• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.419-428
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• 2021

In order to develope a mobile-based greenhouse energy calculation program, firstly, the overall thermal transmittance of 10 types of major covers and 16 types of insulation materials were measured. In addition, to estimate the overall thermal transmittance when the cover and insulation materials were installed in double or triple layers, 24 combinations of double installations and 59 combinations of triple installations were measured using the hotbox. Also, the overall thermal transmittance value for a single material and the thermal resistance value were used to calculate the overall thermal transmittance value at the time of multi-layer installation of covering and insulating materials, and the linear regression equation was derived to correct the error with the measured values. As a result of developing the model for estimating thermal transmittance when installing multiple layers of coverings and insulating materials based on the value of overall thermal transmittance of a single-material, the model evaluation index was 0.90 (good when it is 0.5 or more), indicating that the estimated value was very close to the actual value. In addition, as a result of the on-site test, it was evaluated that the estimated heat saving rate was smaller than the actual value with a relative error of 2%. Based on these results, a mobile-based greenhouse energy calculation program was developed that was implemented as an HTML5 standard web-based mobile web application and was designed to work with various mobile device and PC browsers with N-Screen support. It had functions to provides the overall thermal transmittance(heating load coefficient) for each combination of greenhouse coverings and thermal insulation materials and to evaluate the energy consumption during a specific period of the target greenhouse. It was estimated that an energy-saving greenhouse design would be possible with the optimal selection of coverings and insulation materials according to the region and shape of the greenhouse.

• Tuberculosis and Respiratory Diseases
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• v.54 no.2
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• pp.210-218
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• 2003

Background : Sex specific cross sectional reference values for the lung function indices usually employ a linear model with a term for age and height. The purpose of this study was to determine the effects of the body mass index (BMI), the fat percentage of the body mass and the fat-free mass index (FFMI) on the forced expiratory volume curve. Methods : Between January 2000 and December 2001, a total of 300 subjects, 150 men and 150 women (mean age : $45{\pm}13$ years), with a normal lung function were enrolled in the study sample. This study measured the $FEV_1$, FVC and $FEF_{25-75%}$ from the forced expiratory volume curve by a spirometer and the body composition by a bioelectrical impedance method in all subjects. Multiple regression analysis was used in order to examine the effects of the body composition on the parameters derived from the forced expiratory volume curve. Results : After adjusting for age, the BMI and Fat percentage improved the descriptions of the FVC (p<0.05, $r^2=0.491$) and $FEV_1$ (p<0.05, $r^2=0.654$) in women. In contrast, the FFMI contributed significantly to the FVC (p<0.05, $r^2=0.432$) and $FEV_1$ (p<0.05, $r^2=0.567$) in men. The $FEF_{25-75%}$ correlated with the fat percentage in women (p<0.05, $r^2=0.337$). Conclusion : These results suggest that the BMI, the fat percentage and the FFMI are significant determinants of the forced expiratory volume curve. The plmonary function test, when considering the BMI, the fat percentage and the FFMI, might be useful in clinical applications.

• Journal of dental hygiene science
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• v.13 no.1
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• pp.62-70
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• 2013

The aim of this study was to analyze factors related to xerostomia in adults aged 30 years and over. The data were analyzed using the t-test, one-way ANOVA and multiple linear regression analysis in the SPSS version 12.0 program. The results were as follow. 1. The respondents who were older than 51 years old, unemployed and had less than 3 million won of average monthly income were more aware of xerostomia (p<0.05). 2. The respondents who answered poor and moderate for their general and oral health and the group with duplicate medication and comorbidity were more aware of xerostomia (p<0.05). 3. The respondents that had problems in chewing, communication, ordinary activities and complained of pain discomfort and suffered from anxiety depression were more aware of xerostomia (p<0.001). 4. The respondents that answered frequent dryness on their skin, eyes, lips, and nasal mucosa were more aware of xerostomia (p<0.001). 5. Xerostomia showed highest correlation with quality of life ($\beta$=0.436) followed by the number of medications ($\beta$=0.239), sense of entire body dryness ($\beta$=0.200), feeling of hopelessness ($\beta$=0.160) and number of oral mucosa disease symptoms ($\beta$=0.099) (p<0.05). According to the results of the study, xerostomia may cause deterioration in quality of life. Thus, it is advised to improve the patient management system among dental professions to prevent various complications caused by xerostomia and conduct regular health education on the cause and management method of xerostomia.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.883-893
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• 2019

