• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.143-157
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• 1987

In order to determine the design precipitation, the most probable daily precipitation and annual precipitation at every spot are calculated and iso - precipitation line are drawn. Probability of precipitation and drought phenomena of each gage station are analyzied by the method of frequency analysis from the statistical conceptions. The results summarized in this study are as the follows. 1. Annual mean precipitation in kyungpook area are 1044 mm, about 115 mm less than annual mean precipitation of Korea amounts to l1S9mm, and found to regionally unequal. 2. Monthly mean rainfall of July is 242.2mm, 23.2%, August 174.2mm, 16.7%, June 115mm, 11% and September 114.2mm, 10.9% and Rainfall depth of July-August are more than 40% of annual precipition. This shows notable summer rainy weather by typoon and low pressure storm and seasonal unbalance of water supply. 3. The relation among the maximum precipi.tation per day, per two continuous days and per three contnous days are caculated and the latter is found 31.0% increased rate of the first and the last 48.2% increased rate of first. 4. Probability precipitation in Kyungpook area are shown as 9.0%(5 year), 13.3%(10 year), 17.7%(20 year), 23.1%(50 year), 27.0%(100 year) and 31.1%(200 year) increased rate of each recurrence year compared with observed average annual precipitation. 5. From annual precipitation and maximum daily rainfall data probability of precipitation and precipitation isohyetal line are derived which shown as Table 11 and Fig. 8. 6. Drought days are divided 6 class and analysed results are shown on table 12. Average occurrence time of 10-14 continuous drought days are 2.3 time per year, 15-19 days are 0.9 time per year, 20-24 days are one per six years, 30-34 days are once per nine years and over than 35days are once per 25 years.

• Journal of Korean Elementary Science Education
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• v.26 no.3
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• pp.276-285
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• 2007

The purpose of this study was to observe five teachers' science classes and analyze the patterns of their analogies. To analyze the data, investigator triangulation was used, and the results were as follows: First, among the patterns of analogy used, expressions of similes and metaphors were utilized by all the teachers; teachers with over ten years' experience used adult's daily phrases, and teachers with under ten years' experience tended to use anthropomorphism. Regarding pictorial analogies, these manifested themselves in the sixth period, while teachers with over ten years' experience drew a simple picture about circulation of water, teachers with under one year's experience made additional materials for their analogy and they put an emphasis on the concept of the circulation itself. Teachers tended to use analogs according to their interests; teachers who majored in science education used pictorial analogies for further study. Second, the patterns emerging from the correspondence of the analogy manifested themselves in all the teachers equally, but they have no relation to the teacher's background. Third, routine analogy and artificial analogy appeared equally in consideration of degree of artificiality among the patterns of the analogies used. Regarding routine analogy, most teachers tended to look for analogs from things and experiences from themselves or their own backgrounds rather than those of the students. Regarding artificial analogies, teachers tended to purposely choose analogs to help students to understand; energetic teachers sometimes failed to choose appropriate analogs because they approached the topic with too much intensity. While a teacher who lacked experience and interest in science used many expressions of analogy, a teacher who felt some degree of constraint rarely used expressions of analogy. Fourth, most of the teachers used analogs familiar to their own experiences but students often found understanding these analogs difficult. Therefore, teachers need to make greater efforts to utilize analogs which are especially familiar to students when they attempt to explain science concepts.

• Journal of the Korean Dietetic Association
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• v.15 no.3
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• pp.311-327
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• 2009

This study was performed to determine the health and nutritional risk factors associated with hypertension in Koreans over the age of 50 in a high-income class (more than twice as much family income as the 2005 Korean minimum cost of living, 668,540 Won). A total of 505 subjects aged over 50 from the 2005 Korean National Health and Nutrition Examination Survey (KNHANES) were divided into two groups: A hypertension group (HG) (N=151, Systolic Blood Pressure ${\geq}$140 mmHg or Diastolic Blood Pressure ${\geq}$90 mmHg) and normal group (NG) (N=354). Subjects who took hypertension medicines or underwent diet therapy were excluded. In HG, mean daily alcohol intake and the amount of alcohol consumption per one occasion were significantly higher than in NG, respectively. A greater number of hypertension subjects answered that they drank alcohol to reduce stress as compared to normal subjects. HG also took fewer dietary supplements than NG. Mean body mass index (BMI), waist circumference, fasting blood sugar level, and 2 hour postprandial blood sugar following a glucose load were significantly higher in HG than in NG, respectively. Also, iron, thiamin, and niacin intakes and the consumption frequency of seaweeds were significantly lower in HG than in NG, respectively. Finally, obesity (BMI ${\geq}$25 $kg/m^2$), abdominal obesity (waist circumference ${\geq}$90 cm for males, ${\geq}$ 80 cm for females), high blood sugar level 2 hours after an oral glucose load (${\geq}$140~200 mg/dl), and hypertriglyceridemia (serum TG ${\geq}$200 mg/dl) were related to a significantly higher risk of hypertension in the subjects (odds ratio: 1.884~3.040). In conclusion, dietary factors such as higher alcohol consumption; lower intakes of iron, thiamin, and niacin; lower consumption frequency of seaweeds; and metabolic syndrome were associated with hypertension in the study subjects.

