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We did not find significant correlations between the network parameters and clinical characteristics in the present study.
The obtained condition basically establishes a relationship between the network parameters of the neural system and the number of neurons.
In addition, correlation analyses are performed to understand the relationships between the network parameters and the terrain information.
The condition obtained for the robust stability establishes a matrix-norm relationship between the network parameters of the neural system, and therefore it can easily be verified.
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As can be seen in Figures 5, 7, 8, and 9, due to the random behavior of the proposed algorithm, there is a strict relation between the average number of operations, the network parameters, and the algorithm parameters (including the channel assignment model).
As with the node degree distributions, the network parameters are far more similar for MA and NI than between the observed and random networks.
Still, the response to cold clearly influenced the network parameters.
Otherwise, the network parameters will be altered.
The network parameters provide evidence that our integrated regulatory networks are different from randomly generated networks.
Correlations between the cracked network parameters and the lifetime of the TBC are proposed in this paper.
On this basis, we (i) develop an analytical framework that allows us to analyze the relationship between network parameters and the average localization accuracy obtained in ad hoc sensor networks with a spread-spectrum physical layer, and (ii) show that, for such networks, minimizing the average localization error is equivalent to maximizing the average effective throughput.
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