• Tuberculosis and Respiratory Diseases
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• v.50 no.3
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• pp.320-333
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• 2001

Background : Peak expiratory flow (PEF) provides a simple, quantitative, and reproducible measure of the existence and severity of airflow obstructions. Peak flow meters are designed to monitor the condition asthma patients. There are many reports showing the normal predicted value of PEF in other countries. Studies on healthy Korean adults have been performed in a relatively small sample number and a lower limit for the normal value was not reported. Therefore, an attempt to provide normal predictive PEF value with a lower limit was made. Method : The PEF(Mini-Wright Peak Flow Meter) measurements and spirometry were done in 233 men and 631 women without history of respiratory disease. All subjects were non-smokers with no respiratory symptoms. The normal predictive value and its lower limit were developed by multiple regression analysis. The result was compared with regression equations in other reports. Results : The regression equation for the normal PEF predictive value(L/min) is $25.117+4.587{\times}$Age(year)-$0.064{\times}Age^2+2.931{\times}$Height(cm) in men($R^2=025$), and 146.942-$0.011{\times}Age^2+1.795{\times}$Height(cm)+$0.836{\times}$Weight(kg) in women($R^2=0.21$). The regression equation for the lower limit of this value (L/min) is $25.117+4.587{\times}$Age(year)-$0.064{\times}Age^2+1.936{\times}$Height(cm) in men, and $146.942-0.011{\times}Age^2+1.232{\times}$Height(cm)+$0.481{\times}$Weight(kg) in women. The residuals were normally distributed. The PEF in Korean males was sililar to those reported in British and Japanese subjects. The PEF in Korean females was similar to that in British subjects, but higher than the PEF in Japanese subjects. The lower limit of normal value was 71% of normal predictive PEF value in men and 76% in women. Conclusion : The normal predictive PEF value and its lower limit was measured from 233 male and 631 female asymptomatic, lifelong non-smoking participants. The normal predictive value was different from those of other studies on Korean subjects. Therefore, further studies are required.

• Economic and Environmental Geology
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• v.18 no.1
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• pp.1-9
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• 1985

To understand the characteristics of uranium distribution, and the correlation of the uranium content and major constituents in uraniferous black slates from the Jinsan area of Ogcheon Fold Terrain, forty representative specimens were analyzed by mineralogical and radiometric techniques. According to statistical analysis, the uranium has a positive correlation with organic carbon and limonite, but a negative relation to muscovite and other opaques. The relationship with the highest and meaningful correlation is between log uranium and organic carbon. The log uranium-organic carbon correlation coefficient is 0.845 and these two constituents have about 71.4% association. It suggests that the abundance of organic carbon controlled the uranium precipitation. The relationship of organic carbon to log uranium can be expressed by following regression equation log ($U_3O_8{\times}10^4+1$)=-1.3447+2.5599 log (organic carbon). The multiple regression equation of different major components to log uranium is log ($U_3O_8{\times}10^4+1$)=0.77396+ 0.04465 (organic carbon)+0.00574 (quartz)-0.00964 (muscovite)+0.37827 (biotite)-0.02286 (clay substance)+0.01268 (other silicates)+0.1032 (barite)-0.00224 (apatite)+0.01606 (calcite)+0.08258 (hematite)-0.02406 (limonite)-0.01715 (other opaques).

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.207-228
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• 2007

