• Journal of Nutrition and Health
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• v.52 no.2
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• pp.206-216
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• 2019

Purpose: Management of the metabolic risk factors in diabetes patients is essential for preventing or delaying diabetic complications. This study compared the levels of the metabolic risk factors in diabetes patients according to the income levels, and examined the secular trends in recent decades. Methods: The data from the Korea National Health and Nutrition Examination Survey 1998 ~ 2014 were used. The diabetes patients were divided into three groups based on their household income levels. General information was obtained through self-administered questionnaires, and the blood biomarkers and blood pressure data were obtained from a health examination. Multivariable linear regression models were used to compare the metabolic biomarker levels according to the household income levels, adjusting for potential confounding factors. Results: The fasting blood glucose, hemoglobin A1c, and blood lipid (total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride) levels were similar in the three groups. During the survey period of 16 years, the blood pressure showed a significant decreasing trend with time in all groups (p < 0.001). In contrast, the fasting blood glucose (p = 0.004), total cholesterol (p < 0.001), and LDL-cholesterol levels (p = 0.007) decreased significantly, and the HDL-cholesterol level (p < 0.001) increased significantly in the highest-income groups. In the lowest-income group, the fasting blood glucose (p = 0.02), total cholesterol (p < 0.001), and triglyceride (p = 0.003) levels showed a significant decreasing trend over time. On the other hand, the middle-income group showed no significant change in any of the metabolic risk factors except for blood pressure. Conclusion: The level of management of metabolic risk factors according to the income level of Korean diabetes patients was similar. On the other hand, the highest- and lowest-income groups showed positive trends of management of these factors during 16 years of observation, whereas the middle-income group did not show any improvement.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• 한국노년학
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• v.38 no.4
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• pp.1083-1105
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• 2018

The present study sought to capture day-to-day fluctuation of the daily happiness among Korean urban older adults and to examine whether the within person fluctuation of daily happiness is explained by the social and physical activities the older adults experience each day. We also examined whether the within person association between daily social, physical activities and the daily happiness varies by individual characteristics(i.e. gender, age, educational level and health). In addition, we explored the relationships between the level and fluctuation of daily happiness and the level of global happiness. The data was collected by multi-method approach, which includes general survey, daily diary method and collection of physical activity data through the activity monitors. In total, 175 urban older adults participated for seven days of daily diary survey. The data about the number of steps and the time spent on sedentary activities, light intensity physical activities and moderate to vigorous intensity physical activities were also collected during the same period from 16 sub-samples using activity monitors. Hierarchical linear modeling was applied for the analysis. The results were as below. First, the level of happiness of older adults fluctuated during a week, and the patterns of fluctuation varied by the gender and the health. Second, socializing with their children and friends elevated their levels of happiness. Also the impact of contacts with siblings on the level of daily happiness was greater for the unhealthy group compare to the healthy group. Third, older adults were happier on the days when they walked more, but the level of daily happiness decreased on the days when they spent longer time for low intensity physical activities. Lastly, the higher level of daily happiness were related to the higher level of global happiness, but the degree of fluctuation of daily happiness was not related to the level of global happiness. The implications of these results and suggestions for future research are discussed.

• Sleep Medicine and Psychophysiology
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• v.17 no.1
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• pp.41-49
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• 2010

Unlike the case of adult obstructive sleep apnea syndrome (OSAS), there was no consistent finding on the changes of sleep architecture in childhood OSAS. Further understanding of the sleep electroencephalogram (EEG) should be needed. Non-linear analysis of EEG is particularly useful in giving us a new perspective and in understanding the brain system. The objective of the current study is to compare the sleep architecture and the scaling exponent (${\alpha}$) from detrended fluctuation analysis (DFA) on sleep EEG between OSAS and normal children. Fifteen normal children (8 boys/7 girls, 6.0${\pm}4.3$2.2 years old) and twelve OSAS children (10 boys/2 girls, 6.4${\pm}4.3$3.4 years old) were studied with polysomnography (PSG). Sleep-related variables and OSAS severity indices were obtained. Scaling exponent of DFA were calculated from the EEG channels (C3/A2, C4/A1, O1/A2, and O2/A1), and compared between normal and OSAS children. No difference in sleep architecture was found between OSAS and normal controls except stage 1 sleep (%) and REM sleep latency (min). Stage 1 sleep (%) was significantly higher and REM latency was longer in OSAS group (9.3${\pm}4.3$4.3%, 181.5${\pm}4.3$59.9 min) than in controls (5.6${\pm}4.3$2.8%, 133.5${\pm}4.3$42.0 min). Scaling exponent (${\alpha}$) showed that sleep EEG of OSAS children also followed the 'longrange temporal correlation' characteristics. Value of ${\alpha}$ increased as sleep stages increased from stage 1 to stage 4. Value of ${\alpha}$ from C3/A2, C4/A1, O1/A2, O2/A1 were significantly lower in OSAS than in control (1.36${\pm}4.3$0.05 vs. 1.41${\pm}4.3$0.04, 1.37${\pm}4.3$0.04 vs. 1.41${\pm}4.3$0.04, 1.37${\pm}4.3$0.05 vs. 1.41${\pm}4.3$0.05, and 1.36${\pm}4.3$0.07 vs. 1.41${\pm}4.3$0.05, p<0.05). Higher stage 1 sleep (%) in OSAS children was consistent finding with OSAS adults. Lower $'{\alpha}'$ in OSAS children suggests decrease of self-organized criticality or the decreased piling-up energy of brain system during sleep in OSAS children.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.1
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• pp.78-86
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• 2009

