• Parasites, Hosts and Diseases
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• v.45 no.4
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• pp.255-266
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• 2007

The protective effect of the Synadenium carinatum latex lectin (ScLL), and the possibility of using it as an adjuvant in murine model of vaccination against American cutaneous leishmaniasis, were evaluated. BALB/c mice were immunized with the lectin ScLL (10, 50, 100$[\mu}g$/animal) separately or in association with the soluble Leishmania amazonensis antigen (SLA). After a challenge infection with $10^6$ promastigotes, the injury progression was monitored weekly by measuring the footpad swelling for 10 weeks. ScLL appeared to be capable of conferring partial protection to the animals, being most evident when ScLL was used in concentrations of 50 and 100${\mu}g$/animal. Also the parasite load in the interior of macrophages showed significant reduction (61.7%) when compared to the control group. With regard to the cellular response, ScLL 50 and 100 ${\mu}g$/animal stimulated the delayed-type hypersensitivity (DTH) reaction significantly (P < 0.05) higher than SLA or SLA plus ScLL 10 weeks after the challenge infection. The detection of high levels of IgG2a and the expression of mRNA cytokines, such as IFN-$\gamma$, IL-12, and TNF-$\alpha$ (Th1 profiles), corroborated the protective role of this lectin against cutaneous leishmaniasis. This is the first report of the ScLL effect on leishmaniasis and shows a promising role for ScLL to be explored in other experimental models for treatment of leishmaniasis.

• The Korean Journal of Franchise Management
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• v.11 no.4
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• pp.17-29
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• 2020

Purpose: As the untact culture has spread due to the recent COVID-19, the service industry as well as the beauty salon is shrinking. In order to overcome such a crisis in beauty salons, the lower dimension of beauty salon service quality is divided into servicescape, technical service, and employee service, and actions are taken with the 'perceived value of the customer' as a parameter. In this study, S-O-R(Stimulus-Organism-Response) theory was applied for customer-centered analysis, and gender and regularity were selected as moderator variables to add practical implications. Research design, data, and methodology: All constructs were measured using items developed and used in the previous study. A total of 261 questionnaires were collected online using NaverForm. The data were analyzed using factor analysis, correlation analysis, and measurement model analysis with SPSS 22.0 and AMOS 22.0. After testing the research model and hypothesis for the entire group, a multi-group analysis was conducted by dividing into male and female groups, regular customers, and non-regular customers. Results: First, this study showed that the service environment of beauty salon customers had a negative (-) effect on perceived utilitarian value, and the technical service and perceived hedonic value had a positive (+) effect on the customer's behavioral intention. Technical service and employee service had a positive (+) effect on perceived hedonic value, but perceived hedonic value did not affect customer's behavioral intention. Second, there is no statistically significant difference in each path between male and female groups. Third, there was a statistically significant difference between regular customers and non-regular customers, only the path that the servicescape influences the perceived hedonic value. Conclusion: According to the results of this study, technical service and utilitarian value should be considered in order to induce behavioral intention of customers, and technical service quality should be considered first. Also, operating a beauty salon requires a differentiated approach to the salon servicescape according to the ratio of non-regular and regular customers. The beauty salon servicescape generally showed negative practical value for non-regular customers, but positive for the Hedinic value for servicescape such as comfortable service and clean interior for regular customers.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.163-177
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• 2019

