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A recent review [ 6], highlighting a number of important features which have yet to be explored in paratuberculosis modelling research emphasised treatment of parameter uncertainty and model variability as key areas for improvement.
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Our main results are confirmed by employing a more realistic FitzHugh-Nagumo neuron model, which displays threshold variability, as well as by considering more realistic stochastic synaptic models and realistic signals such as poissonian spike trains.
From our single-cell data, we built a computational model that captures cell variability, as well as population behaviors.
Model strategies aim at enhancing process descriptions, studying parameter sensitivity, uncertainty, inverse parameter identification, model calibration, and effects of spatial variability, as well as generating model emulators and databases.
Although most models show a greater variability in daily streamflow than the observations, the models with the greatest variability (as much as double the observed variability), produce most of their runoff through fast response, surface runoff mechanisms.
The domain analysis is split in domain scope that determines which systems and features are part of video surveillance domain (Figure6) and feature modeling which identifies the commonality and variability as well as the relationship among features in the variability model.
First, because of the use of a composite femur model, morphologic variability as encountered in the operating room is absent.
Bonifacio and Borba (2009) propose an approach, Modelling Scenario Variability as Crosscutting Mechanisms (MSVCM), to deal with the variability of scenarios as a composition of different artifacts: use cases model, feature model, product configuration, and configuration knowledge.
We have modeled this variability as 25% random connections on top of the architecture obtained from the Gabor kernel (see Materials and Methods).
Covered in the first section is an introduction chapter to physiological methods in general, followed by ones on models of absorption, distribution, and elimination, as well as generic PBPK models, variability, uncertainty, and sensitivity analysis as applied to model building and use, and drug drug interactions.
The domain model captures product commonalities and variabilities as well as the particular characteristics of different SPL products.
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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