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Figure 6 illustrates the input with its corresponding directional aid outputs and other relevant intermediary signals.
Figure 1 illustrates the input, processing, and output of the VM placement problem.
The specific algorithm is detailed as follows: As Algorithm 3 illustrates, the input keyword is converted to the form of a hash key by the global index in Skip-Octree.
Figure 1 (blue bars) illustrates the input data.
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Figures 6 and 7 illustrate the input pressure, inner/outer annular pressure and gas flow rate for experiments using 0.3 mm premium composite screen under compressor output pressure 2.5 MPa.
Open image in new window Fig. 13 Projection in 3-D of cluster centres' representations Table 5 Maximum intra-cluster distance Cluster 1 2 3 4 5 Distance (MSE) 0.01 0.02 1.565 0.158 0.14. Figure 14a e illustrate the input images corresponding to the 5 cluster centres that were derived from the CNN RNN architecture.
Example simulations below illustrate the input information required.
The tables 1 and 2 illustrate the input data needed to run the algorithm for this figure.
The components at the top right of Figure 2 illustrate the input of medical data about patients.
Open image in new window Figure 1 The diagram illustrating the inputs and outputs of insulin receptor.
This illustrates the key inputs, outputs/behaviours and outcomes of the intervention.
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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