• Bulletin of the Korean Chemical Society
• /
• v.34 no.5
• /
• pp.1467-1472
• /
• 2013

Metabolomics is a field that studies systematic dynamics and secretion of metabolites from cells to understand biological pathways based on metabolite changes. The metabolic profiling of intact human colorectal tissues was performed using high-resolution magic angle spinning (HR-MAS) NMR spectroscopy, which was unnecessary to extract metabolites from tissues. We used two different groups of samples, which were defined as normal and cancer, from 9 patients with colorectal cancer and investigated the samples in NMR experiments with a water suppression pulse sequence. We applied target profiling and multivariative statistical analysis to the analyzed 1D NMR spectra to identify the metabolites and discriminate between normal and cancer tissues. Cancer tissue showed higher levels of arginine, betaine, glutamate, lysine, taurine and lower levels of glutamine, hypoxanthine, isoleucine, lactate, methionine, pyruvate, tyrosine relative to normal tissue. In the OPLS-DA (orthogonal partial least square discriminant analysis), the score plot showed good separation between the normal and cancer groups. These results suggest that metabolic profiling of colorectal cancer could provide new biomarkers.

• Nuclear Engineering and Technology
• /
• v.56 no.6
• /
• pp.2182-2189
• /
• 2024

The high-purity germanium gamma-ray spectrometer was used to measure the radioisotope in surface water of lakes in a Chinee petroleum industrial area. 92 samples were collected from surface water of three lakes. Activity concentrations of ²³²Th, ²²⁶Ra and ⁴⁰K in three lakes were measured, distributed in the range of 101.8-209.4, 192.1-224.9 and 335.0-548.9 mBq/L, respectively. Results were all within the limits of WHO and China. Potential environmental and health risks were assessed by calculating some radiation hazard indicators, radium equivalent index, annual effective dose, excess lifetime cancer risk, absorbed dose rate, external hazard index, internal hazard index, annual gonadal dose equivalent, activity utilization index and representative gamma index, which ranged 0.38-0.54 Bq/L, 0.06-0.08 mSv/y, 0.23 × 10^-3-0.31 × 10^-3, 0.17-0.24 nGy/h, 1.01 × 10^-3-1.46 × 10^-3, 1.55 × 10^-3-2.02 × 10^-3, 1.16-1.66 μSv/y, 3.13 × 10^-3-4.45 × 10^-3 and 2.60 × 10^-3-3.77 × 10^-3. The results were all at acceptable levels, meaning no impact on human health. The relationship between the electrical conductivity of surface water and the activity concentration of ²³²Th, ²²⁶Ra and ⁴⁰K was evaluated. The electrical conductivity value was 0.241-0.369 mS/cm, showing a significant correlation coefficient between ²²⁶Ra and ⁴⁰K and electrical conductivity. Multivariate statistical methods were used to determine the relationship between the activity concentrations of ²³²Th, ²²⁶Ra, and ⁴⁰K, radiation hazard indicators and electrical conductivity.

• Journal of Korean Society of Environmental Engineers
• /
• v.38 no.2
• /
• pp.71-78
• /
• 2016

The study of water environment system in Changnyeong-Haman weir section using a statistical analysis has been conducted. Statistical analyses used in this study were the correlation analysis, the principal components, and the factor analysis. The purpose of the study is to establish better understanding of relationships between water quality factors in the Changnyeong-Haman weir section which can provide useful information to manage Nakdong river. According to correlation analyses on COD and TOC, it revealed that the value of correlation coefficient was 0.844. Furthermore, the results from the principal component analysis categorized the water quality factors into three factor groups, the first principal factor group included COD, TOC, BOD, pH, water temperature (WT). And, it was observed that the concentration of cyanobacteria in the water body decreased, while the concentrations of the diatoms and the green algae increased after the events of rainfall.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.2
• /
• pp.284-294
• /
• 2018

Statistical methods have been widely used to estimate the remaining battery runtime of mobile smart devices, such as smart phones, smart gears, tablets, and etc. However, existing work available in the literature only considers a particular statistical method. Thus, it is difficult to determine whether statistical methods are applicable to estimating thr remaining battery runtime of mobile devices or not. In this paper, we evaluated the performance of statistical methods applicable to estimating the remaining battery runtime of mobile smart devices. The statistical estimation methods evaluated in this paper are as follows: simple and moving average, linear regression, multivariate adaptive regression splines, auto regressive, polynomial curve fitting, and double and triple exponential smoothing methods. Research results presented in this paper give valuable data of insight to IT engineers who are willing to deploy statistical methods on estimating the remaining battery runtime of mobile smart devices.

• Horticultural Science & Technology
• /
• v.29 no.4
• /
• pp.382-387
• /
• 2011

The chemical composition of essential oils obtained from aerial parts in spearmint, apple mint and chocolate mint, was investigated by gas chromatography/mass spectrometry analyses. (-)-Carvone (33.0%) was quantitatively major compound in spearmint, followed by R-(+)-limonene (11.7%) and ${\beta}$-phellandrene (9.7%); (-)-carvone (37.4%) and germacrene D (11.9%) in apple mint; and (-)-menthol (34.3%), p-menthone (18.4%) and menthofuran (9.8%) in chocolate mint. Hierarchical cluster analysis and principle components analysis showed the clear difference in chemical composition of the three mint oils.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.1
• /
• pp.25-31
• /
• 2003

There are a lot of factors influencing on the automobile fuel economy such as average speed, average acceleration, acceleration sum per kilometer, and so on. In this study, various driving data were recorded during road tests. The accumulated road test mileage in Seoul metropolitan area is around 1,300 kilometers. The data were analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis. The analyzed results show that the average trip time per kilometer is one of the most important factors to fuel consumption and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• KCID journal
• /
• v.18 no.2
• /
• pp.66-75
• /
• 2011

Multivariate statistical analysis was applied to 110 dam heightening reservoir to classify the building conditions for waterfront centered around cultivated area using data of land cover, landscape, additional water quantity, local economic, tourism resources, and accessibility related variables. Five factors were extracted through factor analysis based on eigen value criteria of more than one. These five factors together account for 68.2% of the total variance. Characteristics of five factors for the downstream of dam heightening reservoirs are building conditions of waterfront, economic conditions, additional water quantity, eco-tours, and accessibility of tourism resources respectively. Five clusters were classified through cluster analysis based on factor score. The classified result shows that third cluster has remunerative terms for building waterfront.

• KDI Journal of Economic Policy
• /
• v.14 no.1
• /
• pp.121-145
• /
• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• The Korean Journal of Applied Statistics
• /
• v.33 no.3
• /
• pp.281-296
• /
• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• The Korean Journal of Applied Statistics
• /
• v.37 no.1
• /
• pp.1-12
• /
• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.