• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.1
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• pp.95-103
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• 2019

Lips have a defect in maintenance of moisture due to their thin layer. As aging progresses, lips lose volume and redness, and become wrinkled. Fat grafting and filler surgery have been used to achieve attractive lips, but little research has been reported to develop better materials to replace the present methods. Recently, a study suggests that the increase of adipocyte number can be enhancing the expansion endogenous fat. In previous study, we identified that the efficacy of Magnolia officinalis bark extract (MOBE) was effective on the induction of adipogenic differentiation. In this study, we confirmed that MOBE enhanced the differentiation of human adipose-derived stem cells on the fat mimic 3D structure built by 3D bioprinting method From further experiments in human, we established a method to quantify the severity of lip wrinkle by measurement of standard deviation of gray value using Image J software. Finally, we found that topical treatment with 1% MOBE formulated lip balm significantly improved the lip wrinkle after using for 12 weeks. In conclusion, these findings suggest that MOBE has great potential, as a cosmetic ingredient, to reduce the lip wrinkle through the effect of promoting adipogenic differentiation.

• Journal of Environmental Science International
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• v.28 no.3
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• pp.341-356
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• 2019

In this study, the impacts of local meteorology caused by tidal changes in the West Sea on ozone distributions in the Seoul Metropolitan Area (SMA) were analyzed using a meteorological model (WRF) and an air quality (CMAQ) model. This study was carried out during the day (1200-1800 LST) between August 3 and 9, 2016. The total area of tidal flats along with the tidal changes was calculated to be approximately $912km^2$, based on data provided by the Environmental Geographic Information Service (EGIS) and the Ministry of Oceans and Fisheries (MOF). Modeling was carried out based on three experiments, and the land cover of the tidal flats for each experiment was designed using the coastal wetlands, water bodies (i.e., high tide), and the barren or sparsely vegetated areas (i.e., low tide). The land cover parameters of the coastal wetlands used in this study were improved in the herbaceous wetland of the WRF using updated albedo, roughness length, and soil heat capacity. The results showed that the land cover variation during high tide caused a decrease in temperature (maximum $4.5^{\circ}C$) and planetary boundary layer (PBL) height (maximum 1200 m), and an increase in humidity (maximum 25%) and wind speed (maximum $1.5ms^{-1}$). These meteorological changes increased the ozone concentration (about 5.0 ppb) in the coastal areas including the tidal flats. The increase in the ozone concentration during high tide may be caused by a weak diffusion to the upper layer due to a decrease in the PBL height. The changes in the meteorological variables and ozone concentration during low tide were lesser than those occurring during high tide. This study suggests that the meteorological variations caused by tidal changes have a meaningful effect on the ozone concentration in the SMA.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.73-85
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• 2021

This study proposes an efficient management direction for Korean construction accident cases through a deep learning-based text data classification model. A deep learning model was developed, which categorizes five categories of construction accidents: fall, electric shock, flying object, collapse, and narrowness, which are representative accident types of KOSHA. After initial model tests, the classification accuracy of fall disasters was relatively high, while other types were classified as fall disasters. Through these results, it was analyzed that 1) specific accident-causing behavior, 2) similar sentence structure, and 3) complex accidents corresponding to multiple types affect the results. Two accuracy improvement experiments were then conducted: 1) reclassification, 2) elimination. As a result, the classification performance improved with 185.7% when eliminating complex accidents. Through this, the multicollinearity of complex accidents, including the contents of multiple accident types, was resolved. In conclusion, this study suggests the necessity to independently manage complex accidents while preparing a system to describe the situation of future accidents in detail.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.11
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• pp.1673-1685
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• 2019

Objective: To evaluate the efficacy of treatments based on gonadotrophin-releasing hormone (GnRH), GnRH-prostaglandin $F2{\alpha}$ ($PGF2{\alpha}$), and/or intense exposure to novel rams to induce fertile estrus without the use of steroid hormones in seasonally anestrous Suffolk ewes. Methods: In the first experiment, ewes were treated with one injection of GnRH, two injections of GnRH administered 7 days apart, or a sequence of GnRH-$PGF2{\alpha}$-GnRH (GPG). In the second experiment anestrous ewes were exposed, for 36 days starting on the day of weaning, to groups of four rams of three different breeds that were alternated every day. Besides exposure to the male effect (ME), the ewes were injected with saline solution (ME group, n = 20), with GnRH (ME-GnRH group, n = 20) or with a sequence of GnRH-$PGF2{\alpha}$-GnRH (ME-GPG group, n = 20). The rams used for male-effect were fitted with aprons to prevent mating, and ewes detected in estrus were bred to selected fertile rams. Ovarian activity was monitored by progesterone determinations in both experiments. Results: In the first experiment sustained induction of ovarian activity was not achieved and no ewe was detected in estrus. In the second experiment induction of sustained ovarian activity was achieved in all groups. Most of the ewes were detected in estrus, 76.7% of the ewes were mated during a 36-d breeding period and 71.7% of all the ewes became pregnant during that period. No significant differences between groups were found for any of these variables. However, estrus detection efficiency was higher in the ME-GnRH group than in the ME group (p<0.05). Conclusion: An intense male-effect, that included the continuous presence and frequent alternation of several rams of different breeds, was sufficient to induce ovarian activity and fertile estrus in Suffolk ewes during the period of deep anestrus without the use of hormones, although addition of GnRH improved the efficiency of estrus detection.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Journal of Ginseng Research
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• v.45 no.1
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• pp.66-74
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• 2021

