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In these models each node ( i, j ) has a state space x i j = ( x i j E, x i j H ), where x i j E is an activity variable (representing firing rate) and x i j H is a fatigue variable.
Thus, we follow [1] and assume that in these models each node ( i, j ) in the network has a state space x i j = ( x i j E, x i j H ), where x i j E is an activity variable (representing firing rate) and x i j H is a fatigue variable.
In the models, each node quantifies the relative response of a proteomic or phenotypic entity to perturbations with respect to the basal condition.
In the network models, each node represents the quantitative change of a biological variable, x μ i ((phospho protein level and phenotypic change) in the perturbed condition, μ, relative to the unperturbed condition.
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In this model, each node updates its local variable with a weighted average of its neighbors' values, and each new value is corrupted by an additive noise with zero mean.
By applying the proposed model, each node is able to detect whether the mobility states of the network is relatively static or mobile without the support of the Global Positioning System GPSS).
In our model, each node prepares the information in a proactive mode, but can use it for all different paths passing through, saving the cost and delay in the reactive mode.
Discrete models are characterized by modeling each node individually.
In this model, each node is equipped with one single directional antenna.
In the Erdős- Rényi model, each node is picked with the same probability.
In this failure model, each node has a constant chance of failing per time unit.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com