• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3772-3783
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• 2021

This work proposes a new efficient meta-heuristic optimization algorithm called Gamma Ray Interactions Based Optimization (GRIBO). The algorithm mimics different energy loss processes of a gamma-ray photon during its passage through a matter. The proposed novel algorithm has been applied to search for the global minima of 30 standard benchmark functions. The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification. The results are compared with those obtained by the Particle Swarm Optimization, Genetic Algorithm, Gravitational Search Algorithm and Grey Wolf Optimizer algorithms. The comparisons indicate that the GRIBO algorithm is able to provide very competitive results compared to other well-known meta-heuristics.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.7 no.2
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• pp.69-77
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• 2021

No-carrier-added holmium-166 (n.c.a ¹⁶⁶Ho) separation is performed based on the results of separation conditions using stable isotopes dysprosium (Dy) and holmium (Ho) to minimize radioactive waste from separation optimization procedures. Successful separation of two adjacent lanthanides was achieved by cation-exchange chromatography using a sulfonated resin in the H+ form (BP-800) and α-hydroxyisobutyric acid (α-HIBA) as eluent. For the identification process after separation of stable isotopes, the use of chromogenic reagents alternatively enables on-line detection because the lanthanides are hardly absorb light in the UV-vis region or exhibit radioactivity. Four different chromogenic reagents were pre-tested to evaluate suitable coloring reagents, of which 4-(2-Pyridylazo)resorcinol is the most recommendable considering the sensitivity and specificity for lanthanides. Lanthanide radioisotopes (RI) were monitored for separation with an RI detector using a lab-made separation LC system. Under the proper separation conditions, the n.c.a ¹⁶⁶Ho was effectively obtained from a large amount of 100 mg dysprosium target within 2 hrs.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1037-1048
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• 2022

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (¹³³Ba, ²²Na, ¹³⁷Cs, and ⁶⁰Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an insitu monitoring system.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• Applied Biological Chemistry
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• v.29 no.1
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• pp.10-15
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• 1986

A polyacrylamide gel electrophoresis technique was applied to cytokinin-protein binding assay. Binding of soybean leaf proteins to cytokinin and relative affinities of protein fractions to cytokinin were studied. The electrophoresis technique appeared to be very useful for determination of cytokinin-protein binding, for identification of protein species binding to cytokinin and for comparison of relative affinities of the proteins to cytokinin. The presence of cytokinin-binding proteins in soybean leaves was confirmed from assays with ammonium sulfate precipitation, Sephadex G-25 chromatography, paper chromatography, and electrophoresis. Three groups of cytokinin-binding proteins were identified in the soybean leaf protein extract and two of the three showed low affinity to cytokinin, however, the third one with mobility between $0.0{\sim}0.2$, probably high molecular weight protein (s), showed high affinity in the electrophoretic analysis.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3913-3923
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• 2023

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance « automated identifiers » for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(²⁴¹Am, ⁵⁷Co, ¹³⁷Cs, ⁵⁴Mn, ⁶⁰Co), and then applied to five standard nuclear materials(0.32-4.51% at.²³⁵U), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for ²³⁸U and 4.7-10.5% for ²³⁵U in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (ST) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify ²³⁵U and ²³⁸U isotopes in LEU or natural U-Th samples if a medium energy resolution detector is was in the measurements.

• Nuclear Engineering and Technology
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• v.31 no.4
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• pp.423-431
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• 1999

A radiotracer technique has been developed to monitor the rotational movement of two piston rings in one cylinder during engine operation. The rings were labeled with two different kinds of radioisotopes, i.e. $^{60}$ Co and $^{192}$ Ir, for identification of the top ring from the second ring. The radiotracers were implanted in a small hole bored on the inner side of each piston ring. The rings were installed in a single cylinder hydrogen engine and three Nal scintillation detectors were mounted around the engine block to measure the gamma radiation. The angle of ring-gap orientation was determined from the radiation counts measured with the three detectors during engine operation. Two windows (upper window for $^{60}$ Co and lower window for $^{192}$ Ir) were set on each ratemeter to count radiation from the two isotopes separately. Procedure to convert the radiation counts to the position of the ring gap was established. With the software programmed with MS-Visualbasic, radiation counts were compared with the reference responses that were measured at angular intervals of 10$^{\circ}$for each piston ring in advance of the experiment. The result was used for the evaluation of the relationship between the orientation of ring-gaps and oil consumption. It was found that an increase in the oil consumption rate of a specific operation condition was closely related to the relative phase angle of the two piston rings.

• Applied Biological Chemistry
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• v.15 no.2
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• pp.143-162
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• 1972

Based on the concentration of $^{40}K$ naturally occurring radioisotope of potassium, a method for the determination of total potassium in soils and plants was developed. The method was extended to evaluate the potassium supplying power of soils by taking the ratio of exchangeable potassium to total potassium $(K_{ex}/K_t)$, termed the potassium buffering capacity. Using this as index, it was observed that the release of potassium from soil fellows the from order reaction. A linear relationship was found between the potassium buffering capacity and the release constant of potassium or mica content of the clay. Similarly the potassium buffering capacity was also closely correlated with total uptake of potassium by rice plant. Hence it is concluded that the method for determining of the potassium buffering capacity could be veil applied to characterize the potassium availability of soils. The method for the determination of potassium is characterized by (1) The efficient measurement of the weak beta activity emissions from the samples, (2) identification of $^{40}K$, (3) calculation of total potassium content using the proportional constant of $^{40}K$ of samples to that of the standard. Difference in the potassium supplying power of soils due to soil types was also evaluated with the use of this technique. The degree of the potassium supplying power was in the order of soil types as red-yellow podzolic and lateric soils, basaltic materials(Rvd)> low-humic gley and alluvial soils, alluvial plains and food plains(Apa)> low-humic gley soils, nearly level to sloping local alluvial plains and slopes(Afb)> low-humic gley and alluvial soils, fluvio-marine plains (Fma).

• Journal of Radiation Protection and Research
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• v.48 no.2
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• pp.100-106
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• 2023

Background: As breast tissue expanders consist of metallic materials in the needle guard and ferromagnetic injection port, irradiation can produce radioactivation. Materials and Methods: A CPX4 (Mentor Worldwide LLD) breast tissue expander was exposed using the Versa HD (Elekta) linear accelerator. Two photon energies of 6 and 10 MV-flattening filter free (FFF) beams with 5,000 monitor units (MU) were irradiated to identify the types of radiation. Furthermore, 300 MU with 10 MV-FFF beam was exposed to the CPX4 breast tissue expander by varying the machine dose rates (MDRs) 600, 1,200, and 2,200 MU/min. To assess the instantaneous dose rates (IDRs) solely from the CPX4, a tissue expander was placed outside the treatment room after beam irradiation, and a portable radioisotope identification device was used to identify the types of radiation and measure IDR. Results and Discussion: After 5,000 MU delivery to the CPX4 breast tissue expander, the energy spectrum whose peak energy of 511 keV was found with 10 MV-FFF, while there was no resultant one with 6 MV-FFF. The time of each measurement was 1 minute, and the mean IDRs from the 10 MV-FFF were 0.407, 0.231, and 0.180 μSv/hr for the three successive measurements. Following 10 MV-FFF beam irradiation with 300 MU indicated around the background level from the first measurement regardless of MDRs. Conclusion: As each institute room entry time protocol varies according to the working hours and occupational doses, we suggest an addition of 1 minute from the institutes' own room entry time protocol in patients with CPX4 tissue expander and the case of radiotherapy vaults equipped with a maximum energy of 10 MV photon beams.