In this work, the method is customized to accomodate a multiperiod design.
Its novelty is that it accounts for the specific input/output behavior (or response function) of the particular simulation at hand; i.e., the method is customized or application-driven.
Moreover, a number of these ranking methods are customized to deal with a specific dataset, and thus, not necessarily applicable to all DEA problems.
One of the effective methods is customizing the electrode materials, e.g., engineering pore geometry/size and doping defects/functional groups [11, 12].
In recent times, such a growth method has been customized for the realization of different multicomponent oxide materials.
Moreover, we demonstrated that DnaSP and RDP could accurately identify SNPs and recombination events respectively suggesting, this method could be customized for any set of gene sequences to quickly characterize novel genes and monitor the pneumococcus for 'vaccine escape' strains.
This method can be customized for most ligation mixtures and it is possible to reduce the total time required to carry out this procedure.
Moreover, the method can be customized to fit the requirements of the amyloid system of choice by changing the membrane material, cut off size, peptide labeling strategy, and the fluorophore.
This approach takes advantage of the object oriented programming capabilities of Python for providing full flexibility: methods can be customized with no limitations, while untouched methods keep providing the full features of the parent model class and a seamless integration in the predictive environment.
This implies these methods can be customized to specific features of a particular field.
