• 충청수학회지
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• 제19권4호
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• pp.365-373
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• 2006

In this paper four regular relations, R, $L^*$, $M^*$ and $Q^*$ in a transformation group (X, T) are defined and some of their properties are studied.

• 충청수학회지
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• 제20권4호
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• pp.495-501
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• 2007

In this paper the relative regionally regular relation is defined and it will be given the necessary and sufficient conditions for the relation to be an equivalence relation.

• 국제물리치료학회지
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• 제5권2호
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• pp.701-707
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• 2014

This study was carried out survey to compare using status of physical therapy for elderly persons between urban and rural area in Honam. There were 16 places consisting of general clinics, departments of family medicine, pain medicine, and orthopedics that run outpatient physical therapy. This study distributed 636 questionnaires in total and collected 400 responses. Regionally, 200 responses out of 311 questionnaires from Gwangju and 200 responses out of 325 questionnaires from Jeollanam-do and Jeollabuk-do were collected. Regionally speaking, Gwangju was 62% while Jeollanam-do and Jeollabuk-do regions were 88%, indicating patients in rural areas employed more public transportation, which was statistically significantly different. There was a difference between urban and rural areas with regard to questions regarding improvement with physical therapy. Factors related to the number of physical therapy visits per week that showed a significant difference between urban and rural areas found by the linear regression analysis result were working hours, whether the patient exercised or not, and pain stress. This result suggest that it is necessary to reduce working hours and pain stress experienced by rural elderlies as well as to encourage regular exercise via national polices.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.