In this study, crop coefficients were calculated in two different methods and the results were evaluated. In the first method, appropriateness of GLDAS-based evapotranspiration was evaluated by comparing it with observed data of Cheongmi-cheon (CMC) Flux tower. Then, crop coefficient was calculated by dividing actual evapotranspiration with potential evapotranspiration that derived from GLDAS. In the second method, crop coefficient was determined by using MLR (Multiple Linear Regression) analysis with vegetation index (NDVI, EVI, LAI and SAVI) derived from MODIS and in-situ soil moisture data observed in CMC, In comparison of two crop coefficients over the entire period, for each crop coefficient GLDAS K_c and SM&VI K_c, shows the mean value of 0.412 and 0.378, the bias of 0.031 and -0.004, the RMSE of 0.092 and 0.069, and the Index of Agree (IOA) of 0.944 and 0.958. Overall, both methods showed similar patterns with observed evapotranspiration, but the SM&VI-based method showed better results. One step further, the statistical evaluation of GLDAS K_c and SM&VI K_c in specific period was performed according to the growth phase of the crop. The result shows that GLDAS K_c was better in the early and mid-phase of the crop growth, and SM&VI K_c was better in the latter phase. This result seems to be because of reduced accuracy of MODIS sensors due to yellow dust in spring and rain clouds in summer. If the observational accuracy of the MODIS sensor is improved in subsequent study, the accuracy of the SM&VI-based method will also be improved and this method will be applicable in determining the crop coefficient of unmeasured basin or predicting the crop coefficient of a certain area.

• Journal of Nutrition and Health
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• v.41 no.8
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• pp.733-741
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• 2008

In the present study, we comprehensively examined the associations of plasma levels of total adiponectin and high molecular weight (HMW) adiponectin with the features of cardiometabolic risks including body fat distribution, dyslipidemia, insulin resistance and inflammatory markers in a cross-sectional study of 110 treated hypertensive patients. Blood lipid profiles, high sensitivity C-reactive protein (hsCRP) and homeostasis model assessment of insulin resistance (HOMA- IR) derived from fasting glucose and insulin concentrations were determined. Plasma levels of tumor necrosis factor-${\alpha}$ (TNF-${\alpha}$), interleukin-6 (IL-6) and intercellular adhesion molecule-1 (ICAM-1) were analyzed using ELISA. The results showed that plasma levels of HMW-adiponectin were negatively associated with body mass index (BMI, r = - 0.203, p < 0.05) and waist circumference (r = -0.307, p < 0.01), which was not shown in total adiponectin. Plasma levels of HMW-adiponectin were negatively associated with triglyceride (r = -0.223, p < 0.05) and positively associated with HDL-cholesterol (r = 0.228, p < 0.05). Plasma levels of adiponectin were positively associated with HDL-cholesterol (r = 0.224, p < 0.05). Plasma levels of HMW-adiponectin were negatively associated with hsCRP (r = -0.276, p < 0.01) and IL-6 (r = -0.272, p < 0.01). In addition, there were weak associations between plasma levels of HMWadiponectin and TNF-${\alpha}$ (r = -0.163, p = 0.07) and ICAM-1 (r = -0.158, p = 0.09). However, there were no significant associations of total adiponectin with inflammatory markers except hsCRP (r = -0.203, p < 0.05). Stepwise multiple linear regression analysis showed that only plasma levels of HMW-adiponectin was an independent factor influencing serum levels of hsCRP, a marker of systemic low grade inflammation, after adjusting for age, gender, BMI, waist circumference, alcohol intake, smoking status, blood lipids, total adiponectin and drug use (p < 0.01). These results suggest that HMW-adiponectin, rather than total adiponectin, is likely to be closely associated with the features of cardiometabolic risks in treated hypertensive patients and might be effective biomarker for the prediction of cardiovascular disease.

• Sleep Medicine and Psychophysiology
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• v.27 no.1
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• pp.16-23
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• 2020

Objectives: Sleep disturbance in the elderly is associated with cognitive decline. Sleep quality is known to deteriorate with age, and prospective studies seldom have examined the relationship between sleep quality and cognitive function. This study investigates the relationship between early sleep quality and cognitive function based on six-year follow-up data of community individuals older than 60 years. Methods: The participants included 622 community elderly people older than 60 years from Jinju-Si. The final analysis comprised 322 elderly people. Pittsburgh sleep quality index (PSQI) and the Korean version of Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K) were used to assess early sleep quality and cognitive function after six years. Multiple linear regression analysis was performed to investigate the association between early sleep quality and cognitive function in the elderly. Results: Early sleep quality (PSQI) was significantly associated with the results of the digit span test, clock drawing test (clox 1), and word recall test after six years. Sleep quality (PSQI) decreased significantly after six years, and lower quality of sleep (PSQI) score was associated with higher digit span test score (β = -0.167, p = 0.026) and higher clock drawing test score (β = -0.157, p = 0.031). Lower quality of sleep (PSQI) score was associated with higher word recall test (β = -0.140, p = 0.039). Conclusion: The digit span test, word recall test, and clock drawing task (CLOX 1) shown to be significantly associated to sleep quality can be performed fast and easily in clinical practice. It is important to assess early cognitive function in the elderly with poor sleep quality, and further studies could suggest that these tests may be useful screening tests for early dementia in elderly with poor sleep quality.

