• Journal of the Korean Wood Science and Technology
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• v.48 no.5
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• pp.591-607
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• 2020

This study was aimed to investigate the changes of chemical composition of the volatile organic compounds (VOCs) emitted from red pine needles in the process of needle abscission or senescence. The VOCs in intact, senescent, and litter red pine needle samples were analyzed by headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS). And then, multivariate statistical interpretation of the processed data sets was conducted to investigate similarities and dissimilarities of the needle samples. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to investigate the dataset structure and discrimination between samples, respectively. From the data preview, the levels of major components of VOCs from needles were not significantly different between needle samples. By PCA investigation, the data reduction according to classification based on the chlorophyll a / chlorophyll b (Ca/Cb) ratio were found to be ideal for differentiating intact, senescent, and litter needles. The following OPLS-DA taking Ca/Cb ratio as y-variables showed that needle samples were well grouped on score plot and had the significant discriminant compounds, respectively. Several compounds had significantly correlated with Ca/Cb ratio in a bivariate correlation analysis. Notably, the litter needles had a higher content of oxidized compounds than the intact needles. In summary, we found that chemical compositions of VOCs between intact, senescent, and litter needles are different each other and several compounds reflect characteristic of needle.

• Health Policy and Management
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• v.29 no.3
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• pp.277-287
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• 2019

Background: Diabetes is known as one of the most important ambulatory care sensitive conditions. This study purposed to assess the status of continuity of care (COC) and diabetes-related avoidable hospitalizations (DRAHs) of a group of middle- and old-aged patients and to observe the relationship of the two elements by the two age groups. Methods: This study utilized the National Health Insurance Service's National Sample Cohort data and the subjects are diabetes patients of 45 and over, classified into two groups of 'middle-aged'(45-64 years) and 'old-aged'(${\geq}65years$) patients. The dependent variable was DRAHs, which was defined in accordance with the definition of the Organization for Economic Cooperation and Development "Health Care Quality Indicators" project. COC, as an independent variable, is measured by the COC index in this study. Two-part model (multi-variate and multi-level analyses) was utilized. Results: Factors associated with the status and the number of DRAHs differed by each age group. Meanwhile, the two-part model showed that higher COC was associated with a lower risk of preventable hospitalizations in both middle- and old-aged groups. Conclusion: Study findings can provide health policy insights and implications in order to strengthen the primary care system for further improvement of diabetes management, especially for middle- and old-aged groups.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06b
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• pp.365-372
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• 2006

In this paper, the possibility of statistical modeling from the pulp and peroxide bleaching condition variables to predict optical properties of a bleached chemimechanical pulp used in a newsprint paper machine at Mazandaran Wood and Paper Industries Company (MWPI) was studied. Due to the variations in the opacity and the brightness of the bleached pulp at MWPI and to tackle this problem, it was decided to study the possibility of modeling the bleaching process. To achieve this purpose, Multi-variate Regression Analysis was used for model building and it was found that there is a relationship between independent variables and pulp brightness as well as pulp opacity, consequently, two models were constructed. Then, model validation was carried out using new data set in the bleaching plant at MWPI to test model predictive ability and its performance.

• Health Policy and Management
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• v.27 no.3
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• pp.211-218
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• 2017

Background: Korea set up new diagnosis related group (DRG) as demonstration project in 2009. The new DRG was reformed in 2016. The main purpose of study is to identify the effect of reform on accuracy of payment. Methods: This study collected inpatient data from a hospital which contains medical information and cost from 2015 to 2016. The dependent variables were accuracy of total, bundled, unbundled payment, and payment for procedures. To analyze the effect of reform, this study conducted a multi-variate regression analysis adjusting for confounding variables. Results: The accuracy of payment increased after policy reform. The accuracy of total, bundled, unbundled payment, and payment for procedures significantly increased 3.90%, 2.92%, 9.03%, and 14.57% after policy reform, respectively. The accuracy of unbundled payment showed the largest increase among dependent variables. Conclusion: The results of study imply that policy reform enhanced the accuracy of payment. The government needs to monitor side effects such as increase of non-covered services. Also, leads to a considerable improvement in the value of cost unit accounting as a strategic play a role in development of DRG.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.90-97
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• 1999

Recently, there have been many researches to automate processing and analysing image data in medical field, due to the advance of image processing techniques, the fast communication network and high performance hardware. In this paper, we design and implement the system based on the multi-layer neural network model to be able to analyze, differentiate and count blood cells in the peripheral blood image. To do these, we segment red and white-blood cell in blood image acquired from microscope with CCD(Charge-coupled device) camera and then apply the various feature extraction algorithms to classify. In addition to, we reduce multi-variate feature number using PCA(Principle Component Analysis) to construct more efficient classifier. So, in this paper, we are sure that the proposed system can be applied to a pathological guided system.

