This model can offer information of both the slow-scale and fast-scale dynamics of the system and is more accurate compared with the averaged model.
Xu (Xu et al. 2009) employed the generalized variational principles to decompose a boundary value problem with random microstructure into a slow scale deterministic problem and a fast scale stochastic one using a Green's function based multiscale method.
In this study, by employing the generalized variational principles, a Green-function-based multiscale method is formulated to decompose a boundary value problem with random microstructure into a slow scale deterministic problem and a fast scale stochastic one.
In this work, we address these questions by simulating a two time-scale biologically realistic neural network with dynamics evolving at two time-scales: the fast scale of neurons and synapses and slow scale of homeostatic presynaptic-dependent synaptic scaling.
In numerical results based on Algorithms 2 and 3, only slow scale fading (distance dependent pathloss and shadowing) information is considered as also stated in Sections "Determining the optimum SINR target" and "Mode selection", respectively (see Table 2 for the values of the simulation parameters).
The basic idea is to separate time into two scales, a slow scale for tribological phenomena and a fast scale for dynamics.
Achieving reductions in neonatal mortality globally has been challenging for a variety of reasons, including limited political prioritization of newborn health, inadequate financial commitment to neonatal care by funding sources, and slow scale up of high impact maternal-child interventions.
Within each pair of equations, onedependent variable evolves on a fast time scale and one on a slow scale.
To this end, we proposed low complexity power control and MS algorithms that rely on slow scale CSI.
This model performs well in short-term forecasting of wind speed and wind power owing to the slow scale of meteorological changes.
The slow scale problem corresponds to common engineering practices by smearing out fine-scale microstructures.
