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Article Abstract
HotSpot Health Physics Code is widely used for assessing radiological risks during nuclear incidents and emergencies, providing critical insights into radiation dose distributions and contamination patterns. However, its manual data input and analysis processes limit its efficiency in complex scenarios requiring extensive parameter variations. This study addresses these challenges by introducing a Python-based automation framework for HotSpot. The framework dynamically adjusts key parameters, including radionuclide concentration, weather conditions, and terrain features, generating organized datasets in real-time. Automated visualization and streamlined data extraction enhance precision and reduce analysis time, enabling rapid decision-making. By automating HotSpot, this work advances radiological risk assessment methodologies, improving accuracy, flexibility, and applicability to diverse emergency scenarios.
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| http://dx.doi.org/10.1016/j.apradiso.2025.112150 | DOI Listing |
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