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It is found that the optimal layers to obtain large reduction of protein adsorption and availability of functional groups for binding are obtained by using mixtures of flexible and rod-like molecules.
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A multiobjective optimization problem is presented to determine the optimal layer thickness and optimal closed loop control function for a symmetric cross-ply laminate subjected to thermomechanical loadings.
We train an AdaBoost detector per layer and compare the performance to find the optimal layer for this task.
The optimal layering for a given gas solid system depends on the feed composition and feed velocity.
Furthermore, it was also demonstrated that the drying rate was the maximum corresponding to the optimal layer thickness in microwave thin layer drying process.
The optimal layer stacking pattern is determined by a fitting function that applies temperature difference between plates under abnormal and design conditions.
We used a known learning-curve and introduce a measure named "neuron-curve" to find the optimal layer size & minimum size of training set.
In the end of this paper, some numerical examples are presented to illustrate the effects of marginal distributions, risk dependence structure and reinsurance premium principles on the optimal layer reinsurance.
The evaluated optimal material layer thickness by the proposed methodology is consistent with experimentally measured value, and hence confirms that the optimal layer thickness can be predicted exactly through the average temperature of bulk material at the initial 1st min during pre-heating stage.
For the practical applications, the presence of zero intensity in the reflected light dip center under the angle of measurement is the complementary requirement to determine the optimal layer's thickness.
In this investigation we consider the possibility for optimal layers of reinsurance and retrocession in the design of the insurance industry.
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CEO of Professional Science Editing for Scientists @ prosciediting.com