Korea Research Reactor 1&2 (KRR-1&2), Korea’s first research reactor, began dismantling in 1997. As of 2022, the demolition of general areas such as offices has been completed, and contaminated areas such as reactor rooms remain. On the other hand, construction waste generated in contaminated areas of nuclear facilities cannot be disposed of as general industrial waste. It is predicted that about 5,000 tons of construction waste will be generated if the contaminated area of KRR-1&2 is demolished. In this study, the application plan for the demolition of contaminated area of KRR-1&2 was reviewed through a review of laws and cases related to domestic and overseas disposal. The only method for disposing of construction waste in contaminated areas that can be applied in Korea is clearance in accordance with Nuclear Safety Commission Notice No. 2020-06. In addition, there has been no case of demolishing large-scale nuclear facilities in Korea. Therefore, there are limitations in domestic laws and standards to be applied to the dismantling of contaminated areas of KRR-1&2. The IAEA and the United States specify comprehensive matters such as optimization of radiation protection and minimization of waste products. The EU recommends demolition after decontamination by removing contaminated areas before demolition of buildings. It also presents three options for reuse, recycling, and disposal of buildings and building waste. In particular, in the case of Germany, detailed radioactivity measurement methods for deregulation of buildings and building waste are presented in accordance with the EU’s guidelines. As a result of synthesizing this, it is judged that the EU and Germany building clearance plan will be suitable for domestic application.

The International Atomic Energy Agency recommends the deep geological disposal system as one of the disposal methods for high-level radioactive waste (HLW), such as spent nuclear fuel. The deep geological disposal system disposes of HLW in a deep and stable geological formation to isolate the HLW from the human biosphere and restrict the inflow of radionuclides into the ecosystem. It mainly consists of an engineered barrier and a natural barrier. Safety evaluation using a numerical model has been performed primarily to evaluate the buffer’s long-term stability. However, although the gas generation rate input for long-term stability evaluation is the critical factor that has the most significant influence on the long-term hydraulic-mechanical behavior of the buffer, in-depth research and experimental data are lacking. In this study, the gas generation rate on the interface between the disposal canister and the buffer material, a component of the engineered barrier, was mainly studied. Gas can be generated between the disposal canister and the buffer material due to various causes such as anaerobic corrosion of the disposal canister metal, organic matter decomposition, radiation decomposition, and steam generation due to high temperature. The generation of gas in such a disposal environment increases the pore gas pressure in the buffer and causes internal cracks. The occurred cracks increase the intrinsic permeability of the buffer, which leads to a decrease in the primary performance of the buffer. For this reason, it is essential to apply the appropriate gas generation rate according to the disposal condition and buffer material for accurate long-term stability analysis. Therefore, the theoretical models regarding the estimation of gas generation were summarized through a literature study. The amount of gas generated was estimated according to the disposal environment and material of the disposal canister. It is expected that estimated values might be used to estimate the long-term stability analysis of buffer performance according to the disposal condition.

Prior to the investigations on fuel degradation it is necessary to describe the reference characteristics of the spent fuel. It establishes the initial condition of the reference fuel bundle at the start of dry storage. In a few technology areas, CANDU fuels have not yet developed comprehensive analysis tools anywhere near the levels in the LWR industry. This requires significantly improved computer codes for CANDU fuel design. In KNF, in-house fuel performance code was developed to predict the overall behavior of a fuel rod under normal operating conditions. It includes the analysis modules to predict temperature, pellet cracking and deformation, clad stress and strain at the mid-plane of the pellet and pellet-pellet interfaces, fission gas release and internal gas pressure. The main focus of the code is to provide information on initial conditions prior to dry storage, such as fission gas inventory and its distribution within the fuel pellet, initial volumes of storage spaces and their locations, radial profile of heat generation within the pellet, etc. Potential degradation mechanisms that may affect sheath integrity of CANDU spent fuel during dry storage are: creep rupture under internal gas pressure, sheath oxidation in air environment, stress corrosion cracking, delayed hydride cracking, and sheath splitting due to UO2 oxidation for a defective fuel. To upgrade the developed code that address all the damage mechanisms, the first step was a review of the available technical information on phenomena relevant to fuel integrity. The second step was an examination of the technical bases of all modules of the in-house code, identify and extend the ranges of all modules to required operating ranges. Further improvements being considered include upgrades of the analysis module to achieve sufficient accuracy in key output parameters. The emphasis in the near future will be on validation of the in-house code according to a rigorous and formal methodology. The developed models provide a platform for research and industrial applications, including the design of fuel behavior experiments and prediction of safe operating margins for CANDU spent fuel.

