• Journal of the Korean GEO-environmental Society
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• v.15 no.1
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• pp.63-68
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• 2014

In this study, it was found that determined the photocatalytic degradation of antibiotics (lincomycin, LM) with various catalyst composite of titanium dioxide ($TiO_2$), hydroxyapatite (HAP) and germanium (Ge) under UV-A irradiation. At first, various type of complex catalysts were investigated to compare the enhanced photocatalytic potential. It was observed that in order to obtain the removal efficiencies were $TiO_2/HAP/Ge$ > $TiO_2/Ge$ > $TiO_2/HAP$. The composition of $TiO_2/HAP/Ge$ using a statistical approach based on mixture analysis design, one of response surface method was investigated. The independent variables of $TiO_2$ ($X_1$), HAP ($X_2$) and Ge ($X_3$) which consisted of 6 condition in each variables was set up to determine the effects on LM ($Y_1$) and TOC ($Y_2$) degradation. Regression analysis on analysis of variance (ANOVA) showed significant p-value (p < 0.05) and high coefficients for determination value ($R^2$ of $Y_1=99.28%$ and $R^2$ of $Y_2=98.91%$). Contour plot and response curve showed that the effects of $TiO_2/HAP/Ge$ composition for LM degradation under UV-A irradiation. And the estimated optimal composition for TOC removal ($Y_2$) were $X_1=0.6913$, $X_2=0.2313$ and $X_3=0.0756$ by coded value. By comparison with actual applications, the experimental results were found to be in good agreement with the model's predictions, with mean results for LM and TOC removal of 99.2% and 49.3%, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1309-1320
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• 2015

This study made an attempt to analyse the relationship between operation environment and fleet size per route which represents the level of service for inner-city bus service. Regression analysis method has been adopted as main analysis tool and 98 routes of bus operation status in Ulsan city as of 2013 has also been selected for analysis target. Correlation analysis was performed to identify the relationship between dependent and independent variables. There are three types of model for whole sample, type operation, and bus route operation system. These are the results of the current study. 1. The model developed for whole sample of 98 routes is as follows. Y(Fleet Size)=$-4.532+0.00002877*X_1$(Revenue). This model shows that it is necessary to have more than 140 passengers per day to increase fleet size of each bus route in Ulsan. 2. Models developed by type of operation (which are standard, express, and middle sized) are shown below. Stand Bus : Y(Fleet Size)=$-10.954+0.00004283*X_1$(Revenue). It is identified that more than 153 passengers need to use standard bus to increase fleet size per each standard bus, Middle Sized Bus : Y(Fleet Size)=-0.859+0.00001438*X1(Revenue). For middle sized bus, at least 52 daily passengers are needed to increase number of bus in each route. 3. Models developed for each route operation systems are as belows. Joint Operation Group : Y(Fleet Size)=$-4.786+0.00003028*X_1$(Revenue). Individual Operation Group : Y(Fleet Size)=$-2.339+0.00002030*X_1$(Revenue). These model provide similar result which 140 people is the minimum number of passenger to raise the number of vehicles in each route. This result shows that the route operation systems does not affect the raise number of cars significantly.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.469-480
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• 2017

This study aims to understand the relationship between lineaments and epicenters in Geochang region, Gyungsangnam-do, South Korea. An instrumental observation of earthquakes has been started by Korea Meteorological Administration (KMA) since 1978 and there were 6 earthquakes with magnitude ranging 2 to 2.5 in Geochang region from 1978 to 2016. Lineaments were extracted from LANDSAT 8 satellite image and shaded relief map displayed in 3-dimension using Digital Elevation Model (DEM). Then, lineament density was statistically examined by hotspot analysis. Hexagonal grids were generated to perform the analysis because hexagonal pattern expresses lineaments with less discontinuity than square girds, and the size of the grid was selected to minimize a variance of lineament density. Since hotspot analysis measures the extent of clustering with Z score, Z scores computed with lineaments' frequency ($L_f$), length ($L_d$), and intersection ($L_t$) were used to find lineament clusters in the density map. Furthermore, the Z scores were extracted from the epicenters and examined to see the relevance of each density elements to epicenters. As a result, 15 among 18 densities,recorded as 3 elements in 6 epicenters, were higher than 1.65 which is 95% of the standard normal distribution. This indicates that epicenters coincide with high density area. Especially, $L_f$ and $L_t$ had a significant relationship with epicenter, being located in upper 95% of the standard normal distribution, except for one epicenter in $L_t$. This study can be used to identify potential seismic zones by improving the accuracy of expressing lineaments' spatial distribution and analyzing relationship between lineament density and epicenter. However, additional studies in wider study area with more epicenters are recommended to promote the results.

