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.

본 연구에서는 국내 미기록종인 Euandrolaelaps yamauchii (Ishikawa, 1982)를 채집하여 해당종의 새로운 분포지로서 기록하였다. 또한, Euandrolaelaps yamauchii (Ishikawa)의 완모식표본과 한국산 표본을 함께 비교검경하여 개정된 분류학적 진단정보를 제시하였으며, 검경에 사용 한 현미경사진 및 도판과 국내에 보고된 Euandrolaelaps속에 대한 분류키도 함께 제공하였다.

This research aimed to investigate heterogeneity regarding governance, agricultural productivity, and food security between developed and developing nations. Utilizing a three-stage least squares (3SLS) simultaneous equation model, this study found that governance could positively impact food security through capital accumulation and agricultural productivity in both developed and developing countries. However, the magnitude of these effects differed significantly between country groups, with developed countries showing stronger governance-food security linkages than developing countries. This study reaffirms the importance of governance while showcasing its potential to vary based on a country’s economic level. Additionally, it sheds comprehensive light on impacts of agricultural production and agricultural capital accumulation on food security.

Small and medium-sized manufacturing enterprises(SMEs) have traditionally relied on skilled labor to support multi-variety, small-batch production. However, demographic changes such as low birth rates and aging populations have led to severe labor shortages, prompting increased interest in collaborative robots(cobots) as a viable alternative. Despite this necessity, many SMEs continue to face significant challenges in implementing such technologies due to technical, organizational, and environmental(TOE) constraints. While prior research has mainly focused on technology adoption from the perspective of user organizations, this study adopts a differentiated approach by analyzing adoption factors from the perspective of smart factory experts—specifically, evaluators/mentors and solution providers—who play a critical role in Korea’s policy-driven smart manufacturing environment. Using the Analytic Hierarchy Process(AHP), the study evaluates the relative importance and prioritization of adoption factors across three dimensions: technology, organization, and environment. Survey data collected from 20 smart factory experts indicate that top management support, relative advantage, and safety are key determinants in cobot adoption. Furthermore, the findings reveal that organizational readiness and technical effectiveness have greater influence on implementation decisions than external pressures such as partner pressure. This study provides new insights by incorporating expert perspectives into the adoption framework and offers practical policy and managerial implications to support cobots implementation in the SMEs.

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.