기후변화와 세계시장 개방에 따른 외래 및 돌발해충의 종류는 다양해지고 밀도는 지속적으로 증가하고 있지 만 발생 실태에 관한 자료는 미흡하다. 본 연구는 경남지역 돌발 및 남방계 해충의 발생과 피해조사를 위해 미국선 녀벌레, 썩덩나무노린재, 담배거세미나방 등 3종을 경남 18시·군에서 조사하였다. 미국선녀벌레는 약충기(6월), 성충기(9월) 조사를 진행하였고, 길이가 50cm 이상되는 가지의 밑동에서 위쪽으로 50cm 내 개체를 육안조사하 였다. 썩덩나무노린재는 콩 개화기(7월), 등숙기(9월) 조사를 진행하였고 지점당 집합페로몬 트랩 1개를 설치하 고 1주 후 채집된 마리 수를 조사하였다. 담배거세미나방은 성페로몬 트랩 1개를 설치한 뒤 1주 후 채집된 마리 수를 조사하였다. 미국선녀벌레는 의령, 함안, 함양 등에서 발생이 많았고, 전년대비 발생이 소폭 감소하였다. 썩덩나무노린재는 합천, 밀양, 함양 등에서 발생이 많았고, 담배거세미나방은 전시군에서 발생이 확인되었으 며, 특히 함양, 산청, 사천, 고성 등에서 밀도가 높았다.

최근 기후변화에 따라 아열대 작물 재배가 증가하면서 경남지역 망고 재배면적도 크게 증가하고 있다. 하지만 망고에 발생하는 병해충 발생 정보가 부족하여 2022년부터 현재까지 경남지역에 있는 망고농장을 조사하였으 며, 그 결과 병해 6종, 해충 8종 발생을 확인하였다. 특히 경남지역 망고 포장에서 꽃노랑총채벌레, 대만총채벌레, 볼록총채벌레 등의 발생을 확인하였고, 통영, 함안, 밀양 등에서 총채벌레류에 의한 피해가 발생하였다. 많은 농가에서 총채벌레류 방제를 위해 화학농약에 의존하고 있으나 총채벌레는 반복적인 약제 노출시 빠른 세대 진전으로 인해 저항성 발달이 쉽기때문에 방제에 어려움을 겪고 있다. 이러한 문제를 해결하고자 색상별 끈끈이 트랩의 총채벌레류 유인효과를 확인하였으며, 꽃노랑총채벌레는 노랑, 주황, 연두, 대만총채벌레는 빨강, 노랑, 파랑, 볼록총채벌레는 파랑, 노랑, 빨강 순으로 유인효과가 높은 것을 확인하였다.

Until now, in case of Palaearctic region, only one species, Nesendaeus monochrous in this genus was recorded from China. but the biology and life cycle are not clearly known, yet. This species collected from the bud of Glochidion chodoense in Korea for the first time and closely related to Heterochyromera imerodeus Kojima & Morimoto which was recorded only in Japan,. But Nesendaeus monochrous is easily distinguishable from H. imerodeus by the relatively longer elytra with bisinuately round apices. Heterochyromera imerodeus Kojima & Morimoto can be found from Eurya emarginata and Glochidion obovatum in Japan. So, this two species are closely related each other and need more taxonomic study about the generic synonymy or changing the generic position.

The repeated use of biocidal products, including household insecticides and disinfectants, has led people to seek safer alternatives, such as light traps, ultrasound generators, or repellent lamps. By appearances, these physical/non-chemical alternatives seem appealing to many consumers and are gaining more attention. However, unlike biocidal products, these alternatives are not subject to mandatory approval and registration processes, and no standards for efficacy and safety are in place. Given the increasing numbers of insect traps on the market, there is an urgent need to investigate their efficacy. In the present study, we examined the attraction activity of ten commercially available insect trap products. They varied in size, structure, trapping methods (fan or sticky pad), and attractants. Their efficacies were assessed under both laboratory condition (1.8m x 1.8m x 1.8m) and semi-field room condition (4m x 6m x 2m). Ul*****APTM demonstrated the highest capture rate (%), leading us to further study the role of its attractant. Notably, the capture rate increased by 28.9% when the CO2 attractant was used, displaying statistical differences (P = 0.032). For practical application in the field, consumer acceptance and satisfaction were monitored using 20 participants. The potential of insect traps as viable alternatives to biocidal products is discussed.

모기는 감염병을 매개하는 종으로 전염병 확산 억제를 위해서는 개체수의 감시와 정확한 예측이 필요하다. 본 연구에서는 모기 개체수 및 기상 및 현장 자료를 활용해 모기 개체수 머신러닝 모델을 개발하였다. 모기 개체수는 디지털 모기 측정기(Digital Mosquito Monitoring System, DMS)의 2015 년~2022년의 5월~10월의 자료를 활용하였다. 기상 자료는 기온, 강수량, 풍속, 습도를 사용하였으며, 현장 조사 자료는 현장을 명목척도와 서열척도로 나누어 기록하여, 명목 척도의 경우 원핫 인코딩으 로 변환해 수치화하여 사용하였다. 분석에 사용된 머신러닝 모델은 Artificial Neural Network, Random Forest, Gradient Boosting Machine, Support Vector Machine이며 성능지표로 R2, RMSE를 사용하였다. 연구 결과, Gradient Boosting 모델이 R2 0.4, RMSE 22.45로 가장 좋은 성능을 나타냈다. 현장 조사 자료 를 분석에 활용하였을 때 R2는 증가하였고, RMSE는 감소하였다. 본 연구 결과 모기 개체수에 현장 조사 자료가 예측 정확도를 향상시킬 수 있음을 확인하였다.