Measuring Information Technology(IT) organizations' activities have been limited to mainly measure financial indicators for a long time. However, according to the multifarious functions of Information System, a number of researches have been done for the new trends on measurement methodologies that come with financial measurement as well as new measurement methods. Especially, the researches on IT Balanced Scorecard(BSC), concept from BSC measuring IT activities have been done as well in recent years. BSC provides more advantages than only integration of non-financial measures in a performance measurement system. The core of BSC rests on the cause-and-effect relationships between measures to allow prediction of value chain performance measures to allow prediction of value chain performance measures, communication, and realization of the corporate strategy and incentive controlled actions. More recently, BSC proponents have focused on the need to tie measures together into a causal chain of performance, and to test the validity of these hypothesized effects to guide the development of strategy. Kaplan and Norton[2001] argue that one of the primary benefits of the balanced scorecard is its use in gauging the success of strategy. Norreklit[2000] insist that the cause-and-effect chain is central to the balanced scorecard. The cause-and-effect chain is also central to the IT BSC. However, prior researches on relationship between information system and enterprise strategies as well as connection between various IT performance measurement indicators are not so much studied. Ittner et al.[2003] report that 77% of all surveyed companies with an implemented BSC place no or only little interest on soundly modeled cause-and-effect relationships despite of the importance of cause-and-effect chains as an integral part of BSC. This shortcoming can be explained with one theoretical and one practical reason[Blumenberg and Hinz, 2006]. From a theoretical point of view, causalities within the BSC method and their application are only vaguely described by Kaplan and Norton. From a practical consideration, modeling corporate causalities is a complex task due to tedious data acquisition and following reliability maintenance. However, cause-and effect relationships are an essential part of BSCs because they differentiate performance measurement systems like BSCs from simple key performance indicator(KPI) lists. KPI lists present an ad-hoc collection of measures to managers but do not allow for a comprehensive view on corporate performance. Instead, performance measurement system like BSCs tries to model the relationships of the underlying value chain in cause-and-effect relationships. Therefore, to overcome the deficiencies of causal modeling in IT BSC, sound and robust causal modeling approaches are required in theory as well as in practice for offering a solution. The propose of this study is to suggest critical success factors(CSFs) and KPIs for measuring performance for IT organizations and empirically validate the casual relationships between those CSFs. For this purpose, we define four perspectives of BSC for IT organizations according to Van Grembergen's study[2000] as follows. The Future Orientation perspective represents the human and technology resources needed by IT to deliver its services. The Operational Excellence perspective represents the IT processes employed to develop and deliver the applications. The User Orientation perspective represents the user evaluation of IT. The Business Contribution perspective captures the business value of the IT investments. Each of these perspectives has to be translated into corresponding metrics and measures that assess the current situations. This study suggests 12 CSFs for IT BSC based on the previous IT BSC's studies and COBIT 4.1. These CSFs consist of 51 KPIs. We defines the cause-and-effect relationships among BSC CSFs for IT Organizations as follows. The Future Orientation perspective will have positive effects on the Operational Excellence perspective. Then the Operational Excellence perspective will have positive effects on the User Orientation perspective. Finally, the User Orientation perspective will have positive effects on the Business Contribution perspective. This research tests the validity of these hypothesized casual effects and the sub-hypothesized causal relationships. For the purpose, we used the Partial Least Squares approach to Structural Equation Modeling(or PLS Path Modeling) for analyzing multiple IT BSC CSFs. The PLS path modeling has special abilities that make it more appropriate than other techniques, such as multiple regression and LISREL, when analyzing small sample sizes. Recently the use of PLS path modeling has been gaining interests and use among IS researchers in recent years because of its ability to model latent constructs under conditions of nonormality and with small to medium sample sizes(Chin et al., 2003). The empirical results of our study using PLS path modeling show that the casual effects in IT BSC significantly exist partially in our hypotheses.

• Korea Journal of Hospital Management
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• v.7 no.1
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• pp.41-63
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• 2002

The purpose of this study is to analyze the interrelation of influential factors in organizational conflict and organizational commitment. The data for this study were collected through a self-administered survey with a structured Questionnaire to 1,167 subjects from several nursing staff members, administration staff members and medical technicians of six hospitals. In this analysis frequency test, t-test, ANOVA, hierarchical multiple regression and structural equation model were used. The main findings of this study are as follows. 1. Factors which influence organizational conflict were analyzed. The type of occupation and the year of service were socio-demographic variables which influenced organizational conflict positively. Adjusted R square was 0.03. Perceptions on organizational structure and organizational culture were analyzed with two- level variables that were added. The findings were as follows. Adjusted R square increased to 0.25. The year of service, internal process culture and rational goal culture were positive variables. The design of organizational structure, human relations culture and open system culture were negative variables. 2. Variables which influence organizational commitment were analyzed. Age and the year of service were positive variables, while academic background based on high school education was a negative variable. Adjusted R square was 0.16. Perceptions on organizational structure and organizational culture were analyzed with two-level variables that were added. The findings were as follows. The characteristics of organizational structure, human relations culture and organizational culture were positive variables. Adjusted R square increased to 0.55. The variables of organizational conflict were added in 3 steps. Findings were as follows. The variables of hierarchical conflict showed negative influence and were included in two-level influential variables. Adjusted R square increased to 0.56. 3. Structural equation model was analyzed in order to examine the relation between organizational structure and the variables of organizational culture, organizational conflict and organizational commitment. Thirteen path coefficients out of seventeen path coefficients were significant. Age had negative influence on organizational conflict and positive influence on organizational commitment. The year of service had positive influence on organizational conflict and organizational commitment. The design of organizational structure, human relations culture and open system culture had negative influence on organizational. conflict. They had positive influence on organizational commitment. Internal process culture and rational goal culture had positive influence on organizational conflict. Organizational conflict had negative influence on organizational commitment. The squared multiple correlation of this model was 25.1% in organizational conflict and 52.7% in organizational commitment. The conclusion of this study is as follows. Factors in organizational structure and organizational culture, rather than socio-demographic factors, had a stronger influence on the organizational conflict and organizational commitment of hospitals. In order to decrease organizational conflict, to increase organizational commitment and to maximize the effectiveness of hospital management, it is necessary to understand the overall relation between organizational structure, organizational culture, organizational conflict and organizational commitment, with the effort of improving personalized factors and individual factors of organization management.

