• Korean Journal of Health Education and Promotion
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• v.1 no.1
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• pp.21-36
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• 1983

It is assumed that menarche is affected not only by the biological factors such as nutrition and genetic heritage, but also it is affected by other socio-cultural environmental factors including weather, geographic location, education and level of modernization. Also recent trend of menarche in Korea indicates that a lot of discussion are being generated to the need of sex education as a part of formal school education. The purpose of this study is to develop the school health education program by determine the age of menarche, the factors relavant to time of menarche and psycho-mental state of students at the time in menarche and investigate the present state of school health education relate to menarche of adolescents. The total number of 732 girls was drown from first, second and third grades of 4 middle schools in Seoul. For the data collection the survey was conducted during the period from May 1 to May 20, 1982 by using prepared questionair. The major results are summarized as follow; 1. Mean age at menarche and the percent distribution of menarche experienced. It was observed that about 68.7% of sampled students have been experienced menarche at the time interviewed. For the each group, age at menarche is revealed that among the students about 37.8% are experienced menarche for under 12 years old group, 62.1% for 13 year-old group, 80.6％ for 14 year-old group and 95.5% for over 15 years old. In sum it was found that the mean age at menarche was 12.3 years old, ranged from age at 10 as earlist the age at 15 as latest. 2. Variables associated with age at menarche. 1) There was tendency those student who belong to upper class economic status have had menarche earlier than those student who belong to lower class. Therefore, economic status is closely related to age at menarche. 2) In time of mother's education level, it is also found that those students whose mother's education levels from high school and college are experienced menarche earlier than those students whose mother's education levels from primary school and no-education. 3) However, in connection with home discipline, there was no significant relationship between age at menarche and home disciplines which are being treated "Rigid", "Moderated ", "Indifferent". 4) Degree of communication between parents and daughter about sex matters was found to be associated each others in determination of age at menarche. 5) It was found that high association between mother's menarche age and their daughter's menarche age was observed. Mother's age at menarche earlier trend to be shown also as earlier of their daughters. 6) Those students belong to "D & E" of physical substantiality index are trend to be earlier in menarche than those students in the index "A & B". 3. Psycho-mental state at the time of menarche. Out of the total students 68.2% had at least one or more than one of subjective symptoms. Shyness was shown as most higher prevalent symptom and others are fear, emotional instability, unpleasant feeling, depression, radical behavior, inferior complex and satisfaction appeared. Very few cases are appeared be guilty and stealing feeling. 4. The present status of school health education program related to menarche. As to the source of information about menarche, teacher was a main source with average index 5.88 and the other informants were mother & family member, friends, books and magagines, movies, television, and radio. For the problem solving at menarche, mother & family members were subject to discussion with an average index 6.02 as high. The others for discuss and knowledge about menarche were books, magagine, friends, teachers, and self-learning based on own experienced. The time of learning about menarche, it was learned as highest percentage with 43.2% at a 6 grades of primary school, middle school with 34.4%, 5 grade of primary school with 18.2%, and 4 grade of primary school with 4.0% respectively.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.514-524
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• 2016

The Bio-Climatic impact Assessment System, BioCAS was utilized to produce analysis maps of daily maximum perceived temperature ($PT_{max}$) and excess mortality ($r_{EM}$) over the entire Seoul area on a heat wave event. The spatial resolution was 25 m and the Aug. 5, 2012 was the selected heat event date. The analyzed results were evaluated by comparing with observed health impact data - mortality and morbidity - during heat waves in 2004-2013 and 2006-2011,respectively. They were aggregated for 25 districts in Seoul. Spatial resolution of the comparison was equalized to district to match the lower data resolution of mortality and morbidity. Spatial maximum, minimum, average, and total of $PT_{max}$ and $r_{EM}$ were generated and correlated to the health impact data of mortality and morbidity. Correlation results show that the spatial averages of $PT_{max}$ and $r_{EM}$ were not able to explain the observed health impact. Instead, spatial minimum and maximum of $PT_{max}$ were correlated with mortality (r=0.53) and morbidity (r=0.42),respectively. Spatial maximum of $PT_{max}$, determined by building density, affected increasing morbidity at daytime by heat-related diseases such as sunstroke, whereas spatial minimum, determined by vegetation, affected decreasing mortality at nighttime by reducing heat stress. On the other hand, spatial maximum of $r_{EM}$ was correlated with morbidity (r=0.52) but not with mortality. It may have been affected by the limit of district-level irregularity such as difference in base-line heat vulnerability due to the age structure of the population. Areal distribution of the heat impact by local building and vegetation, such as spatial maximum and minimum, was more important than spatial mean. Such high resolution analyses are able to produce quantitative results in health impact and can also be used for economic analyses of localized urban development.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.117-128
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• 2023

Tree dieback in Geumgang pine forest has occurred in Uljin and Bonghwa since the 2010s. In order to identify status of tree dieback and prevent further damages, a monitoring project for tree dieback in Geumgang pine forest had been launched by Southern regional office of forest service in 2020. This study was conducted to understand the characteristics of tree dieback occurrence and assess the high risk areas using the occurrence data in the project. Pine tree dieback occurred frequently in areas with mountain ridges in high elevation, dry south-facing slopes, mature stands, and high temperature rise in winter. Furthermore, the result of risk assessment showed that 6.2 percent(5,294ha) of Geumgang pine forest(85,000 ha) in total study area are at high risk of tree dieback. As the pine trees in the high risk area are prone to experience the dieback due to temperature and drought-related extreme weather events, regular forest management activities are needed to reduce the drought stress of pine trees. Forest health management for the pine forest with high protection priority can be also useful strategy to counter the risk of decline. This results can be used as the basic information for the adaptive forest management to climate change.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.