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We use our 30 s training sequence for experimentally fitting these parameters, given the following parameter values: min_s = 0.02,hor = 5 and eta_max = 1.
With these parameters, given an edge (u,v ∈E, a spreader s∈V and a piece of information r, λ u,v,s,r) is finally defined as follows [10]: lambda u,v,s,r) = minleft{omega u) cdot phi(r) cdot {gamma}^{delta u,r -1}1},r -1}ht} (1right
Parameters in each block were sampled from their full condition distributions, which are the conditional distributions of these parameters given parameters in other blocks and the phenotypic and marker data.
Through a Monte-Carlo method, we propose a numerical study of such nanodevice, to evaluate tolerances (or uncertainty) on these parameters, given a threshold of efficiency, to facilitate the design of nanoparticles.
Before experimenting with individual parameters, we wanted to find the most coherent set of values of these parameters given a discrete set of values for each parameter as specified earlier.
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Variation of these parameters gives rise to a number of designing options.
Also, a simple self-adaptive choice of these parameters gives surprisingly good results.
Because these parameters give a great influence on the control performance.
These parameters give an indication of the possible optimum mill operating conditions in an idealised condition.
The same values of these parameters give an excellent prediction of the profiles of multicomponent bands.
The parameters, which were used to define geometric characteristics of coastal erosion, were evaluated and some ratios of these parameters gave a constant value.
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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