• Journal of Gastric Cancer
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• v.19 no.3
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• pp.344-354
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• 2019

Purpose: No standard technique has been established for esophagojejunal anastomosis during laparoscopic total gastrectomy (LTG) for gastric cancer owing to the technical difficulty and high complication rate of this procedure. This study was performed to compare the short-term outcomes of circular and linear stapling methods after LTG. Materials and Methods: A total of 106 patients treated between July 2010 and July 2018 were divided into 2 groups according to the following anastomosis procedures: hemi-double-stapling technique (HDST; circular stapling method; group C, n=77) or overlap method (linear stapling method; group L, n= 29). The clinicopathological features and postoperative outcomes, including complications, were analyzed. Multivariate analysis was performed using a logistic regression model to identify the independent risk factors for anastomotic complications. Results: The incidence of anastomotic complications was significantly higher in group C than in group L (28.0% vs. 6.9%, P=0.031). The incidence of anastomosis leakage did not differ between the groups (6.5% vs. 6.9%, P=1.000). However, anastomosis stricture occurred only in group C (13% vs. 0%, P=0.018). Multivariate analysis showed that the anastomosis type was significantly related to the risk of anastomotic complications (P=0.045). Conclusions: The overlap method was superior to the HDST with respect to anastomotic complications, especially anastomosis stricture.

• Geomechanics and Engineering
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• v.8 no.4
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• pp.511-521
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• 2015

It is a common failure type that high-filled embankment slope sideslips. The deformation mechanism and factors influencing the sideslip of embankment slope is the key to reduce the probability of this kind of engineering disaster. Taking Liujiawan high-filled embankment slope as an example, the deformation and failure characteristics of embankment slope and sheet-pile wall are studied, and the factors influencing instability are analyzed, then the correlation of deformation rate of the anti-slide plies and each factor is calculated with multivariate linear regression analysis. The result shows that: (1) The length of anchoring segment is not long enough, and displacement direction of embankment and retaining structure are perpendicular to the trend of the highway; (2) The length of the cantilever segment is so large that the active earth pressures behind the piles are very large. Additionally, the surface drainage is not smooth, which leads to form a potential sliding zone between bottom of the backfill and the primary surface; (3) The thickness of the backfill and the length of the anti-slide pile cantilever segment have positive correlation with the deformation whereas the thickness of anti-slide pile through mudstone has a negative correlation with the deformation. On the other hand the surface water is a little disadvantage on the embankment stability.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• Journal of Korean Medical Science
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• v.33 no.53
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• pp.322.1-322.14
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• 2018

Background: Osteoprotegerin (OPG) plays protective roles against the development of vascular calcification (VC) which greatly contributes to the increased cardiovascular events in patients with chronic kidney disease (CKD). The present study aimed to find the non-traditional, kidney-related cardiovascular risk factors correlated to serum OPG and the effect of serum OPG on the arterial stiffness measured by brachial ankle pulse wave velocity (baPWV) in patients with the pre-dialysis CKD. Methods: We cross-sectionally analyzed the data from the patients in whom baPWV and the serum OPG were measured at the time of enrollment in a prospective pre-dialysis CKD cohort study in Korea. Results: Along with traditional cardiovascular risk factors such as age, diabetes mellitus, pulse pressure, and baPWV, non-traditional, kidney-related factors such as albuminuria, plasma level of hemoglobin, total $CO_2$ content, alkaline phosphatase, and corrected calcium were independent variables for serum OPG in multivariate linear regression. Reciprocally, the serum OPG was positively associated with baPWV in multivariate linear regression. The baPWV in the 3rd and 4th quartile groups of serum OPG were higher than that in the 1st quartile group after adjustments by age, sex and other significant factors for baPWV in linear mixed model. Conclusion: Non-traditional, kidney-related cardiovascular risk factors in addition to traditional cardiovascular risk factors were related to serum level of OPG in CKD. Serum OPG level was significantly related to baPWV. Our study suggests that kidney-related factors involved in CKD-specific pathways for VC play a role in the increased secretion of OPG into circulation in patients with CKD.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.189-192
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• 2006

This study was performed to suggest the mediocre prediction equation of chloride diffusion coefficient which is used to estimate the service life of marine concrete, in order to provide the useful data for concrete mix design of marine concrete. As a result, the mediocre prediction equation of chloride diffusion coefficient which set W/B and mineral admixture replacement ratio as parameters was presented by performing the multivariate non linear regression analysis.

• Analytical Science and Technology
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• v.37 no.1
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• pp.12-24
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• 2024

In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of rice polishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains were analyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Their correlation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensities showed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based on those lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracy performances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model using Si I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy (2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, which is closed related to nutrition, shelf life, appearance, and commercial value of rice products.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.128-135
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• 2022

The comparative evaluation of occupational accident fatality rates (OAFRs) of different countries is complicated owing to the differences in their level of socio-economic development. However, such evaluation is necessary to assess the national occupational safety and health system of a country. This study proposes a statistical method to compare the OAFRs of countries taking into consideration the difference in their level of socio-economic development. We first collected data on the socio-economic indicators and OAFRs of 11 countries over a 30-year period. Next, based on literature survey and statistical correlation analysis, we selected the significant independent variables and built multiple linear regression models to predict OAFR. We also determined the groups of countries having heterogeneous relationships between the independent variables and OAFRs, which are represented by the regression models. The proposed method is demonstrated by comparing the OAFR of Korea with the OAFRs of 10 other developed countries.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.