Narae Kang

Development of a simulation-based assessment to evaluate the clinical competencies of Korean nursing students

OBJECTIVES To describe a simulation-based assessment (SBA) to evaluate the clinical competencies of nursing students in childrens health and to compare its results with grade point average (GPA), self-efficacy, topic-specific knowledge, and self-reported clinical competency using the Six-D Scale. METHODS This cross-sectional, descriptive study recruited nursing students from a childrens health clinical practicum. Students were assigned to either an asthma (n=55) or a type 1 diabetes (n=48) care scenario conducted on a high-fidelity simulator. Clinical competencies were assessed using the global rating scale (GRS) and a checklist. RESULTS Data on 103 students were analyzed. The SBA-GRS indicated that 64.6%-87.3% of students passed. The SBA-GRS showed a statistically significant positive association with the SBA checklist in both the asthma (rho=.763, p<.001) and the type 1 diabetes (rho=.475, p=.001) group. In the asthma group, the SBA-GRS and checklist showed statistically significant associations with GPA (rho=.413, p=.002 vs. r=.508, p<.001) and the Six-D Scale (rho=.266, p=.049 vs. r=.352, p=.008); in the diabetes group, only the SBA checklist showed a statistically significant association with self-efficacy (r=.339, p=.018) and the Six-D Scale (r=.373, p=.009). Four groups by SBA-GRS had statistically significant differences in scores on the SBA checklist in both groups (F=25.757, p<.001 in the asthma group; F=4.790, p=.006 in the diabetes group) and GPA only in the asthma groups (F=6.095, p<.001). CONCLUSION SBA was found to be feasible for nursing students. The GRS and checklist were reasonably correlated with other evaluation methods of student competency, but correlations were better with easier scenarios.

Long-Term Simulation of Daily Streamflow Using Radar Rainfall and the SWAT Model: A Case Study of the Gamcheon Basin of the Nakdong River, Korea

In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study applied and to a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data for , , and were utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods using and , the simulation which utilized reflected the rainfall-runoff characteristics better than the simulations which applied or . However, in the rainy or wet season, the simulations which utilized or were similar to or better than the simulation that applied . This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar.

Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future.

Evaluation for Snowfall Depth Forecasting using Neural Network and Multiple Regression Models

Since snowfall is related to various meteorological variables such as temperature and precipitation, it is generated in nonlinear manner. Therefore this study constructs snowfall forecasting model using neural networks and multiple regression which can consider nonlinear process of snowfall. The study constructs the forecasting models for each station using temperature, precipitation, and snowfall depth observed from starting time of observation to 1999. And snowfalls are calculated for all stations by using temperature and precipitation in the period of 2000 to 2011. From the statistical analysis of the calculated snowfall, the proper model is selected. The selected models show the correlation coefficients of 0.700 to 0.949 and the adjusted determination coefficients of 41.7% to 89.8%. The applicability of neural network models is superior to other model at almost every station. But in some cases multiple regression models show better results than neural network models due to the lack of observational data during learning period and the extreme peak values which are not learned during forecasting period. According to the study, the results of the models confirm the predicting snowfall depth by using temperature and precipitation is possible and show neural network model is better than the existing statistical models.

A Review on the Management of Water Resources Information based on Big Data and Cloud Computing

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

Estimation of Frequency Based Snowfall Depth Considering Climate Change Using Neural Network

In recent years, extreme weather event due to the climate change has been frequently occurred over the world. Meanwhile, Korean peninsula has been suffered from the natural disasters such as snowfall. This study estimated the snowfall depth of climate change by using temperature, precipitation based on KMA-RegCM3 climate model and climate change scenario. We estimated the frequency based daily snowfall depth(50yr, 80yr, 100yr and 200yr) at 18 weather stations for four different target periods(Target I: 1971~2010, Target II: 2011~2040, Target III: 2041~2070, Target IV: 2071~2100) under climate change. Snowfall has nonlinear relationship with temperature and precipitation and so this study used a neural network and multiple regression models which can consider nonlinearity between snowfall and meteorological variables for its forecasting. As the results, the average rate of frequency based snowfall depth will be decreased by 6~18% for Target I and the rate will be continuously decreased in Target II, III and IV. The results of this study could be used as the basic information for the future disaster prevention planning and design criteria related to snowfall.

