The importance of using good run orders is demonstrated by seeking D-optimal run orders for a central composite design in the presence of an AR(1) autocorrelation pattern.
The synthesis of complex functions such as winner- loser -take-all, k-winner- loser -take-all, rank ordering are demonstrated winner- loser -take-all overhead.
A temperature-induced and a Ni-induced enhancement of graphitic ordering is demonstrated.
High-order accuracy of up to the 6th order is demonstrated for problems in linear advection, diffusion and Poisson equation.
The effectiveness of these algorithms, up to the 12th order, is demonstrated by computing all 120 eigenstates of a model C60 molecule to very high precisions.
In particular, a substantial reduction of oxidized groups and sp3 carbons upon annealing was proven by X-ray photoelectron and Raman spectroscopies, while the increase of crystalline order was demonstrated by X-ray diffraction, in terms of higher correlation lengths both along and perpendicular to the graphene planes.
In the Taylor Green vortex flow, the improvements in order behaviour are demonstrated.
Finally, two design examples of 3rd and 5th order lattice wave digital fractional order differentiator (LWDFOD) are demonstrated to justify the design accuracy.
The mechanical effects of increasing turns per inch and combining groups of fibers into higher-order yarn structures are demonstrated.
The second-order convergence rates are demonstrated with a few numerical examples on randomly perturbed quadrilateral and polygonal meshes.
Very low levels of leakage, in the order of 0.05 arem2, are demonstrated by the pHEMTs, which have enabled the implementation of large-geometry, low-noise devices.
