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The model, which belongs to the class of Capacitated Multi-facility Weber Problem, involves bilinear terms for the transportation costs, and nonlinear convex constraints for calculating Euclidean distances.
We fashion a nonlinear integral model, which belongs to the class of quasi-linear viscoelastic models, for solid-like materials, which upon linearization reduces to a linear viscoelastic model.
Alternatively, if we assumed that y i ∗ followed the Poisson distribution, we would end up with a Poisson-Logit model, which belongs to the Linear Exponential Family (LEF) (see Gourieroux et al. 1984; Staub and Winkelmann 2013), and only correct specification of the first moment would be enough for consistency.
In our case, the generalized QLV model, which belongs to this class of models, does it quite well.
We want to infer the autoregressive time-varying network model, which belongs to the overall parameter space that is the union of the parameter spaces of all phases delimited by k changepoints (k = {0,..., k ¯ }).
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As for the former, the difference is inferred by identifying the corresponding pair of model fragments (each of which belongs to each model) which belong to exclusive model fragment classes.
In this paper, two neural dynamic models which belong to the class of recurrent neural networks (RNN) have been formulated for the solution of equilibrium and eigenvalue problems.
To build a battery model with enough precision and suitable complexity, firstly this paper summarizes the seven representative battery models, which belong to the simplified electrochemical models or the equivalent circuit models.
Within this context, FE model updating technique, which belongs to the class of inverse problems in classical mechanics, is used to detect, locate and quantify damage.
Interestingly, all genes thus far identified in this model, except AP2, which belongs to the APETALA2/ ethylene-responsive element binding protein (AP2/EREBP) family, are MADS genes.
As for the latter, the difference is easily inferred by identifying the corresponding pair of model fragments (each of which belongs to each model) both of which give the (global) PPCs of the same class.
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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