Sentence examples for sensitivity of network from inspiring English sources

Simulation results are used for demonstrating the sensitivity of network performance to key system parameters, namely, structural vibration intensity, energy harvesting efficiency of the used piezoelectric material, and the energy storage capacity at the pulse switching sensor nodes.

While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies.

In our simulation studies, we could show that in principle this way the sensitivity of network reconstruction can be increased significantly.

An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations.

The Jacobians can be used to determine the sensitivity of the network outputs to changes in both the network weights and the inputs.

In noisy networks the presence of low grid scores for networks with high bump scores is explained by sensitivity of these network configurations to noise-induced drift.

Starting with Network I, we intend to control the concentration of Tet-ON with self-repression and decrease the sensitivity of the network to low Tc levels.

The challenges that this network poses are first to eliminate expression leaking when Tc is absent and second, to increase the sensitivity of the network to Tc. Beginning with the first challenge an obvious step is to increase "repressors" levels, meaning the total amount of both TetR and Tet-OFF dimmer molecules, when Tc is absent.

Such modifications eventually increased the resistance changes and pressure sensitivity of the network.

These estimates can be seen as estimates of the sensitivity of the network utility to changes of the allocation strategies.

Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8%too 15.5%.