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Purpose This study aimed to investigate the incidence of febrile seizures (FS) associated with coronavirus disease 2019 (COVID-19) in children and examine the variation in these incidences after the Omicron surge. Methods The number of confirmed COVID-19 cases aged below 5 years residing in the Jeonbuk province from January 2020 to June 2022 was obtained from official data released by the Korean government. During the same period, data regarding FS patients with COVID-19 were obtained from all local hospitals capable of FS treatment in Jeonbuk. The data were analyzed retrospectively. Results The number of children under 5 years of age in Jeonbuk was 62,772, of which 33,457 (53.2%) were diagnosed with COVID-19 during the study period. Of these, 476 patients (1.4%) required hospitalization and 64 (0.19%, 44 boys: 20 girls) developed FS. Until 2021, before the Omicron surge, 23.4% of the patients (89 of 381) required hospitalization, but no children with COVID-19 were hospitalized for FS. However, after the Omicron surge in 2022, 16.5% of hospitalized children (64 of 387) experienced FS, despite the decline in hospitalization rates among COVID-19 patients (1.2%). Twenty-five patients (39.1%) had complex FS, and one (1.6%) presented with febrile status epilepticus. Forty-two patients (65.6%) experienced first-time FS, with an average of 1.5 convulsive events. Conclusions During the COVID-19 pandemic, the incidence of FS was approximately 0.19%; however, after the emergence of the Omicron variant, FS occurred more frequently and became more complex.

Deep neural networks (DNNs) have been applied to various fields and achieved high performances. However, they require significant computing resources because of their numerous parameters, even though some of those parameters are redundant and do not contribute to the DNN performance. Recently, to address this problem, many knowledge distillation-based methods have been proposed to compress a large DNN model into a small model. In this paper, we propose a novel knowledge distillation method that can compress a vehicle maker classification system based on a cascaded convolutional neural network (CNN) into a single CNN structure. The system uses mask regions with CNN features (Mask R-CNN) as a preprocessor for the vehicle region detection and has a structure to be used in conjunction with a CNN classifier. By the preprocessor, the classifier can receive the background-removed vehicle image, which allows the classifier to have more attention to the vehicle region. With this cascaded structure, the system can classify the vehicle makers at about 91% performance. Most of all, when we compress the system into a single CNN structure through the proposed knowledge distillation method, it demonstrates about 89% accuracy, in which only about 2% of the accuracy is lost. Our experimental results show that the proposed method is superior to the conventional knowledge distillation method in terms of performance transfer.

Higher quantities of arsenic (As) in Sargassum fusiforme limit its use as a food ingredient. The present study aimed to reduce As in S. fusiforme using sequential processing involving hot water, citric acid, and fermentation. The As content in S. fusiforme of 76.18 mg/kg was reduced to 30.47 mg/kg and 24.45 mg/kg using hot water and citric acid processing, respectively. However, the As content in S. fusiforme was reduced to 9.09 mg/kg by sequential processing with hot water and citric acid. Using response surface methodology, optimal processing conditions for S. fusiforme were determined to be treatment with hot water at 60 °C for 120 min followed by treatment with 0.4% citric acid. To further reduce the As content, the processed S. fusiforme was fermented by Lactobacillus rhamnosus, and the As content was further reduced to 1.64 mg/kg. In addition, the levels of organic acids and amino acids in S. fusiforme pre- and post-fermentation were significantly altered. These results indicated that the As content in S. fusiforme could be effectively reduced using the sequential processing with hot water, citric acid, and L. rhamnosus fermentation, and the organic acid and amino acid levels were significantly altered by L. rhamnosus fermentation.

This paper proposes a novel real-time pose estimation system on embedded devices for a driver and a front passenger. The main goal of the proposed system is to operate in real time with limited hardware resources while preserving the high accuracy. The proposed system is divided into an object detection and a pose estimation. In the object detection, we eliminate the redundant and inaccurate bounding boxes by considering the characteristics of the target image domain. In the pose estimation, a single-person pose estimation with a lightweight deep learning model has been proposed and knowledge distillation has been adopted to maximize the performance while maintaining the high speed. In the experimental results, the proposed pose estimation has up to 9 % of the accuracy and the 9 times less computation compared to the previous methods. The operation speed is 195 frame per second on NVIDIA Jetson TX2.

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.

In this paper, we propose a novel system to generate jaywalking images. To synthesize a pedestrian on the road and label the binary case such as jaywalk or normal-walk, the pre-trained Convolutional Neural Network (CNN) is used to segment the drivable area from the large-scale dataset. The proposed system automatically generates a jaywalker based on existing pedestrian objects in the image. The proposed system performs three main steps. First, we train the existing network with both black box image dataset and object dataset to segment road areas and pedestrians. Second, the generator synthesizes jaywalkers randomly within the road segmentation masks. Third, a CNN classifier is trained using the generated synthetic dataset and performs the inference from natural jaywalking images. The experiment results show that the jaywalking classifier trained with both generated synthetic dataset and the untouched natural dataset has a high accuracy of 0.96, which is 0.08 higher than the accuracy using only the untouched natural dataset on the same model.

The skin is the largest organ of the body. As it is the first line of defense from the external environment, it is the most vulnerable organ to injury. In 2016, there were 500 000 burn patients, and they incurred astronomical medical costs. Researchers have studied a wide range of treatments for skin injuries, including wound dressing, skin tissue engineering, and cell sheets. However, there are limitations to these treatments. They cannot regenerate the full thickness of the skin or heal extensive burn wounds. Many researchers are working towards developing skin bioprinting, which is a promising technology that can potentially be applied to overcome the limitations of current burn treatments. One of the key advantages of this technology is that it can be used to produce biomimetic artificial skin with multiple types of skin cells. Hence, various studies have been conducted using bioprinting technology to generate advanced biomimetic and functional skins containing vasculature, pigmentation, sweat glands and hair follicles. The resulting skin substitutes are expected to have a range of applications including cosmetics, skin disease modeling and drug development. In this chapter, we will review progress in bioprinting technology relating to manufacturing artificial skins.

