We also truncate the flow model at degree 14 and damp using the Bloxham 'strong' norm (Bloxham 1988) which effectively reduces the influence of the model SV above degree 8.
Its elements are given by, where ∆P (l) is the difference between the powers of two MF models at degree l.
Figure 5-right indeed illustrates that model D has the smallest correlation to the mean model at large degrees n with a most significant difference from SH degree 11, thus corresponding to small spatial scales.
From the data obtained, an inverse kinematic model at seven degrees of freedom was realized.
In this model (at 34 degrees C), no liposomes were able to extravasate into the tumor interstitium.
Our method can therefore overcome those computational complexity issues, and identify complex interactions (between two or more factors) that contribute to the response model, at varying degrees of sparsity (controlled by the penalization component).
The final version of CHAOS-6 was obtained by taking the model coefficients from CHAOS-6l (as described above) and replacing the static field Gauss coefficients above degree (n=24) with the static field coefficients from the CHAOS-6h model, truncated at degree (n=110).
Figure 10 again shows that although there are differences with other models, especially at degree 5, the BGS model is well within the range of variability of other models.
When comparing the two SV models truncated at degree 8 (Fig. 6 and Table 2), we obtain differences with similar geographical patterns and comparable intensity values as for the MF model comparison.
A, B, E, F, and H models have comparable mean square difference to the mean model at all SH degrees except for H which deviates significantly for SH degrees 4 6.
