• Journal of the Korean Home Economics Association
• /
• v.38 no.12
• /
• pp.177-188
• /
• 2000

This study was performed to investigate the awareness of an ecological city and its related variables and the willingness to move to an ecological city and its related variables. Data were collected through a questionnaire survey given to 491 residents in Mokpo, Kwangju and Muan in Chollanam Province. Awareness of the ecotogical city was medium level, half of the respondents were not aware of the ecological city. Multiple regression analysis was conducted to estimate the effects of the variables on the awareness of the ecological city. The most significant variable was environmental knowledge. Next, experience in environmental education and homeownership effected on the awareness of the ecological city. Those who knew about environmental pollution and environmental disruption had a self-reported higher degree of awareness of the ecological city. Those who had experience in environmental education as well as those who lived in their own house were more aware of the ecological city. Other variables were not statistically significant. Half of the respondents were willing to move to the ecological city. Seven variables were related with willingness to move to the ecological city. Those who knew about the environmental pollution and environmental disruption preferred to move to the ecological city. Those who lived in multi-housing had more of a willingness to move to the ecological city. People with a higher education as well as the younger group preferred to move to the ecological city. Those who were more aware of the ecological city and had more ecological-oriented values had more willingness to move to the ecological city. Salaried men and professionals were more willing to move to the ecological city than non-employed people. Among those variables environmental knowledge was the only influential variables on willingness to move to the city.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.3
• /
• pp.1330-1334
• /
• 2013

The purpose of this study was to analyze the association between self-reported voice problem and body mass index. Data were collected from the 5th Korea National Health and Nutritional Examination Survey (2010) from 5,811 subjects(2,503 men and 3,308 women) aged 19 years and olders. chi-square, t-test and multi-nominal logistic regression analysis were used that to compare self-reported voice problem and variable(age, sex, hight, weight, waist measurement, body mass index). body mass index(OR=1.028, 95% CI: 1.003-1.056) was independently associated with self-reported voice problem(p<0.031). also over weight-two step obesity (OR=1.765, 95% CI: 1.036-3.006) were independently associated with self-reported voice problem(p<0.036). The results of comparison verified that body mass index are valuable self-reported voice problem of risk factor. when the evaluation were conducted, what was considered body mass index is needed.

• Korean Journal of Labor Studies
• /
• v.23 no.2
• /
• pp.141-167
• /
• 2017

This study carries out an empirical analysis of how workplace innovation affects employment growth. The theoretical model and hypotheses of this study are drawn from the previous research on the employment effects of innovation. I use the data on Workplace Innovation Indicators(2013-14) collected by Labor Foundation. As the regression models for this study, I adopt OLS models whose dependent variable is employment growth rate, and whose main independent variables are the adoption and the intensity(standardized values) of three innovative work practices such as TQM, employee suggestion plans and multi-skill training programs. The results of this study indicate that the adoption of workplace innovation does not have significant effects on employment growth, but that the intensity of workplace innovation has weakly positive effects on employment growth. Besides, the results of this demonstrate show that government-subsided organizational innovation consulting and training hours per capita have positive employment effects, but that wage level and prior employment size have negative ones. Finally the empirical results are outlined, and their limitations and the future direction of research on this topic are discussed.

• Journal of Environmental Health Sciences
• /
• v.41 no.6
• /
• pp.369-379
• /
• 2015

Objectives: The lack of reliable information on environmental pollution and health impacts related to asbestos contamination from abandoned mines has drawn attention to the need for a community health study. This study was performed to evaluate asbestos-related health symptoms among residents near abandoned asbestos mines located in the Chungcheong Provinces. In addition, exposure assessment for asbestos is needed although the exposure to asbestos was in the past. Methods: Past exposure to asbestos among inhabitants near abandoned asbestos mines was estimated by using surface sampling of deposited dust in indoor and outdoor residences. A total of 54 participants were divided into two groups with (34 cases) and without (20 controls) diseases related to asbestos. Surface sampling of deposited dust was carried out in indoor and outdoor residences by collecting 105 samples. Deposited dust for sampling was analyzed by polarization microscope (PLM) and scanning electron microscope?energy dispersive x-ray spectrometer (SEM-EDX) to detect asbestos. Subsequently, the elements of the deposited dust with asbestos were analyzed by SEM-EDX to assess the contribution of sources such as abandoned mines, slate and soil. Results: Among the 105 samples, asbestos was detected by PLM in 29 (27.6%) sampling points, and detected by SEM in 56 (48.6%) sampling points. Asbestos in indoor residences was detected by PLM in four sampling points, and by SEM in 12 sampling points. Asbestos detection in indoor residences may be due to ventilation between indoors and outdoors, and indicates long-term exposure. The asbestos detection rate for outdoor residences in the case group was higher than that in the control group. This can be explained as the case group having had higher exposure to asbestos, and there has been continuous exposure to asbestos in the control group as well as the case group. Conclusion: Past residential asbestos exposure may be associated with asbestosis among local residents near abandoned asbestos mines. Odds ratios were calculated for asbestos detection in outdoor residence by logistic regression analysis. Odds ratio between asbestos detection and asbestosis pulmonum was 3.36 (95% CI 0.90-12.53) (p=0.072), adjusting for age, sex, smoking status and work history with multi-variable logistic regression by PLM analysis method.

