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Small cells in the considered system model are denoted by (mathcal {N}={1,2,dots,N}).
The states in the model are denoted as N p t, N s ′ t, N s t.
Variables in the dimensional model which will appear in scaled form in the dimensionless model are denoted by an asterisk.
The consequences of the model are denoted by Bi; they represent an intensity level associated with the dependent variable y and are the basis of the output space.
We use the following notation: Parameters corresponding to a basic Eisenberg Noe model are denoted by EN, B signifies the incorporation of bankruptcy costs, C cross-holdings, and F fire sales.
Items relevant only to the development of a prediction model are denoted by D, items relating solely to a validation of a prediction model are denoted by V, whereas items relating to both types of study are denoted D V.
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The resulting model is denoted as CASRPBS, whereas the additional "A" represents the assortment decision.
Algorithm 2 will improve the model on (B(x_{k},omegaDelta_{k})) and the resulting model is denoted by (m^{(2)}_{k}).
The approximating model is denoted as g θ, where the subscript θ indicates the U-dimensional parameter vector, which in turn specifies the probability density function.
Therefore, the color-based observation model is denoted as follows: p ( O t e | S t ) ∝ e - λ e ρ e 2 ( E *, E ( S t ) ) (22).
Therefore, the color-based observation model is denoted as follows: p ( O t c | S t ) ∝ e - λ c ρ c 2 ( K *, K ( S t ) ) (10).
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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