• Sleep Medicine and Psychophysiology
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• v.21 no.1
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• pp.21-28
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• 2014

Objective: Few studies have evaluated the personality characteristics of patients with obstructive sleep apnea (OSA) and simple snoring. We investigated the personality characteristics of OSA and simple snoring patients and compared differences in personality between the two groups using the Eysenck Personality Questionnaire (EPQ). Materials and Methods: Two hundred and thirty-seven patients who were suspected to have OSA or simple snoring participated in this study. A self-questionnaire which included the EPQ was administered to all participants. All subjects underwent polysomnography in a sleep laboratory and those with an apnea-hypopnea index (AHI) ${\geq}5$ were included in the OSA group, while those with an AHI <5 were included in the simple snoring group. Results: OSA patients had significantly lower scores for Psychoticism (F=4.563, p=0.034) than simple snorers. There were no significant differences in Extraversion (F=3.029, p=0.083), Lie (F=0.398, p=0.529), or Neuroticism (F=3.367, p=0.068) scores between the two groups. In the correlation analysis of the OSA group, AHI was positively correlated with Extraversion score (r= 0.16, p=0.029) and negatively correlated with Lie score (B=-0.31, p<0.001). Using multiple stepwise linear regression analysis with the four EPQ parameter scores as dependent variables, Lie score was associated with older age (B=0.14, p<0.001) and lower AHI (B=-0.05, p<0.001), Psychoticism score was associated with higher Pittsburgh Sleep Quality Index (PSQI ; B=0.14, p<0.001), Neuroticism score was associated with higher PSQI (B=0.34, p=0.001) and female sex (B=3.15, p=0.003), and Extraversion score was associated with younger age (B=-0.08, p=0.020) and higher body mass index (B=0.26, p=0.023). Conclusion: This study suggests that patients with OSA are significantly less prone to psychotic personality characteristics than those with simple snoring. Among OSA patients, higher AHI was correlated with low falsehood and high extraversion tendencies.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Clinical and Experimental Pediatrics
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• v.46 no.2
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• pp.173-177
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• 2003

Purpose : Recently, different results about factors affecting accurate quantitation of 24-hr urinary protein(24UP) amount using spot urine protein/creatinine ratio(PCR) have been reported. The current study was designed to evaluate correlation between 24UP amounts and PCR in children, and the effect of 24UP amounts, age, sex, and glomerular filtration rate(GFR) on this correlation. Methods : Among 94 patients who visited the department of pediatrics in Busan Paik Hospital from March 2002 to August 2002, 68 patients whose urinary creatinine excretion was ${\geq}15mg/kg/day$ were included in this study. All the patients were divided into I, II/A, B group(I : 24UP<500 mg/day, II : $24UP{\geq}500mg/day$, A : <10 years of age, B : ${\geq}10years$ of age). Pearson correlation analysis was performed between 24UP and PCR to evaluate the relationship. We defined fractional difference between 24UP and PCR, and then performed multiple regression analysis with 24UP amount, age, GFR and fractional difference. Results : There was a strong positive linear correlation between 24UP and PCR(R=0.936, P<0.0001) in all patients, and the correlation was also good in each group. Using PCR cutoff values of 0.5, the PCR provided high sensitivity, specificity, positive and negative predictive value in predicting 24UP amount ${\geq}500mg$. The factors affecting accurate quantitation of proteinuria using spot urine PCR was age, not 24UP amount, GFR or sex. Conclusion : Spot urine PCR is a useful test but has limitations in predicting 24UP amount. Therefore, it should be used only as screening method. Age-adjusted PCR cutoff values may be necessary to predict 24UP amount in children with proteinuria.

• Journal of the korean academy of Pediatric Dentistry
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• v.34 no.2
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• pp.215-221
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• 2007

The aim of this study was to test whether metric measurements of crown length, root length and apex width during tooth development could be a better basis for correlation with age than the classical methods based on subjective estimations of various stages of tooth development. Panoramic radiographs of 120 children, aged 7 to 9 years, were collected from the department of the pediatric dentistry of Chonnam National University Hospital, Korea. The methods of Mornstad was used to estimate age. The structures measured were crown length root length and apex width in panoramic radiographic. The results were as follows : 1. In the boys, it showed higher correlation between lower 2nd molar crown length, lower 1st molar root length or lower 1st permolar apical width and age. In the girls, it showed higher correlation between lower 2nd premolar crown length, lower 2nd molar root length or lower 1st molar apical width and age. 2. With the aid of a multiple regression model, a linear relationship between some of these distances and age was shown. Boy(months) = 43.958 + lower 2nd molar crown length ${\times}$ 4.392 + lower 1st molar root length ${\times}$ 2.255 - lower 1st permolar apical width ${\times}$ 2.046, Girl(months) = 75.213 + lower 2nd premolar crown length ${\times}$ 3.910 lower 2nd molar root length ${\times}$ 2.280 - lower 1st molar apical width ${\times}$ 6.217 Age was estimated in boys and girls using the mathematic model ; the mean difference between chronological and estimated ages was $-2.1{\pm}6.8$ months for boys and $6.1{\pm}6.2$ months for girls. Therefore, it seems to be more accurate and easier than the earlier methods.