The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

This study analyzes the aerodynamic and structural characteristics of an H-Darrieus vertical-axis wind turbine (VAWT) under varying inlet velocities using transient analysis. The k-ε turbulence model and six-DOF were applied to simulate urban environments in the flow analysis, while the structural analysis considered blade momentum of inertia and RPM conditions. The numerical results showed that the drag and lift forces increased by 60% and 53% respectively from the nominal wind speed to the cut-off wind speed conditions. Structural analysis indicated that the maximum Von-Mises stress in the blade did not exceed the yield strength of 69 MPa of PC-ABS, ensuring structural stability. However, the connecting rod exceeded the yield strength of SPCC 270 MPa, suggesting potential failure due to repeated rotational loads. This study confirms that materials with a yield strength of more than 1,100 MPa required for connecting rods to ensure reliable operation at high wind speed. These findings provide important insights for the design of robust VAWTs suitable for extreme environments.

This study aims to optimize the SDC (Spinning Dust Collector) system in amphibious assault vehicle engines through numerical analysis of dust and moisture particle separation efficiency using CFD-DPM. Focusing on an axial cyclone structure, the research evaluates separation efficiency across various particle sizes and flow conditions. The results demonstrate that vortices generated by cyclone blades play a critical role in influencing particle trajectories and improving separation performance. Additionally, the study highlights the significant impact of engine flow conditions and housing design, emphasizing that their careful optimization enhances the system's efficiency in separating dust and water. These findings offer valuable insights into optimizing inlet and outlet flow paths and cyclone housing design, providing a solid foundation for advancing SDC system performance in high-efficiency engines.

Despite their historical use, studies on the genetic functions of mushrooms and varietal improvement via biomolecular techniques are limited compared to other organisms. Recent advancements in CRISPR/Cas9 have enabled precise genetic modifications in mushrooms, with RNP-based systems offering high editing efficiency without foreign gene insertion. In this study, we optimized gene-editing conditions for Ganoderma lucidum (Yongji 2) by utilizing RNP/nanoparticle complexes to enhance efficiency. The optimal conditions included a 0.2 M sorbitol buffer (pH 7.0) and a protoplast-to-complex ratio of 10:1. Among eight gRNAs designed for the catA gene, three were identified with high activity, and PEG-mediated transformation resulted in successful gene edits, primarily involving 1 bp deletions. The editing efficiency reached 7–8%, demonstrating that nanoparticle-supported RNP systems are effective for marker-free gene editing in mushrooms. These findings highlight a promising approach for advancing genetic research and varietal improvement in G. lucidum and other mushroom species.

ars using diesel have always had problems with reducing exhaust fumes, and have been studied steadily in this regard. There were studies on the remanufacturing effect of DOC catalyst deactivated by diesel vehicle smoke reduction device, analysis of vehicle fire accident cases caused by damage to diesel vehicle smoke reduction device, and related studies on the remanufacturing effect of diesel vehicle smoke reduction device DPF. This study also developed an optimized system for complete combustion of smoke generated by institutions using diesel engines in low-temperature exhaust gases. The main systems to be developed are high-performance heaters, burner structures that can maintain ignition in exhaust flows, and exhaust flow control units that reduce exhaust gas backflow effects caused by diesel engines.

This paper addresses the maintenance optimization problem in multi-component systems in which parts are connected in series, carrying out several missions interspersed with scheduled finite breaks. Due to limited time or resources, maintenance actions can be only carried out on a limited set of components. The decision maker then has to decide which components to maintain to ensure a pre-specified performance level during next mission. Most of the existing models in the literature usually assume only one system and enough spare parts. However, there are situations in which maintenance is required for multiple systems of the same type. To overcome this restrictive assumption, this study optimizes the maintenance problem considering the lack of repair parts and cannibalism for many identical systems. This study presents two optimization models with different objectives to solve the problem and analyzes the results so that the decision maker can decide. The results of this study are expected to be used for the maintenance of multiple systems of the same type, such as swarm drones.

공간 샘플링은 공간모델링 연구에 활용되어 샘플링 비용을 줄이면서 모델링의 효율성을 높이는 역할을 한다. 농업분야에서는 기후변화 영향을 예측하고 평가하기 위한 고해상도 공간자료 기반 모델링에 대한 연구 수요가 빠르게 증가하고 있으며, 이에 따라 공간 샘플링의 필요성과 중요성이 증가하고 있다. 본 연구는 국내 농지 공간샘플링 연구를 통해 농업분야 기후변화연구의 공간자료 활용의 효율성을 제고하고자 하였다. 본 연구는 층화랜덤샘플링 을 기반으로 하였으며, 1 km 해상도의 농지 공간격자자료 모집단 (11,386개 격자)에 대해서 RCP 시나리오별 (RCP 4.5/8.5) 연대별 (2030/2050/2080년대) 공간샘플링을 설 계하였다. 국내 농지는 기상 및 토양 특성에 따라 계층화 되었으며, 샘플링 효율 극대화를 위해 최적 층화 및 샘플 배정 최적화를 수행하였다. 최적화는 작물수량, 온실가스 배출량, 해충 분포 확률을 포함하는 16개 목표 변수에 대해 주어진 정밀도 제한 내에서 샘플 수를 최소화하는 방향으로 진행되었다. 샘플링의 정밀도와 정확도 평가는 각각 변동계수 (CV)와 상대적 편향을 기반으로 하였다. 국내 농지 공간격자 모집단 계층화 및 샘플 배정 및 샘플 수 최적화 결과, 전체 농지는 5~21개 계층, 46~69개 샘플 수 수준에서 최적화되었다. 본 연구결과물들은 국내 농업시스템 대표 공간격자로써 널리 활용될 수 있을 것으로 기대된다. 또한, 기후변화 영향예측 공간모델링 연구들에 활용되어 샘플링 비용 및 계산 시간을 줄이면서도 모델의 효율성을 높이는 데에 기여할 수 있다.