Background: Abnormal chloride (Cl-) transport has a detrimental impact on mucociliary clearance in both cystic fibrosis (CF) and non-CF chronic rhinosinusitis. Ginseng is a medicinal plant noted to have anti-inflammatory and antimicrobial properties. The present study aims to assess the capability of red ginseng aqueous extract (RGAE) to promote transepithelial Cl- secretion in nasal epithelium. Methods: Primary murine nasal septal epithelial (MNSE) [wild-type (WT) and transgenic CFTR-/-], fisher-rat-thyroid (FRT) cells expressing human WT CFTR, and TMEM16A-expressing human embryonic kidney cultures were utilized for the present experiments. Ciliary beat frequency (CBF) and airway surface liquid (ASL) depth measurements were performed using micro-optical coherence tomography (μOCT). Mechanisms underlying transepithelial Cl- transport were determined using pharmacologic manipulation in Ussing chambers and whole-cell patch clamp analysis. Results: RGAE (at 30㎍/mL of ginsenosides) significantly increased Cl- transport [measured as change in short-circuit current (ΔISC = ㎂/㎠)] when compared with control in WT and CFTR-/- MNSE (WT vs control = 49.8±2.6 vs 0.1+/-0.2, CFTR-/- = 33.5±1.5 vs 0.2±0.3, p < 0.0001). In FRT cells, the CFTR-mediated ΔISC attributed to RGAE was small (6.8 ± 2.5 vs control, 0.03 ± 0.01, p < 0.05). In patch clamp, TMEM16A-mediated currents were markedly improved with co-administration of RGAE and uridine 5-triphosphate (8406.3 +/- 807.7 pA) over uridine 5-triphosphate (3524.1 +/- 292.4 pA) or RGAE alone (465.2 +/- 90.7 pA) (p < 0.0001). ASL and CBF were significantly greater with RGAE (6.2+/-0.3 ㎛ vs control, 3.9+/-0.09 ㎛; 10.4+/-0.3 Hz vs control, 7.3 ± 0.2 Hz; p < 0.0001) in MNSE. Conclusion: RGAE augments ASL depth and CBF by stimulating Cl- secretion through CaCC, which suggests therapeutic potential in both CF and non-CF chronic rhinosinusitis.

• Journal of Korean Neurosurgical Society
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• v.64 no.5
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• pp.693-704
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• 2021

Objective : Endovascular mechanical thrombectomy (MT) has been regarded as one of the standard treatments for acute ischemic stroke caused by large vessel occlusion. Despite the wide use of stent retrievers for MT, arterial intimal damage caused when deployed stent is pulled has been a certain disadvantage. We hypothesized that statin could protect and stabilize vessel damage after endovascular MT using a stent retriever. In this animal study, we observed the protective effects of the statins towards MT-induced vessel wall injury. Methods : Twenty-eight carotid arteries of fourteen rabbits were used in the experiments with MT using stent retriever. We divided the rabbits into four groups as follows : group 1, negative control; group 2, positive control; group 3, statin before MT; and group 4, statin after MT. After MT procedures, we harvested the carotid arteries and performed histomorphological and immunohistochemical analyses. Results : In histomorphological analysis with hematoxylin and eosin and Masson's trichrome stain, significant intimal thickening (p<0.05) was observed in the positive control (group 2), compared to in the negative control (group 1). Intimal thickening was improved in the statin-administered groups (groups 3 and 4 vs. group 2, p<0.05). We also observed that statin administration after MT (group 4) resulted in a more effective decrease in intimal thickness than statin administration before MT (group 3) (p<0.05). We performed immunohistochemical analysis with the antibodies for tumor necrosis factor-alpha (TNF-α), cluster of differentiation (CD)11b, and CD163. In contrast to the negative control (group 1), the stained percentage areas of all immunological markers were markedly increased in the positive control (group 2) (p<0.05). Based on statin administration, the percentage area of TNF-α staining was significantly reduced (p<0.05) in group 3, compared to the positive control group (group 2). However, significant differences were not observed for CD11b and CD163 staining. In group 4, no significant differences were observed for TNF-α, CD11b, and CD163 staining (p≥0.05). The differences in the percentage areas of the different markers between the statin-administered groups (groups 3 and 4) were also not revealed. Conclusion : We presented that statin administration before and after MT exerted protective effects towards vessel wall injury. The efficacy of statins was greater post-administration than pre-administration. Thus, statin administration in routine prescriptions in the peri-procedural period is strongly advised.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.1
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• pp.49-54
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• 2022

The copper plating process used to fabricate the submicron damascene pattern of Cu wiring for Si wafer was applied to the plating of a PCB pattern of several tens of microns in size using the same organic additives and current density conditions. In this case, the non-uniformity of the plating thickness inside the pattern was observed. In order to quantitatively analyze the cause, a numerical calculation considering the solution flow and electric field was carried out. The calculation confirmed that the depletion of Cu²⁺ ions in the solution occurred relatively earlier at the bottom corner than the upper part of the pattern due to the plating of the sidewall and the bottom at the corner of the pattern bottom. The diffusion coefficient of Cu²⁺ ions is 2.65 10^-10 m²/s, which means that Cu²⁺ ions move at 16.3 ㎛ per second on average. In the cases of small damascene patterns, the velocity of Cu²⁺ ions is high enough to supply sufficient ions to the inside of the patterns, while sufficient time is required to replenish the exhausted copper ions in the case of a PCB pattern having a size of several tens of microns. Therefore, it is found that the thickness uniformity can be improved by reducing the current density to supply sufficient copper ions to the target area.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.