This study was conducted to determine the appropriate seeding dates by verifying the difference in winter survival and productivity of alfalfa according to fall sowing dates in the central area of South Korea. The experiment was conducted for 2 years (2020 and 2021) at the field in the Department of Animal Resources Development, NIAS located in Cheonan. Sowing dates started from September 18 to November 8 with 10 days of intervals during 2020 and 2021; SO1 (September 18), SO2 (September 28), SO3 (October 8), SO4 (October 18), SO5 (October 28), and SO6 (November 8). After sowing, the winter survival rate was measured in the spring of the following year, and the dry matter yield was measured by harvesting at 10% flowering and harvesting five times a year. SO6 failed to winter survival, and SO5 also had a lower winter survival rate than SO1~4 (p<0.05). The average annual dry matter yield of alfalfa linearly decreased with delaying sowing dates (p<0.05). The feed value did not differ in the same year by delaying the sowing date in the same year. These results suggest that sowing date should be started before October 18 to increase winter survival and productivity of alfalfa in the central area of South Korea.

This study was aimed to isolate bacterial inoculants producing chitinase and evaluate their application effects on corn silage. Four corn silages were collected from four beef cattle farms to serve as the sources of bacterial inoculants. All isolates were tested against Fusarium graminearum head blight fungus MHGNU F132 to confirm their antifungal effects. The enzyme activities (carboxylesterase and chitinase) were also measured to isolate the bacterial inoculant. Based on the activities of anti-head blight fungus, carboxylesterase, and chitinase, L. buchneri L11-1 and L. paracasei L9-3 were subjected to silage production. Corn forage (cv. Gwangpyeongok) was ensiled into a 10 L mini silo (5 kg) in quadruplication for 90 days. A 2 × 2 factorial design consists of F. graminearum contamination at 1.0104 cfu/g (UCT (no contamination) vs. CT (contamination)) and inoculant application at 2.1 × 105 cfu/g (CON (no inoculant) vs. INO (inoculant)) used in this study. After 90 days of ensiling, the contents of CP, NDF, and ADF increased (p<0.05) by F. graminearum contamination, while IVDMD, acetate, and aerobic stability decreased (p<0.05). Meanwhile, aerobic stability decreased (p<0.05) by inoculant application. There were interaction effects (p<0.05) on IVNDFD, NH3-N, LAB, and yeast, which were highest in UCT-INO, UCT-CON, CT-INO, and CT-CON & INO, respectively. In conclusion, this study found that mold contamination could negatively impact silage quality, but isolated inoculants had limited effects on IVNDFD and yeast.

Buckwheat (Fagopyrum esculentum), which is a traditional Korean crop, has been known as a health food due to its rich nutrition. This study was conducted to evaluate the change in flavonoid content of flowers and seeds during post-flowering growth of Korean tartary buckwheat variety ‘Hwanggeummiso’, with the aim of providing basic data for the development of functional food and feed additive. Tartary buckwheat took 69 and 99 days from the sowing date to reach the flowering and maturity stages, respectively. As a result of examining the flavonoid components of each part of tartary buckwheat, chlorogenic acid, rutin, and isoquercitrin of flowers increased from the flowering period on 22 May (0 days after flowering) to 42 days after flowering, while quercetin increased until 21 days after flowering and then decreased thereafter. In seeds, chlorogenic acid, rutin, and isoquercitrin were most abundant at the time of seed-bearing on 14 days after flowering, and showed a decreasing tendency thereafter. On the other hand, quercetin showed a tendency to increase until 21 days after flowering and then decrease. Overall, the flavonoid content was higher in flowers than in seeds, with rutin being particularly prominent. Based on this, the possibility as food materials and feed additives was confirmed using buckwheat produced in Korea.

Although the proportion of coal-fired power generation is decreasing, efficient operating technology is needed to continuously invest in facilities and reduce maintenance costs until it is abolished. Boilers, one of the main facilities of power plants, operate for a long time in harsh environments of high temperature and high pressure. In addition, damage due to deterioration is likely to occur depending on the fuel and tube material used. It is very important to judge soundness because damage caused by deterioration adversely affects facility operation. Previously, replication method was used to analyze the progress of deterioration. In the replication method, pre-treatment such as chemical treatment is performed on the boiler tube in the field, the area is reproduced by attaching a film, and the replicated film is determined by an expert in the laboratory with an expensive microscope. However, this method involves substantial costs and time requirements, as well as the possibility of human errors. To address these issues, we developed a mobile health assessment system in this research. Since it is detachable and takes images in real time, this system enables swift evaluations across a broad range and facilitates the assessment of preprocessing quality. In addition, it was intended to reduce existing human mistakes by developing a degradation classification algorithm using the merger cluster method.

With the recent advent of Metaverse, the character industry that reflects the characteristics of users' faces is drawing attention. there is a hassle that users have to select face components such as eyes, nose, and mouth one by one. In this paper, we propose a diffusion-based model that automatically generates characters from content human photographs. Our model generates user artistic characters by reflecting content information such as face angle, direction, and shape of a content human photo. In particular, our model automatically analyzes detailed information such as glasses and whiskers from content photo images and reflects them in artistic characters generated. Our network generates the final character through a three-step: diffusion process, UNet, and denoising processes. We use image encoders and CLIP encoders for the connection between style and input data. In the diffusion process, a collection of noise vectors is gradually added to a style vector to enable lossless learning of the detailed styles. All input values except for the style images are vectorized with CLIP encoders and then learned with noise style vectors in the UNet. Subsequently, noise is removed from the vectors through the UNet to obtain the artistic character image. We demonstrate our performance by comparing the results of other models with our results. Our method reflects content information without loss and generates natural high-definition characters.