Other nanoparticle inks are slow setting and compatible only with ink-jet or screen printing methods, which require several passes in order to make even a thin electrode.
The model problem is formulated to permit direct numerical simulations (DNS) using pseudo-spectral methods, which require periodic boundary conditions.
These methods yield the desired ribozyme sequences within minutes in contrast to the in vitro selection methods, which require weeks.
Simplicity and quick convergence is the advantage of the method in comparison with other much more laborious numerical methods which require extensive computer facilities.
In contrast to similar methods which require a priori knowledge of the mean fluid velocity, this formulation is based on knowledge of the mean pressure gradient only.
To investigate whether these alternatives are able to improve the module detection, we used 15 such measures as the input for four of the top clustering methods which require having a similarity or distance measure as parameter.
Compared to other common methods which require tedious and complicated physical calibration, our method avoids these steps for all possible geometric configurations and is therefore more convenient and robust.
In this paper, a simple and novel tuning method is proposed along with three existing methods, which require only step or frequency response tests.
As no meshing, it can save substantial cost and time on pre-processing, unlike mesh based methods, which require meshing and re-meshing.
Experiments were performed on speech corrupted by a variety of noises, using missing feature compensation methods which require binary masks and probabilistic masks.
Results show both of these methods can be used as efficient frameworks to decompose optimization problems in linear time, in comparison to traditional methods which require polynomial time.
