• Child Health Nursing Research
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• v.12 no.1
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• pp.34-43
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• 2006

Purpose: The purpose of this study was to identify the degree of internet addiction and factors affecting internet addiction in elementary school students. Method: The participants in this study were 1,328 students in 4, 5 or 6 grades of elementary school. They were recruited from two elementary schools. Data collection was conducted using of 6 questionnaires that were modified by the investigator. The data were analyzed with the SPSS win 10.0 program using descriptive statistics, Pearson correlation coefficients and stepwise multiple regression. Results: 1) The mean of total item score for internet addiction was 2.1, which was slightly low. Of respondents in this research 48.4% normally use the internet, while 48.5% addictively use the internet and as high as 3.1% were serious internet-addicted. 2) There was a significant correlation between internet addiction, self-esteem, aggression, impulsivity, parent's support and friend's support(γ= -.15 ～ .44). 3) Stepwise multiple regression analysis showed that amount of time spent on the internet per day, impulsivity, aggression, gender, self-esteem, duration to use of internet, father's age, and the major place where the internet was used were the predictors of internet addiction and accounted for 47% of the variance in internet addiction. Conclusion: Time spend on the internet per day, impulsivity, aggression, gender, self-esteem, duration to use of internet, father's age, the major place where the internet was used accounted for internet addiction in elementary school students. Therefore it is necessary to develop nursing interventions and to further identify the depth of the relationship of the related factors in order to decrease internet addiction.

• Journal of Korean Academy of Nursing
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• v.52 no.5
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• pp.511-521
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• 2022

Purpose: This study aimed to examine the moderating effect of organizational justice on the relationship between self-efficacy and nursing performance among clinical nurses. Methods: In January 2021, a cross-sectional survey was conducted with 224 clinical nurses recruited from a university-affiliated hospital in Suwon, South Korea. Participants completed online-based, self-report structured questionnaires. Collected data were analyzed using multiple regression and a simple model of PROCESS macro with a 95% bias-corrected bootstrap confidence interval. Results: Self-efficacy and organizational justice were found to be significant predictors of nursing performance. These two predictors explained the additional 34.8% variance of nursing performance in the hierarchical regression model, after adjusting the other covariates. In addition, organizational justice moderated the relationship between self-efficacy and nursing performance among the clinical nurses. In particular, at low self-efficacy level, participants with high organizational justice had higher nursing performance compared to those with low organizational justice. Conclusion: Enhancing organizational justice can be used as an organizational strategy for improving the organizational culture in terms of distribution, procedure, and interaction. Ultimately, these efforts will contribute to the improvement of nursing performance through a synergistic effect on organizational justice beyond nurses' individual competency and self-efficacy.

• Tuberculosis and Respiratory Diseases
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• v.85 no.2
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• pp.165-174
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• 2022

Background: One goal of the End tuberculosis (TB) Strategy is to see no TB-affected households experiencing catastrophic costs. Therefore, it is crucial for TB-elimination programs to identify catastrophic costs and their main drivers in order to establish appropriate health and social measures. This study aimed to measure the percent of catastrophic costs experienced by Egyptian TB patients and to identify its determinants. Methods: We conducted a prospective cohort study with 151 Egyptian TB patients recruited from two chest dispensaries from the Cairo governate from May 2019 to May 2020. We used a validated World Health Organization TB patient-cost tool to collect data on patients' demographic information, household income, and direct and indirect expense of seeking TB treatment. We considered catastrophic TB costs to be total costs exceeding 20% of the household's annual income. Results: Of the patients, 33% experienced catastrophic costs. The highest proportion of the total came in the pre-treatment stage. Being the main breadwinner, experiencing job loss, selling property, and the occurrence of early coronavirus disease 2019 lockdown were independent determinants of the incidence of catastrophic costs. Borrowing money and selling property were the most-often reported coping strategies adopted. Conclusion: Despite the availability of free TB care under the Egyptian National TB Program, nearly a third of the TB patients incurred catastrophic costs. Job loss and being the main breadwinner were among the significant predictors of catastrophic costs. Social protection mechanisms, including cash assistance and insurance coverage, are necessary to achieve the goal of the End TB Strategy.

