• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.34 no.3
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• pp.27-54
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• 2021

Objectives : This review was conducted to validate the effectiveness and safety of herbal medicine combined with conventional therapy for rosacea. Methods : Randomized controlled trials(RCTs) reporting the effects of herbal medicine treatment on rosacea were searched through eight electronic databases from 2016 to March 17, 2020. This study collection and data extraction were performed by two independent reviews. The Cochrane risk-of-bias tool was used for the evaluation of the risk of bias in all included RCTs. Mean differences(MD) and Risk ratio(RR) of 95% Confidence intervals(Cls) were calculated and data synthesis was conducted using Review Manager(RevMan, ver.5.4) Results : Eighteen RCTs were included and all trials compared the combined therapy of herbal medicine with conventional western therapy to conventional therapy alone. The effective rate of the combination of herbal medicine with western medicine(RR 1.20, 95% CI : 1.13-1.28, p<0.00001, I²=0%), the effective rate of the combination of herbal medicine with laser-based therapy(RR 1.12, 95% CI : 1.04-1.21, p=0.004, I²=18%) and the effective rate of the combination treatment group using herbal medicine, western medicine and external drugs were all statistically higher that of the control group(RR 1.19, 95% CI : 1.11-1.28, p<0.00001, I²=0%). The score of non transient erythema(MD -0.36, 95% CI : -1.01 0.29, p=0.27, I²=93%), flushing(MD -0.69, 95% CI : -0.97, 0.41, p<0.00001, I²=32%), papules or pustules(MD 0.10, 95% CI : -0.15, 0.35 p=0.44, I²=0%) were also seen in the herbal medicine and western medicine combination group. The overall risk of bias of the included studies was some concerns. No serious adverse effects were observed. Conclusions : This review found the safety and effectiveness of the combined therapy of herbal medicine with conventional western therapy for rosacea.

• Journal of Nutrition and Health
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• v.54 no.1
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• pp.76-94
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• 2021

Purpose: This study was implemented to develop and validate a semi-quantitative food frequency questionnaire (SQ-FFQ) to assess the dietary intake of married migrant women in multicultural families. Methods: The SQ-FFQ consisting of 41 food items was selected based on the information of frequently consumed foods from the preliminary studies on dietary intake survey for married migrant women or a Korean Health and Nutrition Examination survey. Thirty-five subjects who visited the Multicultural Family Support Center in Seoul and Daejeon area completed their three-day diet records and SQ-FFQ 2 times. The reproducibility and relative validity of SQ-FFQ were assessed by comparing the 1st and 2nd SQ-FFQ and by comparison with the three-day diet records, respectively. Results: The reliability of SQ-FFQ, which was examined 1-2 weeks apart, showed no significant difference in the energy and macronutrient intake (carbohydrate, fat, protein), while the average intakes of vitamin A, riboflavin, calcium, and potassium were slightly higher in the 2nd than 1st SQ-FFQ. All correlation coefficients for the reliability for nutrients were statistically significant, ranging from 0.453 to 0.885. The mean intakes of energy, carbohydrate, vitamin C, thiamin, folate, vitamin B12, calcium, and sodium obtained from the SQ-FFQ were greater than those of the 3-day dietary records. The Spearman's correlation coefficient between the 2 methods was the highest for plant fat (r = 0.602; p < 0.001) and relatively higher for energy, carbohydrate, fat, and thiamin ranging from 0.512 to 0.599; and protein, riboflavin, niacin, folate ranging from 0.335 to 0.499, while no significant correlation was observed for vitamin A, C, calcium, iron, and sodium. Conclusion: The developed SQ-FFQ for married migrant women in multicultural families in this study appears to be useful for estimating the nutritional status, particularly energy, carbohydrates, fat, and vitamin B group.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.77-89
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• 2021

In this study, a method has been proposed to improve the performance of hydraulic property estimation model developed by Jeong et al. (2020). In their study, low-dimensional features of the annual groundwater level (GWL) fluctuation patterns extracted based on a Denoising autoencoder (DAE) was used to develop a regression model for predicting hydraulic properties of an aquifer. However, low-dimensional features of the DAE are highly dependent on the precipitation pattern even if the GWL is monitored at the same location, causing uncertainty in hydraulic property estimation of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed based on a conditional variational autoencoder (CVAE). The CVAE trains a statistical relationship between GWL fluctuation and precipitation pattern. The actual GWL and precipitation data monitored on a total of 71 monitoring stations over 10 years in South Korea was applied to validate the effect of using CVAE. As a result, the trained CVAE model reasonably generated GWL fluctuation pattern with the conditioning of various precipitation patterns for all the monitoring locations. Based on the trained CVAE model, the low-dimensional features of the GWL fluctuation pattern without interference of different precipitation patterns were extracted for all monitoring stations, and they were compared to the features extracted based on the DAE. Consequently, it can be confirmed that the statistical consistency of the features extracted using CVAE is improved compared to DAE. Thus, we conclude that the proposed method may be useful in extracting a more accurate feature of GWL fluctuation pattern affected solely by hydraulic characteristics of the aquifer, which would be followed by the improved performance of the previously developed regression model.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.515-530
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• 2021

This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R²) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R² was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R² was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R² of 0.69 and RMSE of 2.88%.

• Knowledge Management Research
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• v.22 no.2
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• pp.119-144
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• 2021

Recently, as competition in the market evolves from the competition among companies to the competition among their supply chains, companies are struggling to enhance their supply chain management (hereinafter SCM). In particular, as blockchain technology with various technical advantages is combined with SCM, a lot of domestic manufacturing and distribution companies are considering the adoption of blockchain-oriented SCM (BOSCM) services today. Thus, it is an important academic topic to examine the factors affecting the use of blockchain-oriented SCM. However, most prior studies on blockchain and SCMs have designed their research models based on Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT), which are suitable for explaining individual's acceptance of information technology rather than companies'. Under this background, this study presents a novel model of blockchain-oriented SCM acceptance model based on the Technology-Organization-Environment (TOE) framework to consider companies as the unit of analysis. In addition, Value-based Adoption Model (VAM) is applied to the research model in order to consider the benefits and the sacrifices caused by a new information system comprehensively. To validate the proposed research model, a survey of 126 companies were collected. Among them, by applying PLS-SEM (Partial Least Squares Structural Equation Modeling) with data of 122 companies, the research model was verified. As a result, 'business innovation', 'tracking and tracing', 'security enhancement' and 'cost' from technology viewpoint are found to significantly affect 'perceived value', which in turn affects 'intention to use blockchain-oriented SCM'. Also, 'organization readiness' is found to affect 'intention to use' with statistical significance. However, it is found that 'complexity' and 'regulation environment' have little impact on 'perceived value' and 'intention to use', respectively. It is expected that the findings of this study contribute to preparing practical and policy alternatives for facilitating blockchain-oriented SCM adoption in Korean firms.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of Food Hygiene and Safety
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• v.35 no.6
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• pp.544-551
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• 2020

The aim of this study was to develop and validate a simultaneous analytical method for berberine and palmatine, which are representative substances of Phellodendron amurense, and to evaluate the antioxidant activity. We evaluated the specificity, linearity, precision, accuracy, limit of detection (LOD), and limit of quantification (LOQ) of analytical methods for berberine and palmatine using high-performance liquid chromatography. Our result showed that the correlation coefficients of the calibration curve for berberine and palmatine exhibited 0.9999. The LODs for berberine and palmatine were 0.32 to 0.35 µg/mL and the LOQs were 0.97 to 1.06 µg/mL, respectively. The inter-day and intra-day precision values for berberine and palmatine were from 0.12 to 1.93 and 0.19 to 2.89%, respectively. The inter-day and intra-day accuracies were 98.43-101.45% and 92.39-100.60%, respectively. In addition, the simultaneous analytical method was validated for the detection of berberine and palmatine. Moreover, we conducted FRAP and NaNO2 scavenging activity assays to measure the antioxidant activities of berberine and palmatine, and both showed antioxidant activity. These results suggest that P.amurense could be a potential natural resource for antioxidant activity and that the efficacy can be confirmed by investigating the content of the berberine and palmatine.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.231-241
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• 2021

This study intended to present strategies and implications for attracting new students and customized education to university officials through research on the participation of workers' academic aspirations in university education. Thus, variables were derived by analyzing prior data, and causal settings between variables and questionnaires were developed. Subject to the survey, 331 workers interested in participating in university education were collected through interpersonal interviews. The collected data were dataized, and reliability and feasibility verification and frequency analysis were conducted. Finally, we validate the fit of the structural equation model and the causal relationship for each concept. Therefore, the results of the validation show the following implications. First, university officials should be motivated by a mentor and mentee system with experienced people who have switched to a suitable vocational group through university education. It will also be necessary to develop and disseminate programs so that they can continue to develop themselves for the future. To this end, it will be necessary to help them understand their aptitude and strengths through consultation with experts. Second, university officials should strengthen public relations so that prospective students can know the cases and information of the job transformation of the admitted workers through recommendations. It will also be necessary to develop university education programs that can self-develop, accept various ideas through "public contest", and provide accurate information about university education to workers through re-processing. Third, university officials should provide workers with a program that allows them to catch two rabbits: job transformation and self-improvement through university education. In other words, it is necessary to stimulate the motivation of workers by providing various information such as visiting advanced overseas companies, obtaining various certificates, moving between departments of blue-collar and white-collar, and transfer opportunities. Fourth, university officials should actively promote university education programs related to this by participating in university education and receiving systematic education and the flow of social environment. Finally, university officials will need to consult and promote workers so that they can self-develop when they participate in college education, and they will have to figure out what they need for self-development through demand surveys and analysis.

• Journal of Life Science
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• v.32 no.1
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• pp.29-35
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• 2022

This study was conducted to analyze the genetic polymorphisms of dopamine receptor D4 (DRD4) in horse breeds and its association with substrate characteristics in Jeju crossbreds (Jeju Horse × Thoroughbred). Polymorphisms in DRD4 are candidate genes associated with temperament in various mammals, including humans. Single nucleotide polymorphism (SNP) G292A in the exon 3 region of the horse DRD4 has a reported association with curiosity and vigilance in thoroughbreds. Sanger sequencing was used to identify polymorphisms of the mutations in DRD4 in three horse breeds. The SNP frequency in Jeju horses was significantly different from the frequency in other breeds. Character evaluation, conducted in the Jeju crossbreds and scored using a temperament test and contact test, revealed a high correlation between each test. Comparison of the polymorphism in the DRD4 of horses and the results of the character evaluation revealed lower scores for all temperaments in horses carrying allele A. Comparison of the SNP of G292A and blood dopamine levels in Jeju crossbreds showed 2.87 times higher levels for the GA type than for the GG type. This study identified an association between DRD4 polymorphism and various test methods for evaluating horse temperament and levels of neurotransmitters. Further research could validate the use of this gene as a genetic marker for character evaluation.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.