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The initial state vector at a node is formed by measuring the forces and displacements at that node; the state vectors of the adjacent nodes are predicted by TM with crack parameters included.
For instance, networks with 50 nodes are predicted with an AUC of roughly 0.65, when 50 samples are available.
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Data from the 'sleeping' nodes were predicted using probabilistic models.
The next observation time for these nodes is predicted only based on the latest observations of their neighborhood and the virtual leader.
The next observation time these nodes is predicted only based on the latest observations of their neighborhood and the virtual leader.
Several network model nodes were predicted by RCR to increase or decrease in activity across multiple data sets.
Here, red Nodes were predicted to be part of 'TM' region and blue ones to be part of 'nTM' by determination of the residue conservation at each variable motif position of the given data sets like described in section "Topology separation and prediction of discriminative motifs".
According to the principle "The less influential the node is, the lower centrality the node has, the earlier the node is predicted, the less rating error is"., we propose a prediction node sequence generation approach based on anti-centrality sort.
DEACTIVATING A DEACTIVATING assignment occurs when a child node is predicted active but the current node is predicted inactive.
After transmission, the state of the channel used by the node is predicted according to link quality, residual energy, and belief state space.
An activating node can have descendant nodes that are predicted active if their assessed type is not activating (i.e. the node has been deactivated or negated).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com