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Various devices, such as pilot flames and recirculation methods, are designed for this purpose.
Thermal recovery methods are designed to reduce the viscosity of the remaining oil in place by increasing its temperature.
The methods are designed to detect shared variation using data from multiple individuals, but can also process single genomes.
First, these methods are designed to be applied to original event logs.
However, many selection methods are designed for the fault free situation only.
Traditional finite difference (FD) methods are designed to be exact for low degree polynomials.
Both methods are designed to deliver intensity-modulated beams as determined by inverse planning software.
Several methods are designed to combine multiple eigenfunctions using isocontours or characteristic values of the eigenfunctions.
Most of the current unsupervised feature selection methods are designed to process only numerical datasets.
Domain decomposition methods are designed to deal with coupled or transmission problems for partial differential equations.
Most existing estimation and testing methods are designed for models without endogeneity.
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