TY  - JOUR
AU  - Dragović, Snežana D.
AU  - Janković-Mandić, Ljiljana
AU  - Onjia, Antonije
AU  - Bacić, G
PY  - 2006
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/937
AB  - The specific activities of primordial radionuclides in soil samples from 21 different locations in Serbia and Montenegro were determined by gamma-ray spectrometry. The results obtained were compared with those from other studies conducted worldwide. Concentrations of radionuclides in soils analyzed in this study ranged from 1.28 to 4.80 ppm for uranium, from 5.26 to 19.0 ppm for thorium, and from 0.97% to 2.87% for potassium. The mean concentrations of U (2.76 ppm) and Th (10.4 ppm) are similar to the world average (2.64 and 11.1 ppm for U and Th, respectively), whereas the mean concentration of K (1.98%) is about 1.4 times higher than world average value (1.37%).
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Radiation Measurements
T1  - Distribution of primordial radionuclides in surface soils from Serbia and Montenegro
EP  - 616
IS  - 5
SP  - 611
VL  - 41
DO  - 10.1016/j.radmeas.2006.03.007
ER  - 

@article{
author = "Dragović, Snežana D. and Janković-Mandić, Ljiljana and Onjia, Antonije and Bacić, G",
year = "2006",
abstract = "The specific activities of primordial radionuclides in soil samples from 21 different locations in Serbia and Montenegro were determined by gamma-ray spectrometry. The results obtained were compared with those from other studies conducted worldwide. Concentrations of radionuclides in soils analyzed in this study ranged from 1.28 to 4.80 ppm for uranium, from 5.26 to 19.0 ppm for thorium, and from 0.97% to 2.87% for potassium. The mean concentrations of U (2.76 ppm) and Th (10.4 ppm) are similar to the world average (2.64 and 11.1 ppm for U and Th, respectively), whereas the mean concentration of K (1.98%) is about 1.4 times higher than world average value (1.37%).",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Radiation Measurements",
title = "Distribution of primordial radionuclides in surface soils from Serbia and Montenegro",
pages = "616-611",
number = "5",
volume = "41",
doi = "10.1016/j.radmeas.2006.03.007"
}

Dragović, S. D., Janković-Mandić, L., Onjia, A.,& Bacić, G.. (2006). Distribution of primordial radionuclides in surface soils from Serbia and Montenegro. in Radiation Measurements
Pergamon-Elsevier Science Ltd, Oxford., 41(5), 611-616.
https://doi.org/10.1016/j.radmeas.2006.03.007

Dragović SD, Janković-Mandić L, Onjia A, Bacić G. Distribution of primordial radionuclides in surface soils from Serbia and Montenegro. in Radiation Measurements. 2006;41(5):611-616.
doi:10.1016/j.radmeas.2006.03.007 .

Dragović, Snežana D., Janković-Mandić, Ljiljana, Onjia, Antonije, Bacić, G, "Distribution of primordial radionuclides in surface soils from Serbia and Montenegro" in Radiation Measurements, 41, no. 5 (2006):611-616,
https://doi.org/10.1016/j.radmeas.2006.03.007 . .

TY  - JOUR
AU  - Dragović, Snežana D.
AU  - Onjia, Antonije
AU  - Stanković, Srboljub J.
AU  - Aničin, Ivan V.
AU  - Bacić, G
PY  - 2005
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/814
AB  - An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray spectrometry was developed and optimized. A three-layer feed-forward ANN with back-propagation learning algorithm was used to model uncertainties of measurement of activity levels of eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137 and Be-7) in soil samples as a function of measurement time. It was shown that the neural network provides useful data even from small experimental databases. The performance of the optimized neural network was found to be very good, with correlation coefficients (R-2) between measured and predicted uncertainties ranging from 0.9050 to 0.9915. The correlation coefficients did not significantly deteriorate when the network was tested on samples with greatly different uranium-to-thorium (U-238/Th-232) ratios. The differences between measured and predicted uncertainties were not influenced by the absolute values of uncertainties of measured radionuclide activities. Once the ANN is trained, it could be employed in analyzing soil samples regardless of the U-238/Th-232 ratio. It was concluded that a considerable saving in time could be obtained using the trained neural network model for predicting the measurement times needed to attain the desired statistical accuracy.
PB  - Elsevier, Amsterdam
T2  - Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors And
T1  - Artificial neural network modelling of uncertainty in gamma-ray spectrometry
EP  - 463
IS  - 2-3
SP  - 455
VL  - 540
DO  - 10.1016/j.nima.2004.11.045
ER  - 

@article{
author = "Dragović, Snežana D. and Onjia, Antonije and Stanković, Srboljub J. and Aničin, Ivan V. and Bacić, G",
year = "2005",
abstract = "An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray spectrometry was developed and optimized. A three-layer feed-forward ANN with back-propagation learning algorithm was used to model uncertainties of measurement of activity levels of eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137 and Be-7) in soil samples as a function of measurement time. It was shown that the neural network provides useful data even from small experimental databases. The performance of the optimized neural network was found to be very good, with correlation coefficients (R-2) between measured and predicted uncertainties ranging from 0.9050 to 0.9915. The correlation coefficients did not significantly deteriorate when the network was tested on samples with greatly different uranium-to-thorium (U-238/Th-232) ratios. The differences between measured and predicted uncertainties were not influenced by the absolute values of uncertainties of measured radionuclide activities. Once the ANN is trained, it could be employed in analyzing soil samples regardless of the U-238/Th-232 ratio. It was concluded that a considerable saving in time could be obtained using the trained neural network model for predicting the measurement times needed to attain the desired statistical accuracy.",
publisher = "Elsevier, Amsterdam",
journal = "Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors And",
title = "Artificial neural network modelling of uncertainty in gamma-ray spectrometry",
pages = "463-455",
number = "2-3",
volume = "540",
doi = "10.1016/j.nima.2004.11.045"
}

Dragović, S. D., Onjia, A., Stanković, S. J., Aničin, I. V.,& Bacić, G.. (2005). Artificial neural network modelling of uncertainty in gamma-ray spectrometry. in Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors And
Elsevier, Amsterdam., 540(2-3), 455-463.
https://doi.org/10.1016/j.nima.2004.11.045

Dragović SD, Onjia A, Stanković SJ, Aničin IV, Bacić G. Artificial neural network modelling of uncertainty in gamma-ray spectrometry. in Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors And. 2005;540(2-3):455-463.
doi:10.1016/j.nima.2004.11.045 .

Dragović, Snežana D., Onjia, Antonije, Stanković, Srboljub J., Aničin, Ivan V., Bacić, G, "Artificial neural network modelling of uncertainty in gamma-ray spectrometry" in Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors And, 540, no. 2-3 (2005):455-463,
https://doi.org/10.1016/j.nima.2004.11.045 . .