• Journal of dental hygiene science
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• v.12 no.6
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• pp.607-616
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• 2012

The purpose of this study was to examine the self-rated oral health status and oral health concern of 6,094 adults over the age of 19, which were both related to subjective oral health awareness, based on the second-year (2008) raw data of the 4th National Health and Nutrition Survey. 1. As for subjective oral health awareness, 49.4 percent replied they were in bad oral health when they were asked about self-rated oral health status. Regarding oral health concern, 62.6 percent answered they were sort of concerned about oral health. 2. As to oral health indexes by sociodemographic characteristics, there were statistically significant differences in oral health indexes according to gender, age, academic credential, monthly mean household income, frequency of eating between meals and toothbrushing frequency. Smoking made no statistically significant differences to oral health indexes (p<0.000). 3. Concerning self-rated oral health status by sociodemographic characteristics, no significant differences were found according to gender, age and academic credential, and there were statistically significant differences according to monthly mean household income and smoking (p<0.000), frequency of eating between meals (p<0.018), toothbrushing frequency (p<0.003). 4. In relation to oral health concern by sociodemographic characteristics, gender and smoking made no significant differences, and statistically significant differences were found according to age (p<0.003), academic credential, monthly mean household income, frequency of eating between meals and toothbrushing frequency (p<0.000). 5. In regard to the relationship between subjective oral health awareness and oral health indexes, none of the oral health indexes had a significant relationship to self-rated oral health status, and there were statistically significant differences in oral health concern according to functioning teeth index (p<0.011) and community periodontal index (p<0.017).

• Journal of Life Science
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• v.31 no.3
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• pp.287-297
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• 2021

Cornus officinalis Siebold & Zucc. is traditionally used as an edible and medicinal plant in many countries in East Asia. Previous studies have shown the pharmacological potential of extracts and components of C. officinalis, but comparative analysis of the composition of the leaf, stem, and fruit extracts has been insufficient to date. In the present study, the content of active antioxidant and anti-inflammatory ingredients was verified in different C. officinalis parts (under-ripe sansuyu, ripe sansuyu, seed, leaf, stem, and dried sansuyu). One active component, morroniside, was high in fruit (under-ripe and ripe sansuyu), while loganin was high in fruit (under-ripe sansuyu) and cornin was high in seeds. Total polyphenol contents were highest in fruit (ripe sansuyu) and flavonoids were highest in leaves. DPPH radical scavenging activity was highest in leaves, followed by seeds and then ripe sansuyu extract. The anti-inflammatory efficacy of leaf extracts of C. officinalis (LCO) was further investigated by measuring their effects on levels of nitric oxide (NO) and the pro-inflammatory cytokines interleukin (IL)-1β and IL-6 in RAW 264.7 macrophages. Non-cytotoxic concentrations of LCO effectively decreased the lipopolysaccharide (LPS)-induced expression of inducible NO synthase, resulting in decreased NO production. LCO also significantly suppressed LPS-induced production and expression of IL-1β and IL-6. Taken together, the present findings suggest that C. officinalis leaves have potential as natural materials for the development of antioxidant and anti-inflammatory agents.

• Journal of The Korean Association For Science Education
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• v.42 no.3
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• pp.325-340
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• 2022

This study attempts to analyze the causes of low affective achievement of elementary, middle, and high school students in Korea in science. To this end, a total of 27 students, three to four students per grade, were interviewed by grade from the fourth grade of elementary school to the first grade of high school, and a total of nine teachers were interviewed by school level. In the interview, related questions were asked in five sub-areas of the 'Indicators of Positive Experiences about Science': 'Science Academic Emotion', 'Science-Related Self-Concept', 'Science Learning Motivation', 'Science-Related Career Aspiration', and 'Science-Related Attitude'. Interview contents were recorded, transcribed, and categorized. As a result of examining the causes of low science academic emotion, it was found that students experienced negative emotions when experiments are not carried out properly, scientific theories and terms are difficult, and recording the inquiry results is burdensome. In addition, students responded that science-related self-concept changed negatively due to poor science grades, difficult scientific terms, and a large amount of learning. The reasons for the decline in science learning motivation were the lack of awareness of relationship between science class content and daily life, difficulty in science class content, poor science grades, and lack of relevance to one's interest or career path. The main reason for the decline in science-related career aspirations was that they feel their career path was not related to science, and due to poor science performance. Science-related attitudes changed negatively due to difficulties in science classes or negative feelings about science classes, and high school students recognized the ambivalence of science on society. Based on the results of the interview, support for experiments and basic science education, improvement of elementary school supplementary textbook 'experiment & observation', development of teaching and learning materials, and provision of science-related career information were proposed.