• Journal of Life Science
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• v.33 no.7
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• pp.565-573
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• 2023

In light of the complex interactions between the host animal and its resident gut microbiomes, studies of these microbial communities as a means to improve cattle production are important. This study was conducted to analyze the intestinal microorganisms of Holstein (HT) and Jersey (JS), raised in Korea and to clarify the differences in microbial structures according to cattle species through next-generation sequencing. The alpha-diversity analysis revealed that most species richness and diversity indices were significantly higher in JS than in HT whereas phylogenetic diversity, which is the sum of taxonomic distances, is not significant. Microbial composition analysis showed that the intestinal microbial community structure of the two groups differed. In the both groups, a significant correlation was observed among the distribution of several microbes at the family level. In particular, a highly significant correlation (p<0.0001) among a variety of microbial distributions was found in JS. Beta-diversity analyis was to performed to statistically verify whether a difference exists in the intestinal microbial community structure of the two groups. Principal coordinate analysis and unweighted pair group method with arithmetic mean (UPGMA) clustering analysis showed separation between the HT and JS clusters. Meanwhile, permutational multivariate analysis of variance (PERMANOVA) revealed that their microbial structures are significantly different (p<0.0001). LEfSe biomarker analysis was performed to discover the differenc microbial features between the two groups. We found that several microbes, such as Firmicutes, Bacilli, Moraxellaceae and Pseudomonadales account for most of the difference in intestinal microbial community structure between the two groups.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.157-165
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• 2006

The change of external/internal factors of the cell rquires specific biological functions to maintain life. Such functions encourage particular genes to jnteract/regulate each other in multiple ways. Accordingly, we applied a linear decomposition model IFSA, which derives hidden variables, called the 'expression mode' that corresponds to the functions. To interpret gene interaction/regulation, we used a cross-correlation method given an expression mode. Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. These methods assume that gene expression is controlled by a linear combination of uncorrelated/indepdendent latent variables. However these methods have some difficulty in grouping similar patterns which are slightly time-delayed or asymmetric since only exactly matched Patterns are considered. In order to overcome this, we employ the (IFSA) method of [1] to locate phase- and shut-invariant features. Membership scoring functions play an important role to classify genes since linear decomposition models basically aim at data reduction not but at grouping data. We address a new function essential to the IFSA method. In this paper we stress that IFSA is useful in grouping functionally-related genes in the presence of time-shift and expression phase variance. Ultimately, we propose a new approach to investigate the multiple interaction information of genes.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.47-75
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• 2009

Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the average variance extracted (AVE). However, there was little discussion of direct (47 (28.66%)), indirect, and total effect in the SEM models. Based on these findings, we suggest general guidelines for the use of SEM and propose some recommendations on concerning issues of latent variables models, raw data, sample size, data screening, reporting parameter estimated, model fit statistics, multivariate normality, confirmatory factor analysis, reliabilities and the decomposition of effects.

• Journal of Distribution Research
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• v.17 no.5
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• pp.85-111
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• 2012

Introduction : The purpose of this study was to examine the relationship between perceptual characteristics of consumers and intent of relational continuance for PB(Private Brand) products in discount stores. This study was conducted as an empirical study based on survey. For the empirical study, factors of PB products as characteristics perceived by consumers such as perceived quality, store image, brand image and perceived value were deduced from preceding studies. The effect of such factors on intent of relational continuance mediated by brand trust and brand equity of PB products was structurally examined. Research Model : Based on theory analysis and hypotheses, constructed a Structural Equation Model(SEM). The research model is shown in Figure 1. Research Method : This paper is based on s qualitative study of selected literature and empirical data. The survey for empirical study was carried out on consumers in Gyeonggi and Busan between January 2012 and May 2012. 300 surveys were distributed and 253 (84.3%) of them were returned. After excluding omissions and insincere responses, 245 surveys (81.6%) were used for final analysis as effective samples. Result : First of all, the Reliability was carried out for instrument used. The lower limit of 0.7 for Cronbach's Alpha as suggested by Hair et al. (1998). And Construct validity was established by carrying out exploratory factor analysis by Varimax rotation for all. Four factor result for the consumer's perceptual characteristics of PB Products, two mediating factors and one dependent factor. All constructs included in research framework have acceptable validity and reliability. Table 1 shows the factor loading, eigen value, explained variance and Cronbach's alpha for each factor. In order to assure validity of constructs, I implemented Confirmatory Factor Analysis (CFA), using AMOS 20.0. In confirmatory factor analysis, researcher can take control over the specification of indicators for each factor by hypothesizing that a specific factor is loaded with the relevant indicators. Moreover, CFA is particularly useful in the validation of scale for the measurement of specific construct. CFA result summarized Table 2 shows that the fit measures of all constructs fulfill the recommended level and loadings are significant. To test causal relationship between constructs in the research model, used AMOS 20.0 that provides a graphic module as method for analysing Structural Equation Modeling. The result of hypothesis test is shown in Table 3. As a result of empirical study, perceived quality, brand image and perceived value as selected attributes for PB products showed significantly positive (+) effect on brand trust and brand equity. Furthermore, brand trust and brand equity showed significantly positive (+) effect on intent of relational continuance. However, store image of discount stores selling the PB products was analyzed to have positive (+) effect on brand trust and no significant effect on brand equity. Discussion : Based on the results of this study, the relationship between overall quality, store image, brand image and value perceived by consumers about PB products and intent of relational continuance was structurally verified as being mediated by brand trust and brand equity. Looking at the results, a strategic approach that maximizes brand trust and equity value for PB products by large discount stores is required on top of basic efforts to improve quality, brand image and value of PB products in order to maximize consumer's intent of relational continuance and to continuously attract repeated purchase of products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.257-269
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• 2021

