이 연구는 1세대 스마트 온실의 재배환경 데이터와 장미 절 화의 품질 특성 데이터를 수집하고 그 요인들 간의 상관 관계 를 분석하여 절화수명 예측 및 최적 환경 조성의 기초 자료를 얻고자 수행되었다. 이를 위해, 토경재배(SC) 및 암면배지경 양액재배(RWH) 하우스 각 1개소를 선정하여 1년간 기온, 상 대습도(RH) 및 수증기압차(VPD), 일적산광량(DLI), 근권온도 등의 환경 데이터와 매월 말 수확된 장미 ‘Miss Holland’ 절 화의 품질 특성 데이터를 수집하였으며, 이 데이터와 절화수 명과의 상관관계를 분석하였다. 절화수명은 10월과 11월을 제외하고는 SC 하우스에서 RWH 하우스보다 더 길었다. 절 화수명과 환경 및 생육 특성 간의 상관관계 분석에서 SC 하우 스의 상관계수는 RWH 하우스보다 조금 더 높았으며, 절화수 명 예측을 위한 요소들도 두 하우스 간에 차이가 있었다. SC 하우스의 절화수명 Y=0.848X1+0.366X2-0.591X3+2.224X4- 0.171X5+0.47X6+0.321X7+9.836X8-110.219(X1-X8: 최고 RH, RH 일교차, DLI, pH, Hunter’s b value, EC, 절화 장, 잎 두께; R2=0.544)로 예측되었고, RWH 하우스의 절화수명 Y=-1.291X1+52.026X2-0.094X3+0.448X4-3.84X5+0.624X6 - 8.528X7+28.45(X1-X7: 경경, 야간 VPD, 최고 근권온도, 최 저 근권온도, 기온 일교차, RH 일교차, 최고 VPD; R2=0.5243) 로 예측되었다. 이 두 모델식으로부터 SC 하우스에서는 RH, EC 및 pH가, 그리고 RWH 하우스에서는 근권 온도가 절화수명에 더 큰 영향을 미친다는 것을 추론할 수 있다. 따라서 각 재배 방법에 따라 장미의 절화수명에 더 큰 영향을 미치는 환경적 요인을 효율적으로 관리할 필요가 있다.

For the disposal of radioactive waste from nuclear facilities, assessing their radioactivity inventories is essential. As a result, countries with nuclear facilities are implementing assessment schemes tailored to their respective policies and available resources for radioactive waste management. This paper specifically describes the assessment scheme for radioactivity inventory applied to metal waste generated during the dismantling of the Japan Power Demonstration Reactor (JPDR), a 1.25 MW BWR. The distinctive aspect of the Japanese approach lies in the fact that, for a pair of a key nuclide and a difficult-to-measure (DTM) nuclide that lack a significant correlation in their concentrations, the mean activity concentration method was used. In this method, an arithmetic average of all measurements of the DTM nuclide from representative drums, including MDAs (Minimum Detectable Activities), was assigned to the concentration of the DTM nuclide for all drums, regardless of the concentration of its paired key nuclide. Conversely, for a specific pair of a key nuclide and a DTM nuclide with a significant correlation, the scaling factor method was applied, as is common in many other countries. This Japanese case can serve as a valuable reference for Korea, which does not have the option of using the mean activity concentration method in its assessment scheme.

To ensure the long-term supply and sustainability of uranium fuel, exploring alternative resources is essential, particularly considering that terrestrial reserves of uranium are limited (about 4.6 million tons). Since the amount of uranium dissolved in seawater is approximately 1000 times that of terrestrial reserves (i.e., about 4.5 billion tons), uranium extraction from seawater (UES) can be an alternative resource. However, the ultra-low concentration of uranium in seawater (about 3.3 ppb) poses a significant challenge in achieving economic feasibility for UES. This paper introduces case studies on the cost analysis of systems for recovering uranium from seawater, specifically focusing on braided fiber-based adsorbents developed by JAEA and ORNL. The cost analysis has been conducted based on using the deployment of these adsorbents on the bottom of the sea, which is a passive deployment method, thereby reducing the total costs of recovery. The analysis results can be used to identify R&D areas necessary for reducing cost components, making UES economically feasible.