An increase in EGR rate can reduce NOx emissions. On the other hand, an excessive EGR rate is a major cause of incomplete combustion. Precise position control of the EGR valve that is optimized for the operating conditions of the engine should be supported to meet the strict emission regulations. Accordingly, this study performed mathematical modeling and control theory for characteristic analysis of an EGR valve motor. Because it is a step for controlling the position of the EGR valve motor using the Microchip development tool, this study analyzed the characteristics of the motor for each opening and closing section according to the input value of the duty ratio of PWM (Pulse Width Modulation).

Oil spills from disasters such as the sinking of ships and the discharge of oily wastes cause serious environmental problems. Polydimethylsiloxane(PDMS) sponges are valuable tools for isolating spilled oil. Here, we propose new PDMS sponges with bio-inspired design and enhanced absorption capacities. 3D printing was used to produce templates having negative designs, and after being filled with PDMS, the templates were selectively dissolved. Through this, PDMS sponges with well-interconnected and controlled porosities were produced within 10% error. The wettability of sponges with various pore sizes and line widths was investigated. The surfaces were found to be highly hydrophobic, with water contact angles of 100-143°, and oleophilic, with oil contact angles of ∼0°. The sponge fabricated with line width of 200 μm and pore size of 400 μm showed the highest hydrophobicity and oleophilicity. These parameters were used to produce the surfaces of hollow sponges having bio-inspired design that mimics the water absorption and storage functions of cactus. Repeated oil-water separation testing was conducted, and the absorption capacities were compared with those of non-hollow and conventional sponges. The new design showed absorption capacity up to 3.7 times that of the sponges. The bio-inspired PDMS sponge provides a significant advance in oil–water separation ability.

Recently, novice driver or weak drivers was difficult to understand the movement characteristics of the carand are immature sense of width and length of the car according to various each driver’s sex and age, model. Tocomplement this problem, the use of rear sensor and the camera is increased. And the parking assistance system thatimproves the convenience of parking the driver is being developed. Accordingly, parking guide system is needed toreflect the difference in the steering angle and correct the error distance.In this study, it is proposed that the turning radius during backward by complementing the existing Ackerman Jentaudtype. And it develops more accurate parking guideline to be able to generat algorithm by applying the formula to propose a steering wheel angle sensor value derived through the handle. Key Words : Ackerman jeantaud type, Calibration, CAN, Parking system * Corresponding Author : Seon-Bong Lee(Keimyung Univ.)Tel: +82-10-5180-5067 email: seonbong@kmu.ac.krReceived January 20, 2015Accepted May 7, 2015Revised February 13, 2015Published May 31, 2015

Purpose: To use fluorophotometry to measure corneal epithelial barrier function after excimer laser photorefractive keratectomy (PRK). Setting: Seoul National University Hospital, Seoul, Korea. Methods: Twenty-five eyes of 21 patients (13 women, 8 men) had PRK to correct myopia. Corneal epithelial healing time was measured and corneal epithelial permeability to sodium fluorescein evaluated by fluorophotometry 1, 2, and 3 weeks after surgery. Results: Epithelial permeability showed a statistically significant increase 1 week after surgery and returned to its preoperative level 1 week later. Comparative studies according to epithelial healing day and corrected diopter showed results that were not statistically significant (P & .05). Conclusion: These results suggest that PRK delays complete reconstruction of corneal epithelial barrier function. In humans, the corneal epithelium regained its normal barrier function 2 weeks after PRK. Thus, at least during these weeks, care should be taken to minimize further epithelial trauma.

The purposes of this study are to elucidate the retinal changes of heat shock protein 70.1 (hsp70.1) knockout mice and to compare them between in normal and in retinal degeneration (rd) mice. Eyes of hsp70.1 wild type (+/+) and knockout (-/-) mice in the C57BL/6 or FVB genetic backgrounds respectively, which were reared in the normal environment, were examined by fundus photography, electroretinography, light microscopy, terminal dUTP nick-end labeling (TUNEL) stain, and immunohistochemistry. In C57BL/6 mice, fundus photography showed no changes between hsp70.1+/+ and -/- mice at 1 and 6 months of age. Electroretinographic examination showed a tendency of decreased amplitude of a- and b-wave with aging in both genotype, but there were not different statistically. The ratios of the thickness of inner nuclear and outer nuclear layer to the retinal thickness were respectively decreased with aging in both genotype, but there were not different statstically. TUNEL assay showed a few positively labeled cells in the ganglion cell, inner nuclear and outer nuclear layers and the immunohistochemistry showed no immunopositivity of hsp70 in the inner segments of photoreceptor cell layer in both genotype. In rd mice, fundus photography showed a narrowing of the retinal vessels at the age of 4 weeks, however, there were no differences of retinal changes including pigment epithelial layer in both genotype. Electroretinographic examination at the postnatal 2, 3 and 4 weeks showed no differences between them. Loss of photoreceptor cell and outer nuclear layers showed no differences in both genotype. In conclusion, there were no differences of the retinal changes at least under the normal environmental condition in hsp70.1+/+ and -/- mice. These results show that hsp70.1-/- mice can be used to study the role of hsp70.1 to the external stress to the retina.