본 연구는 관수조건에 따른 지피식물의 생육특성을 파악하고 수분 스트레스 후 생육 회복력을 평가하 기 위하여 수행되었다. 지피식물로 많이 사용되는 초본류 4종을 대상으로 피복의 목적을 달성할 수 있 는 적정 식재간격을 도출하였으며, 또한 관수처리 유형에 따른 생육 및 피복률의 변화를 분석하였다. 식물마다 피복효과를 달성하기 위한 적정 식재간격에 차이가 있었다. 돌단풍은 30～35cm, 송엽국은 30～40cm간격, 꽃잔디는 40cm이상, 돌나물은 20cm미만의 식재간격이 효과적이다. 관수처리에 따른 생육특성을 분석한 결과 관수가 지속적으로 이루어지면 초장이 길지만 처리기간 동안 생육의 변화는 거의 없었다. 피복률은 관수처리와 식물에 따라 차이가 있다. 송엽국과 돌단풍은 무관수 20일 후 관수가 이루어지면 피복률도 회복되었으며, 꽃잔디는 관수가 이루어지고 나서 시간이 경과된 후에 피복률을 회복하였다. 피복이 전체적으로 이루어진 이후에는 토양 수분부족에 의한 피복률의 변화가 거의 없었 으나 피복 초기에 수분이 공급되지 않으면 피복에 어려움이 있다. 꽃잔디, 송엽국, 돌단풍은 일정기간 무관수가 지속되더라도 다시 관수가 이루어지면 회복이 될 수 있다. 즉 식물관리에 있어서 종 마다 차이 는 있으나 관수관리가 중요하며, 일정기간 무관수 후 관수가 이루어지면 생육이 회복될 수 있다.

이 연구는 초분광 영상으로 두 품종의 콩(청자 3호, 대찬)의 들불병을 진단할 수 있는 모델과 다중분광 영상센서를 개발하기 위해 수행되었다. 무처리구와 들불병 처리구에서 5 nm full width at half maximum (FWHM)으로 구성된 원시 초분광 중심파장들의 콩 식물 영역 반사율들을 추출하여 10 nm FWHM으로 병합한 후, t-test로 차이가 나타난 blue, green, red, red edge, NIR1 및 NIR2 각 영역에서 선정된 대표 밴드로 121개의 식생지수를 계산하였다. 식생지수를 입력변수로 support vector machine (SVM), random forest (RF), extra tree (EXT), extreme gradient boosting (XGB)의 머신러닝 기법과 shapley additive explanation 변수 선택 기법을 적용하여 들불병 진단에 가장 적절한 모델을 선정하고 사용된 식생지수와 파라미터를 나타내었다. T-test 결과 품종에 상관없이 blue 1개(420 nm), green 2개(500, 540 nm), red 1개(600 nm), red edge 2개(680, 700 nm), NIR1 2개(780, 840 nm), NIR2 1개(920 nm)의 총 9개 대표 밴드들이 선택되었고, 성능 평가를 통해 선정된 모델에 청자 3호의 경우 SVM모델(OA=0.86, KC=0.72, 10 VIs)이 선정되었으나 혼동행렬 분석 결과 정상오분류가 적은 RF모델이 선택되었다. RF모델(식 생지수 : RE/Blue, NSI, GDVI, Green/Blue, 파라미터 : max_depth=6, n_estimators=100)은 OA=0.81, KC=0.60, precision=0.86, recall=0.81, F1 score=0.80의 성능을 나타내었다. 대찬은 EXT모델(식생지수 : YVI, RE/Green, 2YVI, 파라미터 : max_depth=8, n_estimators=10)이 선정되 었고, OA=0.86, KC=0.72, precision=0.86, recall=0.86, F1 score=0.86의 성능을 나타내었다.

Research is currently being conducted in the field of carbon reduction–related construction technologies, focusing on using industrial waste as a replacement for cement or as aggregates. However, the existing research is limited as carbon reduction is only achieved by reducing the amount of cement used. With the imperative of carbon neutrality, the development of carbon reduction technology is also necessary in the construction field. To address this, we plan to develop carbon reduction technology by introducing biochar—a carbon-sequestration material—into construction practices. Therefore, this study aims to comprehend the effect of the carbonization degree of biochar on the hydration reaction of cement, emphasizing the development of carbon-sequestration construction technology. Therefore, physical and chemical properties, such as surface and crystal structures, were analyzed to determine the effect of varying carbonization degrees on cement composites, contributing valuable insights into the broader field of sustainable construction.

The purpose of this study was to analyze microstructural changes and evaluate the mechanical properties of TWIP steel subjected to variations in heat treatment, in order to identify optimal process conditions for enhancing the performance of TWIP steel. For this purpose, a homogenization heat treatment was conducted at 1,200 °C for 2 h, followed by hot rolling at temperature exceeding 1,100 °C and cold rolling. Annealing heat treatment is achieved using a muffle furnace in the range of 600 °C to 1,000 °C. The microstructure characterization was performed with an optical microscope and X-ray diffraction. Mechanical properties are evaluated using micro Vickers hardness, tensile test, and ECO index (UTS × Elongation). The specimens annealed at 900 °C and 1,000 °C experienced a significant decrease in hardness and strength due to decarburization. Consequently, the decarburization phenomenon is closely related to the heat treatment process and mechanical properties of TWIP steel, and the effect of the microstructure change during annealing heat treatment.

