• Korean Journal of Ichthyology
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• v.36 no.3
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• pp.253-262
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• 2024

This study revealed the reproductive biology of the Olive Flounder, Paralichthys olivaceus, in the middle of the West Sea of Korea. We collected samples using stow net and drag net fisheries in coastal waters from January to December 2023. Monthly gonodosomatic index and maturity stage results showed that the spawning period was April to June for females and March to June for males. Females grew faster than the males. The fecundity ranged from 90,387 to 994,658 number of eggs, and the relationship between TL and fecundity (F) was 0.01TL^2.4896 (R²=0.2862). The gonadosomatic index (GSI) exhibited its highest values in May for females and in March for males, coinciding with the primary spawning period. The total length required for 50% sexual maturity was determined through a logistic regression model and the females was estimated to be 49.4 cm and the males was estimated to be 36.5 cm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Weed & Turfgrass Science
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• v.7 no.3
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• pp.275-282
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• 2018

Miscanthus species are known as a genus of eco-friendly and low-maintenance cost ornamental grasses. Plug and cutting methods were tested for multi-propagation of most promising ornamental Miscanthus species in greenhouse and field plot. The plug formation period with three different cell sizes with four cultivars (M. sinensis 'Andersson', 'Strictus', 'Gracillimus', 'Variegatus') were evaluated the seedling development stages with two irrigation types of the over-head and the bottom watering in greenhouse and field plot afterward during 2015-2016 season. In seedling plug test, the size of tray cell affected the plug formation. Bottom irrigation resulted positively on plant height, weight, root and tiller development compared with the over-head irrigation. Plug cell size affected the plant growth in the field after transplanting. All of the 3 Miscanthus species showed higher rates of successful propagation at the lower nodes before inflorescence formation (vegetative growth stage). To analyze the survival factors of M. xgiganteus cutting, the cutting time, node part, and culm diameter were tested as independent variables with the binary logistic model. The survival probability was influenced by node part and culm diameter significantly. The third and fifth node parts showed 0.12 (8X higher failure probability) and 0.02 (50X higher failure probability) times less survival probability. It means the survival probability will be increased by using older and lower part of cuttings during a vegetative growth stage before inflorescences of M. xgiganteus.

• Journal of Hospice and Palliative Care
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• v.18 no.2
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• pp.128-135
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• 2015

Purpose: As the National Health Insurance Service (NHIS) began to cover home oxygen therapy (HOT) services from 2006, it is expected that the new services have contributed to overall positive outcome of patients with chronic obstructive pulmonary disease (COPD). We examined whether the usage of HOT has helped slow down the progression of COPD. Methods: We examined hospital claim data (N=10,798) of COPD inpatients who were treated in 2007~2012. We performed ${\chi}^2$ tests to analyze the differences in the changes to respiratory impairment grades. Multiple logistic regression analysis was used to identify factors that are associated with the use of HOT. Finally, a generalized linear mixed model was used to examine association between the HOT treatment and changes to respiratory impairment grades. Results: A total of 2,490 patients had grade 1 respiratory impairment, and patients with grades 2 or 3 totaled 8,308. The OR for use of HOT was lower in grade 3 patients than others (OR: 0.33, 95% CI: 0.30~0.37). The maintenance/mitigation in all grades, those who used HOT had a higher OR than non-users (OR: 1.41, 95% CI: 1.23~1.61). Conclusion: HOT was effective in maintaining or mitigating the respiratory impairment in COPD patients.

• Clinical and Experimental Pediatrics
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• v.50 no.10
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• pp.987-994
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• 2007

