In the analysis procedure, the approximation of the response, y, was determined using a quadratic polynomial regression model as a function of the pyrolysis parameters using Eq. 1, which has linear and quadratic terms in addition to an interaction term, where b is the regression coefficient, χ is the independent parameter, and e is the experimental error.
An essential part of the solution procedure is the approximation of the joint-confidence region.
In the present work we extend the FEMOS computational platform in the study of the Drift Diffusion model (DD) [5, 6] and focus on the issue of endowing the simulation tool of a consistent, stable and accurate procedure for the approximation of electron and hole current densities in the device.
During the first rounds, the procedure solves the approximation problem of Eq. 2 using only those mutation pairs that best fit the multiplicative model, and then gradually extends the subset to also include pairs with larger residual errors (see Methods for details).
In addition, combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process.
On the other hand, Li et al. [7] introduced two steps of iterative procedures for the approximation of common fixed point of a nonexpansive semigroup on a nonempty closed convex subset in a Hilbert space.
In 2009, motivated and inspired by Moudafi [11], Shahzad and Udomene [12] introduced and studied the iterative procedures for the approximation of common fixed points of asymptotically nonexpansive mappings in a real Banach space with uniformly Gâteaux differentiable norm and uniform normal structure.
In 2009, motivated and inspired by Marino and Xu [20], Li et al. [21] introduced the following general iterative procedures for the approximation of common fixed points of a nonexpansive semigroup { T ( s ) : s ≥ 0 } on a nonempty, closed and convex subset K in a Hilbert space: y n = α n γ f ( y n ) + ( I − α n A ) 1 t n ∫ 0 t n T ( s ) y n d s, n ≥ 1, (1.11).
Throughout the operation of the approximation procedure, we ensured that the weight matrix remained symmetric.
The usual procedure involves approximation of the torn ends of the muscle with absorbable suture material.
Interestingly, the majority of the mutation pairs selected towards the end of the approximation procedure shared a specific function (Figure 2).
