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This study demonstrates how machine learning can be used to boost optimization algorithms in an industrial application.
Significant changes in LC shape and volume at the time of boost that deviate from the original plan for WBI with sequential boost can be addressed by adaptive replanning at the first boost fraction.
In boost mode, high efficiency can be achieved at high boost ratios.
Significant changes in LC shape and volume at the time of boost from the original plan for WBI with sequential boost can be addressed by the online replanning at the first boost fraction.
Our findings provide a basis for novel strategies for how boost vaccination to defined antigens can be selectively improved.
Stage I and II breast cancer patients undergoing BCT can be effectively managed with electron, photon, or interstitial implant boost techniques.
We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier.
We show how these techniques can boost and enhance wireless networking operation in the 60 GHz band.
CT simulation at boost planning could allow for a reduction in the boost volumes.
Boost-HiC can also be used to compare contact maps at an improved resolution.