The bacterial soft-rot disease is one of the most critical diseases in vegetables such as Chinese cabbage. The researchers isolated two bacteria (Pseudomonas kribbensis and Pantoea vagans) from diseased tissue samples of Chinese cabbages and confirmed them as being the strains that cause soft-rot disease. Lactic-acid bacteria (LAB), were screened and used to control soft-rot disease bacteria. The researchers tested the treatments with hypochlorous acid water (HAW) and LAB supernatant to control soft-rot disease bacteria. The tests confirmed that treatments with the HAW (over 120 ppm) or LAB (Lactobacillus plantarum PL203) culture supernatants (0.5 mL) completely controlled both P. kribbensis and P. vagans.

The rapid development of computer vision and deep learning has enabled these technologies to be applied to the automated classification and counting of microscope images, thereby relieving of some burden from pathologists in terms of performing tedious microscopic examination for analysis of a large number of slides for pathological lesions. Recently, the use of these digital methods has expanded into the field of medical image analysis. In this study, the Inception-v3 deep learning model was used for classification of chondrocytes from knee joints of rats. Knee joints were extracted, fixed in neutral buffered formalin, decalcified, processed and embedded in paraffin, and hematoxylin and eosin (H&E) stained. The H&E stained slides were converted into whole slide imaging (WSI), and the images were cropped to 79 × 79 pixels. The images were divided into training (60.42%) and test (39.58%) sets (46,349 and 30,360 images, respectively). Then, images containing chondrocytes were classified by Inception-v3 and accuracy was calculated. We visualized the images containing chondrocytes in WSIs by adding colored dots to patches. When images of chondrocytes in knee joints were evaluated, the accuracy was within the range of 91.20 ± 8.43%. Therefore, it is considered that the Inception-v3 deep learning model was able to distinguish chondrocytes from non-chondrocytes in knee joints of rats with a relatively high accuracy. The above results taken together confirmed that this deep learning model could classify the chondrocytes and this promising approach will provide pathologists a fast and accurate analysis of diverse tissue structures.

Embryo transfer (ET) in the animal is an important procedure to generate genetically engineered animals and conserve genetic resources. For ET experiments in mice, pseudopregnant recipients are usually prepared with proestrus stage of females and vasectomized males. However, this conventional method is inefficient because the size of female colonies should be large to select only the proestrus stage in the estrous cycle and the surgical procedures are required to generate vasectomized males. In this study, we established a simple and efficient protocol to prepare ET recipients using the estrous synchronization with hormone injection and the mating with wild male mice. The delivery rate of ET recipients tended to be increased with estrous synchronization using hormone injection (100%) compared to the conventional method (71%). Further, natural pregnancy of the recipients, induced by mating with a wild male, significantly enhanced the birth rate of ET offspring than the conventional method (33% vs. 13%). Based on the results, we concluded that our new protocol using hormone injection to ET recipients and mating with wild males could be more efficient and simpler compared to the conventional method.

The objective of this study was to develop a simultaneous method for 8 amino acids including alanine, arginine, glutathione, lysine, ornithine, methionine, threonine and tryptophan in veterinary products using LC-MS/MS. To optimize MS analytical condition of 8 amino acids, each parameter was established by multiple reaction monitoring in positive mode. The chromatogram separation was achieved on a C18 column with mobile phase of 0.1% formic acid in D.W. and 0.1% formic acid in acetonitrile for green technology at a flow rate of 0.4mL/min for 5 min with gradient elution. The developed method was validated for mass accuracy, precision, linearity in veterinary products. Calibration curves were linear over the calibration ranges (0.5 – 10 mg/L) for all the analytes r2>0.99. Average recoveries were 92.96 – 105.61% and relative standard deviations (RSD) were 0.27 – 3.5%. The limit of detection (LOD) and the limit of quantification (LOQ) were 0.04 – 0.83 mg/L and 0.12 – 2.52 mg/L, respectively. All values were corresponded with the criteria ranges requested by CD 2002/657/EC. The application of this method will be helpful in quality control analysis of amino acids in veterinary products.

