Sentence examples for modeling of unstable from inspiring English sources

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But whereas the neurological basis of expressionism makes the physical mark of the artist on the medium central to her art, Dennis' modeling of unstable neurologies and the delusions they impel is disclosed through an assemblage of the objects and materials of industrial production and consumption that appears seriously askew, what objects would look like if they could be neurotic.

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Animal models of unstable atherosclerotic plaques may provide insight into the molecular underpinnings of atherosclerosis and mechanisms by which its progression may be halted or even reversed [ 48].

Consequently, we conclude the poor performance on C- and N-containing compounds is also a result of the poor performance of the model on unstable material and not because of a systematic issue with modeling certain elements.

The model, originally devised in [ 24] to model evolution of unstable gene amplification and then analyzed mathematically by other, is presented in Fig.  1b.

In the model of the unstable shaft fracture, dexamethasone decreased the BV of the callus by 33% (Figs 2 and 4, 95% CI 16 to 49; p < 0.001).

Today, the standard setup for biomechanical fracture models of dorsally unstable DRF is a dorsal wedge osteotomy 20 mm proximal to the articular surface [ 4- 9].

In this paper a periodic method is presented for the design of look ahead system models for unstable digital controllers.

The model distinguishes various forms of unstable and explosive behaviour according to specific regions of the parameter space that open up the potential for testing these forms of extreme behaviour.

Humans with spinocerebellar Ataxia 1 (spinocerebellarebellataxiaxia 2 (SCand, and spinocerebellar ataxia 3 (SCand, and their corresponding mouse models, have large numbers of unstable polyglutamine (polyQ, CAG) repeats in the Ataxin-1 [ 23, 24], Ataxin-2 [ 25], and Ataxin-3 [ 26- 28] proteins, respectively.

Originally applied to decision trees, bagging can be used with any classification or regression model and it is especially effective in conjunction with utilization of unstable nonlinear models (i.e., a small change in the training set can cause a significant change in the model's learned parameters).

The method is applicable to stable, non-minimum phase, irrational, and to a certain class of unstable plant models.

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