Recently, there seems to besome problems in the urban visual landscape as a result of continuous economic growth and industrial development. At the same time, the public has begun to be aware of the importance of visual resources, and the necessity for visual landscape conservation and improvement. Therefore, the development of evaluative indicators for systematic visual landscape planning and design is urgent. The purpose ofthis study is to discover evaluative models and indicators for the diagnosis of urban visual landscapes. This study included the selection of 18 physical indicators(statistical data) by literature reviews, adoption of field and questionnaire surveys at 12 autonomous districts in Seoul and surrounding major mountain valleys and river streams(i.e. Mt. Nam and Han-River). The content of the questionnaire is scenic beauty. Moreover, the linear regression analysis between the scenic beauty mean scores and the physical indicator scores figure out the scenic beauty prediction model. As this study suggests, the most important indicators in urban visual landscapes are 'Greens', 'Park' and 'the number of apartment buildings(higher than 20 stories).' Based on the results, greens and parks should be priority elements to considerin urban landscape planning and design. Moreover, since the number of apartment buildings that are higher than 20 stories has a negative correlation with the scenic beauty score, it can be used as basic data for landscape planning. For the scenic beauty prediction models and evaluative indicators suggest a direction of urban management, each indicator becomes basic data for visual landscape planning and design. In following studies, if physical indicators and case studies are added, the scenic beauty prediction models and evaluative indicators could be more synthetic and systematic. Moreover, the development of physical indicators in three dimensions(3D)(i.e. results from visual district analysis, view surface analysis) could be expected to obtain more general and varied results.

• 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.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.117-120
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• 2011

Purpose: There are 3 warnings for Glucagon tests. First, EDTA tubes that already contain Aprotinin must be used for plasma collection. Second, for freezer storage of centrifuged plasma, glass tubes must be used. Last, glass tubes must be used for testing procedure. So we compared the glucagon results of next 3 situation to those of control group. First, We compared to results by tubes without Aprotinin and with aprotinin. Second, we compared to results by tubes(plastic vs glass) for plasma storage. Third, we compared to results by tubes(plastic vs glass) for testing. We tried to evaluate the results of the 3 different condition. Materials and Methods: 40 healthy adults were studied with normal results on the general medical check up and laboratory tests. We compared the results of 3 different condition belows: Blood were collected in EDTA tube containing aprotinin and plasma was stored in the glass tube for 3 days in a freezer and results were obtained by tests in the glass tubes. Results from EDTA plasma without aprotinin, results from platic tubes for freezer stroage, results from plastic tube when testing. Simple linear regression analysis and paired t-test using SPSS were done for statistical analysis. Commercial glucagon kit(RIA-method)which made by Siemens company were used. Results: Correlation coefficient between results of EDTA tubes with Aprotinin vs without Aprotinin was r=0.783 (p=0.064). Result of specimen in plastic tubes stored 3 days in a freezer showed lower value compared to those in glass tube(r=0.979, p=0.005). Also, results of testing in plastic tubes showed lower values than those testing in glass tubes. (r=0.754, p<0.001). Conclusion: It is recommended for glucagon determination to use EDTA tube with Aprotinin which is a inhibitor of protein breakdown enzyme. Results of plastic tube when storage and testing showed lower value than those of glass tubes, so it is recommended to store and test in glass tubes.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.