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In order to validate dose evaluation methods for radiation shielding design of accelerator facilities, absorbed dose distributions in a plastic phantom of 30×30×30 cm3 slab were measured for 40- and 65-MeV quasi-monoenergetic neutrons with a tissue-equivalent proportional counter and a tissue-equivalent ionization chamber.
A systematic evaluation of the efficacy of training methods for radiation safety education in oncology nursing has not been documented prior to this study.
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In the present paper, a simplified method for radiation streaming through arbitrary rectangular section bent ducts including slits with a plane source is derived on the basis of the same consideration as earlier.
The simplified method for radiation streaming through bent ducts with a point source was previously derived by an optics analogy considering only components passing through the duct without enormous preparatory calculations.
Recent work by McClarren and Hauck (2010) [31] suggests that the filtered spherical harmonics method represents an efficient, robust, and accurate method for radiation transport, at least in the two-dimensional (2D) case.
This will facilitate a direct coupling of the boundary element method with other numerical techniques, such as the finite element method for radiation and scattering in a non-uniform flow region.
We provide the first open dataset to the radiation oncology community, thus allowing researchers to compare methods for optimizing radiation dose delivery.
To optimize biodosimetry methods for estimating radiation exposure after a large-scale radiological event, all likely radiation qualities, modes of exposure and exposure times should be considered while designing assays that will be useful for triage [ 1].
Here we present an open dataset – the first of its kind – to the radiation oncology community, which will allow researchers to compare methods for optimizing radiation dose delivery.
The main methods for dispersing radiation are discussed here.
Predictive data mining offers variety of methods for solar radiation predictions where artificial neural network is one of the reliable and accurate methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com