• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.

• Journal of Biosystems Engineering
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• v.27 no.3
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• pp.219-226
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• 2002

Desorption equilibrium moisture contents of oak mushroom were measured by the static method using salt solutions at flour temperature levels of 35$\^{C}$, 45$\^{C}$, 55$\^{C}$ and 6$\^{C}$ and five relative humidity levels in the range from 11.0% to 90.8%. EMC data were fitted to the modified Henderson, Chung-Pfost, modified Halsey and modified Oswin models using nonlinear regression analysis. Drying tests far oak mushroom were conducted in an experimental dryer equipped with air conditioning unit. The drying test were performed in triplicate at flour air temperatures of 35$\^{C}$, 45$\^{C}$, 55$\^{C}$ and 65$\^{C}$ and three relative humidities of 30%, 50% and 70% respectively. Measured moisture ratio data were fitted to the selected four drying models(Lewis, Page, simplified diffusion and Thompson models) using stepwise multiple regression analysis. The results of comparing root mean square errors for EMC models showed that modified Halsey was the best model, and modified Oswin models could be available far oak mushroom. The results of comparing coefficients of determination and root mean square errors of moisture ratio for four drying models showed that Page model were found to fit adequately to all drying test data with a coefficient of determination of 0.9990 and root mean square error of moisture ratio of 0.00739.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.420-428
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• 2016

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And $NDVI_{UAV}$ and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to $NDVI_{UAV}$ and other agro-meteorological factors were well reflected in the model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3301-3309
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• 2013

This study was done to identify the relationships of self-esteem, major satisfaction and career motivation and to examine the factors influencing on career motivation among 369 college nursing students. Data were analyzed using t-test, one-way ANOVA, Pearson's correlation coefficients and stepwise multiple regression with the SPSS Win 18.0 Program. There were significant positive correlations between self-esteem and career motivation, between major satisfaction and career motivation, between self-esteem and major satisfaction. The predictors on career motivation were major satisfaction (${\beta}$=.48) and self-esteem (${\beta}$=.23) and the model explained 34.5% (F=17.84, p<.001) of the variance. The findings indicate the following studies are needed to explore various factors influencing on the career motivation and to develop the career motivation programs for college nursing students.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2323-2326
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• 2015

It is urgently necessary to be aware of the distribution and risk areas of liver fluke, Opisthorchis viverrini, for proper allocation of prevention and control measures. This study aimed to investigate the human behavior, and environmental factors influencing the distribution in Surin Province of Thailand, and to build a model using stepwise multiple regression analysis with a geographic information system (GIS) on environment and climate data. The relationship between the human behavior, attitudes (<50%; $X_{111}$), environmental factors like population density ($148-169pop/km^2$; $X_{73}$), and land use as wetland ($X_{64}$), were correlated with the liver fluke disease distribution at 0.000, 0.034, and 0.006 levels, respectively. Multiple regression analysis, by equations OV= -0.599 + 0.005(population density ($148-169pop/km^2$); $X_{73}$) + 0.040 (human attitude (<50%); $X_{111}$) +0.022 (land used (wetland; X64), was used to predict the distribution of liver fluke. OV is the patients of liver fluke infection, R Square= 0.878, and, Adjust R Square= 0.849. By GIS analysis, we found Si Narong, Sangkha, Phanom Dong Rak, Mueang Surin, Non Narai, Samrong Thap, Chumphon Buri, and Rattanaburi to have the highest distributions in Surin province. In conclusion, the combination of GIS and statistical analysis can help simulate the spatial distribution and risk areas of liver fluke, and thus may be an important tool for future planning of prevention and control measures.

• Journal of Korean Academy of Nursing Administration
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• v.5 no.1
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• pp.77-86
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• 1999

The purpose of this study is to look into nurse's legal duty and to analyze influencing factors, by reading through precedents in nursing-related malpractice cases. 157 clinic nurses and 40 head nurses who were working in 3 university hospitals in the area of Seoul and KyongKi province were surveyed with pre-set questionnaires which were based on 20 cases and to compare precedents with nurses' attitudes. Independent variables were demographic characteristics, job characteristics, experience and education, and personal characteristics. Dependent variables were the extents of understanding in their duty. To analyze the extent of duty understanding t-text. ANOVA, and multiple regression were performed according to the characteristics of each study subject. Main findings are as follows; 1. $X^2$ test was performed for each hospitals and job category. The existence of religion and job-related advisers were significantly different according to hospitals and job categories. The existences of the orientation for nursing, education, and meeting were significantly different according to hospitals. 2. The extent of duty understanding according to the subject characteristics was assessed by means of median. Each case was given the median. 12 cases(46%)out of 26 cases demonstrated the median of more than 6 showing high extent of understanding. 3. When $X^2$ test was performed. the extent of duty understanding was significantly different in the areas of marriage status. the existence of meetings. and the experience of having dispute. When multiple regression(stepwise method) was performed, above variables were not statistically significant. 4.As result of comparative analysis on precedents and nurse's attitudes. it is 4 cases that nurse's attitudes is more active than precedents and it is 9 cases that nurse's attitudes is more passive than precedents and it is 4 cases that nurse's attitudes is parallel with precedents. This study does not present statistical model which can integrate all the cases. The significance of this study is in that it generalized and assessed the nurses' understanding of duty based on nursing -care which was legally disputed and compared nurse's attitude with precedents.

