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.

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.

This study investigated the flowering response of three Korean native Aster species, namely A. hayatae, A. spathulifolius, and A. koraiensis, to varying photoperiods. Three-month-old plants propagated from cuttings were grown under four different photoperiods: 9, 12, 14, and 16 h. Aster hayatae flowered under all conditions, with flowering rates of 92%, 85%, 65%, and 27% under 9-, 12-, 14-, and 16-h photoperiods, respectively. Flowering in A. hayatae was promoted by shorter photoperiods, classifying it as a facultative short-day plant. Aster spathulifolius flowered only under 9- and 12-h photoperiods, with no significant difference between these treatments, suggesting that the species is an obligate short-day plant. However, given the low A. spathulifolius flowering rates of 27% and 13% under 9- and 12-h photoperiods, respectively, further research is required. Aster koraiensis did not flower under any photoperiod, possibly due to vernalization requirements or juvenility. These findings offer valuable insights into the photoperiodic flowering responses of these three Korean native Aster species, enhancing our understanding of their ecological traits and potential horticultural applications.

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

Business model(BM) innovation is widely known as a differentiated strategy and strategic framework for companies to secure a sustainable competitive advantage in an uncertain environment. While prior research has studied new business models in accordance with changes in manufacturing trends such as digitalization and servitization, empirical understanding of the dynamic processes of BM innovation is still lacking. This study addresses this gap by proposing an analytical framework of the BM innovation matrix that classifies companies' BM innovation cases into four types according to the degree of BM change and the influential level of the industry/market outcome through a critical literature review on business models and dynamics. Drawing on this framework, we conduct longitudinal case studies of leading global 3D printing firms to examine the dynamic processes and external environmental factors that shape the evolution of BM innovation. Our findings reveal previously underexplored patterns of co-evolution between firms’ business models and their broader industrial and market environments. This study has the significance of constructing a framework for dynamically analyzing BM innovation based on longitudinal case studies of emerging 3D printing companies. We presented implications for companies seeking successful commercialization of emerging technologies, such as the strategic usefulness of the BM innovation framework and the importance of co-evolution with industrial structure and environmental factors in the process of change.

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.