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Although the results of this approach may not be as quickly seen as in the disease-specific approach, it allows the system in its entirety to be strengthened.
And this is the intrinsic value of the EBA approach: it allows for climate change adaption while meeting other human development goals.
Because all cellular components can be individually accessed using this approach, it allows manipulation of the tumor cells, as well as of the keratinocyte and stromal cell populations.
By using our approach, it allows us to reduce the number of the parameters of the algorithm respecting both the original design of the algorithm and its performance.
Compared to the classical continuum approach, it allows taking into account micromechanical parameters in the behaviour of a granular material and simulating complex loadings without costly model sophistication.
That is the greatest value, we believe, of the cross-training approach: It allows people to use the linear improvement techniques they know and understand to produce a nonlinear result.
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The construction of our typology was based on an unsupervised statistical approach; it allowed an impartial reduction of all the information present in our complex dataset and then a robust classification.
Unlike previous approaches, it allows control to be examined within a uniform and consistent framework.
The contribution of this approach is threefold: first, unlike existing approaches, it allows for the verification of several requirements at once.
Compared to other approaches, it allows for modeling of a whole family of XML schemas related to a particular application domain.
These results indicate that our initial heuristic is a good candidate for our approach as it allows the approach to try to re-allocate a high number of tasks in the second phase as migrating tasks.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com