• The Journal of Korean Physical Therapy
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• v.25 no.4
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• pp.204-209
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• 2013

Purpose: Children with cerebral palsy generally have a high incidence of respiratory problem, resulted from poor coughing, airway clearance problem, respiratory muscle weakness, kyphoscoliosis and so forth. The purpose of this study is to investigate the possible factors that can be affected to forced vital capacity (FVC) in children with cerebral palsy. Methods: Total thirty six children with diplegic and hemiplegic cerebral palsy were recruited in this study. They were evaluated by general demographic data (i.e., age, gender, body mass index (BMI)) and variables related to respiratory functions (i.e., chest mobility, waist mobility, maximal phonation time, and maximum inspiratory/expiratory pressure (MIP/MEP)). The correlation between forced vital capacity and the rested variables were analyzed, and multiple regression with stepwise method was conducted to predict respiratory function, in terms of FVC as the dependent variable, and demographic and other respiratory variables as the independent variable. Results: FVC showed a significant correlation with waist mobility (r=0.59, p<0.01), maximal phonation time (r=0.48, p<0.05), MIP (r=0.73, p<0.01), and MEP (r=0.60, p<0.01). In addition, the multiple regression analysis model indicated that FVC could be predicted by the assessment of each waist mobility and MIP. Conclusion: These finding suggest that respiratory function is related to body size and respiratory muscle strength, and that BMI, waist mobility, and MIP can be predictable factors to affected respiratory function in term of FVC.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.331-340
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• 2022

A descriptive study was attempted to identify the factors affecting of nursing organization culture, communication competence, and professionalism on nursing service quality of small and medium-sized hospital nurses. The study were 166 nurses from hospital nurses located in G metropolitan city, and the data collection period was from August 22 to August 30, 2019. Data were analyzed PASW 18.0 program. As a result, the factors affecting on nursing service quality were professionalism(𝛽=.485, p<.001), communication competence(𝛽=.354, p<.001), hierarchy-oriented organization culture(𝛽=.154, p<.01). The adequacy of the multiple regression model was very significant at the F statistic 64.75(p<.001), explanatory power of 54.6%. Based on this study, it can be useful as basic data for exploring ways to improve nursing service quality and developing nursing intervention programs.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.3-20
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• 2019

With advancements in technology of virtual reality, it is used for various purposes in many fields such as medical care and healthcare, but as the same time there are also increasing reports of nausea, eye fatigue, dizziness, and headache from users. These symptoms of motion sickness are referred to as cybersickness, and various researches are under way to solve the cybersickness problem because it can cause inconvenience to the user and cause adverse effects such as discomfort or stress. However, there is no official standard for the causes and solutions of cybersickness at present. This is also related to the absence of tools to quantitatively measure the cybersickness. In order to overcome these limitations, this study proposed quantitative and objective cybersickness evaluation method. We measured 128-channel EEG waves from ten participants experiencing visually stimulated virtual reality. We calculated the relative power of delta and alpha in 11 regions (left, middle, right frontal, parietal, occipital and left, right temporal lobe). Multiple regression models were obtained in a stepwise manner with the motion sickness susceptibility questionnaire (MSSQ) scores indicating the susceptibility of the subject to the motion sickness. A multiple regression model with the highest under the area ROC curve (AUC) was derived. In the multiple regression model derived from this study, it was possible to distinguish cybersickness by accuracy of 95.1% with 11 explanatory variables (P_D.MF, P_D.LP, P_D.MP, P_D.RP, P_D.MO, P_A.LF, P_A.MF, P_A.RF, P_A.LP, P_A.RP, P_A.MO). In summary, in this study, objective response to cybersickness was confirmed through 128 channels of EEG. The analysis results showed that there was a clearly distinguished reaction at a specific part of the brain. Using the results and analytical methods of this study, it is expected that it will be useful for the future studies related to the cybersickness.

• 한국체육학회지인문사회과학편
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• v.51 no.3
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• pp.373-382
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• 2012

The purpose of this study is to predict VO₂max with body index and submaximal metabolic responses. The subjects are consisted of 250 male aging from 18 to 34 and we separated them into two groups randomly; 179 for a sample, 71 for a cross-validation group. They went through maximal exercise testing with Bruce protocol, and we measured the metabolic responses in the end of the first(3 minute) and second stage(6 minute). To predict VO₂max, we applied multiple regression analysis to the sample with stepwise method. Model 1's variables are weight, 6 minute HR and 6 minute VO₂(R=0.64, SEE=4.74, CV=11.7%, p<.01), and the equation is VO₂max(ml/kg/min)= 72.256-0.340(Weight)-0.220(6minHR)+0.013(6minVO₂). Model 2's variables are weight, 6 minute HR, 6 minute VO₂, and 6 minute VCO₂(R=0.67, SEE=4.59, CV=11.3%, p<.01), and the equation is VO₂max(ml/kg/min)= 68.699-0.277(Weight) -0.206(6minHR)+0.020(6minVO₂)-0.009(6minVCO₂). And the result did not show multicolinearity for both models. Model 2 demonstrated more correlation compared to Model 1. However, when we conducted cross-validation of those models with 71 men, measured VO₂max and estimated VO₂ Max had statistical significance with correlation (R=0.53, 0.56, P<.01). Although both models are functional with validity considering their simplicity and utility, Model 2 has more accuracy.