According to Article 4 and 5 of the Nuclear Safety and Security Commission (NSSC) Notice No. 2020-6, radioactive waste packages should be classified by radioactive levels, and finally permanently shipped to underground or surface disposal facilities. The level of the radioactive waste package is determined based on the concentrations of the radionuclides suggested in Article 8 of NSSC Notice No. 2021-26. Since most of the radionuclides in radioactive wastes are beta nuclides, chemical separation and quantification of the target nuclides are essential. Conventional methods to classify chemically non-volatile radionuclides such as Tc-99, Sr-90, Nb- 94, Fe-55 take a lot of time (about 5 days) and have low efficiency. An automated non-volatile nuclide analysis system based on the continuous chemical separation method of radionuclides has been developed to compensate for this disadvantages of the conventional method in this study. The features of the automated non-volatile nuclide separation system are as follows. First, the amount of secondary waste generated during the chemical separation process is very small. That is, by adopting an open-bed resin column method instead of a closed-bed resin column method, additional fittings and connector are unnecessary during the chemical separation. In addition, because the peristaltic pump is supplied for the sample and solution respectively, it is great effective to prevent cross-contamination between radioactive samples and the acid stock solution for analysis. Second, the factors that may affect results, such as solution amount, operating time and flow rate, are almost constant. By mechanically controlling the flow rate precisely, the operating time and additional factors required during the separation process can be adjusted and predicted in advance, and the uncertainty of the chemical separation process can be significantly reduced. Finally, it is highly usable not only in the continuous separation process but also in the individual separation process. It can be applied to the individual separation process because the user can set the individual sequence using the program. As a result of the performance evaluation of the automation system, recovery rates of about 80–90% and reproducibility within 5% were secured for all of the radionuclides. Furthermore, it was confirmed that the actual work time was reduced by more than 50% compared to the previous manual method. (It was confirmed that the operation time required during the separation process was reduced from 6 days to 3 days.) Based on these results, the automation system is expected to improve the safety of workers in radiation exposure, reduce human error, and improve data reliability.

Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in Ada- Boost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.

We fabricated 3 types of ETL, mp TiO2, ZnO, and ZnO coated on mp TiO2(ZMT) to compare the photoelectric conversion efficiency (PCE) and fill factor (FF) of Perovskite solar cells. The structure of the cells was FTO/ETL/Perovskite (CH3NH3PbI3)/spiro-MeOTAD/Ag. SEM morphology assessment of the ETLs showed that mp TiO2 was porous, ZnO was flat, and the ZMT porous surface was filled with a thin layer. Via XRD measurements, the crystal structures of mp TiO2 and ZnO ETL were found to be anatase and wurtzite, respectively. The XPS patterns showing energy bonding of mp TiO2, ZnO, and ZMT O 1s confirmed these materials to be metal oxides such as ETL. The electrical characteristics of the Perovskite solar cells were measured using a solar simulator. Perovskite solar cells with ZMT ETL showed showed PCE of 10.29 % than that of conventional mp TiO2 ETL devices. This was considered a result of preventing Perovskite from seeping into the ETL and preventing recombination of electrons and holes.

We introduce the Transformable Reflective Telescope (TRT) kit that applies an aluminum profile as a base plate for precise, stable, and lightweight optical system. It has been utilized for optical surface measurements, developing alignment and baffle systems, observing celestial objects, and various educational purposes through Research & Education projects. We upgraded the TRT kit using the aluminum profile and truss and isogrid structures for a high-end optical test device that can be used for prototyping of precision telescopes or satellite optical systems. Thanks to the substantial aluminum profile and lightweight design, mechanical deformation by self-weight is reduced to maximum 67.5 μm, which is an acceptable misalignment error compared to its tolerance limits. From the analysis results of non-linear vibration simulations, we have verified that the kit survives in harsh vibration environments. The primary mirror and secondary mirror modules are precisely aligned within 50 μm positioning error using the high accuracy surface finished aluminum profile and optomechanical parts. The cross laser module helps to align the secondary mirror to fine-tune the optical system. The TRT kit with the precision aluminum mirror guarantees high quality optical performance of 5.53 μm Full Width at Half Maximum (FWHM) at the field center.

농촌 종점 물류는 농촌 주민들의 온라인 쇼핑에 영향을 주는 중요한 요인임. 농촌 인터넷 쇼핑의 서비스 품질은 양호한 종점 물류 배송과 지원이 필요함. 농촌 이커머스의 발전에 의 하여 농촌 주민들이 서비스의 품질에 대한 기대치도 높아지고 있음. 본 연구에서는 이커머스 만족도를 통한 농촌 종점 물류 서비스 품질이 농촌 주민의 온라인 구매행위에 어떠한 영향 미치는지 검토하고, 농촌 종점 물류 서비스의 발전을 위한 개 선 및 발전전략을 제시하였음. 본 연구에서는 구조방정식모델 (SEM)과 온충린의 매개효과 테스트를 적용하여 분석하였음. 1. 신뢰성과 서비스 편의성은 만족도에 긍정적 영향을 미치며, 원가절약은 만족도에 부정적 영향을 미침. 2. 만족도는 인터넷 쇼핑에 긍정적인 영향을 미치는 것으로 나타났음. 3. 만족도는 반응성과 구매행위 사이에 매개효과가 있고 경제와 구매행위 사이에도 매개효과가 있는 것으로 나타났음.

