Sentence examples for network decay from inspiring English sources

From our assumptions, there are two main forces that can affect to which extent the imperfectness of conditional cooperation spreads through the network (decay) and that, therefore, may determine whether cooperation can be sufficiently sustained across time: the average connectivity (total number of links) and the presence of highly connected players (distribution of links).

To test if these network decay mechanisms are sufficient to also "trim" the machinery to cell sized dimensions, we have modified kinetic model parameters as well as the G-actin diffusion coefficient within reasonable ranges.

(C) Can network decay associated with array treadmilling alone account for the finite size of the lamellum in cells, or are additional mechanisms necessary to break down and depolymerize the network?

Indeed, we clearly observed that degree distributions of the merged network decayed as a power law (Fig 3D), similar to the separate SI and HC networks (supplementary Fig S7a online).

The average OD for the two smaller networks decay more rapidly, but this does not imply that the distribution of OD across the network is less structured.

Previous literature has suggested that the in-degree distribution of gene regulatory networks decays exponentially [ 100- 102].

In such a network, RNA decay is confounded with self-regulation.

Travel, if essential, would have been easier by boat: archaeology suggests that much of the Roman road network was decaying, and in many places scrub and forest was taking back land which had been farmed for centuries.

Besides edge density, the decrease of coherent FFL type 2 of regulation including heat shock protein at 48°C could be related to the fact that the frequency of recurring sub-graphs or network motifs decays with increasing temperature.

We proved that a function with m jumping discontinuity points can be approximated by a simplest neural network and a decay RBF neural network in L2(R) by each ɛ error, and a function with m jumping discontinuity point y= f(x), x∈E⊂ℝd can be constructively approximated by a decay RBF neural network in L2 Rd) by each ε>0 error.

With no proven method to set the parameter, all sparsity levels are considered, from a diagonal-only network (only RNA decay) through a fully connected network.