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What remains is to push these models from the drawing board into the operating room is consumer demand.
The goal of the present study was to describe the biomedical uses of these models from two studies.
In situ 3-D microtomography has been instrumental to assess these models from experimental evolution of average void density and size.
In this review, the use of these models from the perspective of an Ag innovation company's trait discovery and development program will be discussed.
Moreover, methods often employed to design these models from data are also biased by the former structure and by their nature, which is close to prototype identification algorithms.
A simulation (or refinement) relation between two state-based models allows to structure these models from an abstract view to a concrete view.
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While estimates from these models come from the training set, the prediction properties must be evaluated in the validation set.
But, of course, these models abstract from very important parts of reality, and the next step to do is what?
These models learned from solutions calculated by a validated multibody model of the knee.
The prediction error (absolute) of these models varies from 3.72% to 8.70%.
The proportion of information explained by these models ranges from 0.18 to 0.63.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com