• Journal of Nutrition and Health
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• v.51 no.6
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• pp.567-579
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• 2018

Purpose: This study examined the mediation effects of dietary habits, nutrient intake, daily life in the relationship between the working hours of Korean shift workers and metabolic syndrome. Methods: Data were collected from the sixth (2013-2015) Korea National Health and Nutrition Examination Survey (KNHANES). The stochastic regression imputation was used to fill missing data. Statistical analysis was performed in Korean shift workers with metabolic syndrome using the SPSS 24 program for Windows and a structural equation model (SEM) using an analysis of moment structure (AMOS) 21.0 package. Results: The model fitted the data well in terms of the goodness of fit index (GFI) = 0.939, root mean square error of approximation (RMSEA) = 0.025, normed fit index (NFI) = 0.917, Tucker-Lewis index (TLI) = 0.984, comparative fit index (CFI) = 0.987, and adjusted goodness of fit index (AGFI) = 0.915. Specific mediation effect of dietary habits (p = 0.023) was statistically significant in the impact of the working hours of shift workers on nutrient intake, and specific mediation effect of daily life (p = 0.019) was statistically significant in the impact of the working hours of shift workers on metabolic syndrome. On the other hand, the dietary habits, nutrient intake and daily life had no significant multiple mediator effects on the working hours of shift workers with metabolic syndrome. Conclusion: The appropriate model suggests that working hours have direct effect on the daily life, which has the mediation effect on the risk of metabolic syndrome in shift workers.

• Journal of Korean Society of Forest Science
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• v.84 no.2
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• pp.226-238
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• 1995

This research was conducted to investigate the influential factors of the runoff of water and hillslope erosion caused by the large-scale harvesting operation. It was carried out on harvested sites (13ha) and non-harvested sites(13ha) in Seoul National University Research Forest [(Mt.) Paekunsan], from 1993 to 1994. 1. The amount of runoff of water was increased as the unit of rainfall increases, and the amount of runoff on harvested sites was larger than that of non-harvested sites by 28% in the first year and 24.5% in the second year after harvesting. According to the multiple regression equation for surface runoff, unit and number of rainfall, amount of hillslope erosion and soil bulk density showed statistically significance($R^2$=0.91). 2. The amount of hillslope erosion on harvested sites was larger than that of non-harvested sites by 7 times during the first year of harvesting and 2 times during the second year. 3. The multiple regression equations for hillslope erosion showed that soil bulk density, surface runoff of water and unit of rainfall(these factors were not controllable) had statistically significance($R^2$=0.74). 4. Soil runoff in harvested and non harvested sites were maximum 6.7% and 1% of the amount of hillslope erosion, respectively during the first year of harvesting. And the second year of harvesting soil runoff in harvested and non harvested cites were maximum 5.7% and 1.9%of the amount of hillslope erosion. From the above results, when in planning for timber harvesting, the buffer strip-woods zone must be remained to diminish soil and water runoff and to preserve water quality.