Okadaic acid (OA) group toxins, including OA and its analogs, such as dinophysis toxins (DTXs), have been reported to cause diarrheal shellfish poisoning (DSP). These toxins are primarily produced by dinoflagellates and are accumulated in bivalves. Recently, the presence of Dinophysis sp., a causative alga of DSP, has been reported along the coasts of Korea, posing a potential risk of contamination to domestic seafood and exerting an impact on both the production and consumption of marine products. Accordingly, the European Food Safety Authority (EFSA) and the World Health Organization (WHO) have established standards for the permissible levels of OA group toxins in marine products for safety management. Additionally, in line with international initiatives, the domestic inclusion and regulation of DTX2 among the substances falling under the purview of management outlined by the 2022 diarrheal shellfish toxin standard have been implemented. In this study, we reviewed the physicochemical properties of OA group toxins, their various exposure routes (such as acute toxicity, genotoxicity, reproductive and developmental toxicity), and the relative toxicity factors associated with these toxins. We also performed a comparative assessment of the methods employed for toxin analysis across different countries. Furthermore, we aimed to conduct a broad review of human exposure cases and assess the international guideline for risk management of OA group toxins.

Silkworms have been developed for food and medicinal purposes, and several silkworm products are commercially available. However, there has been no research on their shelf stability. This study investigated the shelf stability of freeze-dried silkworms during storage at various temperature and humidity conditions, using three lipid oxidation indicators: peroxide, acid, and TBA value. The higher the temperature and relative humidity are, the more significant the three lipid oxidation indicators increase. Among these indicators, the TBA and peroxide values show the highest temperature dependence and humidity dependence, respectively. This study could contribute to the standardization of the shelf life of products made from freeze-dried silkworms.

막대한 예산이 투입되는 국가연구를 기획·관리·확산하는 연구관리 담당자들은 매우 중요한 인력이나, 역할에 관한 연구는 부족한 실정이다. 본 연구는 연구관리 전문기관 담당자에게 필요한 역량요소를 도출하고 역량의 중요도를 확인하고자 하였다. 이를 위해 4가 지 주요요인과 16개의 하위요인을 도출하였고, AHP 기법을 기반으로 설문조사(연구관리 담 당자와 연구자 90명 대상)를 추진하여 우선순위를 도출하였다. 전체 응답자 기준으로 역량의 중요도는 협력적 태도, 소통능력, 연구지식, 목표관리능력과 신의성실 순으로 확인되었으나, 담당자의 경우, 업무와 관련한 역량(규정지식, 판단능력, 정책지식)이 중요하다고 보는 반면, 연구자들의 경우 자신들을 보다 더 지원해 줄 수 있는 역량(협력적 태도, 소통능력)을 보다 더 중시하는 시각차를 볼 수 있었다. 이번 연구는 전문기관 연구관리 담당자의 채용과 역량 개발에 활용할 수 있을 것으로 사료된다.

본 연구는 ESG 경영에 대한 에너지 관련 산업과 기업의 전략적 발전방향 모색을 위하여 글로벌 ESG 경영에서 선도적인 역량과 경험을 갖고 있는 것으로 평가되는 한국중부발전의 사례를 분석해 성공 요인을 도출하고자 하였다. 사례 분석 결과 한국중부발전은 한 발 앞서 ESG 경영에 관심을 갖기 시작해 기후위기 대응과 청정에너지 및 신재 생에너지 사업 분야로 진출하였고, 동반성장과 지역사회 공헌, 부패방지와 윤리경영에 역점을 두고 미래성장과 지 속가능경영을 계속해 객관적인 수치와 실적을 기반으로 우수한 성과를 달성한 것으로 확인되었다. 또한 한국 중부 발전이 글로벌 ESG 경영에 성공할 수 있었던 것은 기존의 글로벌 경영 역량 보유와 이를 제고하고자 전문인력 양 성을 바탕으로 전사적인 조직을 중심으로 하는 지속적인 노력, ESG 리스크에 대한 선제적 관리와 수치목표 중심의 성과관리, 동반성장과 상생경영의 기업문화, 글로벌 ESG 경영에 대한 CEO와 경영진의 판단과 의지가 주요한 요 인으로 작용하였기 때문이라고 분석되었다.

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

This study was aimed to determine the changes in CO2 concentration according to the temperatures of daytime and nighttime in the CO2 supplemental greenhouse, and to compare calculated supplementary CO2 concentration during winter and spring cultivation seasons. CO2 concentrations in experimental greenhouses were analyzed by selecting representative days with different average temperatures due to differences in integrated solar radiation at the growth stage of leaf area index (LAI) 2.0 during the winter season of 2022 and 2023 years. The CO2 concentration was 459, 299, 275, and 239 μmol·mol-1, respectively at 1, 2, 3, and 4 p.m. after the CO2 supplementary time (10:00-13:00) under the higher temperature (HT, > 18°C daytime temp. avg. 31.7, 26.8, 23.8, and 22.4°C, respectively), while it was 500, 368, 366, 364 μmol·mol-1, respectively under the lower temperature (LT, < 18°C daytime temp. avg. 22.0, 18.9, 15.0, and 13.7°C, respectively), indicating the CO2 reduction was significantly higher in the HT than that of LT. During the nighttime, the concentration of CO2 gradually increased from 6 p.m. (346 μmol·mol-1) to 3 a.m. (454 μmol·mol-1) in the HT with a rate of 11 μmol·mol-1 per hour (240 tomatoes, leaf area 330m2), while the increase was very lesser under the LT. During the spring season, the CO2 concentration measured just before the start of CO2 fertilization (7:30 a.m.) in the CO2 enrichment greenhouse was 3-4 times higher in the HT (>15°C nighttime temperature avg.) than that of LT (< 15°C nighttime temperature avg.), and the calculated amount of CO2 fertilization on the day was also lower in HT. All the integrated results indicate that CO2 concentrations during the nighttime varies depending on the temperature, and the increased CO2 is a major source of CO2 for photosynthesis after sunrise, and it is necessary to develop a model formula for CO2 supplement considering the nighttime CO2 concentration.