• Journal of Environmental Science International
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• v.17 no.9
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• pp.981-992
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• 2008

This study examined how to produce new methods of copper (II) sulfate crystallization by using a small-scale chemistry tool such as small-scale reaction surface and petri dish. The making of copper(II) sulfate is included in the 5th grade elementary science textbooks. Various copper(II) compounds were reacted with a 2 M sulfuric acid solution. The result of this study is as follows: Seven small amounts of copper(II) compounds were reacted with a few drops of 2 M sulfuric acid solution at room temperature to make a copper(II) sulfate crystal of triclinic shape. Using the petri dish method, a copper(II) sulfate crystal could be identified within one hour of reacting copper(II) hydroxide, copper(II) carbonate, copper(II) nitrate, copper(II) perchlorate, cupric(II) formate from a few drops of 2 M sulfuric acid solution at room temperature. When using the lap top method for copper(II) perchlorate, cupric formate, a proper crystal could be identified within one hour as well. SSC methods were used for the first time to make a copper sulfate crystal via chemical reaction. We can make a copper(II) sulfate crystal using a simple method which is easier, safer and saves time in class. And since a small quantity of chemicals are being used in SSC chemical methods, waste is greatly reduced. This lessens the amount of environmental problems caused by the experiment. This can be helpful in preserving nature. In addition the cost of chemical and laboratory equipment is greatly reduced because it uses material that we find in our daily lives. There will be continued study of small-scale methods such as improvement of new programs, study and training of teachers, and securing SSC tools. I would like to suggest such as SSC methods are applicable in elementary School Science. I would like it to become a wide spread program.

• Journal of Environmental Science International
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• v.26 no.6
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• pp.729-739
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• 2017

The Weather Research and Forecasting (WRF) model and Vegetation Photosynthesis and Respiration Model (VPRM) were coupled to simulate atmospheric $CO_2$ concentrations. The performance of the WRF-VPRM to simulate regional scale $CO_2$ concentration was estimated over coastal basin areas. Either Hestia 2011(HST) or Vulcan 2002(VUL) anthropogenic $CO_2$ emission data were used in two numerical experiments for the study regions. Simulated meteorological variables were validated with ground and background $CO_2$ measurement data, and the results show that the model captured temporal variations of $CO_2$ concentration on a daily basis. $CO_2$ directional analysis revealed that the dominant $CO_2$ emission sources are located S and SW. The simulated Net Ecosystem Exchange (NEE) agreed relatively well with measured $CO_2$ fluxes at each vegetation class site, showing approximately 40% at max improvement at shrub areas.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.243-251
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• 2008

The cyber classes of an e-Learning system have been considered as one of the important form of education. Especially. some of non-major(liberal arts and science) and major subjects are held in cyber classes. However, there is no or little study of effectiveness and function for the students' position. In this study, we analyzed log files in the e-learning system. and classified login and learning hour patterns of students. who were enrolled in the cyber classes. into hourly pattern in a day, daily pattern in a week, and weekly pattern in a semester. We proposed general ideas to improve effectiveness and function of current e-learning. Over 50% of logins were for less than 30 minutes learning and there is wasteful use of e-learning system resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Communications of Mathematical Education
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• v.25 no.3
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• pp.497-524
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• 2011

This study was to understand students' learning about the function of math combined with molecular motions of science using the block scheduling. It was based on the revised Class Structure Model of Lee et al.(2010) where MBL as a tool was used to increase students' participation and understanding in the integrated concepts. The researcher provided the 6th grade students who lived in Sung Nam-Si, Kyung Gi-Do with 8 unit lessons, consisting of 5 stages of CSM. As a result of the study, the integrated education of Mathematics and Science showed synergic effect in studying both subjects and brought a positive result in gradual mathematization. It may be hard to combine all the contents of mathematics and science together. However, learning the relation between volume and pressure, and between volume and temperature of gas used as an example of function shown in our daily life was appropriate through Fogarty's integrated education model because it supported the objective of both subjects. Also, it was a good idea to develop CSM because it was composed of the contents from both subjects held in the same period of a year. Through the five stages, students were able to establish and generalize the definitions and the concepts of function.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1653-1661
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• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.