Conservation Activities on Korean Rare and Endemic Plants -with a Special Reference to the Korea National Arboretum

Symposium Pape

Structural dynamics and divergence of the polygalacturonase gene family in land plants

A distinct feature of eukaryotic genomes is the presence of gene families. The polygalacturonase (PG) (EC3.2.1.15) gene family is one of the largest gene families in plants. PG is a pectin-digesting enzyme with a glycoside hydrolase 28 domain. It is involved in numerous plant developmental processes. The evolutionary processes accounting for the functional divergence and the specialized functions of PGs in land plants are unclear. Here, phylogenetic and gene structure analysis of PG genes in algae and land plants revealed that land plant PG genes resulted from differential intron gain and loss, with the latter event predominating. PG genes in land plants contained 15 homologous intron blocks and 13 novel intron blocks. Intron position and phase were not conserved between PGs of algae and land plants but conserved among PG genes of land plants from moss to vascular plants, indicating that the current introns in the PGs in land plants appeared after the split between unicellular algae and multicelluar land plants. These findings demonstrate that the functional divergence and differentiation of PGs in land plants is attributable to intronic loss. Moreover, they underscore the importance of intron gain and loss in genomic adaptation to selective pressure

On the Estimation of the Mission Performance Index of Unmanned Surface Vehicles Based on the Mission Coverage Area

For mission planning and replanning of multiple unmanned surface vehicles (USVs), it is important to estimate each USV’s mission performance in terms of sea surveillance (e.g., illegal ship control). In this study, a mission performance index (MPI) is proposed based on the mission coverage area for estimating the USVs’ mission performance of illegal ship control. The penalty value is considered in the MPI calculation procedure owing to the track-off of the USV. In addition, the USV simulation is conducted under illegal ship control, and the MPI is calculated based on changing the mission coverage area. The results show that the MPI increases with the path width of the mission coverage area

Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning

Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owing to severe occlusion, wide pose variation, and even scale variation. This makes pest counting more challenging. To address these issues, this study proposes a new pest counting model referred to as multiscale and deformable attention CenterNet (Mada-CenterNet) for internal low-resolution (LR) and high-resolution (HR) joint feature learning. Compared with the conventional CenterNet, the proposed Mada-CenterNet adopts a multiscale heatmap generation approach in a two-step fashion to predict LR and HR heatmaps adaptively learned to scale variations, that is, changes in the number of pests. In addition, to overcome the pose and occlusion problems, a new between-hourglass skip connection based on deformable and multiscale attention is designed to ensure internal LR and HR joint feature learning and incorporate geometric deformation, thereby resulting in an improved pest counting accuracy. Through experiments, the proposed Mada-CenterNet is verified to generate the HR heatmap more accurately and improve pest counting accuracy owing to multiscale heatmap generation, joint internal feature learning, and deformable and multiscale attention. In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale. The experimental results show that the proposed model outperforms state-of-the-art crowd counting and object detection models

Three Qualities of OTT Services: A Mixed Methods Approach

Since over-the-top (OTT) services emerged as a new way of consuming video contents, OTT markets have grown exponentially and the competition among the OTT services has been intensified. Nonetheless, only limited scholarly attention has been paid to identifying user’s motivation to use OTT service. Therefore, we employ a developmental sequential mixed methods approach to identify quality factors and to examine their effect on post-subscription experiences and continuance intention. In a qualitative study, we derived six factors that are important to users’ intention to continue to use the subscription. Based on the identified factors, we develop a research model with three groups of qualities modified from IS success model. The proposed research model will be validated with data collected through a quantitative research with a survey to OTT service users