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  • Küçük Resim
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    A Monte Carlo-based approach to determine effective atomic numbers of low-Z explosives in landmines
    (TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2025) Özşahin Toker, Melis; Yavaş, Kübra; Toker, Ozan; İçelli, Orhan
    Landmines pose significant humanitarian and strategic challenges, threatening both civilian populations and military operations worldwide. This study presents a practical simulation method based on Rayleigh and Compton scattering ratios to determine the effective atomic number of low atomic number (low-Z) explosives used in landmines. Utilizing the Monte Carlo N-Particle (MCNP) simulation program, Rayleigh/Compton scattering ratios were obtained using a Ge(Li) detector at a scattering angle of 115° within a simulated geometry. The simulation results were found to be in good agreement with experimental data, confirming the reliability of the method. Pure elements with atomic numbers ranging from 3 to 20 and various explosives were irradiated with photons of 59.54 keV energy to obtain scattering spectra. The effective atomic numbers calculated using Rayleigh/Compton scattering ratios were compared with five different theoretical methods, yielding consistent results. These findings demonstrate that the proposed method can reliably determine the effective atomic number of low-Z elements and explosives containing these elements. Additionally, the study confirms that MCNP simulations can be effectively utilized in various fields such as defense industry, radiation safety, medical applications, and radiation dosimetry.
  • Küçük Resim
    Öğe
    Machine learning-based estimation of occupational radiation dose in interventional cardiology
    (OXFORD UNIV PRESS, GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND, 2025) Hisiroglu, Kevser A.; Toker, Ozan; Toker Özşahin, Melis; İçelli, Orhan
    In interventional cardiology, occupational radiation exposure for medical personnel can reach high levels, underscoring the critical need for effective radiation protection and monitoring methods. This study employs machine learning algorithms to estimate radiation doses received by personnel within a virtual 3D angiography room designed to reflect realistic clinical settings. Monte Carlo simulations generated radiation data across various scenarios, accounting for personnel positions, radiation source distance, and exposure angles typical in angiography. The simulation data were used to train five machine-learning algorithms (Gradient Boosting, K-nearest neighbors, Random Forest, Linear Regression, and Decision Tree). Key findings showed that machine learning models, particularly Gradient Boosting, could effectively predict dose levels by utilizing spatial and operational parameters without requiring physical dosemeter. This study provides a framework that could streamline radiation monitoring practices, making dose assessments more accessible and efficient for routine use in clinical environments.