• Journal of Yeungnam Medical Science
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• v.10 no.2
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• pp.345-359
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• 1993

This study was conducted to identify the factors related to the blood pressure in school children. The study subjects were 144 boys and 140 girls aged 10 years old, 4th grade student of one elementary school in Taegu City. Blood pressure and 18 variables for 284 school children were measured in May 1992. A weight-for-height index, R$\ddot{o}$hrer index(weight/$height^3$) was calculated for each individual as a measure of obesity. Body fat percent, fat weight, and lean body mass were measured by impedance fat meter(Model SIF-891). Serum total cholesterol, high-density lipoprotein, triglyceride, uric acid, total protein, and electrolyte were measured by automated clinical chemistry analyzer(Hitachi 4020). Low-density lipoprotein and atherogenic index were calculated by the equation. The variables assessed were sex, systolic blood pressure, diastolic blood pressure. R$\ddot{o}$hrer index, body fat percent, body fat weight, lean body mass, total cholesterol, high-density lipoprotein, low-density lipoprotein, uric acid, total protein, serum electrolyte and blood glucose. The mean systolic and diastolic blood pressures for boys were 104.1mmHg and 66.6mmHg and those for girls were 102.9mmHg and 67.5mmHg. Systolic blood pressure of boys was positively correlated with height, weight, R$\ddot{o}$hrer index, fat weight, lean body mass, and triglyceride. Systolic blood pressure of girls was positively correlated with height, weight, lean body mass, and uric acid. Diastolic blood pressure of boys was positively correlated with weight. R$\ddot{o}$hrer index, and lean body mass. Diastolic blood pressure of girls was positively correlated with height, weight, fat weight, and lean body mass and negatively with total serum protein. In multiple regression analysis for the systolic blood pressure, the significant independent variables were R$\ddot{o}$hrer index and serum sodium in boys, and serum uric acid, fat percent, and R$\ddot{o}$hrer index in girls. In multiple regression analysis of the diastolic blood pressure, the significant independent variables were R$\ddot{o}$hrer index in boys and total serum protein in girls. This study indicated that important factors influencing blood pressure in school children were R$\ddot{o}$hrer index, fat percent, serum sodium, uric acid, and total protein. R$\ddot{o}$hrer index was most important among these. It is recommended to enforce the nutritional education for diet control of obesity to prevent hypertention in children and to adopt healthy life-styles that promote good health and prevent development of cardiovascular risk factors.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.15 no.2
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• pp.124-134
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• 2005

To investigate the exposure effect of polynuclear aromatic hydrocarbons (PAHs), we measured airborne total PAHs as an external dose, urinary 1-hydroxypyrene (1-OHP) as an internal dose of PAHs exposure, and analyzed the relationship between urinary 1-OHP concentration and PAHs exposure. The study population contained 44 workers in steel-pipe coating and paint manufacture industries. The airborne PAHs was obtained during survey day, and urine were sampled at the end of shift. Personal information on age, body weight, height, eniployment duration, smoking habit, and alcohol consumption was obtained by a structured questionnaire. Airborne PAHs were analyzed by the gas chromatograph with mass selective detector. Urinary 1-OHP levels were analyzed by the high performance liquid chromatograph with ultraviolet wavelength detector. For statistical estimation, t-test, ${\chi}^2$-test, analysis of variance, correlation analysis, arid regression analysis were executed by SPSS/PC (Windows version 10). The mean of environmental total PAHs was $87.8{\pm}7.81{\mu}g/m^3$. The mean concentration ($526.5{\pm}2.85{\mu}g/m^3$) of workers in steel-pipe coating industries using coal tar enamel was the higher than that ($17.5{\pm}3.36{\mu}g/m^3$) of workers in paint manufacture industries using coal tar paint. The mean of urinary 1-OHP concentration ($51.63{\pm}3.144{\mu}\;mol/mol$ creatinine) of workers in steel-pipe coating industries was the higher than that ($2.33{\pm}4.709{\mu}\;mol/mol$ creatinine) of workers in paint manufacture industries. The mean of urinary 1-OHP concentration of smokers was the higher than that of non-smokers. There was significant correlation between the urinary concentration of 1-OHP and the environmental concentration of PAHs (r=O.S48, p<0.001), pyrene(r=0.859, p<0.001), and urinary cotinine (r=0.324, p<0.05). The regression equation between the urinary concentration of 1-OHP in ${\mu}g/g$ creatinine($C_{1-OHP}$) and airborne concentration of PAHs (or pyrene) in ${\mu}g/m^3$ ($C_{PAHs}$ or Cpyrene) is: Log ($C_{1-OHP}$)=-0.650+0.889×Log($C_{PAHs}$), where $R^2=0.694$ and n=38 for p<0.001.Log ($C_{1-OHP}$)=1.087+0.707${\times}$Log(Cpyrene), where $R^2=0.713$ and n=38 for p<0.001. From the results of stepwise multiple regression analysis about 1-OHP, significant independents were total PAHs and urinary cotinine (adjusted $R^2=0.743$, p<0.001). In this study, there were significant correlation between the urinary concentration of 1-OHP and the airborne concentration of PAHs. The urinary 1-OHP was effective index as a biomarker of airborne PAHs in workplace. But it was influenced by non-occupational PAHs source, smoking.

