우리나라 접목선인장 산업은 화훼 수출액의 43.9%를 차지하 며, 주로 비모란선인장(Gymnocalycium mihanovichii) 품종을 삼각주선인장(Selenicereus sp.) 대목에 접목하여 재배한다. 그 러나 농가에서 재배되는 접목선인장 비모란의 바이러스 감염률 이 80%에 달하고 있어 바이러스 검정을 통한 품종 개발과 보급 이 매우 중요하다. 2019년 분홍색 색상과 자구발생 특성이 우수 한 GG171077-32와 GG151099-28을 계통간 교배하여 2020 년 실생묘 15개체를 양성하여 기내 배양한 삼각주선인장에 접 목하였다. 그 중 분홍색의 색상과 자구 발생 특성이 우수하였던 4개체를 선발하여 2021년 1차 특성검정을 위한 계통으로 증식 하였다. 2021년 특성검정 계통 중 진분홍색을 띄며 자구 발생이 우수한 GG191177-7 계통을 선발하였다. 이후 2년간 농가 3개 소에서 실증시험을 병행하여 특성검정한 결과 GG191177-7 계통을 최종 선발하고 2024년 ‘핑크문’으로 명명하였다. 핑크 문’ 품종은 RHS Color Chart N66A의 진한 분홍색이며, 편원형으로 능수는 8.0개이고, 가시색은 갈색이며 길이는 3.8mm였 다. 모구의 구폭은 43.4mm였고 주당 평균 자구수는 18.4개였 다. 1차 선발 계통 37개체를 대상으로 real-time PCR을 이용 한 바이러스 검정 결과, cactus virus X (CVX)와 pitaya virus X (PiVX)의 단독 감염률은 각각 16.2%였고, CVX와 PiVX의 혼합 감염률은 32.4%였으며, 바이러스 미검출 개체의 비율은 35.1%였다. 바이러스 미검출 개체에서 생산된 자구를 증식하여 ‘핑크문’ 품종을 육성하였다.

This paper examines recent trends in stadium design and construction through a comprehensive case study and literature review. It highlights the innovative use of advanced building materials, such as high-performance composites and sustainable concrete mixes, which enhance structural integrity while reducing environmental impact. The integration of smart technologies—including IoT technologies, building information modeling (BIM), and digital twin—is explored for its role in improving operational efficiency, safety, and maintenance processes. Additionally, the study reviews the development of cutting-edge engineering techniques like seismic design, advanced AI-based structural analysis, which streamline construction processes and optimize resource usage. Emphasizing sustainability, the paper also discusses strategies for energy-efficient designs and renewable energy integration. Overall, the findings demonstrate how interdisciplinary approaches combining material science, smart technology, and sustainable engineering are shaping the future of stadium construction.

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

In this study, the evaluation items related to the effectiveness evaluation of the LVC (Live, Virtual, Constructive) training system of the Air Force were derived and the weights of each item were analyzed. The LVC training system evaluation items for AHP (Analytic Hierarchy Process) analysis were divided into three layers, and according to the level, 3 items were derived at level 1, 11 items at level 2, and 33 items at level 3. For weight analysis of evaluation items, an AHP-based pairwise comparison questionnaire was conducted for Air Force experts related to the LVC training system. As a result of the survey, related items such as (1) Achievement of education and training goals (53.8%), (1.2) Large-scale mission and operational performance (25.5%), and (1.2.1) Teamwork among training participants (19.4%) was highly rated. Also, it was confirmed that the weights of evaluation items were not different for each expert group, that is, the priority for importance was evaluated in the same order between the policy department and the working department. Through these analysis results, it will be possible to use them as evaluation criteria for new LVC-related projects of the Air Force and selection of introduction systems.

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

느타리 1 kg 봉지재배를 통해 살균배지의 주요 화학성과 2주기까지의 버섯 수량간 관계를 분석한 결과, 버섯 수확량은 pH, 총질소 함량, CN율과 2차 함수식에서 매우 높은 상관을 보였다. pH는 4.9~5.0의 범위에서, 총 질소량은 2.0~2.2%에서, CN율은 20~22.5에서 가장 높은 수량성을 보였다. 1주기 버섯은 pH, 총질소량 및 CN율과 높은 상관을 보였고 2주기 버섯은 CN율 외에는 상관이 나타나지 않았다. 그러나 수량합계가 1주기 수량보다 3종의 화학성과의 관계에서 모두 더 높은 상관이 나타났다. 그리하여 본 연구에서 배지의 pH, 총질소 함량 및 CN율은 전 수확기간에 걸쳐서 버섯수량에 큰 영향을 미친다는 결론을 얻을 수 있었다.

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

This study examined the physicochemical properties of radish pickle containing different natural preservatives (grapefruit seed extract, green tea extract, rosemary, or olive) stored for 0, 1, 2, 3, and 4 weeks. The hardness and color of the radish pickles with the grapefruit seed extract was higher than the other radish pickles during storage from week 0 to week 4. A 14.52% and 13.80% decrease in hardness and color were observed in the radish pickles with grapefruit seed extract (GFE), respectively. In addition, the total phenolic content was highest in the GFE in natural preservatives. Based on the results, GFE was selected as the optimal natural preservatives, and the growth of total viable bacteria and yeast were evaluated. The total viable bacteria and yeast showed similar patterns to the control. These results are expected to be useful in producing radish pickles with optimal quality and contribute to the development of various foods in the food industry.

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.