Here, we experimentally assess the strength and pattern of combined ontogenetic/phenological selection in the wild that results from anthropogenic alteration of ontogeny, and consider the potential significance of such selection for the recovery of endangered populations with partial captive propagation.
In fishes, partial captive propagation, where some or all members of a population are bred in captivity and their offspring are released back into the wild (e.g. hatcheries), is often employed to increase numbers of individuals that enter and survive such critical periods or to avert such critical periods altogether.
Existing models for partial discharge (PD) propagation based on a single attenuation constant are unable to explain how each frequency component travels with a different propagation velocity.
Considering the numerical solutions of level set function, if we substitute,, for, in (16), respectively, by extending the propagation partial diffusion equation, we obtain the numerical approximation differential equation with the time step : (18).
Artificial neural network model, based on multilayered partial recurrent back-propagation algorithm has been used.
Thus, if we want a better estimate, we need to study propagation along (partial X).
The findings substantiate and extend previous findings that the central PC is part of the anatomical substrate that facilitates propagation from partial to generalized seizures.
In-depth inspection of the deformation process reveals that, at the onset of plastic deformation, dislocation embryos initiate from the locations of surface defects, and the plastic deformation is dominated by the nucleation and propagation of partial dislocations under the considered temperature.
Traditional models of neocortical partial seizure initiation and propagation are characterized by a "Jacksonian" march, as seizures spread from a site of initiation and cascade outward to more distant areas (Jackson 1958).
The variables were then used as input for QSAR model generation using partial least squares and back-propagation neural network.
