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Can an Invasive Prey Species Induce Morphological and Behavioral Changes in an Endemic Predator? Evidence from a South Korean Snake(Oocatochus rufodorsatus)

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Asian Herpetological Research 2014年第4期5卷 245-254页

作者： Jun-Haeng HEO Heon-Joo LEE Il-Hun KIM Jonathan J.FONG Ja-Kyeong KIM sumin jeong Daesik PARK Department of Biology Kangwon National University Division of Science Education Kangwon National University

Introduction of an invasive prey species into an ecosystem may affect an endemic predator＇s fitness by altering the prey-predator system. Successful adaptation may allow the endemic predator to eat and control the invasive species, while unsuccessful adaptation may result in extinction of the predator. We examine the possible effects of the invasive North American bullfrog （Rana [Lithobates] catesbeiana） on the endemic Red-backed rat snake （Oocatochus rufodorsatus） in South Korea. We do so by comparing the morphology and behavior of adult and hatchling snakes from bullfrog-exposed （Taean） and bullfrog-unexposed （Hongcheon） populations. Among the seven morphological characteristics investigated, relative tail length （tail length/snout-vent length） of both adults and hatchlings from Taean was significantly greater than that of adults and hatchlings from Hongcheon. Also, adult snakes from Taean had a signiifcantly shorter latency of ifrst tongue lfick in response to prey compared to adults from Hongcheon. This difference was not observed in hatchlings. In other snake species, a longer relative tail length and shorter latency of ifrst tongue lfick are known to improve foraging efifciency, and these characters may be adaptations ofO. rufodorsatus to prey on bullfrogs. This study provides preliminary evidence that the presence of an invasive prey species may cause morphological and behavioral changes in an endemic predator.

关键词： invasive prey bullfrog Rana catesbeiana Oocatochus rufodorsatus predator response

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Automatic Examination of Condition of Used Books with YOLO-Based Object Detection Framework

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Computer Systems Science & Engineering 2023年第11期47卷 1611-1632页

作者： sumin Hong Jin-Woo jeong Department of Industrial Engineering Seoul National University of Science and TechnologySeoul01811Korea Department of Data Science Seoul National University of Science and TechnologySeoul01811Korea

As the demand for used books has grown in recent years,various online/offline market platforms have emerged to support the trade in used *** price of used books can depend on various factors,such as the state of preservation(i.e.,condition),the value of possession,and so ***,some online platforms provide a reference document to evaluate the condition of used books,but it is still not trivial for individual sellers to determine the *** lack of a standard quantitative method to assess the condition of the used book would confuse both sellers and consumers,thereby decreasing the user experience of the online secondhand ***,this paper discusses the automatic examination of the condition of used books based on deep learning *** this work,we present a book damage detection system based on various You Only Look Once(YOLO)object detection *** YOLOv5,YOLOR,and YOLOX,we also introduce various training configurations that can be applied to improve ***,a combination of different augmentation strategies including flip,rotation,crop,mosaic,and mixup was used for *** train and validate our system,a book damage dataset composed of a total of 620 book images with 3,989 annotations,containing six types of damages(i.e.,Wear,Spot,Notch,Barcode,Tag,and Ripped),collected from the library books is *** evaluated each model trained with different configurations to figure out their detection accuracy as well as training *** experimental results showed that YOLOX trained with its best training configuration yielded the best performance in terms of detection accuracy,by achieving 60.0%(mAP@.5:.95)and 72.9%(mAP@.5)for book damage ***,YOLOX performed worst in terms of training efficiency,indicating that there is a trade-off between accuracy and *** on the findings from the study,we discuss the feasibility and limitations of our system and future research directi

关键词： Deep learning object detection damage detection book damage

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