To overcome this issue, in a recent work, it has been shown that under some restrictive conditions, the cooperativity of an LTV system can be ensured by a static linear transformation of coordinates.
It has been shown that the time step stability limit is a function of the graphene parameters and more restrictive than the classical HIE-FDTD constraint.
It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels.
Here, it is shown that under a very restrictive condition, that is, when the source-receiver azimuth is constant, the two-step approach can be directly extended to non-zero-offset data.
Recently, in [2], which was the motivation for this paper of ours, it was shown that, under some more restrictive conditions than the ones proposed here, for the case (k=2), max-type system (1) has a unique periodic solution with period ω and that every solution to the system converges to the periodic solution.
It is shown that the presented criterion is less restrictive or more general than those in [15, 16].
It remains to be shown that any biological considerations are sufficiently restrictive to narrow down the range of potential methodologies in any meaningful way.
It is shown that these conditions are not too restrictive for practical swirling flows in exhaust nozzles, and that they allow the solution of the wave equation in terms of confluent hypergeometric functions (instead of Bessel functions in the case of rigid body rotation).
It is shown that both SIMP and PEAK functions can be over-restrictive in that the material is assigned only for probabilities close to one.
It is shown that, in general, the codes are too much restrictive as far as the maximum torsional reinforcement is concerned.