Given the limited terrestrial reserves of uranium (approximately 4.6 million tons), exploring alternative resources is necessary to secure a sustainable, long-term supply of nuclear energy. Uranium extraction from seawater (UES) is a potential solution since the amount of uranium dissolved in seawater (approximately 4.5 billion tons) is about 1,000 times that of terrestrial reserves. However, due to the ultra-low concentration of uranium in seawater (approximately 3.3 ppb), making UES economically viable is a challenging task. In this paper, we explore the potential of using thermal discharge from domestic nuclear power plants for uranium extraction. The motivation for this comes from previous research showing that the adsorption capacity of amidoxime-based adsorbents is proportional to the temperature of the seawater in which they are deployed. Specifically, a study conducted in Japan found that a 10°C increase in seawater temperature resulted in a 1.5-fold increase in adsorption capacity.

Given the limited terrestrial reserves of uranium (about 4.6 million tons), exploring alternative resources is essential to ensure the long-term supply and sustainability of nuclear energy. Uranium extraction from seawater (UES) is a potential solution to this issue since the amount of uranium dissolved in seawater (about 4.5 billion tons) is approximately 1000 times that of terrestrial reserves. However, the ultra-low concentration of uranium in seawater (about 3.3 ppb) makes it a challenging task to make UES economically feasible. This paper provides an overview of the current status of UES technology, which has evolved over the past seven decades. Starting from inorganic adsorbents such as hydrous titanium oxide in the 1960s, amidoxime-based fibrous adsorbents gained the most attention until the early 2010s due to their ease of deployment in actual seawater conditions and high affinity for uranium. Nowadays, research on organic adsorbents with microstructures is prevailing due to their ability to easily control surface area and compositions. In addition, this study identifies the key issues that need to be addressed to make UES technology economically viable.

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

영농형 태양광 발전은 농경지에서 작물을 생산함과 동시에 식물이 요구하는 광포화점 이상의 광을 이용하여 전기를 생산 하는 시스템이다. 새로운 농가 소득원의 개발을 위하여 포도 원에 태양광 패널을 설치하고 수체의 생육과 과실 발육 특성 을 평가하여 영농형 태양광의 활용성을 탐색하고 향후 재배기 술을 개발하는 데 필요한 정보를 제공하고자 연구를 진행하였 다. 152 × 68 × 3.5cm 크기의 구조물에 영농형 150Wp (36cell) 모듈을 포도나무 재식열에 따라 배치하고, 과원의 환경과 식물 생육을 분석하였다. 무처리에는 겨울철 풍속이 0.4－0.6m·s-1 에 도달하였으나, 시설 설치구에서는 0.01－0.02m·s-1에 머 물렀다. 삽수 수피의 탄수화물함량은 시설 설치구에서 183－ 184m·g-1으로 무처리구(181－198mg·g-1)에 비해 큰 차이가 없으며 삽수의 발아율도 큰 차이가 없었다. 잎의 엽록소의 함 량은 처리구에서 높게 나타났다. 수확후 과실의 특성으로는 과립중, 과방중, 당도, 과피색의 차이는 없었다. 다만 시설구 에서 숙기가 5－7일정도 늦어졌으며, 변색기의 착색에는 약 간 차이가 있었다. 영농형 태양광 패널을 설치한 과원에서 포 도나무와 과실의 발육은 유의차가 없었고, 설치구에서 착색 이 지연되었다. 이러한 결과는 향후 포도원에서 영농형 태양 광 시설을 설치하여 포도를 생산하는 기술 개발에 필요한 정 보로 활용될 수 있을 것이다.

Republic of Korea (ROK) is operating the Integrated Environmental Radiation Monitoring Network (IERNet) in preparation for a radioactive emergency based on Article 105 of the Nuclear Safety Act (Monitoring of Nationwide Radioactive Environment). 215 radiation monitoring posts are monitoring a wide area, but their location is fixed, so they can’t cover areas where the post is not equipped around the Nuclear Power Plants (NPPs). For this, a mobile radiation monitoring system was developed using a drone or vehicle. However, there are disadvantages: it is performed only at a specific cycle, and an additional workforce is required. In this study, a radiation monitoring system using public transportation was developed to solve the above problems. Considering the range of dose rates from environmental radiation to high radiation doses in accidents, the detector was designed by combining NaI (TI) (in the low-dose area) and GM detector (in the high-dose area). Field test was conducted by installed on a city bus operated by Yeonggwang-gun to confirm the performance of the radiation monitoring system. As a result of the field test, it was confirmed that data is transmitted from the module to the server program in both directions. Based on this study, it will be possible to improve the radiation monitoring capability near nuclear facilities.

