The mass transfer kinetics are highly dependent on the dimensionality of the system.
Together, these data illustrate that YAP/TAZ signaling is responsive to hydrogel stiffness and degradability, but the outcome is dependent on the dimensionality of cell-biomaterial interactions.
The optimal number of mixtures in each state is likely to be more dependent on feature dimensionality, but for the above-mentioned features, the highest UARs were obtained when five or six mixtures were used.
Recently, several researchers have alternatively studied two-dimensional (2D) thin films [9, 10] to overcome the limitations of 1D nanostructured materials whose thermal properties are highly dependent on their dimensionality and morphology [3, 11 13].
Due to the moderate regularization assumptions, our adaptive kernel density estimator is sensitive to the sparse sampling problem whose effect is highly dependent on the dimensionality.
Furthermore, since α increases with the dimensionality, the algorithm has inverse dependency on dimensionality.
The exact dependency on dimensionality is not trivial to analyze.
The research of nanoparticles with unique dimensionality dependent on the chemical-physical properties of nanoscale matter has propelled efforts towards controllable fabrication and in-depth characterization of inorganic nanostructures with desirable compositional and geometric features.
One-dimensional perovskite oxide nanostructures, including nanowires, nanorods, nanotubes, nanofibers, and nanobelts, have attracted much attention from scientists for their unique physical properties dependent on the size and dimensionality [1 5].
Given the aforementioned theoretical work plus our own simulation of a smaller scaled data set and reduction of the high dimensional numeric data (see Additional file 1), we are convinced that the clustering of the samples using the mod k-prototypes algorithm is not dependent on the scale or dimensionality of the data.
The computational power needed is dependent on three primary factors: the dimensionality of the input data, the complexity of the model and the complexity of the training algorithm.