This study aimed to secure basic data for the development and operation of well-aging education programs by analyzing the physical, mental, and socio-economic needs of well-aging education for successful aging. The research tool was developed as a questionnaire to investigate the perception of well aging and the needs of well-aging education in terms of physical, mental, and socio-economic aspects. In February 2021, 1949 adults over the age of 19 were surveyed through an online and mobile survey by Gallup Korea. Descriptive statistics analysis, variance analysis, Borich needs analysis, and IPA analysis were conducted to analyze the needs of well-aging education. The results revealed economic power, exercise, and chronic disease management to be high in terms of the overall priority of the education needs for well-aging, and infectious disease management, independence, and social responsibility were surveyed in the order of low education needs. In terms of economic power, education needs were highest among all age groups except for the middle-age group (35-49 years old), 82.4% of all respondents, and education needs for exercise and chronic disease management were highest in the middle-age group. Therefore, it is necessary to develop well-aging education programs for each life cycle. These results are expected to be used as empirical data in establishing a platform for developing and operating educational programs for well aging.

• Journal of radiological science and technology
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• v.36 no.4
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• pp.291-297
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• 2013

This study is aimed to evaluate the effect of $T_{1/2}$ upon count rates in the analysis of dynamic scan using NaI (Tl) scintillation camera, and suggest a new quality control method with this effects. We producted a point source with $^{99m}TcO_4^-$ of 18.5 to 185 MBq in the 2 mL syringes, and acquired 30 frames of dynamic images with 10 to 60 seconds each using Infinia gamma camera (GE, USA). In the second experiment, 90 frames of dynamic images were acquired from 74 MBq point source by 5 gamma cameras (Infinia 2, Forte 2, Argus 1). There were not significant differences in average count rates of the sources with 18.5 to 92.5 MBq in the analysis of 10 to 60 seconds/frame with 10 seconds interval in the first experiment (p>0.05). But there were significantly low average count rates with the sources over 111 MBq activity at 60 seconds/frame (p<0.01). According to the second analysis results of linear regression by count rates of 5 gamma cameras those were acquired during 90 minutes, counting efficiency of fourth gamma camera was most low as 0.0064%, and gradient and coefficient of variation was high as 0.0042 and 0.229 each. We could not find abnormal fluctuation in $x^2$ test with count rates (p>0.02), and we could find the homogeneity of variance in Levene's F-test among the gamma cameras (p>0.05). At the correlation analysis, there was only correlation between counting efficiency and gradient as significant negative correlation (r=-0.90, p<0.05). Lastly, according to the results of calculation of $T_{1/2}$ error from change of gradient with -0.25% to +0.25%, if $T_{1/2}$ is relatively long, or gradient is high, the error increase relationally. When estimate the value of 4th camera which has highest gradient from the above mentioned result, we could not see $T_{1/2}$ error within 60 minutes at that value. In conclusion, it is necessary for the scintillation gamma camera in medical field to manage hard for the quality of radiation measurement. Especially, we found a tendency that count rate changes over time at this study, and we proved that it can effect $T_{1/2}$. And also, there is need of appropriate phantoms and the method of quality management like this study, because there are not any advice or limitation degrees for domestic medical purpose scintillation camera.