• Journal of radiological science and technology
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• v.39 no.1
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• pp.59-69
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• 2016

The objective of this study was to analyze the factors influencing integrated bolus peak timing in contrast-enhanced computed tomographic angiography (CTA) and to determine a method of calculating personal peak time. The optimal time was calculated by performing multiple linear regression analysis, after finding the influence factors through correlation analysis between integrated peak time of contrast medium and personal measured value by monitoring CTA scans. The radiation exposure dose in CTA was $716.53mGy{\cdot}cm$ and the radiation exposure dose in monitoring scan was 15.52 mGy (2 - 34 mGy). The results were statistically significant (p < .01). Regression analysis revealed, a -0.160 times decrease with a one-step increase in heart rate in male, and -0.004, -0.174, and 0.006 times decrease with one-step in DBP, heart rate, and blood sugar, respectively, in female. In a consistency test of peak time by calculating measured peak time and peak time by using the regression equation, the consistency was determined to be very high for male and female. This study could prevent unnecessary dose exposure by encouraging in clinic calculation of personal integrated peak time of contrast medium prior to examination.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.18
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• pp.54-87
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• 1975

The effects of various weather factors on yield components of rice, year variation of yield components within regions, and regional differences of yield components within year were investigated at three Crop Experiment Stations O.R.D., Suweon, Iri, Milyang, and at nine provincial Offices of Rural Development for eight years from 1966 to 1973 for the purpose of providing information required in improving cultural practices and predicting the yield level of rice. The experimental results analyzed by standard partial regression analysis are summarized as follows: 1. When rice was grown in ordinary seasonal culture the number of panicles greatly affected rice yield compared to other yield components. However, when rice was seeded in ordinary season and transplanted late, and transplanted in ordinary season in the northern area the ratio of ripening was closely related to the rice yield. 2. The number of panicles showed the greatest year variation when the Jinheung variety was grown in the northern area. The ripening ratio or 1, 000 grain weight also greatly varied due to years. However, the number of spikelets per unit area showed the greatest effects on yield of the Tongil variety. 2. Regional variation of yield components was classified into five groups; 1) Vegetation dependable type (V), 2) Partial vegetation dependable type (P), 3) Medium type (M), 4) Partial ripening dependable type (P.R), and 5) Ripening dependable type (R). In general, the number of kernel of rice in the southern area showed the greatest partial regression coefficient among yield components. However, in the mid-northern part of country the ripening ratio was one of the component!; affecting rice yield most. 4. A multivariate equation was obtained for both normal planting and late planting by log-transforming from the multiplication of each component of four yield components to additive fashion. It revealed that a more accurate yield could be estimated from the above equation in both cases of ordinary seasonal culture and late transplanting. 5. A highly positive correlation coefficient was obtained between the number of tillers from 20 days after transplanting and the number of panicles at each(tillering) stage 20 days after transplanting in normal planting and late planting methods. 6. A close relationship was found between the number of panicles and weather factors 21 to 30 days, after transplanting. 7. The average temperature 31 to 40 days after transplanting was greatly responsible for the maximum number of tillers while the number of duration of sunshine hours per day 11 to 30 days after transplantation was responsible for that character. The effect of water temperature was negligible. 8. No reasonable prediction for number of panicles was calculated from using either number of tillers or climatic factors. The number of panicles could early be estimated formulating a multiple equation using number of tillers 20 days after transplantation and maximum temperature, temperature range and duration of sunshine for the period of 20 days from 20 to 40 days after transplantation. 9. The effects of maximum temperature and day length 25 to 34 days before heading, on kernel number per panicle, were great in the mid-northern area. However, the minimum temperature and day length greatly affected the kernel number per panicle in the southern area. The maximum temperature had a negative relationship with the kernel number per panicle in the southern area. 10. The maximum temperature was highly responsible for an increased ripening ratio. On the other hand, the minimum temperature at pre-heading and early ripening stages showed an adverse effect on ripening ratio. 11. The 1, 000 grain weight was greatly affected by the maximum temperature during pre- or mid-ripening stage and was negatively associated with the minimum temperature over the entire ripening period.