• Korean Journal of Environmental Biology
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• v.41 no.2
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• pp.109-125
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• 2023

This study was conducted to select target fish species as baseline research for accumulation analysis of major hazardous chemicals entering the aquatic ecosystem in Korea and to analyze the impact on fish community. The test bed was selected from a sewage treatment plant, which could directly confirm the impact of the inflow of harmful chemicals, and the Geum River estuary where harmful chemicals introduced into the water system were concentrated. A multivariable metric model was developed to select target candidate fish species for hazardous chemical analysis. Details consisted of seven metrics: (1) commercially useful metric, (2) top-carnivorous species metric, (3) pollution fish indicator metric, (4) tolerance fish metric, (5) common abundant metric, (6) sampling availability (collectability) metric, and (7) widely distributed fish metric. Based on seven metric models for candidate fish species, eight species were selected as target candidates. The co-occurring dominant fish with target candidates was tolerant (50%), indicating that the highest abundance of tolerant species could be used as a water pollution indicator. A multi-metric fish-based model analysis for aquatic ecosystem health evaluation showed that the ecosystem health was diagnosed as "bad conditions". Physicochemical water quality variables also influenced fish feeding and tolerance guild in the testbed. Eight water quality parameters appeared high at the T1 site, indicating a large impact of discharging water from the sewage treatment plant. T2 site showed massive algal bloom, with chlorophyll concentration about 15 times higher compared to the reference site.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.2
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• pp.147-157
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• 2023

Centella asiatica extract is widely used as a raw material for cosmetics due to its various effects, but it is difficult to expect penetration into the skin due to its high molecular weight and low solubility. In order to solve these problems, lipid-based liposomes of various types were developed to increase skin absorption. Therefore, in this study, we tried to increase the skin absorption rate by preparing transfersomes using surfactants as edge activators in existing liposomes. Liposome and transfersomes containing Span 80 and Tween 20, 60, 80, and 85, respectively, were prepared using a high-pressure homogenizer, and we evaluated the particle size, polydispersity index, zeta potential, and skin absorption rate. As a result, there was almost no change in the physical properties of particle size, polydispersity index and zeta potential from 25 ℃ to 60 d, and the particle size of transfersomes containing Tween 20, 60, and 80 increased after 60 d at 45 ℃. Madecassoside, main substances of the Centella asiatica extract was used as an standard and madecassoside was measured and calculated when measuring the skin absorption rate using Franz diffusion cells. As a result, formulations containing Tween 20 were the most, whereas formulations containing Span 80 were the least. According to the skin absorption coefficient (Kp) value, all formulations showed 'very fast', and the absorption rate was similar or greater than that of liposomes, except for formulations containing Span 80. Through this, it was confirmed that the larger the HLB value of the nonionic surfactant, the smaller the particle size of the transfersome, and the increased skin absorption rate due to the increased flexibility of the vesicle membrane. Through this study, transfersome using surfactant as an edge activator can be expected to solve local skin problems not only as a cosmetic raw material or product, but also by increasing skin absorption.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• Journal of Practical Agriculture & Fisheries Research
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• v.18 no.1
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• pp.79-91
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• 2016

Quality factors which characterize 11 kinds of farm-manufactured apple Jangachi and commercial Jangachi, have been studied in order to provide a guideline to improve the quality and marketing strategy of farm-manufactured pickled apples. Moisture content ranged from 74% to 84% and 81% to 91% in Doenjang Jangachi and vinegar Jangachi, respectively; 38% to 64% in Kochujang Jangachi; 57% to 64% in radish Kochujang Jangachi. Moisture content was 89% in Doenjang Jangachi. Even though moisture content of apple Kochujang Jangachi indicated 48% which is lower than that of radish Jangachi, it was higher than that of a persimmon pickled in Kochujang (38%) and that of Japanese apricot Jangachi (49%). pH and titratable acidity, two indicators used to determine the appropriate ripening period of Jangachi, were pH 3.4~5.6, 0.03~0.14%, respectively. The pH ranged from 5.2 to 5.6 in radish Jangachi; 3.4 to 4.1 in Cucumber Jangachi. pH of persimmon Jangachi, Japanese apricot Jangachi and apple Jangachi showed 4.1, 3.5 and 4.1, respectively. Compared with the pH of traditional Jangachi (3.03~5.36), pH of all of the above Jangachi fall into an appropriate range. The brix of apple Jangachi (30%) was 12% to 18% higher than that of Kochujang radish Jangachi, but it was relatively lower than that of persimmon Jangachi (39%) and that of Japanese apricot Jangachi (49%). Salinity of Jangachi varied depending on which marinating material was used. Salinity in the descending order according to each marinating material demonstrated Kanjang (6% to 13%), Doenjang (7%), Kochujang (3% to 4%). Salinity of apple Jangachi was 3.28% which was relatively lower than that of commercial Jangachi which used either Kanjang or Doenjang as its marinating material. Chromaticity test shows that the brightness value of apple Jangachi (54.70) was similar to that of cucumber Jangachi (50.86, 56.02); the redness value and yellowness of apple Jangachi (16.21 and 26.78) were higher than the redness value (7.27 to 11.23) and the yellowness value (10.62 to 14.69) of radish Kochujang Jangachi. Sensory Characteristics value of apple Jangachi, along with radish and cucumber Jangachi in its color, odor and taste (7.00, 7.50, 7.00, respectively) placed high on the list implying higher preference. However, overall preference value of apple Jangachi was 6.83 which was lower than that of Japanese apricot Jangachi or that of radish Jangachi. The result can be explained by the tendency of people preferring crispy Jangachi and points out that the texture of apple Jangachi needs to be improved to gain popularity. Furthermore, for increased sales of apple Jangachi as a niche product, more rigorous market testing is required.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.175-190
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• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.