Blood Glucose Control and Related Factors at a Camp for Korean Children and Adolescents with Type 1 Diabetes

ABSTRACT Data about Asian children and adolescents with type 1 diabetes are sparse. This study’s objectives were to describe blood glucose (BG) levels and related factors at a camp for Korean children and adolescents with type 1 diabetes. This descriptive study was conducted January 8–10, 2015. The participants, 24 children and adolescents, were recruited for a 3-day residential diabetes camp. Data on 24 campers were analyzed. Their mean age was 13.4 (± 1.7) years; 44.4% were boys, and mean HgbA1c was 8.5% (± 1.4%). Results revealed that BG levels were maintained safely: The mean BG level during the 3-day stay was 171.1 (± 33.3) mg/dl. Multiple regression analysis showed that insulin adjustment for hyperglycemia (standardized β = .426; t = 2.431; p = .030) and snacks for hypoglycemia (standardized β = –.719; t = –3.723; p = .003) at the camp were the only independent contributors to mean BG levels during the 3-day study period. No demographic or clinical factor was found to be associated with the mean BG level. This is the first study of its kind to be conducted in an Asian population, presumably because the prevalence of type 1 diabetes in Asia is low and diabetes camps are a novel concept. Further research is recommended to assess the characteristics of campers (e.g., diet, activity levels, and cultural background) and to determine how the health outcomes of children and adolescents with type 1 diabetes are affected by camp programs.

Diabetes‐Specific Quality of Life of Korean Children and Adolescents With Type 1 Diabetes

PURPOSE The purpose of this study was to explore the quality of life of children in Korea with type 1 diabetes and related factors. METHODS Children were recruited from a diabetes camp. Data were collected using four instruments: the PedsQL™ 3.2 Diabetes Module, Self-Efficacy for Diabetes Self-Management, the Center for Epidemiological Studies Depression Scale for Children, and the Diabetes Management Behavior Scale. RESULTS Children who were older (t = 2.197, p = .041), male (t = -3.579, p = .002), and less depressed (t = -2.859, p = .010) were more likely to have better quality of life. CONCLUSION Further research is needed in children with type 1 diabetes in countries where this disease is rare, and governmental support and public awareness are limited.

A comparative study on the simple Two-parameter monthly water balance model and Kajiyama formula for monthly runoff estimation

ABSTRACT A two-parameter monthly water balance model to simulate runoff can be used for a water resources planning programme and climate impact studies. However, the model estimates two parameters of transformation of time scale (c) and of the field capacity (SC) by a trial-and-error method. This study suggests a modified methodology to estimate the parameters c and SC using the meteorological and geological conditions. The modified model is compared with the Kajiyama formula to simulate the runoff in the Han River and International Hydrological Programme representative basins in South Korea. We show that the estimated c and SC can be used as the initial or optimal values for the monthly runoff simulation study in the model. EDITOR M.C. Acreman; ASSOCIATE EDITOR S. Kanae

Applicability of Spatial Interpolation Methods for the Estimation of Rainfall Field

In recent, the natural disaster like localized heavy rainfall due to the climate change is increasing. Therefore, it is important issue that the precise observation of rainfall and accurate spatial distribution of the rainfall for fast recovery of damaged region. Thus, researches on the use of the radar rainfall data have been performed. But there is a limitation in the estimation of spatial distribution of rainfall using rain gauge. Accordingly, this study uses the Kriging method which is a spatial interpolation method, to measure the rainfall field in Namgang river dam basin. The purpose of this study is to apply KED(Kriging with External Drift) with OK(Ordinary Kriging) and CK(Co-Kriging), generally used in Korea, to estimate rainfall field and compare each method for evaluate the applicability of each method. As a result of the quantitative assessment, the OK method using the raingauge only has 0.978 of correlation coefficient, 0.915 of slope best-fit line, and 0.957 of and shows an excellent result that MAE, RMSE, MSSE, and MRE are the closest to zero. Then KED and CK are in order of their good results. But the quantitative assessment alone has limitations in the evaluation of the methods for the precise estimation of the spatial distribution of rainfall. Thus, it is considered that there is a need to application of more sophisticated methods which can quantify the spatial distribution and this can be used to compare the similarity of rainfall field.