강원특별자치도 18개 시군을 대상으로 감나무와 콩에 발생하는 미국선녀벌레, 썩덩나무노린재, 담배거세미 나방의 발생을 조사하였다. 돌발해충인 미국선녀벌레는 약충기인 6월과 성충기인 10월에 발생조사를 진행하고 남방계해충인 담배거세미나방과 썩덩나무노린재는 7월과 9월 콩 재배기에 발생조사를 진행하였다. 미국선녀 벌레는 강원지역에 있는 감나무에서 발생을 확인할 수 없었고. 감나무 주변 기타 수목에서 가지당 평균 6.7마리 발생하였다. 담배거세미나방은 7월, 속초를 제외한 모든 시군에서 발생이 되었으며, 춘천, 횡성에서 트랩당 60마 리가 넘는 발생밀도를 보였으며, 원주, 강릉, 화천 등 에서는 트랩당 1마리로 낮은 발생밀도를 나타냈다. 썩덩나무 노린재는 7월 춘천, 속초, 홍천 등 7개 시군을 제외한 11개 시군에서 트랩에 포획되는 것을 확인하였으며, 화천에 서 트랩당 26마리로 최고발생밀도를 보였다.

The fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), which is native to tropical and subtropical regions of the Western Hemisphere is now annually arrives in Korea. In this study, we developed loop-mediated isothermal amplification (LAMP) assay, one of the main merits of which is a rapid identification of target species. Five among 11 FAW-specific loci tested successfully provided a consistent reaction when ten FAWs, which were collected from eight localities in four countries were tested, whereas the 13 non-target species were not amplified. To increase in-field applicability of the method all life stages, reaction time, and different periods after death was tested using the quick extracted DNA. Our FAW diagnostic protocol can be completed within 30 min, from the process of extracting genomic DNA from an egg or a 1st instar larva to species determination.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

High-entropy alloys (HEAs) are attracting attention because of their excellent properties and functions; however, they are relatively expensive compared with commercial alloys. Therefore, various efforts have been made to reduce the cost of raw materials. In this study, MIM is attempted using coarse equiatomic CoCrFeMnNi HEA powders. The mixing ratio (powder:binder) for HEA feedstock preparation is explored using torque rheometer. The block-shaped green parts are fabricated through a metal injection molding process using feedstock. The thermal debinding conditions are explored by thermogravimetric analysis, and solvent and thermal debinding are performed. It is densified under various sintering conditions considering the melting point of the HEA. The final product, which contains a small amount of non-FCC phase, is manufactured at a sintering temperature of 1250oC.

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.

The carbon anode material for lithium-ion battery was prepared by pyrolysis fuel oil and waste polyethylene terephthalate (PET) additive. The pitch was synthesized as a medium material for carbon anode by heat treatment. The waste PET additive improved the softening point and thermal stability of the pitch. La and Lc of the anode material (heat-treated pitch) increased at higher treatment temperature but decreased by waste PET additive. The electric capacity was evaluated based on effects of defective cavity and developed graphite interlayer, respectively. When the La and Lc of the anode material decreased, the electric capacity by cavity increased based on defective graphite structure. Therefore, the addition of waste PET causes the improved capacity by the cavity. The anode material which has a high efficiency (over 95%) and C-rate (95%, 2 C/0.1 C) was obtained by controlling the process of heat treatment and PET addition. The mechanism of lithium-ion insertion was discussed based on effects of defective cavity and developed graphite interlayer.