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We combined many primer design constraint functions with weights into the fitness function.
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We consider various objective functions with weight and perimeter constraints.
This function gives us Hurwitz-type zeta functions with weight α.
A weighted mean was used as the summarization function with weights being given by the peak areas (e.g., signals with larger peak area were weighted more heavily).
In order to obtain the best suitable conditions for alpha-galactosidase production, an objective function with weights and bias was used.
24 Non-linear transformation consists of weighting the accepted values of the parameters, θi, by a quantity that depends on the distance between si and sobs and then deriving corrected values of the parameters (θi*) from a non-linear regression: θi* = θi + g(W, sobs) − g(W, si) where g(W, s) denotes a feed-forward neural network regression function, with weights W adjusted from the simulated data.
Afterward, Araci et al. have introduced weighted ( h, q ) -Genocchi polynomials and defined ( h, q ) -zeta-type function weighteight α by applying the Mellin transformation to the generating function of the ( h, q ) -Genocchi polynomials with weight α which interpolates for ( h, q ) -Genocchi polynomials with weight α at negative integers (for details, see [3]).
In this paper, we study the two-variable Dirichlet L-function with weight α.
The aim of this paper is to construct q-Dirichlet type L-functions with weight α.
By using (4), we define a two-variable q-Dirichlet L-function with weight α as follows.
In this paper, we also consider a ( h, q ) -zeta-type function weighteight α and derive some interesting properties.
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