In this study, we analyzed the structural and mechanical properties of aluminum foams fabricated using aluminum powders of varying sizes and mixtures. The effects of sintering and pore structure at each size on the integrity and mechanical properties of the foams were investigated. Structural characteristics were examined using scanning electron microscopy and micro–computed tomography, while mechanical properties were evaluated through compression testing. The experimental results demonstrated that smaller powder sizes improved foam integrity, reduced porosity and pore size, and resulted in thinner cell walls. In combination, these effects increased compressive strength as the powder size decreased. The findings of this study contribute to the understanding and improvement of the mechanical properties of aluminum foams and highlight their potential for use in a wide range of applications.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

Italian ryegrass (Lolium multiflorum Lam., IRG) is a widely cultivated winter forage crop known for its high yield and nutritional value. This study evaluated the processing characteristics and feeding performance of IRG-based pellets in Hanwoo cattle (Bos taurus coreanae) and Korean native black goats (Capra hircus). IRG was harvested at the optimal growth stage and processed into two pellet formulations: IRG ≥80% (with up to 20% soybean meal) and 100% IRG. Feeding trials were conducted under ad libitum feeding conditions. Hanwoo cattle showed higher intake of 100% IRG pellets (7.9 kg/day/head) than IRG ≥80% pellets (7.5 kg/day/head, p<0.05), with similar average daily gain (0.9 ± 0.4 kg/day/head). Conversely, black goats exhibited significantly lower intake of IRG ≥80% pellets (54.6 g/day/head) compared to 100% IRG pellets (266 g/day/head), likely due to reduced palatability associated with soybean meal inclusion. These findings suggest that IRG pellets are suitable for Hanwoo cattle, while further optimization of pellet size and formulation is required to improve acceptance in goats. Future studies should assess long-term impacts on digestion, rumen fermentation, and metabolic responses.

This study was conducted from 2022 to 2024 at the Grassland and Forage Crops Division, National Institute of Animal Science (RDA), in Cheonan, Korea, to develop a medium-maturing variety of Italian ryegrass (Lolium multiflorum Lam.). The newly developed tetraploid cultivar, named ‘Spider’, is characterized by its green leaves, semi-erect growth habit in late autumn, and erect growth habit in mid-spring. With a heading date of May 16, ‘Spider’ is classified as a medium-maturing variety. Compared to the control cultivar ‘Kowinmaster’, ‘Spider’ has a 1.0 mm wider leaf blade, a 1.6 cm longer leaf blade, and is 5 cm taller in plant height. Its dry matter yield (10,169 kg/ha) is significantly higher than that of ‘Kowinmaster’ (p<0.05). The crude protein content of ‘Spider’ is 10.4%, which is 0.2% higher than that of the control. Additionally, ‘Spider’ has a neutral detergent fiber (NDF) content of 49.5% and an acid detergent fiber (ADF) content of 26.6%, showing a 2.2% lower NDF and a 0.2% higher ADF compared to ‘Kowinearly’.

The commercial feed additive, native rumen microbes (RC), derived from a diverse microbial community isolated from the rumen of Hanwoo steers is being explored to enhance rumen fermentation and improve ruminant feed utilization. This study evaluated the impact of native rumen microbes supplementation on methane emissions, microbial diversity, and fermentation efficiency on in vitro assessment. Treatments were as follows: CON (basal diet, without RC); T1 (basal diet + 0.1% RC); T2 (basal diet + 0.2% RC). Rumen fermentation parameters, total gas, and methane production were assessed at 12, 24, and 48 h of incubations. The in vitro gas production was carried out using the Ankom RF Gas Production System. Supplementation of RC significantly reduced the total gas production at 12, 24, and 48 hours of incubation (p < 0.05). Volatile fatty acid concentrations were increased, while acetate and propionate were decreased (p < 0.05) at 48 h by the supplementation of RC. Notably, the 0.1% inclusion level of RC significantly reduced methane production by 28.30% and 21.21% at 12 and 24 hours. Furthermore, microbial diversity analysis revealed significant shifts (p < 0.05) in bacterial composition between the control and treatment groups, while supplementation also promoted the growth of bacterial populations, such as Succiniclasticum. These findings suggest that native rumen microbes supplementation, particularly at 0.1% inclusion level, can enhance rumen microbial composition while significantly reducing methane production in vitro.

Background: Heat stress during summer impairs reproductive performance in sows, causing summer infertility. Cytochrome P450 1A2 (CYP1A2) plays a crucial role in steroid hormone metabolism in the liver and has been shown to be upregulated under stress conditions. However, the effect of seasonal heat stress on CYP1A2 expression in sow ovaries remains poorly understood. Methods: Ovaries were collected from crossbred sows during different seasons in Korea. The control group (CON) was exposed to optimal temperatures for swine rearing (22.0-24.5℃) during spring, while the heat-stressed group (HS) experienced high temperatures (30.5-37.0℃) during summer. Temperature-humidity index (THI) was calculated to assess thermal stress severity. CYP1A2 expression was analyzed using Western blotting and immunohistochemistry (IHC), and these values were statistically compared with THI by Pearson’s correlation analysis. Results: The HS exhibited significantly higher THI values compared to the CON (88.11 vs. 66.67). Western blot analysis revealed significant upregulation of CYP1A2 expression in the HS compared to CON (1.89 vs. 1.00). IHC demonstrated that CYP1A2 was specifically localized in granulosa cells of mature follicles, with a significantly higher proportion of CYP1A2-positive follicles in the HS (15.92%) compared to CON (9.08%). When the obtained values were compared with THI values, CYP1A2 expression showed a strong positive correlation with THI (r = 0.41 and 0.74). Conclusions: This study revealed that heat stress can alter the ovarian microenvironment, including the expression of CYP1A2. These findings emphasize the need for effective thermal management strategies to mitigate heat stress-induced reproductive dysfunction in livestock production.

본 연구에서는 광체꽃벌속의 미기록종 Ceratina iwatai Yasumatsu, 1936을 한국에서 처음으로 보고한다. 본 종의 기재, 분포정보, 그리고 삽화를 제공한다.

The casting manufacturing process of aluminum automotive wheels often involves processing various wheel models during stages such as flow forming, machining, packaging, and delivery. Traditionally, separate equipment or production lines were required for each model, which led to higher facility investment costs and increased labor costs for classification. However, the implementation of machine learning-based model classification technology has made it possible to automatically and accurately distinguish between different wheel models, resulting in significant cost savings and enhanced production efficiency. Additionally, this approach helps prevent product mix-ups during the final inspection process and allows for the quick and precise identification of wheel models during packaging and delivery, reducing shipping errors and improving customer satisfaction. Despite these benefits, the high cost of machine learning equipment presents a challenge for small and medium-sized enterprises(SMEs) to adopt such technologies. Therefore, this paper analyzes the characteristics of existing machine learning architectures applicable to the automotive wheel manufacturing process and proposes a custom CNN(Convolutional Neural Network) that can be used efficiently and cost-effectively.