Browsing by Author "Hançerlioğullari A."

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Advanced power conversion efficiency in inventive plasma for hybrid toroidal reactor
(2013-01-01) Hançerlioğullari A.; Cini M.; Güdal M.
Apex hybrid reactor has a good potential to utilize uranium and thorium fuels in the future. This toroidal reactor is a type of system that facilitates the occurrence of the nuclear fusion and fission events together. The most important feature of hybrid reactor is that the first wall surrounding the plasma is liquid. The advantages of utilizing a liquid wall are high power density capacity good power transformation productivity, the magnitude of the reactor's operational duration, low failure percentage, short maintenance time and the inclusion of the system's simple technology and material. The analysis has been made using the MCNP Monte Carlo code and ENDF/B-V-VI nuclear data. Around the fusion chamber, molten salts Flibe (LI 2 BeF 4 ), lead-lithium (PbLi), Li-Sn, thin-lityum (Li 20 Sn 80 ) have used as cooling materials. APEX reactor has modeled in the torus form by adding nuclear materials of low significance in the specified percentages between 0 and 12 % to the molten salts. In this study, the neutronic performance of the APEX fusion reactor using various molten salts has been investigated. The nuclear parameters of Apex reactor has been searched for Flibe (LI 2 BeF 4 ) and Li-Sn, for blanket layers. In case of usage of the Flibe (LI 2 BeF 4 ), PbLi, and thin-lityum (Li 20 Sn 80 ) salt solutions at APEX toroidal reactors, fissile material production per source neutron, tritium production speed, total fission rate, energy reproduction factor has been calculated, the results obtained for both salt solutions are compared. © 2013 Springer Science+Business Media New York.
Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm
(2023-05-01) Mohamed A.A.A.; Hançerlioğullari A.; Rahebi J.; Ray M.K.; Roy S.
This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images’ features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method’s performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.
Natural radionuclide and toxic metal contents of rock salts from mines in Central Anatolia of Turkey
(2022-01-01) Hançerlioğullari A.; Eyüboğlu K.
In this study, the natural radionuclide andpotentially toxic heavy metal contents of rock salt samples collected from three different salt mines located in Çankırı city in the Central Anatolia of Turkey were determined using gamma-ray and X-ray fluorescence (XRF) spectrometry.Also, concentrations of radionuclides and heavy metalsin different table salts (sea, lake, and Himalayan) consumed in Turkey were determined to compare with rock salt. The average activity concentration of 226Ra, 232Th and 40K measured in rock salt samples were found as 1.4, 5.6 and 34.0 Bqkg−1, respectively.The average concentration of Ti, Mn, Fe, Ni, Cu, Zn, Zr and Pb in rock salt samples was analysed as 64.8, 12.9, 504.6, 3.6, 1.8, 2.0, 1.6 and 1.4 µgg−1, respectively. The concentration of Cr, Co, As, Cd, and Hg was below the detection limits.Also, the total annual effective dose (AED) and daily intake of potentially toxic heavy metals due to an intake of the radionuclides and heavy metals from rock salt samples wereestimated. The average value of AED was estimated as 8.4 μSv, which is significantly lower than the average annual effective dose of 290 μSv received by the ingestion of natural radionuclides.
Radiological assessment of internal exposure resulting from ingestion of natural radionuclides in Arachis hypogaea L. grown in Turkey
(2020-01-01) Karataşli M.; Turhan S.; Abugoufa A.H.A.; Gören E.; Kurnaz A.; Hançerlioğullari A.
Groundnut (Arachis hypogaea L.) is one of the most important of all legumes and contains appreciable amounts of dietary oil and protein. Groundnut is added to many foods to enhance their levels of high-quality protein in diets lacking in nutrition. In this study, 51 groundnut samples were collected from the Mediterranean region of Turkey and analysed for naturally occurring radioactive isotopes of radium (226Ra), thorium (232Th) and potassium (40K). The activity concentrations of 226Ra, 232Th and 40K in groundnut samples varied from 2.9 ± 0.8 to 7.6 ± 1.0 Bq kg-1 (dw), with an average of 5.4 Bq kg-1 (dw); 4.4 ± 0.9 to 10.7 ± 1.2 Bq kg-1 (dw), with an average of 6.9 Bq kg-1 (dw) and 246.3 ± 18.2 to 541.8 ± 40.1 Bq kg-1 (dw), with an average of 427.1 Bq kg-1 (dw), respectively. The annual effective radiation dose was estimated to assess the health hazards caused by the ingestion of groundnut samples based on the measured activity concentrations of the radionuclides contained in them. The annual effective radiation dose varied from 6.5 to 10.1 μSv y-1, with an average of 8.3 ± 0.1 μSv y-1. The results revealed that consumption of Turkish groundnuts does not pose any radiological health hazards.