This study aims to review the relations between the dyeing conditions (i.e., dye concentration, addition amounts of salt and alkali, and dyeing temperature) and dyeing properties and color fastness to light for identifying the optimal dyeing conditions when dyed regenerated woody fibers were obtained through the defibration of waste medium density fiberboard (MDF) using reactive Red H-E3B (Bis-monochlorotriazine (MCT)/MCT type) and reactive Red RB133% (Bis-MCT/Vinyl sulphone type). The dyeing yield (K/S) obtained using two types of reactive dyes increased as the dye concentration increased by 1-10% (on the weight of fiber (OWF)). In addition, the K/S of H-E3B was higher than that of RB133% irrespective of the dye concentration. The color difference of H-E3B after ultraviolet (UV) radiation was lower than that of RB133%, denoting good resistance to discoloration by UV. As the amount of sodium sulfate increased, the color difference and K/S also increased, and the adequate salt content was determined to be 50-70 g/L. Further, the color difference and K/S significantly increased only the addition of 2 g/L of sodium carbonate; however, almost no difference was observed when more than 2 g/L of sodium carbonate was added. The addition amount of sodium carbonate was adequate 5-10 g/L to dyeing the fiber and the pH at this addition level was 10. The dyeing yield of H-E3B increased when the dyeing temperature increased; however, it subsequently decreased after the dyeing temperature became $80^{\circ}C$. The dyeing yield of RB133% was almost the same up to $60-70^{\circ}C$ but declined subsequently. Thus, the adequate temperatures were $80^{\circ}C$ and $60^{\circ}C$ for H-E3B and RB133%, respectively. If the waste MDF woody fiber was dyed under the aforementioned optimal conditions, dyed regenerated woody fiber can be obtained having the following colors: 1.5 to 2.0R with the H-E3B dye and 9.6 to 10.0 PR with RB133%.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.267-280
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• 2007

Various single-valued design optimality criteria such as D-, G-, and V-optimality are used often in constructing optimal experimental designs for mixture experiments in a constrained region R where lower and upper bound constraints are imposed on the ingredients proportions. Even though they are optimal in the strict sense of particular optimality criterion used, it is known that their performance is unsatisfactory with respect to the prediction capability over a constrained region. (Vining et at., 1993; Khuri et at., 1999) We assume the quadratic polynomial model as the mixture response surface model and are interested in finding efficient designs in the constrained design space for a mixture. In this paper, we make an expanded list of candidate design points by adding interior points to the extreme vertices, edge midpoints, constrained face centroids and the overall centroid. Then, we want to propose a robust design with respect to D-optimality, G-optimality, V-optimality and distance-based U-optimality. Comparing scaled prediction variance quantile plots (SPVQP) of robust designs with that of recommended designs in Khuri et al. (1999) and Vining et al. (1993) in the well-known examples of a four-component fertilizer experiment as well as McLean and Anderson's Railroad Flare Experiment, robust designs turned out to be superior to those recommended designs.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.1
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• pp.133-143
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• 2002

The textile industry is petting increased effort to manufacture the value-added products that gives the differentiated characters at every level of fiber and fabric production. The color is an important element to be used strategically in order to push up the value-added design. The colors of apparel products have a close relationship with the skin colors of consumers and their preference colors. This study was carried out to cluster the skin colors of the Korean women into several similar skin colors and to analyze their preference colors by the classified groups. We measured the skin colors of 354 Korean women. With color spectrometer, JX-777, we measured 4 points of the body; cheek with removing cosmetics off, forehead, rear neck and arm on the interior part near elbow. All subjects had been shown with 40 color chips and answered the preference colors and preference colors of apparel. Data were analysed to classify skin colors using K-means Cluster Analysis and Duncan test, Frequency and Chi square test on the preference colors about the clustered 3 groups. In doing so, we used in SPSS Win 10 statistical package. Findings were as fellows: 1) The skin colors of the Korean women were clustered into YR, R, and Y skin colors. The majority of the subjects, 324 observations had YR skin colors and the subjects were classified into 3 kinds of skin color groups who had YR skin colors. 2) The average skin colors of total 324 subjects was 5.23YR 6.49/4.09 in Munsell Color System(MCS), 66.56 in L value, 10.53 in a value, and 20.67 in b value. 3) The average skin color of Type 1 was 7.98YR 6.24/4.14 in MCS, 64.10 in L value, 15.05 in a value, and 24.0 in b value. For Type 2 was 7.30 YR 6.56/3.28 in MCS, 67.24 in L value, 6.89 in a value, and 18.4 in b value, and Type 3 was 7.01 YR 7.20/4.38 in MCS, 73.53 in L value, L 16.04 in a value, and 24.87 in b value. 4) The average face color of total 324 subjects was 7.31YR 6.65/3.56 in MCS, 68.13 in L value, 9.53 in a value, and 20.18 in b value. 5) The average face color of Type 1 was 4.19 YR 6.92/5.05 in MCS, 70.78 in L value, 13.2 in a value, and 25.32 in b value. For Type 2 was 5.24YR 6.33/3.79 in MCS, 64.94 in L value, 9.84 in a value, and 19.08 in b value. Type 3 was 5.4YR 6.85/4.68 in MCS, 70.1 in L value, 11.73 in a value, and 23.92 in b value. 6) The difference of mean values between the clustered 3 skin color groups showed significantly different except the a value of neck and H value of cheeks and H value of foreheads. 7) All 3 groups showed that the most preference colors and the most preference colors of apparel was 5R 4/14. and their preference colors were much more than the preference colors of apparel.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.2
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• pp.303-314
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• 2002

