Determination of isotopes activity ratio using gamma ray spectroscopy based on neural network model

Artificial neural network modeling in environmental radioactivity studies - A review

The development of nuclear technologies has directed environmental radioactivity research toward continuously improving existing and developing new models for different interpolation, optimization, and classification tasks. Due to their adaptability to new data without knowing the actual modeling function, artificial neural networks (ANNs) are extensively used to resolve the tasks for which the application of traditional statistical methods has not provided an adequate response. This study presents an overview of ANN-based modeling in environmental radioactivity studies, including identifying and quantifying radionuclides, predicting their migration in the environment, mapping their distribution, optimizing measurement methodologies, monitoring processes in nuclear plants, and real-time data analysis. Special attention is paid to highlighting the scope of the different case studies and discussing the techniques used in model development over time. The performances of ANNs are evaluated from the perspective of prediction accuracy, emphasizing the advantages and limitations encountered in their use. The most critical elements in model optimization were identified as network structure, selection of input parameters, the properties of input data set, and applied learning algorithm. The analysis of strategies and methods for improving the performance of ANNs has shown that developing integrated and hybrid artificial intelligent tools could provide a new path in environmental radioactivity modeling toward more reliable outcomes and higher accuracy predictions. The review highlights the potential of neural networks and challenges in their application in environmental radioactivity studies and proposes directions for future research.

Assessment of erosion rate in a rainfed agricultural area on the Northwestern coast of Egypt using 137Cs measurements

In the present work, the use of the fallout radionuclide ¹³⁷Cs as a tracer for estimating soil redistribution rates over the past 60 years was tested in Egypt at an agricultural field with a smooth slope (0-10%) located in Ras El-Hekma on the Northwestern coast. The average annual soil erosion rate was derived from ¹³⁷Cs inventories (Bq m^-2) and by using the conversion Diffusion and Migration Model (DMM). The activity concentrations of ¹³⁷Cs with uncertainty ranging between 7.5 and 20.9% were measured using HPGe gamma spectrometry. Approximately exponential shape for the distribution of ¹³⁷Cs activity concentration was observed within the upper 20 cm of the soil profile associated with the reference site while for the study sites, the ¹³⁷Cs depth distribution profiles indicate generally an increase of activity reaching a maximum peak of ¹³⁷Cs usually at a depth of 10-15 cm followed by an abrupt exponential decrease of ¹³⁷Cs activity concentration with depth. The measured average reference inventory for this area was 697 Bq m^-2 with a coefficient of variation of 23%. This value is close to the estimated reference inventory using the conversion model, which was 744 Bq m^-2. Transect sampling strategy was adopted at the study area where three parallel transects were chosen based on the common slope. The estimated average annual erosion rate for the three transects was found to be 7.5 t ha^-1 y^-1, and sediment delivery ratio was approximately 98%, which shows the moderate vulnerability of the Ras El-Hekma area to water erosion.

Estimation of liquid density using artificial neural network in gamma-ray scattering measurement

The feasibility of an artificial neural network for the estimation of the liquid density, in gamma scattering measurement, has been investigated in this paper. The liquid density was estimated using a well-trained artificial neural network model with only two input parameters: the scattering angle and the ratio of the area under a single scattering peak for a liquid relative to that for water. It is worth noting that the whole training data was generated by carrying out the Monte Carlo simulation using Monte Carlo N-Particle code. The results indicated that the artificial neural network model exhibits a good correlation between the estimated and reference densities, at all the investigated scattering angles, with a relative error below 5.5 %. Next, the trained model is used to predict the liquid density with the input data of being the experimatal data, which yield the relative deviation between the predicted density and the reference one, mostly less than 5 % (only three cases with deviation in the range from 5-8.1 %). The obtained results demonstrated that the model developed in this work gives more accurate results within the defined conditions.

Neutron-induced mass shift of tin isotopes recognized using inductively coupled plasma mass spectrometry as an isotopic fingerprint on neutron reactions

The phenomenon of isotope shift in response to neutron irradiation was investigated. The high fission-neutron fluence of 1.44×10¹⁸ cm^-2 from the second Egyptian research reactor (ETRR-2) was used to induce a mass shift in a metallic tin sample. A triple-quadrupole inductively coupled plasma mass spectrometry system was used to analyze the isotope abundances in the mass range from A=111 to A=125. The problem of mass bias due to kinetic fractionation on use of the inductively coupled plasma mass spectrometry system was treated by our calibrating the mass bias with a standard reference tin solution and building a response function. The response function was used to obtain absolute abundances in each mass range instead of isotope ratios. The results show that the mass shifts in the irradiated sample are dependent on the cross section of the neutron capture reaction in the tin isotopes.