Tıbbi Görüntüleme Teknikleri Programı / Medical Imaging Techniques Programhttp://hdl.handle.net/20.500.12566/5592023-10-02T13:19:20Z2023-10-02T13:19:20ZCalculation of double differential neutron cross-sections of 56Fe and 90Zr isotopesÖzdoğan, HasanÜncü, Yiğit AliŞekerci, MertKaplan, Abdullahhttp://hdl.handle.net/20.500.12566/16742023-07-19T12:50:07Z2023-01-01T00:00:00ZCalculation of double differential neutron cross-sections of 56Fe and 90Zr isotopes
Özdoğan, Hasan; Üncü, Yiğit Ali; Şekerci, Mert; Kaplan, Abdullah
This study is concerned with the calculations of double differential neutron cross-sections of the structural fusion materials of 56Fe and 90Zr isotopes that are bombarded with protons. Calculations were performed using the level density models of the TALYS 1.95 code and PHITS 3.22 Monte Carlo code. Constant Temperature Fermi Gas, Back Shifted Fermi Gas, and Generalized Super Fluid Models were employed for level density models. Calculations were performed at 22.2 MeV proton energies. Calculations were compared with the experimental data taken from Experimental Nuclear Reaction Data (EXFOR). In conclusion, the results showed that the level density model results of TALYS 1.95 codes for the double differential neutron cross-sections of 56Fe and 90Zr isotopes are consistent with experimental data. On the other hand, PHITS 3.22 results gave lower cross-section values than experimental data at 120 and 150°.
2023-01-01T00:00:00ZEstimations for the production cross sections of medical 61, 64, 67Cu radioisotopes by using bayesian regularized artificial neural networks in (p, α) reactionsÜncü, Yiğit AliÖzdoğan, Hasanhttp://hdl.handle.net/20.500.12566/16732023-07-19T11:53:23Z2023-01-01T00:00:00ZEstimations for the production cross sections of medical 61, 64, 67Cu radioisotopes by using bayesian regularized artificial neural networks in (p, α) reactions
Üncü, Yiğit Ali; Özdoğan, Hasan
Copper (Cu), which is produced in cyclotrons or reactors, is a significant tracer in the human body. Bayesian regularized artificial neural networks (ANNs) algorithm, which is one of the ANN approaches, was used in analyzing the production cross sections of 61Cu, 64Cu, and 67Cu radioisotopes in (p,α) reactions in the present study. The production cross sections of 61Cu, 64Cu, and 67Cu radioisotopes in (p,α) reactions were assessed by making use of the ANN algorithm and TALYS 1.95 codes. The estimated cross section data were then compared to the data found in the TALYS-Based Evaluated Nuclear Reaction Library 2019 (TENDL) and Experimental Nuclear Reaction Data (EXFOR) Library. ANN results were shown to yield successful correlation coefficients of 0.99477, 0.98665, and 0.99313 for training, testing, and all processes, respectively. Furthermore, the mean square error (MSE) results of ANN prediction were calculated to be 3.6 (training) and 11.84 (testing) mb for all the (p,α.) reactions. It was concluded that the ANN algorithm yielded successful results since ANN estimations were suitable for experimental data, TALYS 1.95 calculations, and TENDL data.
2023-01-01T00:00:00ZA study on the cross-section data of 43,44m,46,47Sc isotopes via (d,x) reactions on natural abundance targets under the effects of deuteron optical modelsŞekerci, MertÖzdoğan, HasanKaplan, Abdullahhttp://hdl.handle.net/20.500.12566/16722023-07-19T11:06:18Z2023-01-01T00:00:00ZA study on the cross-section data of 43,44m,46,47Sc isotopes via (d,x) reactions on natural abundance targets under the effects of deuteron optical models
Şekerci, Mert; Özdoğan, Hasan; Kaplan, Abdullah
Many studies have investigated the influence of theoretical models and factors involved in the acquisition of cross-section data of a nuclear reaction. The implications of different models of various variables such as level density, gamma strength function, and optical potentials on cross-section calculations whether used solo or jointly are investigated in a significant portion of the works conducted in this perspective. The aim of this particular study is to investigate the influence of different optical models on the cross-section calculations in production of several scandium isotopes, known for various medical uses, from several targets with natural abundances by (d,x) reactions. For this purpose, the cross-section calculations using five available deuteron optical models of TALYS code in natTi(d,x)43Sc, natTi(d,x)44mSc, natTi(d,x)46Sc, natTi(d,x)47Sc, natV(d,x)47Sc and natCr(d,x)47Sc reactions were performed and the obtained calculation results were compared with the experimental cross-section data gathered from the literature. To understand whether there is a significant and consistent relationship between the experimental data and the calculation results, both have been plotted together and analyzed with the naked-eye. In addition, the calculations of the mean standardized deviation, the mean relative deviation, the mean ratio and the mean square logarithmic deviation were performed in order to evaluate the results numerically.
2023-01-01T00:00:00ZEstimating currents from action potentials using single and multi-output neural network modelsSüleymanoğlu, SelimŞengül Ayan, SevgiÖzdoğan, Hasanhttp://hdl.handle.net/20.500.12566/15982023-05-31T21:00:40Z2022-01-01T00:00:00ZEstimating currents from action potentials using single and multi-output neural network models
Süleymanoğlu, Selim; Şengül Ayan, Sevgi; Özdoğan, Hasan
Ion channels are water-filled pores formed by membrane proteins abundant in excitable cells' plasma
membranes. Ion channels are responsible for converting signals into biological responses. They are primarily
engaged in the generation of short-term action potentials via the combined activity of particular ionic
currents. The goal of this study is to provide a neural network model that was used to predict 13 ionic currents
(such as sodium and calcium channels) from different action potential (AP) shapes. We use a single-cell model
to perform electrophysiological simulations and obtain AP and 13 current shapes based on variations in the
ion channel conductance in cardiomyocytes, which we then compare to experimental results. Constantly
increasing and decreasing the conductance of each ion channel produces 880 different sets of AP shapes and
current shapes, as well as one standard AP shape and 13 standard current shapes without causing any
changes in the conductance of any other ion channel. Next, we calculate the AP difference shapes and feed
them into our neural network along with the passage of time, in order to demonstrate how the dynamics of
action potential induction, movement of the action potential, and the release of neurotransmitters affect the
function of ion channel function. As a starting point for these calculations, the Hodgkin-Huxley model is
utilized. In this study, we demonstrate that using only AP shapes and MATLAB's neural network tool, it is
possible to predict changed ion channel currents with high prediction accuracy.
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