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We also combine our method with three other existing methods, such as COACH, MCL and MCODE.
We also combine the loss and RTT measurements using throughput = 1460Bytes[Max Transmission Unit]/ RTT * sqrt(loss)) from [mathis].
We also combine our novel measurements with statistical mechanical models to better elucidate the mechanisms of these molecular machines.
Moreover, we also combine the MFS with the popular finite element method (FEM), as error bounds are provided.
We also combine these methods with discontinuity capturing schemes (DC) to suppress oscillations that may occur in proximity of boundaries or internal layers.
We also combine this method with random sampling points based on the Chebyshev probability measure to further increase the accuracy of the recovery of the gPC coefficients.
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For each SNP-risk model combination, we also combined the two scores for the African and Hispanic populations using Fisher's χ approximation (18), and show this as a combined P-value estimate for the SNP.
We also combined the elements in this source with the notion rhetorical power.
Finally, we also combined our CUDA parallel scheme with MPI to make the code run on GPU clusters.
We also combined LplA labeling with our previous biotin ligase labeling, to simultaneously image the dynamics of two different receptors, coexpressed in the same cell.
We also combined a polyacrylic acid-modified titanium peroxide nanoparticle (PAA-TiOx NPs) with X-ray irradiation to achieve an anticancer effect.
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