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The velocity and acceleration response matrices can then be calculated by finite difference methods.

The BC covariance matrices can then be obtained from the MAC results via duality transformations [2].

These response matrices can then be applied to the developed MAFVRO method to determine the structural modal parameters.

c The different omic matrices can then be analyzed independently, for detecting cell subpopulations and ranking the genomic features etc. d Finally, multi-omics integration can be performed to identify coherent features from different omics that separate different subpopulations.

The state and output matrices can then be determined as: left[ {begin{array}{c}{varvec{A}_d}varvec{C} end{array}} right] = left[ {begin{array}{c}{{{hat {varvec{X}}}_{i + 1}}}{{{varvec{Y}}_{ileft| i right.}}}end{array}} right]hat {varvec{X}}_i^dagger (13).

The lipid extracted from various biological matrices can then be analyzed by multiple MS platforms, either by detecting the lipids by shotgun lipidomics or after separation by liquid chromatography to detect and quantify lipids of lower abundance.

Similar(54)

The matrix can then be restructured as low-rank estimation.

The rotation matrix can then be computed as (15) (15).

The received signal matrix can then be expressed as (1).

The dynamical matrix can then be employed to compute the phonon spectra by evaluating its eigenvalues.

With the alignment of all labels a confusion matrix can then be computed.

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