Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

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Recently, Kim, Levine, and Allen have successfully demonstrated that the intertwined model of psychological reactance is applicable for message features other than freedom threat (i.e., personal insult, poor argument). The supporting evidence was obtained where resistance prevailed. The current study further extends the utility of the intertwined model by replicating Kim et al.'s experiment in a content domain where persuasive boomerang was observable. Consistent with Kim et al.'s findings, results indicate that both poor argument and personal insult produced negative thoughts and anger in an intertwined manner as freedom threat does. The factor structure of reactance remained similar whether the message produced resistance (i.e., freedom threat, poor argument) or persuasive boomerang (i.e., personal insult). Anger constituted a more powerful sub-construct of reactance than negative cognition across conditions.

Diverse pheromones and pheromone-based traps, as well as images acquired from insects captured by pheromone-based traps, have been studied and developed to monitor the presence and abundance of pests and to protect plants. The purpose of this study is to construct models that detect three species of pest moths in pheromone trap images using deep learning object detection methods and compare their speed and accuracy. Moth images in pheromone traps were collected for training and evaluation of deep learning detectors. Collected images were then subjected to a labeling process that defines the ground truths of target objects for their box locations and classes. Because there were a few negative objects in the dataset, non-target insects were labeled as unknown class and images of non-target insects were added to the dataset. Moreover, data augmentation methods were applied to the training process, and parameters of detectors that were pre-trained with the COCO dataset were used as initial parameter values. Seven detectors—Faster R-CNN ResNet 101, Faster R-CNN ResNet 50, Faster R-CNN Inception v.2, R-FCN ResNet 101, Retinanet ResNet 50, Retinanet Mobile v.2, and SSD Inception v.2 were trained and evaluated. Faster R-CNN ResNet 101 detector exhibited the highest accuracy (mAP as 90.25), and seven different detector types showed different accuracy and speed. Furthermore, when unexpected insects were included in the collected images, a four-class detector with an unknown class (non-target insect) showed lower detection error than a three-class detector.

It is unknown whether the presence of metabolic syndrome (MetS) affects the incidence of laryngeal cancer. The aim of this national population-based retrospective study was to analyze the relationship between MetS and the incidence of laryngeal cancer. Patients with laryngeal cancer (ICD-10: C32) between 2009 and 2010 were retrospectively identified and tracked until 2015 using the Korean Health Insurance claims database. During the seven-year follow-up period, 5,322 subjects were newly diagnosed with larynx cancer. The mean age of people with laryngeal cancer was much higher than those without (63.29 vs. 47.7 years, p < 0.0001), and the incidence of larynx cancer in men was much higher than that in women (93.16% vs. 6.84%, p < 0.0001). Age, gender, smoking status, alcohol intake, and exercise-adjusted hazard ratios indicated that participants with MetS had a 1.13-fold higher hazard of having larynx cancer than those without MetS. The number of MetS components was a strong risk factor for laryngeal cancer with a higher risk estimate of this cancer in both ex- and current smokers as well as people who have never smoked. MetS was found to be an independent risk factor for the incidence of laryngeal cancer. In Korea, MetS and its components are significantly associated with the development of laryngeal cancer.