본 연구는 거베라 주산지인 경북과 경남 지역의 농가 재배 및 스마트팜 현황을 조사하고 분석하고자 수행하였다. 조사방법은 2020년 81 농가를 대상으로 대면 설문조사를 실시하였다. 거베라 경영주 연령은 50∼60대가 71.6%로 가장 많았고, 30∼ 40대가 16%, 70세 이상이 9.9%, 30세 이하는 2.5% 순이었다. 거베라 재배 경력은 5년 이하가 37%였으며, 6∼10년 25.9%, 11∼20년 29.6%, 21년 이상 농가가 7.4%였다. 재배 면적은 0.3 ∼0.6ha가 61.7%로 가장 많았고, 0.3ha 미만이 30.9%, 0.6ha 이상이 7.4%였으며, 전체 평균재배 면적은 0.37ha였다. 거베라 재배 농가당 평균 인력은 자가 노동력 2.1명, 고용 노동력 0.8명 이었다. 재배 방식에서 토경 88.9%, 양액재배 농가는 11.1%였다. 국산품종을 전체 또는 일부라도 재배하고 있는 농가는 61.7%였고 외국산 거베라 품종만 재배하고 있는 농가는 38.3% 였다. 거베라 병해충 발생조사에서 병 발생이 많았다고 응답한 농가는 91.4%였으며, 충 발생이 많았다고 응답한 농가는 98.8% 였다. 스마트팜 시스템을 설치한 농가는 12.3%였으며, 설치하지 않은 농가에서 향후 설치할 의향이 있다고 응답한 농가는 76.9% 였다. 향후 경영계획을 묻는 질문에서 현상유지 80.2%, 축소 8.6%, 경영확대 11.1%였으며, 경영 확대를 대답한 농가는 경북 지역이 8농가로 경남지역 1농가 보다 많았다.

COVID-19와 같은 전염병 감염 시나리오 전반에 걸쳐 펩타이드 기반 치료법을 발견하고 설계하는 개발 시대의 추세는 보다 효율적이고 저렴한 치료 환경으로 발전할 수 있습니다. 결과적으로, 그들의 단백질 분해 약화는 천연 펩타이드 약물의 단점 중 하나입니다. 펩티도미메틱스는 이 단점을 해결하는 데 도움이 될 수 있습니다. 이 리뷰에서 펩타이드 및 펩타이드 기반 약물 발견은 숙주 안지 오텐신 전환 효소-2(ACE2) 수용체 및 바이러스 스파이크 (S)단백질의 연관성을 포함하는 중증 코로나바이러스 폐색전 증후군(SARS-CoV-2)의 주요 진입 기전 중 하나를 표적으로 요약했습니다. 또한, 펩타이드 기반의 새로운 치료법을 통해 COVID-19에 대해 연구된 단백질, 펩타이드 및 기타 가능한 조치의 이점을 다룹니다. 그리고 펩타이드 기반 약물 치료 환경의 개요는 진화적 관점, 구조적 특성, 작동 한계값 및 치료 영역에 대한 설명으로 구성된다

We present an updated version of the multilayer spectral inversion (MLSI) recently proposed as a technique to infer the physical parameters of plasmas in the solar chromosphere from a strong absorption line. In the original MLSI, the absorption prole was constant over each layer of the chromosphere, whereas the source function was allowed to vary with optical depth. In our updated MLSI, the absorption prole is allowed to vary with optical depth in each layer and kept continuous at the interface of two adjacent layers. We also propose a new set of physical requirements for the parameters useful in the constrained model tting. We apply this updated MLSI to two sets of Hα and Ca ii line spectral data taken by the Fast Imaging Solar Spectrograph (FISS) from a quiet region and an active region, respectively. We nd that the new version of the MLSI satisfactorily ts most of the observed line proles of various features, including a network feature, an internetwork feature, a mottle feature in a quiet region, and a plage feature, a superpenumbral bril, an umbral feature, and a fast down ow feature in an active region. The MLSI can also yield physically reasonable estimates of hydrogen temperature and nonthermal speed as well as Doppler velocities at different atmospheric levels. We conclude that the MLSI is a very useful tool to analyze the Hα line and the Ca ii 8542 line spectral daya, and will promote the investigation of physical processes occurring in the solar photosphere and chromosphere.