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    Evaluation of Energy-Dependent Gamma-Ray Interaction Mechanisms in Biological Macro Molecules
    (Taylor & Francis Inc, 2026) Cakir, Yusuf; Ogul, Hasan; Erkoyuncu, Ilhami; Akman, Ferdi
    This study investigates the gamma-ray interaction mechanisms and radiation-induced damage in selected proteins (casein, lactoferrin, and lysozyme) and fatty acids (caproic acid, capric acid, and docosahexaenoic acid) using Monte Carlo simulations and theoretical code. The gamma-ray interaction parameters were determined using WinXCOM and compared with GEANT4 and FLUKA simulations in the energy range of 0.04 to 2 MeV. In addition, the number and average energy of secondary electrons generated by photon interactions were obtained using the GEANT4 simulation tool. Radiation-induced damage was quantified through displacement per atom (DPA) and total ionizing dose (TID) calculations using the FLUKA code at representative photon energies of 0.08 MeV, 0.5 MeV, and 1 MeV. The results showed strong agreement (<3% deviation) between the theoretical and simulation outputs, validating the reliability of the applied methods. Lactoferrin exhibited the highest mu/rho, mu, and lowest half-value layer values due to its higher density and heavier elemental composition, whereas the fatty acids showed weaker attenuation capability. Secondary electron production was highest in lactoferrin and lowest in the fatty acids. The DPA and TID analyses revealed that fatty acids are more susceptible to displacement damage at lower energies, while proteins exhibited higher structural disturbance at higher photon energies. In short, the findings demonstrate that gamma-ray interaction probability, secondary electron production, and radiation damage strongly depend on molecular density, composition, and photon energy. These results could provide valuable information for radiation effects on biomolecules, with implications for radiobiology, food irradiation, and medical radiation applications.
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    Öğe
    Kernel Principal Component Analysis for Allen-Cahn Equations
    (Mdpi, 2024) Cakir, Yusuf; Uzunca, Murat
    Different researchers have analyzed effective computational methods that maintain the precision of Allen-Cahn (AC) equations and their constant security. This article presents a method known as the reduced-order model technique by utilizing kernel principle component analysis (KPCA), a nonlinear variation of traditional principal component analysis (PCA). KPCA is utilized on the data matrix created using discrete solution vectors of the AC equation. In order to achieve discrete solutions, small variations are applied for dividing up extraterrestrial elements, while Kahan's method is used for temporal calculations. Handling the process of backmapping from small-scale space involves utilizing a non-iterative formula rooted in the concept of the multidimensional scaling (MDS) method. Using KPCA, we show that simplified sorting methods preserve the dissipation of the energy structure. The effectiveness of simplified solutions from linear PCA and KPCA, the retention of invariants, and computational speeds are shown through one-, two-, and three-dimensional AC equations.