• Journal of the Society of Cosmetic Scientists of Korea
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• v.41 no.4
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• pp.315-324
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• 2015

In this study, we prepared liquid crystal emulsion composed of amphiphilic substance $C_{14-22}$ alcohol, $C_{12-20}$ alkyl glucoside, behenyl alcohol and studied liquid crystal emulsion of properties and in vitro skin permeation. The results of formulation experiments, the clear liquid crystalline structure was observed in the ratio of $C_{14-22}$ alcohol 0.8%, $C_{12-20}$ alkyl glucoside 3.2%, behenyl alcohol 4% in the formulation. The results of physical property measurements, the viscosity of liquid crystal emulsion and O/W emulsion applied as a control group was respectively $1871.26{\sim}1.15Pa{\cdot}s$, $1768.69{\sim}1.14Pa{\cdot}s$ and the shear stress of O/W emulsion was 178.68 ~ 909.18 Pa, that of liquid crystal emulsion was 190.45 ~ 919.38 Pa. The storage modulus of O/W emulsion was 3428.53 ~ 9157.45 Pa, that of liquid crystal emulsion was 4487.82 ~ 8195.59 Pa. The tan (delta) value of O/W emulsion which means a ratio of viscosity to elasticity was 0.43 ~ 0.19, and that of liquid crystal emulsion was 0.23 ~ 0.25. The water content value on the skin for liquid crystal emulsion was significantly higher from 1 h to 6 h compared with that of O/W emulsion and the transepidermal water loss on the skin was significantly superior in skin moisture loss suppression from 30 min to 4 h compared with that of O/W emulsion. The results of skin permeation using glycyrrhizic acid, the result of skin permeation amount of liquid crystal emulsion for 24 h was $64.58{\mu}g/cm^2$, that of O/W emulsion was $37.07{\mu}g/cm^2$, that of butylene glycol solution was $41.05{\mu}g/cm^2$. Hourly permeability results, it is showed that skin penetration effect of the liquid crystal emulsion increases after 8 h. These results suggest that liquid crystal emulsions are effective for skin moisturizing effect and function as potential efficacy ingredient delivery system for the transdermal delivery.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.657-664
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• 2021

In this study, we investigated the relationships between the air quality (PM_2.5, PM₁₀, O₃) concentrations and local geographical characteristics (terrain heights, building area ratios, population density in 9 km × 9 km gridded subareas) in the Seoul metropolitan area. To analyze the terrain heights and building area ratios, we used the geographic information system data provided by the NGII (National Geographic Information Institute). Also, we used the administrative districts and population provided by KOSIS (Korean Statistical Information Service) to estimate population densities. We analyzed the PM_2.5, PM₁₀, and O₃ concentrations measured at the 146 AQMSs (air quality monitoring system) within the Seoul metropolitan area. The analysis period is from January 2010 to December 2020, and the monthly concentrations were calculated by averaging the hourly concentrations. The terrain is high in the northern and eastern parts of Gyeonggi-do and low near the west coastline. The distributions of building area ratios and population densities were similar to each other. During the analysis period, the monthly PM_2.5 and PM₁₀ concentrations at 146 AQMSs were high from January to March. The O₃ concentrations were high from April to June. The population densities were negatively correlated with PM_2.5, PM₁₀, and O₃ concentrations (weakly with PM_2.5 and PM₁₀ but strongly with O₃). On the other hand, the AQMS heights showed no significant correlation with the pollutant concentrations, implying that further studies on the relationship between terrain heights and pollutant concentrations should be accompanied.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• 한국노년학
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• v.41 no.2
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• pp.217-240
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• 2021

The purpose of this study is to examine the effect of care workers' employment characteristics and perception of facility directors' transformational leadership on quality of service through a hierarchical linear model. For this aim, survey data were collected amongst 240 older adults and 200 care workers who are affiliated within 45 long-term care facilities in Seoul, and analyzed using SPSS 26.0 and HLM 8.0. As a result, one's perception of transformational leadership had a positive effect, whereas, among employment characteristics, employment type and working hours had negative effects on quality of service. Regular workers with fewer working hours and higher awareness of transformational leadership toward the director provided higher quality of service. But wage, total experience and tenure didn't meaningfully affect it. Therefore, the following suggestions were presented. First, it is necessary to reorganize incentive, salary systems and budgets, changing the status of temporary workers' hourly wage system into that of regular workers' monthly one in order to strengthen employment security with acknowledging fundamental professional values through reinforcement of expertise. Reinforcement of long-term care's publicness and establishment of base facilities are also suggested. Second, maintaining appropriate hours of work and rest including annual leave under the Labor Standards Act is needed. Also, increasing the salary of and decreasing working hours for night shift workers are required. Third, education and intervention for inspiring transformational leadership of directors and strengthening qualification standards of them are required.

• Clean Technology
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• v.28 no.3
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• pp.218-226
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• 2022

As a result of the recent social transformation towards a hydrogen economy and carbon-neutrality, the demands for hydrogen energy have been increasing rapidly worldwide. As such, eco-friendly hydrogen production technologies that do not produce carbon dioxide (CO₂) emissions are being focused on. Among them, ammonia (NH₃) is an economical hydrogen carrier that can easily produce hydrogen (H₂). In this study, Ru/Al₂O₃ catalyst coated onmetallic monolith for hydrogen production from ammonia was prepared by a dip-coating method using a catalyst slurry mixture composed of Ru/Al₂O₃ catalyst, inorganic binder (alumina sol) and organic binder (methyl cellulose). At the optimized 1:1:0.1 weight ratio of catalyst/inorganic binder/organic binder, the amount of catalyst coated on the metallic monolith after one cycle coating was about 61.6 g L^-1. The uniform thickness (about 42 ㎛) and crystal structure of the catalyst coated on the metallic monolith surface were confirmed through scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis. Also, a numerical optimization regression equation for NH3 conversion according to the independent variables of reaction temperature (400-600 ℃) and gas hourly space velocity (1,000-5,000 h^-1) was calculated by response surface methodology (RSM). This model indicated a determination coefficient (R²) of 0.991 and had statistically significant predictors. This regression model could contribute to the commercial process design of hydrogen production by ammonia decomposition.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.5
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• pp.580-586
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• 1995

Soil temperatures at depths of 1~5cm are important to the germination and emergence of dry seeded-rice. An automated weather station was used to monitor the hourly weather parameters at Experiment Farm, Kyung Hee University from April 21 to May 30 in 1994. The data was analyzed to figure out the 24-hour temporal changes in air 1.5m above ground and soil temperatures under ground of 0, 2.5, 5, 10 and 20cm. The fluctuations of soil temperature were greatest at the soil surface and decreased with increasing depth. Mean soil temperatures at depth of 2.5cm were about 3$^{\circ}C$ higher than mean air temperatures during the observation period. Although mean soil temperatures at depth of 2.5cm during 10 or 15 days after April 21, May 1 and May 11 showed almost same temperatures, the distribution patterns of temperature regime were different from each other. Rice cultivars, Hwasung, Seohae, Nampung, IR60 and CR155, were seeded at depth of 2.5cm on April 21, May 1 and May 11, respectively. The periods of seedling emergence(PSE) varied in accordance with cultivars and seeding dates. PSE was correlated with accumulated daily mean air temperatures and accumulated hours classified by temperature regimes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.28-36
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• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.