The colors of apparel have become an important element to be used strategically in order to give differentiated character at the level of fiber and fabric production. The colors of apparel have a close relationship with the skin colors of consumers and their preference colors. This study was carried out to classify the skin colors of Korean elderly women into several similar skin colors and to analyse their preference colors. Sample size was 471 Korean elderly women. With color spectrometer, JX-777, we measured 4 points of the body; cheek with removing cosmetics off, forehead, rear neck and arm on the interior part near elbow. All subjects had been shown with 40 color chips and answered the preference colors of apparel and the preference colors. Data weirs analysed to classify skin colors using K-means Cluster Analysis and Duncan test. Independent variables for Cluster Analysis were 12 variables out of L value, a value and b value of 4 points. In doing so, we used SPSS WIN 10 statistical package. Findings were as follows: 1) The skin colors of the Korean elderly women were composed of skin colors of YR, R, and Y. 2) 355 subjects were classified into 4 kinds of skin color groups. 3) The average face color of type 1 was 6.7YR 5.1/4.3 and 56 observations out of 355 subjects were composed of Type 1 and of Type 2 was 6.1YR 6.1/4.5 and 166 observations out of 355 and of 3 Type 6. YR 4.8/4.2 and 75 observations out of 355 and of Type was 6.17 YR 5.7/4.7 and 58 observations out of 355. 4) The average skin color of Type 1 was 7.0YR 5.9/4.4 and of Type 2 was 7.2YR 6.3/4.2 and of Type 3 was 7.0YR 6.2/4.2 and of Type 4 was 7.6YR 5.4/4.2 respectively. 5) The mean values of 12 variables between the 4 classified face color and skin color groups showed significantly different except H value of skin color. 6) All 4 groups showed that the most preference color of apparel and the most preference color were 2.5R 5/14 respectively.

• Journal of the Korean Wood Science and Technology
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• v.40 no.6
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• pp.370-377
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• 2012

This study was conducted to investigate the color change of heat-treated wood made of Korean pine (Pinus koraiensis S. et Z.), and lodgepole pine (Pinus contorta Dougl). The wood samples were heat-treated at 9 different treatment combinations of temperature (170, 180, 200, 220, and $230^{\circ}C$), and time (90, 120, 180, 240, 270 min.) set up by a response surface method. Wood color was measured using a colorimeter, and the samples were installed for evaluating the resistance of termite in the field test sites set up in Jinju. The lightness decreased with the increase of treatment temperature and treatment time. The color differences were evaluated by the method of National Bureau of Standards (NBS), and response surface models to predict the color change were fitted as a function of treatment temperature and treatment time. The $R^2$ values of the fitted models of heat-treated Korean pine and lodgepole pines were 0.92, and 0.88, respectively, showing the potential way for the prediction of color changes. Severe damage was found from the samples in the field test site, which means that the manufactured heat-treated wood did not show the termite resistance enough for the outdoor use.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.