곤충 Laccifer lacca의 분비물인 stick lac에서 얻어지는 락색소는 물에 용해되는 붉은색 식용 색소로, 식품에 붉 은 색을 더하는 착색제이다. 주요 성분은 안트라퀴논계 기 반의 laccaic acid로 A, B, C, D, E로 구성된다. 본 연구 의 목적은 HPLC-DAD를 활용하여 laccaic acid를 정량적 으로 분석할 수 있는 방법을 검증하는 것과 섭취 노출량 을 산출하는 것이다. 제안된 분석법은 특이성, 선형성, LOD, LOQ, 정확성, 정확도 등 다양한 검증 항목에서 평 가되었으며, 매트릭스 효과 또한 충분히 고려되었다. 모든 매트릭스에서 검량선은 높은 선형성 (R20.999)을 보였고, 락색소의 MDL과 MQL은 각각 1.93 mg/kg와 5.86 mg/ kg로 확인되었다. 일내 및 일간 회수율 시험에서 정확도 는 91.5-100.9% 사이였고, 정밀도는 0.8-2.6%RSD로 AOAC 기준을 충족하였다. 한국에서 유통되는 다양한 식 품들을 수집하여 락색소를 모니터링하여 식품 유형별 락 색소 섭취 노출량을 평가하였다. 락색소 섭취 수준의 범 위는 국민전체, 섭취자 각각 0.0000-0.0027 mg/kg·bw/day 와 0.0000 -0.0306 mg/kg·bw/day였다. 개발된 분석법은 다 양한 식품 유형에 적용 가능하여 락색소 추출물의 효과적 인 모니터링을 가능하게 하며, 섭취량 평가를 통해 락색 소의 안전성 평가에 추가적으로 활용될 수 있을 것이다.

Background: During daily activities, stability is maintained to enhance the function of muscles surrounding the lumbopelvic and hip structures. The core muscles such as the rectus abdominis (RA), external oblique (EO), and internal oblique (IO) responsible for this stability. One effective static exercise for strengthening these muscles is the plank. Objects: This study aimed to compare abdominal electromyographic activation when an unstable support surface was applied to the upper and lower extremities during a standard and a knee plank. Methods: A total of 30 adults (15 males and 15 females) participated in this study. Surface electromyography electrodes were placed on the RA, EO, and IO muscles. Data were collected under six conditions for males, including two plank postures (standard and knee planks) and three unstable surface conditions (a dynamic balance cushion applied to the upper extremities, lower extremities, or no cushion). Female participants performed only the knee plank. A mixed-effects model with a random intercept was used to analyze muscle activation across plank posture and surface position, with statistical significance set at α = 0.05. Results: In males, the standard plank elicited greater muscle activation than the knee plank across all muscles (p < 0.0001). Additionally, applying an unstable support surface to the upper extremities significantly increased muscle activation in both plank postures in EO and IO (p < 0.001). Other muscle, RA, was only affected by the unstable support surface in the standard plank position only. In females, the knee plank with upper limb instability resulted in the highest muscle activation for all muscles. Conclusion: These findings highlight the importance of surface instability in core muscle engagement. However, future studies should further investigate abdominal muscle activation with a more detailed analysis and the inclusion of a control group to enhance comparative validity.

본 연구에서는 국내에서 처음 발견된 Aulacophoroides hoffmanni (Takahashi, 1937)를 최초로 보고한다. 2024년 야외조사에서 한국의 서부 2개 지역의 등나무(Wisteria floribunda (Willdenow))에서 심각한 피해를 주고 있는 A. hoffmanni를 확인하였다. 무시성충의 상세한 형태 기재와 계측값을 제시하였으며, 생체 및 슬라이드 표본 사진을 포함하였다. 이 종은 등나무에 심각한 피해를 주고 있어 관상용 등나무의 주요 해충이 될 것으로 예상된다.

This study is a continuation of previous research (Bae & Kim, 2024) that identified ways to improve the fit of commercially available domestic and international leotards for women in their 30–40s through a survey and evaluation of wearing conditions. The previous study revealed that women in this age group struggle to find well-fitting leotards, despite knowing their body measurements. This issue arises because most apparel industries produce garments for women in their 20s, and grading differences do not sufficiently address the increases in bust, waist, and abdominal girth typical of women in their 30s and 40s. Therefore, this study developed leotard patterns that improve fit and comfort based on the physical characteristics of women in their 30s and 40s. Body surface data of the participants were collected using 3D body scanning technology, forming the basis for pattern design. Experimental garments were created to evaluate compression and determine the suitable pattern reduction rate based on material elasticity. Thus, a pattern reduction rate that offers suitable wearing comfort was proposed. Leotard patterns were developed from experimental results in the most popular styles: camisole and sleeved types. The final leotard patterns were presented after synthesizing results from wearers’ evaluations, compression assessments, and expert appearance evaluations. The final leotards were deemed appropriate for women in their 30s and 40s, demonstrating improvements in comfort, movement stability, compression, and appearance.

Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.

Background: When shorter muscles are restored to normal, non-specific low back pain, which is the mechanism that causes pain along with muscle relaxation, disappears. Among the stretching methods, self-stretching is a good exercise method not only for treatment but also for preventive purposes. Objectives: To find out what is the most effective method of self-stretching, which is often used in treatment and preventive arbitration, which can reduce muscle activity and increase spinal flexibility in patients with non-specific back pain. Design: Randomized controlled trials. Methods: Three self-stretching exercise methods for erector spinae are randomly assigned to 13 people each, and three self-stretching exercise methods are performed for each study subject. Surface electromyography using, was performed to measure the erector spinae muscle activity. flexibility was measured through Trunk flexion test in a standing position. Muscle activity and flexibility were measured before and after stretching. Results: The difference in muscle activity and flexibility in the three groups, including cat stretching, seated stretching, and Reclining stretching, all showed statistically significant differences. There was no statistically significant difference in comparison between the three groups of stretch muscle activity. However, a statistically significant difference was shown in the comparison of the variability between the three groups of flexibility. In a post-mortem analysis comparing the amount of change in flexibility, the flexibility of the seated stretching in the chair increased significantly compared to the cat stretching group. Conclusion: It can be confirmed that the stretching method of the erector spinae, cat stretching, seated stretching, and Reclining stretching, all have positive effects on muscle activity and flexibility.

We analyzed the mineral composition of compacted calcium bentonite (GJ-I) and uncompressed sodium bentonite (MX80), both of which were exposed for two years in the YS03 borehole. The YS03 borehole is characterized by a high concentration of anaerobic microorganisms, including sulfate-reducing bacteria, elevated levels of hydrogen sulfide, and high pH conditions. The compacted Ca bentonite showed minimal alteration, with a small amount of new magnetite formation. However, an X-ray diffraction (XRD) analysis revealed that the uncompressed Na bentonite underwent a complete transformation from montmorillonite to muscovite, goethite, and magnetite. Therefore, it is suspected that the compactness of the bentonite significantly impacts the rate of alteration. Furthermore, an X-ray fluorescence (XRF) analysis demonstrated a marked increase in iron oxide in the Na bentonite, whereas key elements of montmorillonite such as alumina (Al2O3), silica (SiO2), and magnesium oxide (MgO) showed substantial decreases. The presumed cause of the alteration in the uncompressed MX80 bentonite is the presence of Fe cations coupled with a high pH environment. We believe that Fe cations, which were likely released from the corrosion of cast iron, played a significant role in altering the montmorillonite lattice.

Efficient and safe maritime navigation in complex and congested coastal regions requires advanced route optimization methods that surpass the limitations of traditional shortest-path algorithms. This study applies Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) reinforcement learning (RL) algorithms to generate and refine optimal ship routes in East Asian waters, focusing on passages from Shanghai to Busan and Ulsan to Daesan. Operating within a grid-based representation of the marine environment and considering constraints such as restricted areas and Traffic Separation Schemes (TSS), both DQN and PPO learn policies prioritizing safety and operational efficiency. Comparative analyses with actual vessel routes demonstrate that RL-based methods yield shorter and safer paths. Among these methods, PPO outperforms DQN, providing more stable and coherent routes. Post-processing with the Douglas-Peucker (DP) algorithm further simplifies the paths for practical navigational use. The findings underscore the potential of RL in enhancing navigational safety, reducing travel distance, and advancing autonomous ship navigation technologies.

