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Radon - Springer Nature

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Last Updated: 20 January 2023

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Influence of fiber type and amount of reinforcing fibers on radon exhalation in uranium tailings’ overburden under high temperature environment

Between the radon exhalation rate and the type and amount of reinforcing fibers in the overburden, a linear function was established, and the correlation analysis technique was used to determine the relationship between the radon exhalation rate and the number and amount of reinforcing fibers in the overburden. The radon exhalation rate of all treatments is high, according to the study, the radon exhalation rate of the jute reinforcement therapy is the lowest under the same dosage, followed by polypropylene reinforcement, and the effect of the glass fiber reinforcement treatment is the highest.

Source link: https://doi.org/10.1007/s10967-023-08763-1


Age-dependent potential health risk assessment due to radioactive radon-222 in the environs of highly populated Durgapur industrial zone and nearby Bakreswar hot spring, India

India's southern province, Bakreswar hot spring, a new article includes a research on radon-222 in the water bodies of the city Durgapur [non-geothermal area] and a nearby Bakreswar hot spring [geothermal province]. For the first time, age-dependent potential health risk assessments of the residents of Durgapur and Bakreswar due to radon exposure were carried out. Due to radon exposure at Durgapur and Bakreswar, the present study is the first attempt to determine the mean ingestion/and inhalation dose per annum, total effective dose [TED] per annum, and the health risk assessment for cancer in adults, children, and infants. For the adults, children, and infants, the radiation profile maps relating to radon concentration and associated contour maps of health risk factors [HRF] were also prepared for the first time. Several methods were also suggested to reduce radon in water and buildings. To shield people from radon danger, the researchers also suggested banning certain water sources to shield people from radon exposure.

Source link: https://doi.org/10.1007/s10653-023-01478-y


Classification and prediction of radon stable zone in uranium tailings pond: a case study from uranium tailings pond in sothern China

The radon concentration in the uranium tailings pond is divided into a stable zone, asymptotically stable zone, and unstable zone, as well as the WOA-BP neural network algorithm for early warning of stability zone. The MAPE is reduced by 20. 1 percent by splitting the stable area, according to the study, and the WOA-BP neural network algorithm's MAPE is 8. 22% lower than the BP neural network algorithm's 8. 22% lower than the BP neural network algorithm's 8. 22% lower than that of the BP neural network algorithm's 8. 22% lower than the BP neural network algorithm's 8. 22% lower than the BP neural network algorithm's 8. 22% lower than that the MAPE is reduced by dividing the MAPE is reduced by 20. 6 percent by 20. 16 TIA reducing the WOA-BP's's, which is 8. 22060's are 8. 26. 20's 8. 23. 2's 8. 22. 26% lower than the 8. 21. 6.

Source link: https://doi.org/10.1007/s10967-023-08764-0


A novel deep learning simulation to predict radon activity concentration in soil layers

It is the second most common cause of pulmonary cancer after the use of cigarette smoking, according to the World Health Organization. We propose a new deep learning model to predict the radon concentration in soil layers by utilizing various transport methods such as diffusion, advection, decay, and generation mechanisms in this paper. Then a comparison of the radon concentration by the analytical method and the proposed deep learning framework is shown, which shows a good correlation. The generation and advection techniques can raise the radon activity concentration at the ground surface, according to three realistic and compared scenarios.

Source link: https://doi.org/10.1007/s10967-022-08735-x


Image Reconstruction Technique Using Radon Transform

Image reconstruction is a vital processing step in the analysis of medical images. This paper discusses the image reconstruction framework that was created using a filter backprojection algorithm based on Radon transform in MATLAB. To prepare the images for further examination of the images, image data and image pre-processing was carried out. By Radon convert's sample image of CT slice to sinogram, the reconstructed image was created using filtered backprojection. As the reconstructed image has a high similarity to the original photo, the reconstruction is precise within the framework of a proposed image reconstruction system.

Source link: https://doi.org/10.1007/978-981-19-1577-2_55


Resolution Analysis of Inverting the Generalized N-Dimensional Radon Transform in $$\pmb {\mathbf {\mathbb {R}}^n}$$ R n from Discrete Data

The continuous results are shown by g, and the f0̆30c=2̆11b^*2̆12cg f u2217 B g r a2217 B g g. The value of g on a regular lattice with step size O(0̆3f5) O consists of the value of g. x_02̆208835 x 0 a e. Pick a point x 0 o. e. S u2208 S, i. e.

Source link: https://doi.org/10.1007/s00041-022-09975-x


Lung cancer mortality attributable to residential radon: a systematic scoping review

Residential radon is the second risk factor of lung cancer in the general population and the first in never-smokers. For several countries, radon-attributable lung cancer deaths have been determined. Previous studies have compared radon attributable lung cancer deaths. Original studies with attributable mortality results related to radon exposure were included in our review. We reviewed 24 studies describing radon attributable mortality from 14 study centers around the world. Overall, 13 studies used risk models based on cohorts of miners, 8 used risks from residential radon case-control studies, and 3 used both risk model methods; 8 used both risk model methods. PAF values ranged from 3 to 12% for household risk sources and from 7 to 25% for miner risk sources, according to high-quality journals. The incidence of lung cancer mortality among researchers in Radon PAF varies widely. Large parts of the variance is due to differences in the risk source used and the assumed hazard characterization.

Source link: https://doi.org/10.1038/s41370-022-00506-w

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions