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As the coupling between the layers increases, the transmission of stress waves through the layered medium is enhanced, and stress localization diminishes.
But this quantity also improves with increased number of input layers, meaning the extra information provided by additional layers increases the sensitivity of input signal to large trees.
The locations of peaks shift toward higher frequencies as the number of graphene layers increases.
These behaviours appear to be repeating as the number of layers increases.
The thickness of the interlamellar amorphous layers increases only slightly.
The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases.
At the same time the complexity of the network also increases as the number of layers increases.
The computation time of the high-order thin layer method increases linearly when the number of layers increases.
As the number of layers increases, the strength of the specimens slowly decreases.
The thickness of the modified layers increases as the nitriding time increases.
The conductivities of the composites increase as the number of ordered carbon layers increases.
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