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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.
In the network model, each node is stationary and knows its location by using GPS.
Further, in this model, each node maps the measured traffic load condition into backoff parameters locally and dynamically.
In our model, each node is assumed to be connected to each other node in a probabilistic manner.
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Discrete models are characterized by modeling each node individually.
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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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com