In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

In order to reduce the area of the high-level radioactive waste (HLW) repository, a buffer material with high thermal conductivity is required. This is because if the thermal conductivity of the buffer material is high, the distance between the disposal tunnels and the deposition holes can be reduced. Sand, which is a natural material and has higher thermal conductivity than bentonite, is added to bentonite to develop an enhanced buffer material. For the sand-bentonite mixture, it is important which sand to use and how much to add because an enhanced buffer material should satisfy both hydraulic (H) and mechanical (M) performance criteria while improving thermal conductivity (T). In this study, we would like to show what type of sand and how much sand should be added to develop an enhanced buffer material by adding sand to Gyeongju bentonite, a representative bentonite in Korea. For this purpose, the thermal conductivity, hydraulic conductivity, and swelling pressure of the sand-Gyeongju bentonite mixture according to the sand addition rate were measured. It is more efficient to use silica sand with smaller particles than Jumunjin sand which is a representative sand in Korea as an additive for an enhanced buffer material than using the Jumunjin sand. In order for the sand-Gyeongju bentonite buffer material to satisfy both the hydraulic and mechanical performance criteria as a buffer material while increasing the thermal conductivity, it is judged that the optimum dry density is 1.7 g/cm3 at least and the optimum sand addition rate is 10% at most.

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

This study examined the subacute oral toxicity of Dendropanax morbiferus H.Lév leaves hot-water extracts (DMWE) using male and female Spargue-Dawley rats. Rats were orally administered the DMWE at dose levels of 0, 250, 500, 1,000, and 2,000 mg/kg body weight (BW) for four weeks. For experimental period, clinical signs and the number of deaths were examined, and feed intake and BW of all experimental animals were measured once a week for four weeks. At the end of the experiment, blood samples were collected from all rats, and all animals were euthanized and autopsies were performed to collect major organs. No dead animals were found during the experimental period. In addition, no differences were found between control and DMWE-treated groups in feed intakes, BW changes, organ weights, clinical signs, hematological parameters, and serum biochemical parameters. The results of this study provided evidence that oral administration of DMWE at the dose of 2,000 mg/kg BW is safe in rats and may not exert severe toxic effects.

In this study, the acute toxicity of Dendropanax morbiferus H.Lév leaf hot-water extracts (DMWE) was examined in male and female ICR mice. Mice were orally administered the DMWE at dose levels of 0, 250, 500, 1,000 and 2,000 mg/kg body weight (BW) for single-dose toxicity test. There were no significant differences in change of BW between control and all DMWE treated-groups. In hematological and blood biochemical analysis, none of the parameters were affected by the DMWE. Similarly, there were no significant effects on markers for liver and kidney functions in all DMWE treated-groups. Since there were no adverse effects of the DMWE in single oral toxicity tests, even at the highest doses, it was concluded that the lethal dose 50 (LD50) of DMWE is estimated at > 2,000 mg/kg BW.

Wolfiporia cocos is an edible fungus commercially cultivated in Asia. To investigate metabolic changes of W. cocos mycelia under both light and dark culture conditions, gas chromatography mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) analyses were performed. In terms of the total amount of sugars, alcohols, amino acids, organic acids, fatty acids, and purines, there no significant differences between the W. cocos mycelia cultivated under light (L) or dark (D) conditions (p < 0.05). However, there were some differences with respect to the production of particular sugars and proteins. The levels of trehalose (L: 17.2 ± 0.3% vs. D: 13.9 ± 1.6%), maltose (L: 0.9 ± 0.1% vs. D: 0.3 ± 0.1%), turanose (L: 0.7 ± 0.2% vs. D: 0.1 ± 0.1%), glutamine (L: 1.6 ± 0.3% vs. D: 0.7 ± 0.2%), and proline (L: 0.3 ± 0% vs. D: 0.1 ± 0%) were all significantly higher under light condition (p < 0.05). In contrast, the levels of galactose (L: 13.7 ± 1.2% vs. D: 17.6 ± 2.0%), aspartic acid (L: 0.6 ± 0.1 % vs. D: 0.9 ± 0.1%), cystathionine (L: 0.6 ± 0.1% vs. D: 0.8 ± 0 %), and malic acid (L: 0.7 ± 0.1% vs. D: 1.2 ± 0.1%) were higher under the dark condition. It is worth noting that the amount of pachymic acid, a pharmaceutically active compound of W. cocos, was 1.68 times greater under the light condition (p < 0.05).

The buffer block, which is one of the main components of the engineering barrier system, plays an essential role in mitigating groundwater infiltration and radionuclide transport in a high-level nuclear waste repository. To achieve those purposes, the compacted buffer block must satisfy the functional safety criteria for dry density, water content, and many other components. In this study, the compation curves of the compacted bentonite-sand mixtures were evaluated to identify the relationship between the dry density and the water content of the buffer material. The floating die press at 10 MPa and the cold isostatic press at 40 MPa were applied to compaction of a buffer block with a diameter of 100 mm and a thickness of 10 mm. The condition of a bentonite-sand mixing ratio was 6:4, 7:3, 8:2, and 9:1 with 9 to 21% water content. As a result, the maximum dry density increases, the optimum moisture content decreases as the sand content of buffer material increases. This study can provide the conditions for manufacturing the compacted bentonite-sand buffer block.