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Each user's requirements are generated at random from the thresholded normal distribution defined by their individual PDS i function.
In this simulation, we assume that user i has a product demand split that is generated from a normal distribution defined by its mean μ i, and its standard deviation σ I, with the value then thresholded to clip it within the range [0,8], i.e.: PDS i = min(max 0,N(μ i,σ i )),8).
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PD: I think it's somewhat overdue.
PD: I don't have a blanket statement to make here.
PD: I don't know about targeting every type of cancer.
PD: I'm not claiming we're going to do that.
The trend is first estimated by averaging PD i (f) over two periods: PD ⌢ i ( f ) = ∠ 1 M ∑ m ∈ C e j PD m ( f ) (17).
Nevertheless, the resulting PD i [ k] is still a circular data.
This phenomenon is revealed by PD i (2f0 t)) which tends towards low phase values.
Thus, over a few periods, PD i (f) has also a non-constant trend.
This trend is then removed before computing the standard deviation: σ i ( f ) = std i PD i ( f ) − PD ⌢ i ( f ) = − 2 log 1 M ∑ m ∈ C e j ( PD m ( f ) − PD ⌢ m ( f ) ) (18).
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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