefore, although the animal models support the concept that IL-33-induced AHR is IL-13 dependent, whether mast cells are critical in these models is uncertain.
Since the genetic risk model is uncertain for most of the SNPs considered, we performed these tests on the co-dominant models only.
The gene association that appears in the model is uncertain and the gene PFLU4620 is annotated as a putative PEP synthase.
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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