This study investigated EFL college-level learners’ expectation on and their experience in an online English-medium instruction (EMI) course focusing on how participants interacted with their classmates and the instructor in their online class (zoom session) based on assumptions and rationales of Interaction Hypothesis and classroom interaction research. Analyses of questionnaire, observation, and interview data revealed that participants’ experience of interaction and their perception of interaction opportunities in the zoom session were significantly related to how they would evaluate the course-taking experience. It was also found that cognitive strategy such as participants’ preparation for each class rather than L2 confidence was more relevant to their level of satisfaction with the course. Results of analyses suggested that an online class could be more effective than a face-to-face class in terms of engaging EFL adult learners in an academic course offered in participants’ L2, English. Based on study results, suggestions on how to increase interaction opportunities in online EMI course are made.

농촌진흥청 국립원예특작과학원에서는 2018년 오리엔탈-트럼펫(OT) 종간잡종나리 ‘Pink Bella’를 개발하였다. 2008년 연노란색 OT 종간잡종나리 ‘Valparadiso’와 붉은색의 오리엔탈나리 ‘Scalini’를 각각 모본과 부본으로 화주 절단 수분법과 주 두교배법으로 각 3화를 인공교배하였고, 교배 후 미숙한 3개의 꼬투리를 수확하여 배가 형성된 배주를 기내에서 배양하여 잡종을 획득한 후 재배하였다. 육묘한 배양묘로부터 2011년 분홍색의 OT 종간잡종 나리 ‘OTO-11-43’ 계통을 개체 선발하였다. 2012년부터 2017년까지 선발된 계통은 자구와 인편번식, 조직배양을 이용하여 번식 및 양구한 후 1, 2차 생육특성 검정을 실시하였다. 2018년 3차 생육특성검정 및 소비자 기호도 평가를 수행한 결과 화색 및 화형에 대한 기호도가 높은 분홍색(RHS, RP62C)의 조기개화성 절화용 OT 종간잡종 나리 ‘Pink Bella’를 육성하였다. 3배체의 OT 종간잡종 나리로 초장은 131.7cm로 초장신장성이 우수하였다. ‘Pink Bella’의 화폭은 18.6cm이며 대조품종 ‘Table Dance’의 18.4cm와 유사한 크기였으며, 내화피의 폭, 길이 역시 대조품종과 통계적인 차이가 없었다. ‘Pink Bella’의 개화기는 6월 15일로 대조품종 ‘Table Dance’의 6월 28일에 비교하여 개화기가 13일 단축된 것으로 나타났으며 통계적으로 유의하였다.

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

Chrysanthemum boreal, C. indicum, and C. indicum var. albescens are well-known wild Chrysanthemum species used for traditional medicine in Korea. In this study, volatile compounds from three wild Chrysanthemums were identified according to four different flowering stages and analyzed using HS-SPME-GC-MS to determine the temporal variation of the volatiles. As a result, 132, 151, and 142 peaks were identified from C. boreale, C. indicum, and C. indicum var. albescens, respectively. Furthermore, 70 out of 132 peaks were identified in C. boreale with a matching ratio of >90% from library search. In addition, 85/151 and 76/142 peaks were identified from C. indicum and C. indicum var. albescens. Forty-nine volatile compounds were found commonly in all three wild Chrysanthemums through all four different flowering stages. However, six, seven, and five unique compounds were detected only in C. boreale, C. indicum, and C. indicum var. albescens, respectively. One hundred volatile compounds were selected for multivariate analysis considering volatile compounds overlapped with each other. The one-way ANOVA (p < 0.05) detected significant differences from 77 out of 100 volatile compounds. In addition, PLS-DA showed the different profiles of volatile compounds according to four different flowering stages in each wild Chrysanthemum. PC1 of each Chrysanthemum accounted for 45.8 56.9, and 11.9% in C. boreale, C. indicum, and C. indicum var. albescens, respectively. PC1 of C. boreale and C. indicum clearly separated the BF stage and the other three stages. Conversely, PC1 of C . indicum var. albescens showed a difference in the composition of volatile compounds between the BF/BO and HO/FO stages. In addition, the different profiles of volatile compounds could be visualized using a heatmap from three wild Chrysanthemums according to four different flowering stages. This study will help improve particular volatile compounds in three wild Chrysanthemums both in quality and quantity.