• The Journal of Korean Academic Society of Nursing Education
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• v.24 no.1
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• pp.72-79
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• 2018

Purpose: The purpose of this study was to investigate the effects of positive thinking and self-esteem on school adaptation among freshmen in a nursing department. Methods: Data were collected by questionnaires from 172 nursing students in the month of December 2017. The collected data were analyzed using descriptive statistics, an independent t-test, ANOVA, Pearson's correlation coefficient, and stepwise multiple regression. Results: A positive correlation was found for school adaptation with positive thinking and self-esteem. Positive thinking, self-esteem, major satisfaction and interpersonal relationships were all significant predictors of school adaptation. The model explained 43.6% of the variables. Conclusion: The results of this study suggest that positive thinking should be considered when developing strategies to increase school adaptation among freshmen in nursing departments.

• Asian Journal of Business Environment
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• v.4 no.3
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• pp.5-12
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• 2014

Purpose - This study is aimed at examining Indian consumers' online shopping motivations, information search, and shopping intentions. The study intends to reveal the relationship between online shopping motivations, information search, and shopping intentions. Research design, data, and methodology - The study employs factor analysis to verify correct loading of items on corresponding factors, and to confirm the applicability of constructs in the Indian context. The model was verified using stepwise regression analysis. Results -The findings show that hedonic and utilitarian motivations significantly affect online information search and shopping intentions. The information search is a significant predictor of online purchase intention. Conclusions - Hedonic and utilitarian motivations are the salient factors affecting online information search and purchase intentions. Marketers are required to design websites that foster an enjoyable online experience. This will attract customers who will browse the website for a longer duration. More time devoted to information search will ensure brand building and loyalty.

• Journal of Korean Public Health Nursing
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• v.30 no.3
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• pp.395-404
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• 2016

Purpose: The purpose of this study was to investigate the effects of positive thinking and subjective happiness on school adaptation in nursing students. Methods: Data were collected by questionnaires from 282 nursing students in the month of November 2013. The collected data were analyzed using descriptive statistics, independent t-test, ANOVA, Pearson's correlation coefficient, and stepwise multiple regression. Results: A positive correlation was found for school adaptation with positive thinking and subjective happiness. Positive thinking, subjective happiness, and major satisfaction were all significant predictors of school adaptation. The model explained 30.2% of the valuables. Conclusion: The results of this study suggest that positive thinking should be considered when developing strategies to increase school adaptation in nursing students.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.647-659
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• 2017

Garlic and onion are grown in major cultivation regions that depend on the crop condition and the meteorology of the production area. Therefore, when yields are to be predicted, it is reasonable to use a statistical model in which both the crop and the meteorological elements are considered. In this paper, using a multiple linear regression model, we predicted garlic and onion yields in major cultivation regions. We used the MODIS NDVI that reflects the crop conditions, and six meteorological elements for 7 major cultivation regions from 2006 to 2015. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, the MODIS NDVI in February was chosen the significant independent variable of the garlic and onion yield prediction model. In the case of meteorological elements, the garlic yield prediction model were the mean temperature (March), the rainfall (November, March), the relative humidity (April), and the duration time of sunshine (April, May). Also, the rainfall (November), the duration time of sunshine (January), the relative humidity (April), and the minimum temperature (June) were chosen among the variables as the significant meteorological elements of the onion yield prediction model. MODIS NDVI and meteorological elements in the model explain 84.4%, 75.9% of the garlic and onion with a root mean square error (RMSE) of 42.57 kg/10a, 340.29 kg/10a. These lead to the result that the characteristics of variations in garlic and onion growth according to MODIS NDVI and other meteorological elements were well reflected in the model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• 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.

• Anxiety and mood
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• v.9 no.1
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• pp.54-60
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• 2013

Objectives : Posttraumatic stress disorder (PTSD) is classified as an anxiety disorder. PTSD occurrence is known to be increased in middle-aged and older people, female, and individuals with a previous history of psychiatric disorders, lower education levels, low socioeconomic status, and severely injured patients. Anxiety symptoms are also related to later development of PTSD. In this study, we investigate the influences of injury severity and sociodemographic factors on severe anxiety in PTSD patients with no previous history of psychiatric disorders. Methods : Forty-one PTSD patients without previous history of psychiatric disorders were recruited from the psychiatric clinic at Chosun University Hospital. Subjects underwent psychiatric and physical examinations including the Injury Severity Score (ISS), Beck Anxiety Inventory (BAI), and Korean-Wechsler Adult Intelligence Scale (K-WAIS). We defined severe anxiety as a BAI scores of 30 or more. Logistic regression analyses and multi-step model selection were applied to identify predictive factors for severe anxiety. Results : In univariate analysis, age, ISS, and socioeconomic status were found to be significant factors. Through multivariate logistic regression analyses and a stepwise model selection, we found the combination of age and ISS to be the best-fitted model for affecting severe anxiety in PTSD patients without a previous history of psychiatric disorders. Conclusion : Our findings suggest that the combination of age and ISS could develop severe anxiety in PTSD patients with no previous history of psychiatric disorders.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.969-977
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• 2016

Climate change affects the growth of crops which were planted especially in fields, and it becomes more important to use climate data to predict the yields of the major vagetables. The variation of the crop products caused by climate change is one of the significant factors for the discrepancy of the demand and supply, and leads to the price instability. In this paper, using a panel regression model, we predicted the garlic yields with the weather conditions of different regions. More specifically we used the panel data of the several climate variables for 15 main garlic production areas from 2006 to 2015. Seven variables (average temperature, average maximum temperature, average minimum temperature, average surface temperature, cumulative precipitation, average relative humidity, cumulative duration time of sunshine) for each month were considered, and most significant 7 variables were selected from the total 84 variables by the stepwise regression. The random effects model was chosen by the Hausman test. The average maximum temperature (January), the cumulative precipitation (March, October), the cumulative duration time of sunshine (April, October) were chosen among the variables as the significant climate variables of the model

• 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.