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Radiation: Applications01:17

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The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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The radiation pressure applied by an electromagnetic wave on a perfectly absorbing surface equals the energy density of the wave. The wave's momentum also gets transferred to the surface when an electromagnetic wave is entirely absorbed by it. The rate at which momentum is transmitted to an absorbing surface perpendicular to the propagation direction equals the force on the surface.
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Colossus: bridging the gap between big data and radiation epidemiology.

Eric Giunta1, Benjamin French2, Linda Walsh3

  • 1Kansas State University, Manhattan, KS, United States of America.

Journal of Radiological Protection : Official Journal of the Society for Radiological Protection
|October 3, 2025
PubMed
Summary

The R package Colossus aids radiation epidemiology research by fitting complex models to big data. It now includes methods to handle exposure uncertainties, improving risk assessment for radiation exposure.

Keywords:
Million Person StudyMonte Carlo maximum likelihoodbig datafrequentist model averagingradiation epidemiologyuncertainty

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Area of Science:

  • Radiation Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Addressing complex questions in radiation epidemiology requires advanced software for big data analysis.
  • Existing tools may lack the flexibility and computational power needed for intricate exposure uncertainties.

Purpose of the Study:

  • To introduce Colossus, an open-source R package designed for complex modeling in radiation epidemiology.
  • To enhance Colossus with methods for propagating covariate and exposure uncertainties in risk models.

Main Methods:

  • Development of the open-source R package Colossus.
  • Implementation of multi-core processing for faster analyses.
  • Integration of methods for analyzing multiple exposure realizations, including frequentist model averaging and Monte Carlo maximum likelihood, guided by NCRP Commentary 34.

Main Results:

  • Colossus leverages R's flexibility and multi-core systems for efficient big data analysis.
  • The package now incorporates advanced techniques to handle covariate and exposure uncertainties.
  • New methods allow for the application of complex risk models to datasets with intricate exposure uncertainties.

Conclusions:

  • Colossus provides a flexible and powerful platform for radiation epidemiology research using big data.
  • The inclusion of uncertainty propagation methods enhances the reliability of risk assessments in radiation studies.
  • Colossus is designed for future expansion, promising continued advancements in the field.