Identification of a potential pathway of the exotic black weevil (Coleoptera: Curculionidae) in South Korea

The black weevil, Aclees taiwanensis Kôno (Coleoptera: Curculionidae), is one of the primary pests of fig trees in southeastern Asia and southern Europe. Thought to be of subtropical and tropical Asian origin, including China, the weevil was first found in southern areas of South Korea in July 2020. Subsequently, it was found in the following five cities: Haenam, Hampyeong, Jindo, Sinan, and Tongyeong (RDA 2020). Attempts to trace a possible pathway for the exotic black weevil suggested that this species probably followed pathways of illegal importation of infested plants from Taiwan and was unintentionally introduced into South Korea based on analysis of a Pest Information System (PIS) database, a phylogenetic analysis of mitochondrial cytochrome c oxidase subunit I gene (COI) sequences data, and interviews with fig growers. In addition, this exotic weevil could expand to other regions of South Korea since proper control methods for this weevil pest have not yet been developed and some fig trees are cultivated using eco-friendly farming practices. Therefore, constant monitoring will be required for the invasive alien weevil species which seriously damages the trunk of fig trees

PG-RCNN: Semantic Surface Point Generation for 3D Object Detection

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and occlusion. Two-stage detectors with point cloud completion approaches tackle this problem by adding more points to the regions of interest (RoIs) with a pre-trained network. However, these methods generate dense point clouds of objects for all region proposals, assuming that objects always exist in the RoIs. This leads to the indiscriminate point generation for incorrect proposals as well. Motivated by this, we propose Point Generation R-CNN (PG-RCNN), a novel end-to-end detector that generates semantic surface points of foreground objects for accurate detection. Our method uses a jointly trained RoI point generation module to process the contextual information of RoIs and estimate the complete shape and displacement of foreground objects. For every generated point, PG-RCNN assigns a semantic feature that indicates the estimated foreground probability. Extensive experiments show that the point clouds generated by our method provide geometrically and semantically rich information for refining false positive and misaligned proposals. PG-RCNN achieves competitive performance on the KITTI benchmark, with significantly fewer parameters than state-of-the-art models. The code is available at https://github.com/quotation2520/PG-RCNN.Comment: Accepted by ICCV 202

Estimation of utility weights for human papilloma virus-related health states according to disease severity

Scenarios for the different HPV-related health states. (DOCX 38 kb

Recipients\u27 Perception of Service Quality, Satisfaction and their Behavioral Intention in Home-delivered Meal Program

This study was conducted to evaluate the recipients’ perception of service quality, satisfaction, and behavioral intention in home delivery program. Using structural equation modeling, this paper shows that food quality and volunteer’s attitude stimuli that enhance satisfaction. Results also suggest that satisfaction mediate the relationship between food quality and behavioral intentions. The results are theoretically and practically meaningful because they address the relationships among three types of perceived quality (food, kindness, and responsiveness), satisfaction, and behavioral intentions in the meals-on-wheels program