Radioactivity of radiostrontiums, Sr-89 and Sr-90, which are both pure beta-emitters, are generally measured via Cherenkov counting. However, the determination of Cherenkov counting efficiencies of radiostrontiums requires a complicated procedure due to the presence of Y-90 (also a pure betaemitter) which is the daughter nuclide of Sr-90. In this study, we have developed a machine learning approach using a linear regression model which allows an easier and simultaneous determination of the Cherenkov counting efficiencies of the radiostrontiums. The linear regression model was employed because total net Cherenkov count (Ct) from the three beta-emitters at time t after the separation of Y- 90, can be expressed as a linear combination of their respective time-varying radioactivities with their respective coefficients (parameters) being their counting efficiencies: Ct = εSr-90[ASr-90·exp(–λSr-90·t)] + εSr-89[ASr-89·exp(–λSr-89·t)] + εY-90[ASr-90·exp(1–λSr-90·t)], where ε is a counting efficiency, A is an initial activity, λ is a decay constant and t is time after the separation of Y-90, Thus, if we train the model with multiple Cherenkov counts measured from the three beta emitters, then we can obtain their estimates for counting efficiencies (so-called parameters) straightforward. For this, the model has been trained by two methods: Ordinary Least Squares (OLS) and Bayesian linear regression (BLR), for which two software packages, PyMC3 and Stan were employed to compare their performances. The results showed that the accuracy of the OLS was worse than that of the BLR. Particularly, the counting efficiency of Sr-90 was estimated to be smaller than 0, which is an unrealistic value. On the other hand, the estimates of the BLR gave realistic values which are close to the true values. Additionally, the BLR was able to provide a distribution for each counting efficiency (so-called “posterior”) from which various types of inference can be made including median and credible interval in the Bayesian statistics which is analogous to, but different from confidence interval in the Frequentist statistics. In the results of the BLR, the Stan package gave more accurate estimates than the PyMC3 package. Therefore, it is expected that counting efficiencies of the radiostrontiums including radioyttrium can be determined at the same time, more easily and accurately, by using the BLR with the Stan package and that the activities of radiostrontium also can be determined more easily by using the BLR if we know their counting efficiencies in advance. It is worth noting that the usage of the linear regression model in this study was different from the usual one where the trained model is used to predict a response value (count) from a set of unseen regressor values (activities).

Once a radioactive material is released from the nuclear power plant (NPP) by accident, it is necessary to understand the behavior of radioactive plume to protect residents adequately. For this, it is essential to measure the radiation dose rate around NPPs at important locations. Our previous study developed a movable radiation detector that can be installed quickly in an accident to measure gamma dose rate in areas where environmental radiation monitoring system is not installed. The data measured by the detector are transmitted to the server in real-time through LoRA wireless communications. There are two methods to use LoRA communications; one is self-network, and the other is the network provided by the mobile carrier. A signal receiver, called a gateway, should be equipped near the installation location of radiation detectors to use a self-network without using the mobile carrier’s system. In other words, the movable radiation detectors we made can function if there should be any gateway near them. The distance capable of communication between gateway and detector is about 8 km in an open area without significant obstacles. Korea has many significant obstacles, such as mountains around most NPPs. Thus, the gateways could be installed in the proper position before the accident to operate the movable radiation detectors without problems. If the gateway is located at a high position like a mountain top, it could cover a wide area. In this study, the elevation database in the area around the NPPs was collected and analyzed to determine where gateways should be installed. The analysis range is limited in the urgent protective action planning zone. The optimization was also performed to minimize the number of gateways.

The fruit stalk of Hovenia dulcis (H.dulcis) is traditionally used to relieve hangovers in Korea. Theracurmin is a highly absorbable curcumin preparation which increases the bioavailability of curcumin. Curcumin is known for its antioxidant and anti-inflammatory effects. However, the role of this combination in lowering alcohol levels in the body, thereby alleviating the severity of alcohol-induced hangover has not been investigated. Therefore, we conducted a study to investigate the eliminatory effects of a health drink containing the extract of the H. dulcis fruit stalk and theracurmin (theracurmin drink) on ethanol-induced hangover in rats. The theracurmin drink delivered orally to rats 30 mins before the administration of 40% ethanol (5 g/kg body weight), lowered the concentration of ethanol and acetaldehyde in the blood samples collected 1, 3, and 5 h after ethanol administration. Furthermore, the theracurmin drink increased the activities of alcohol dehydrogenase and aldehyde dehydrogenase enzymes. The effectiveness of the theracurmin drink was thus superior to that of other health drink products, suggesting that its consumption may alleviate or prevent an alcohol-induced hangover.