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In the present contribution, the optimal values for the free parameter γ of this preconditioner are searched both numerically and theoretically; the later with the aid of linear-stability analysis and with the condition number of the system of NS equations.
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This contribution addresses the optimal design of the biomass supply chain as it is crucial to ensure long term viability of such a project.
And these two methods develop the work of solvability of ODEs (1.1), which makes contribution to the optimal control theory.
We sketch the most important functionality below: The robustness check calculates how strongly the value contribution of the optimal roadmap is affected by variations in the input parameters.
The robustness check calculates how strongly the value contribution of the optimal roadmap is affected by variations in the input parameters.
For each scenario, we performed a robustness check based on planning model prototype, calculating how strongly the value contribution of the optimal project roadmap is affected by varying the input parameters.
On the other hand, for modular (for instance multi-domains) structures, the w α of the eigenvectors that correspond to the minor domains are large, and these eigenvectors give a non-negligible contribution to the optimal HP.
The present contribution deals with the optimal tuning of two different types of dynamic vibration absorbers (DVA) by using ant colony optimization, namely the vibrating blade DVA and the multi-mode DVA.
This negative relationship between the contribution rate and the optimal retirement age in Casamatta et al.'s model heavily relies on the assumption that individuals ignore the impact of their decision on the aggregate constraint.
Our main contribution is that the optimal beamforming vectors can efficiently be computed using determinant maximization techniques through an iterative QL-QR algorithm based on a MSE balancing method.
Hence, over entire tracks, a hit probability p h (n) in a global sorting is defined as p h n = NH n TSF, 1 ≤ n ≤ 40 (6)where NH(n) denotes the number of times that the n th pulse, in terms of the contribution priority, hits the optimal codevector and TSF the total number of testing subframes.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com