• Journal of Distribution Research
• /
• v.16 no.2
• /
• pp.95-118
• /
• 2011

The purpose of this study is as follows. First, we investigate empirically the effects of social network properties such as social network density and centrality of a franchisee on its information sharing with various subjects such as the franchisor and other franchisees in the franchise system. Second, we examine exploratively if tie strength between a franchisee and its franchisor plays a moderating role on the relationship between social network properties and information sharing. The study model was established as shown in

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.29 no.3
• /
• pp.162-168
• /
• 2017

Shallow-water bathymetry survey has been conducted using high definition color images obtained at the altitude of 100 m above sea level using a drone. Shallow-water bathymetry data are one of the most important input data for the research of beach erosion problems. Especially, accurate bathymetry data within closure depth are critically important, because most of the interesting phenomena occur in the surf zone. However, it is extremely difficult to obtain accurate bathymetry data due to wave-induced currents and breaking waves in this region. Therefore, optical remote sensing technique using a small drone is considered to be attractive alternative. This paper presents the potential utilization of image processing algorithms using multi-variable linear regression applied to red, green, blue and grey band images for estimating shallow water depth using a drone with HD camera. Optical remote sensing analysis conducted at Wolpo beach showed promising results. Estimated water depths within 5 m showed correlation coefficient of 0.99 and maximum error of 0.2 m compared with water depth surveyed through manual as well as ship-board echo-sounder measurements.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.9
• /
• pp.93-102
• /
• 2019

This study examined the nurse's emotional exhaustion and influencing factors, and the relationship between emotional burnout and hospital nursing workload using multilevel logistic regression analysis. The study subjects were 3,083 nurses of 65 hospitals, who participated in the training conducted by the Healthcare Industry Trade Union in 2010 and responded to the questionnaire. First, 71.6% of nurses experienced emotional exhaustion, and the average score of emotional exhaustion was 33.53. Second, the 'non - nursing work experience' showed a significant effect on emotional exhaustion. Third, the 'sufficient nursing staff' variable increased the emotional exhaustion. Fourth, the nursing grades were correlated significantly with emotional exhaustion compared to those with more than four beds per nurse. Fifth, in the case of working in the internal ward, the shorter the clinical career, the higher the emotional exhaustion. In other words, higher emotional exhaustion was associated with more nursing work experience, more perceived insufficient nursing staff, more nurses per bed, the department of internal medicine, and a shorter clinical career.

• Journal of the Korean earth science society
• /
• v.38 no.4
• /
• pp.283-292
• /
• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Journal of the Korean Society of Radiology
• /
• v.10 no.3
• /
• pp.145-152
• /
• 2016

This study, targeting a cancer patient undergoing radiation treatment, conducted this research with the aim of looking into the relevance between family support belonging to a patient's primary environment, social support consisting of medical personnel, and the quality of life; this study set 199 patients available for investigation from Jan. 25, 2012 until April 30, 2012 as research subjects among the cancer patients undergoing radiation treatment at the Radiation Oncology Department of a university hospital located in Seoul Metropolitan City. In the analysis of collected data, this study conducted t-test using SPSS/WIN 18.0 Statistical Program, and looked into the relevancy between independent variables including social support, and the quality of life as a dependent variable using analysis of variance, correlation analysis and multi-regression analysis. Conclusively, it was found that the higher the family support perceived by a cancer patient undergoing radiation treatment, the higher the quality of his/her life; thus, this study could learn that there exists a significant relation between family support and the quality of life. Accordingly, it is thought that it's necessary to develop an intervention strategy which makes it possible to intensify family support and social support, etc. for the purpose of improving the quality of life of cancer patients undergoing radiation treatment; further, this study thinks that it's necessary to do additional research which could analyze diverse aspects by subdividing the future quality of life by area.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.9
• /
• pp.351-360
• /
• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.