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In contrast to more traditional methods, parameters are considered to be stochastic rather than fixed.
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The methods' parameters were chosen according to previously used values (e.g., α) or according to preliminary tests.
In the PSO-NTVENN method, parameters are determined by using a sequential neural network approximation.
As with process parameters, the method parameters are the detailed steps of a method attribute, such as the extraction time for sample preparation.
The method parameters are usually chosen so that a Runge-Kutta method has appropriate efficiency, order and stability characteristics.
Objects and method parameters are often templated so that they may be in-lined by the compiler.
The following method parameters were used: injector, 120 °C; column, 60 °C; detector, 120 °C; and current, 150 mV.
EQ.PET filtering was not applied to CT since the method parameters were already optimised for each reconstruction.
Automated generation of method parameters is needed in benchmarking scenarios where manual or random generation of parameters are not suitable, do not scale or are too costly.
The data reduction software developed here is designed to be flexible and a discussion of the effects of varying method parameters is provided.
Thus, systematic investigations of crucial method parameters were performed by employing well characterized Au nanoparticles (Au-NPs) as a defined model system.
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