• Journal of Korean Society of Forest Science
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• v.19 no.1
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• pp.49-54
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• 1973

Surveys of stock volume on steep and vast expanse of mountains, involves various difficulties. And it is extremely uneconomical in forest management point of view, to spend lots of time and man power for surveying such tree volume as the value is much cheaper in comparison with volume and weight. Therefore, a stand volume table estimate easely stock volume per hectare basis from aerial photographs was prepared and correlations to stand volume among factors affecting tree volumation, were studied. Data were 114 places selected from planted Korean white pine, Pinus koreiensis Sieb. et Zucc. stands in Kwangnung Experiment Forest and were computed and analysed by the means of the quantification in the multi-variate statistical analysis. Electronic Data Processing System was applied for data processing at Korean Instiute of Science and Technology. Coefficients of multiple correlations of stand volume table was ranged 0.85~0.88.

• Health Policy and Management
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• v.28 no.4
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• pp.392-401
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• 2018

Background: Physical activity (PA) is critical for maintaining the health and well-being of older people in community and also institutional settings. The purpose of this study was to examine the current status of PA and related individual and organizational factors among older nursing home residents with and without dementia. Methods: This is a secondary data analysis study of a nationwide survey of 92 long-term care facilities in Korea, and the study sample includes older residents with dementia (n= 753) and without dementia (n= 480). The level of PA was measured by PA time and whether or not residents had an outdoor activity over the past 3 days. Multi-variate, multi-level analyses were conducted. Results: More than half of the sample in both groups had no or less than 1 hour of PA. About one out of four older people in our sample had no outdoor activity over the past 3 days regardless of whether or not they had dementia. Among the people without dementia, several individual-level factors were associated with PA, including dependency for activities of daily living, social activity participation, and caregivers' belief in the functional improvement of residents. Unlike the non-dementia group, individual- and organizationallevel factors including staffing level and having the relevant equipment for PA were associated with PA among those with dementia. Conclusion: Study findings provide evidence on the lack of PA among older nursing home residents, and the importance of institutional capacity with regard to human resources and physical equipment for promoting PA among people with dementia, in particular. Policies and supports are needed to promote the implementation of healthy aging programs including PA for older nursing home residents. Such programs should be person-centered with consideration to the physical and cognitive status of individual residents.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.41-53
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• 2015

In meteorological field, many researchers have tried to develop micro scale weather analysis modules for providing real-time weather information service in the metropolitan area. This effort enables us to cope with various economic and social harms coming from serious change in the micro meteorology of a metropolitan area due to rapid urbanization such as quantitative expansions in its urban activity, growth of population, and building concentration. The accuracy of the micro scale weather analysis modules (MSWAM) directly related to usefulness and quality of the real-time weather information service in the metropolitan area. This paper design a evaluation system along with verification tools that sufficiently accommodate spatio-temporal characteristics of the outputs of the MSWAM. For this we proposes a test for the equality of mean vectors of the output series of the MSWAM and corresponding observed time series by using a spectral analysis technique. As a byproduct, a time series cluster analysis method, using a function of the test statistic as the distance measure, is developed. A real data application is given to demonstrate the utility of the method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Journal of muscle and joint health
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• v.25 no.2
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• pp.157-166
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• 2018

Purpose: This study aimed to identify the effect of the level of alcohol drinking and smoking period on the occurrence of lumbar and femoral neck osteoporosis in men aged 50 years and over. Methods: This study analyzed the data of 1,410 men based on results of a National Survey on Nutritional Health for the period 2010-2012. Complex sample multi-variate logistic regression analysis was conducted to determine whether alcohol drinking and smoking period were associated with osteoporosis in men aged 50 years older. Results: The risk of osteoporosis in the femoral neck was 3.43 times (95% confidence interval [CI]=1.15~10.29) higher in men who consumed less alcohol and smoked for a long time compared to men who smoked more than 40 years and consumed much alcohol at once. The risk of osteoporosis in the lumbar spine was 3.45 times (95% confidence interval [CI]=1.69~7.04) higher respectively. Men who consumed much alcohol at a time and smoked less than 40 years were 1.54 (1/0.65) times (95% confidence interval [CI]=0.45~0.93) more likely to be classified as normal than the men who consumed much alcohol and had a long smoking period. Conclusion: The interaction of excessive drinking and smoking has a more detrimental effect on bone mineral density.