Purpose : This study investigated the clinical significance of AN in children and adolescents with obesity induced metabolic complications. Methods : Forty-nine patients who had obesity induced metabolic complications were participated in this cross-sectional study. Obesity induced metabolic complications are as follows: hypertension, dyslipidemia, impaired fasting glucose (IFG), impaired glucose tolerance (IGT), nonalcoholic steatohepatitis (NASH), homeostasis model assessment of insulin resistance (HOMA-IR)>3.16. Clinical characteristics, such as, age, percentage-weight-for-height (PWH), pubertal status, blood pressure (BP), fasting plasma insulin level, fasting and post-oral glucose tolerance test 2-hour glucose levels, liver function test, lipid profile, HOMA-IR were compared according to the presence of AN. Results : Sixty-five percent of patients had AN, 57.1% NASH, 57.1% dyslipidemia, 55.1% hypertension, 46.9% IFG, 24.5% HOMA-IR>3.16 and 16.2% IGT. The patients who were moderately to severely obese with AN had higher incidence of IGT and HOMA-IR>3.16. The patients with AN had significantly higher diastolic BP ($79.4{\pm}6.9$ vs $75.4{\pm}5.6mmHg$), fasting levels of plasma insulin ($10.6{\pm}6.0$ vs $6.2{\pm}5.4{\mu}IU/mL$), HOMA-IR index ($2.6{\pm}1.4$ vs $1.4{\pm}1.3$) and PWH ($42.4{\pm}13.0$ vs $34.3{\pm}1.8%$). The increasing tendency for the presence of AN was significantly related to the cumulative number of obesity induced metabolic complications. Binary logistic regression analysis revealed that the presence of AN was significantly associated with fasting plasma insulin level, PWH and IFG. Conclusion : AN could be useful as a clinical surrogate of obesity induced metabolic complications.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.1
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• pp.72-79
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• 2021

The increasing use of deicing salts has caused various environmental problems, including crop damage along the motorway where deicing salts are sprayed during winter. Deicing salts used on roads have been reported to negatively affect crops, but little information is known about their impact on crops. A seedling emergence assay was conducted to evaluate the effects of deicing salts on crops using oilseed rape (Brassica napus) as a model plant. We tested five chloride deicing salts consisting of NaCl, CaCl2, or MgCl2 and 1 non-chloride deicing salt (SM-3) at a range of concentrations (25, 50, 100, 200, and 400 mM), and untreated control. Regardless of deicing salts, they significantly delayed and reduced seedling emergence of oilseed rape with increasing salt concentration. Non-linear regression analysis of seedling emergence with a range of salt concentrations by fitting to the log-logistic model revealed that the chloride deicing salts reduced seedling emergence more than the non-chloride deicing salt SM-3. The GR50 value, the concentration causing 50% seedling emergence, of SM-3 was 47.1 mM, while those of the chloride deicing salts ranged from 30.7 mM (PC-10) to 37.5 mM (ES-1), showing approximately 10 mM difference between non-chloride and chloride deicing salts. Our findings suggest that seedling emergence assay is a useful tool to estimate the potential damage caused by deicing salts on crops.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Journal of dental hygiene science
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• v.17 no.1
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• pp.38-45
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• 2017

We evaluated the association between sleep duration, dental caries, and periodontitis by using representative nationwide data. We examined 8,356 subjects aged ${\geq}19$ years who participated in the sixth Korea National Health and Nutrition Examination Survey (2013~2014). Sleep duration were grouped into ${\leq}5$, 6, 7, 8, and ${\geq}9$ hours. Presence of dental caries was defined as caries in ${\geq}1$ permanent tooth on dental examination. Periodontal status was assessed by using the community periodontal index (CPI), and a CPI code of ${\geq}3$ was defined as periodontitis. A chi-square test and multiple logistic regression analysis were used to determine statistical significance. Model 1 was adjusted for age and sex, model 2 for household income, educational level, and marital status plus model 1, and model 3 for smoking status, alcohol consumption, blood pressure level, fasting blood glucose level, total cholesterol level, and body mass index plus model 2. The prevalence of dental caries according to sleep duration showed a U-shaped curve of 33.4%, 29.4%, 28.4%, 29.4%, and 31.8% with ${\leq}5$, 6, 7, 8, and ${\geq}9$ hours of sleep, respectively. In the fully adjusted model 3, the risk of developing dental caries was significantly higher with ${\leq}5$ than with 7 hours of sleep (odds ratio, 1.23; 95% confidence interval, 1.06~1.43). The prevalence of periodontitis according to sleep duration showed a U-shaped curve of 34.4%, 28.6%, 28.1%, 31.3%, and 32.5%, respectively. The risk of periodontitis was significantly higher with ${\geq}9$ than with 7 hours of sleep in models 1 and 2, whereas the significant association disappeared in model 3. In a nationally representative sample, sleep duration was significantly associated with dental caries formation and weakly associated with periodontitis. Adequate sleep is required to prevent oral diseases such as dental caries and periodontitis.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.