국내 천적농업이 활성화되기 위해서 토착천적을 개발하여야 한다. 토착천적들을 개발하기 위하여 시설재배 지 내에 다양한 비작물성 식물들을 확보하는 것이 필요하다. 식물의 종류가 많으면 이에 서식하는 기주곤충이 늘어나고 그 곤충들의 토착천적들이 증가한다. 이러한 시설재배지내에 식물 종다양성 확보를 위하여 재배작물 사이의 공간에 경합되지 않는 곳에는 다양한 잡초를 증식하거나 새로운 천적유지 식물을 개발하여 다양한 기주 곤충을 증식시켜야 한다. 현재 가장 많이 사용하는 것으로서 보리를 이용한 콜레마니진딧벌 천적유지식물이 개발되었지만, 그 외에도 담배, 천사의나팔 김장배추, 쑥, 소리쟁이, 냉이 등 다양한 천적유지식물들을 개발할 수 있다. 즉, 토착천적을 개발하기 위하여 생태적으로 안정된 작물재배환경을 유지하는게 필요하고 이러한 안정 된 생태계 속에서 효과적인 천적을 통한 해충방제가 이루어질 수 있다.

United States Nuclear Regulatory Commission (U.S. NRC) specifies regulations on obtaining licenses and describes the technical position on the average waste concentration, also known as Concentration Averaging and Encapsulation Branch Technical Position (CA BTP); CA BTP helps classify blendable waste and discrete items and address concentration averaging. The technical position details are reviewed and compared in a real environment in Korea. A few cases of concentration averaging based on the application of CA BTP to domestic radioactive waste are presented, and the feasibility of the application is assessed. The radioactive waste considered herein does not satisfy the Disposal Concentration Limit (DCL) of the second-phase disposal facility while applying the preliminary classification. However, if CA BTP is applied when the radioactive waste is mixed with other radioactive waste items in a large and heavy container, it can be disposed of at the second-phase disposal facility in Gyeongju Repository. To apply the CA BTP of the U.S. NRC, it is necessary to investigate the safety assessment conditions of the US and Korea.

At a time when the demand for drones is increasing, a plan to utilize drone images was sought for efficient promotion of cadastral resurvey. To achieve the purpose of this study, the technical and legal status of drone images was reviewed, and through this, the possibility of using it for cadastral resurvey was primarily reviewed. subsequently, an experiment was conducted targeting the project district to examine whether drone images were applied to boundary extraction, which is the primary process of cadastral resurvey. As a result of the experiment, it was found that boundary extraction from images is possible. However, in some cases, it is impossible due to field conditions or image quality. Therefore, it is necessary first to apply cases where boundary extraction is possible to cadastral resurvey and seek solutions for some impossible cases. In particular, the image quality problem may have problems with the current technology, but it will also have problems with the existing drone equipment. So, standard for drone calibration should also be established. Finally, the cadastral resurvey surveying procedure using drones was also presented.

목적 : 천연 항균, 항산화 물질인 망고스틴의 크산톤 화합물로 코팅된 콘택트렌즈를 제조하여 안과적 질환 예 방을 위한 기능성 콘택트렌즈의 물리·화학적 특성을 연구하였다. 방법 : 콘택트렌즈를 제조하여 IPN기술을 통해 감마망고스틴을 코팅하였다. 제조된 렌즈를 ISO와 식약처기준 을 참고하여 광투과율, 함수율, 산소투과율(Dk/t), 항균, 항산화성 실험을 진행하였다. 결과 : 감마망고스틴 코팅 콘택트렌즈의 가시광선투과율은 93%이다. 자외선은 70% 이상 청광은 30% 이상 차 단한다. 항균 실험에서는 감마망고스틴 코팅 콘택트렌즈의 항균성이 일반렌즈에 비해 5배 이상 효과 있고, 항산화 는 시험별 17.49, 28.46, 36.99%의 항산화율을 보였다. 결론 : 감마망고스틴으로 코팅된 콘택트렌즈는 UV와 청광의 차단율이 일반렌즈에 비해 매우 뛰어나다. 또한, 일반렌즈에 없는 항균, 항산화 활성을 보여 안과적 질환을 예방할 수 있을 것으로 보인다.

목적 : 건강한 20대를 대상으로 각막내피세포의 형태학적 분석을 실시하고, 양안, 성별, 굴절교정수술 여부, 콘택트렌즈 착용여부, 코로나바이러스-19의 감염에 따른 차이를 알아보고자 하였다. 방법 : 충청남도 일개 대학교에서 20대 남녀 87명(174안)을 대상으로 하였다. 각막내피세포의 형태학적 분석 은 비접촉식 경면현미경을 이용하여 검사하였고, 각막내피세포의 밀도, 내피세포의 다형성, 육각형비율, 그리고 중심각막두께를 분석하였다. SPSS 18.0 프로그램을 이용하여 양안, 성별, 그리고 그 외에 조사한 변수에 따른 각 막내피세포의 밀도, 다형성 및 육각형 비율을 측정하였다. 결과 : 우안과 좌안에서 각막내피세포의 밀도는 각각 2,977.7±238.7 cells/mm2와 3,008.9±236.7 cells/mm2(p=0.056)이었고, 남성과 여성은 각각 2,970.9±237.9 cells/mm2와 2,983.6±241.8 cells/mm2로 통 계적으로 유의한 차이가 없었다(p=0.807). 각막내피세포의 다형성과 육각형 비율도 양안, 성별, 굴절교정수술 여 부. 코로나바이러스-19 감염 유무에 따라 유의한 차이가 없었다(p>0.050). 그러나 각막내피세포의 다형성과 육각 형 비율은 콘택트렌즈 착용 군과 대조군에서 각각 30.7±4.5%와 61.9±5.4% 그리고 28.2±4.0%와 64.7±4.7% 를 보였다(각각 p=0.018와 p=0.029). 결과 : 한국 20대 성인을 대상으로 분석한 각막내피세포 밀도의 평균값은 2,993.3±237.5 cells/mm2이었고, 양안과 성별에 따른 차이는 없었다.

In 2022, research for native prokaryotic species in Korea reported 10 unrecorded bacterial strains affiliated to phyla Actinomycetota, Bacillota, and Pseudomonadota. The strains formed monophyletic clades with the most closely related species (with ≥98.7% sequence similarity) in the 16S rRNA gene sequencing. Among them, four species of the phylum Actinomycetota, two species of the phylum Bacillota, and four species of the phylum Pseudomonadota have not been reported in Korea, suggesting unrecorded species in Korea. Information on strains such as Gram staining reaction, colony and cell morphology, biochemical characteristics, and isolation sources were provided in the species description.

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMYnormal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).