• Journal of Forest and Environmental Science
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• v.29 no.1
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• pp.38-48
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• 2013

Decomposition of plant material is an important component in the study of forest ecosystem because of its critical role in nutrient cycling. Different tree species has different nutrient release patterns, which are related to leaf litter quantitative traits and seasonal environmental factors. The quantitative traits of leaf litter are important predictors of decomposition and decomposition rates increase with greater nutrient availability in the forest ecosystems. At the ecosystem level, litter quantitative traits are most often related to the physical and chemical characteristics of the litter, for example, leaf toughness and leaf mass per unit area, and lignin content tannin and total phenolics. Thus, the analysis of litter quantitative traits and decomposition are highly important for the understanding of nutrient cycling in forest ecosystems. By studying the role of litter quantitative traits on decomposition and nutrient cycling in forest ecosystems will provide a valuable insight to how quantitative traits influence ecosystem nutrient dynamics. Such knowledge will contribute to future forest management and conservation practices.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Journal of Korean Academy of Nursing
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• v.40 no.4
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• pp.589-598
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• 2010

Purpose: The purpose of this study was to investigate the factors that increase of the risk for falls in low-income elders in urban areas. Methods: The participants were elderly people registered in one of public health centers in one city. Data were collected by interviewing the elders, assessing their environmental risk factors, and surveying relevant secondary data from the public health center records. For data analysis, descriptive statistics and multiple logistic regression were performed using SPSS version 14. Results: Stroke, diabetes, visual deficits, frequency of dizziness, use of assistive devices and moderate depression were statistically significant risk factors. The comorbidity of chronic diseases with other factors including depression, visual deficit, dizziness, and use of assistive devices significantly increased the risk of falls. From multiple logistic regression analysis, statistically significant predictors of falls were found to be stroke, total environmental risk scores, comorbiditiy of diabetes with visual deficits, and with depression. Conclusion: Fall prevention interventions should be multifactorial, especially for the elders with stroke or diabetes, who were identified in this study as the high risk group for falls. A fall risk assessment tool for low-income elders should include both the intrinsic factors like depression, dizziness, and use of assistive devices, and the extrinsic factors.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.5
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• pp.146-153
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• 2010

Urban development typically results in many species being confined to small, isolated and degraded habitat fragments. Fragment size and isolation underpin many studies of modified landscape to prevent biodiversity loss. However, habitat characteristics such as vegetation structure and edge effects are less frequently incorporated in planning. The relative influence of biogeographic (e.g. size, isolation) and vegetation parameters on assemblages is poorly understood, but critical for conservation management. In this study, the relative importance of biogeographic and vegetation parameters in explaining the diversity of forest-interior dwelling birds in forest fragments in Chungcheong Province Area. Fragment size and vegetation characteristics were consistently important predictors of bird diversity. Forestinterior bird richness was influenced by fragment size (0.437), wood age (0.332), wood diameter (0.068), and patch shape (-0.079). To preserve bird diversity of Chungcheong Province Area, it is important to consider differing responses of bird diversity to landscape change, move beyond a focus primarily on spatial attributes (size, isolation) to recognize that landscape change also has profound effects on habitat composition and quality. The result is very useful for long-term aspect of biodiversity conservation plan in regional scale.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.403-411
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• 2021

This study discusses the influence of economic factors on the clothing exports from China and 15 South and Southeast Asian countries to the United States. A basic gravity trade model with three predictors, including the GDP value produced by exporting and importing countries and their geographical distance was established to explain the bilateral trade patterns. The conventional approach of multiple regression and the novel approach of Artificial Neural Networks (ANNs) were developed based on the value of clothing exports from 2012 to 2018 and applied to the trade pattern prediction of 2019. The results showed that ANNs can achieve a more accurate prediction in bilateral trade patterns than the commonly-used econometric analysis of the basic gravity trade model. Future studies can examine the predictive power of ANNs on an extended gravity model of trade that includes explanatory variables in social and environmental areas, such as policy, initiative, agreement, and infrastructure for trade facilitation, which are crucial for policymaking and managerial consideration. More research should be conducted for the examination of the balance between developing countries' economic growth and their social and environmental sustainability and for the application of more advanced machine-learning algorithms of global trade flow examination.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.