Honam University

The purpose of this study is to empirically examine government intervention in the venture capital ecosystem, focusing on the case of South Korea. Drawing on prior literature, government policy intervention in venture capital markets is conceptually classified into direct intervention, indirect intervention, and temporal intervention. Direct intervention refers to cases in which the government acts as an active investor and directly participates in investment decision-making, whereas indirect intervention involves the government supplying capital through privately managed venture capital funds while delegating investment selection and management to private actors. Temporal intervention denotes the strategic adjustment of these policy instruments over different stages of market development. Using annual data from the Korea Venture Capital Association and the Korea Fund of Funds covering the period from 2004 to 2022, this study applies time-series econometric analysis to evaluate the effectiveness of indirect government intervention. The empirical results indicate that the current Korean policy regime is best characterized as an indirect intervention model, in which public funds are invested as limited partners in privately managed venture capital funds. The findings show that early-stage venture investment increases with a higher proportion of early-stage–oriented funds and greater use of preferred stock instruments, while an expansion in total venture fund formation is associated with a decline in early-stage investment. Based on these results, the study proposes six policy recommendations aimed at enhancing the effectiveness of indirect government intervention in promoting early-stage venture investment.

Lipoprotein(a) [Lp(a)] has recently regained attention in prognostic research. However, data remain limited for patients without significant coronary artery stenosis confirmed angiographically and those who do not undergo percutaneous coronary intervention (PCI), and studies focusing specifically on patients with coronary artery spasm (CAS) are even more scarce. In this study, a total of 1,373 patients with positive intracoronary provocation testing with acetylcholine (ACH) and insignificant coronary artery stenosis were divided into two groups based on Lp(a) levels: the high Lp(a) group (≥50 mg/dL) and the low Lp(a) group (< 50 mg/dL). The primary endpoint was major adverse cardiovascular events (MACE); secondary endpoints included major adverse cardiovascular and cerebrovascular events (MACCE1) and MACCE1 with recurrent angina (MACCE2). Multiple imputation was followed by inverse probability of treatment weighting (IPTW), and Cox regression analysis was used to analyze 10-year clinical outcomes. There was no significant difference in MACE, MACCE1, or MACCE2 between the two groups. Before IPTW adjustment, the high Lp(a) group had a higher incidence of revascularization (0.9

Fabric-reinforced cementitious matrix (FRCM) systems have been developed as strengthening systems that offer several advantages over fiber-reinforced polymer systems, including improved fire resistance and compatibility with wet surfaces. This paper presents the results of an experimental study on the shear resistance contribution of RC beams externally strengthened with FRCM systems. Shear tests were performed on 12 RC beams. The experimental variables included different types of textile grids (distinguished by impregnation type and surface coating), various strengthening configurations (side-bonding and U-wrapping), and different spacings of internal transverse reinforcement. The results showed that external strengthening with FRCM composites effectively enhanced the shear strength of RC beams. The measured shear resistance contribution varied depending on the textile grid type and was significantly influenced by both the mechanical properties of the textile grid and the amount of internal transverse reinforcement. In particular, the shear strength contribution of the FRCM composite increased with increasing axial stiffness ratio between the external FRCM reinforcement and the internal transverse reinforcement. Finally, a comparison with prediction models from previous studies revealed that the experimentally measured FRCM shear contributions were higher than those predicted theoretically.

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within +/- 1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25-30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18-88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships.

This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 x 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot-frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone, thereby resulting in both model compression and improved inference speed. Additionally, YOLOv5-P, based on PP-PicoDet, was configured as a comparative model to quantitatively evaluate performance. Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance, with an mAP 0.5 of 89.6%, mAP 0.5-0.95 of 66.7%, precision of 91.3%, and recall of 85.6%, while maintaining a lightweight model size of 6.45 MB. In contrast, YOLOv5-P exhibited a smaller model size of 4.03 MB, but showed lower performance with an mAP 0.5 of 70.3%, mAP 0.5-0.95 of 35.2%, precision of 62.3%, and recall of 74.1%. This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.