곤충호텔은 곤충이 서식할 수 있도록 인위적으로 만든 구조 물로서 정원이나 텃밭 등 다양한 곳에서 활용되고 있다. 하지만 식생유형, 재료, 설치 방식 등에 따른 곤충 유입 효과에 대한 검증은 부족하며, 국가 또는 지역에 따른 곤충의 종류와 생육환 경이 다름에도 불구하고 곤충호텔에 대한 국내 연구는 전무한 실정이다. 따라서 본 연구에서는 국립수목원을 대상으로 구체적 인 식생유형 및 재료에 따른 유입 곤충 특성을 확인하고, 국내 실정에 맞는 효과적인 곤충호텔 설치 및 관리방안에 대한 기준 을 제시하고자 하였다. 조사구는 세 가지 식생유형(초지, 침엽수 림, 활엽수림)으로 구분하였으며, 각 식생의 조사구에 두 가지 재료(참나무, 잣나무)를 활용한 곤충호텔을 설치하였다. 조사는 2년동안(2022~2023) 진행되었으며, 매년 4~9월까지 주 1회 씩(총 48회) 곤충호텔의 유입 곤충을 직접 채집하였다. 곤충호 텔에서 채집된 곤충은 총 9목 46과 129종 3,057개체로, 2022 년에는 7목 34과 85종 1,750개체, 2023년에는 8목 35과 77종 1,307개체가 출현하였다. 연도별로 비교하면 1차년도에 유입 된 곤충의 개체수가 2차년도 보다 약 1.3배 많았고, 재료의 부식 에 따른 곤충의 구성도 달라졌다. 식생유형에 따라 구분하면 기간에 상관없이 활엽수림에서 유입 곤충이 가장 많았고, 침엽 수림에서 가장 적었다. 또한 재료에 따른 구분에서는 참나무가 잣나무보다 유입 곤충이 많았다. 참나무의 경우 유입 곤충의 연도별 차이가 적은 반면, 잣나무는 1년차에 비해 2년차에 약 2.3배 줄었다. 상관분석과 계층적 군집분석을 통한 곤충의 유입 특성은 식생유형보다는 재료의 영향이 큰 것으로 확인되었고, 식생유형만 비교했을 경우 초지에 비해 활엽수림과 침엽수림의 유사성이 높았다. 결론적으로 생물 다양성 증진을 목적으로 곤 충호텔을 설치하고자 한다면 초지나 활엽수림에 참나무 재료를 사용하는 것이 유리하며, 잣나무 재료를 이용한다면 1년 주기로 재료 교체가 필요할 것으로 판단된다. 하지만 특정 재료를 선호 하는 곤충의 기주특이성이 확인되었기 때문에 다양한 종류의 재료를 같이 사용하는 것도 하나의 방법이라고 사료된다. 향후 이를 기반으로 전시원에서 실질적으로 활용할 수 있는 곤충호텔 모델 개발로 확대해 나갈 예정이다.

This study was conducted to investigate changes in immunoglobulin G (IgG) concentration, nutrient content, and microbial communities of fresh and heat-treated Holstein colostrum collected from a colostrum bank operated by a local agricultural technology center in Gyeongsangbuk-do, South Korea. Of the 16 colostrum samples, 8 were heated at 60℃ for 30 min under a pressure of 0.9–1 bar. The colostrum samples were stored at −70℃ until use, at which time they were thawed at 50–55℃ in a water bath to analyze IgG levels, chemical composition, and microbiome, which was identified by 16S rRNA gene sequencing using the Illumina MiSeq-PE250 platform. The IgG concentrations were similar in fresh and heat-treated colostrum. The fat, protein, and lactose contents also did not differ in these samples. However, somatic cell count (SCC) was lower in heat-treated colostrum than those in fresh colostrum (p<0.05). At the phylum level for the microbiome of fresh colostrum, Proteobacteria (44.16%) was the most abundant taxa, followed by Bacteroidota (33.26%), Firmicutes (10.04%), Actinobacteriota (7.14%), and a marginal difference in the order of abundance was observed in heat-treated colostrum. At the genus level, bacteria belonging to Sphingomonas, Delftia, Ochrobactrum, Simplicispira, and Lactobacillus were more abundant (p<0.05) in the heat-treated colostrum, while the abundance of Acinetobacter in the fresh colostrum was four times more (p<0.05) than that in the heat-treated colostrum. Our results demonstrated that heating does not affect IgG level and colostrum composition but reduces SCC (p<0.05), suggesting that heat-treated colostrum can potentially be put to further use (e.g., feeding Hanwoo calves) without compromising its quality. Differences in the microbiome between the fresh and heat-treated colostrum were limited. Further studies are required to extensively investigate the quality and safety of colostrum collected from dairy farms to ensure better utilization and processing at a local agricultural technology center.

Rapidly changing environmental factors due to climate change are increasing the uncertainty of crop growth, and the importance of crop yield prediction for food security is becoming increasingly evident in Republic of Korea. Traditionally, crop yield prediction models have been developed by using statistical techniques such as regression models and correlation analysis. However, as machine learning technique develops, it is able to predict the crop yield more accurate than the statistical techniques. This study aims at proposing the onion yield prediction framework to accurately predict the onion yield by using various environmental factor data. Temperature, humidity, precipitation, solar radiation, and wind speed are considered as climate factors and irrigation water and nitrogen application rate are considered as soil factors. To improve the performance of the prediction model, ensemble learning technique is applied to the proposed framework. The coefficient of determination of the proposed stacked ensemble framework is 0.96, which is a 24.68% improvement over the coefficient of determination of 0.77 of the existing single machine learning model. This framework can be applied to the particular farmland so that each farm can get their customized prediction model, which is visualized by the web system.