• Journal of Animal Science and Technology
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• v.49 no.5
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• pp.559-568
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• 2007

Recently, copy number variations (CNV) of genes or genomic segments have been intensively studied and various analysis methods have been developed. In this study, quantitative oligonucleotide ligation assay (qOLA) was applied to investigate CNV of KIT gene in the Landrace breed. A combined assay using qOLA and pyrosequencing, 6 genotype classes, I1/I1 or I3/i (IBe), I1/I2 or I3/IP, I1/I3, I1/IP or I2/i (IBe), I2/I2and I2/IP, were identified from 44 Landrace pigs. Genotype assignment using grouping features of measurements on a scatter plot showed 100% agreement with those using a statistical assignment by PROC FASTCLUS procedure implemented in the SAS package. Two versions (3100 and 3130) of ABI sequencers gave the same genotyping results, indicating there was no influence on qOLA by different versions of instrument, however, the means of standard deviation and coefficient of variation from the qOLA on a ABI 3130 (2.33 and 4.10) was lower than those from the qOLA on a ABI 3100 (2.67 and 4.81). Effect of proteinase K treatment on the PCR product followed by qOLA was very clear because noise peaks were disappeared and the observed ration fit better to the reference ratio corresponding to each genotype.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Environmental Science International
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• v.12 no.6
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• pp.665-676
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• 2003

To investigate the chemical characteristics of PM$\_$2.5/ in Seoul, Korea, atmospheric particulate matters were collected using a PM$\_$10/ dichotomous sampler including PM$\_$10/ and PM$\_$2.5/ inlet during the period of October 2000 to September 2001. The Inductively Coupled Plasma-Mass Spectromety (ICP-MS), ion Chromatography (IC) methods were used to determine the concentration of both metal and ionic species. A statistical analysis was performed for the heavy metals data set using a principal component analysis (PCA) to derived important factors inherent in the interactions among the variables. The mean concentrations of ambient PM$\_$2.5/ and PM/sub10/ were 24.47 and 45.27 $\mu\textrm{g}$/㎥, respectively. PM$\_$2.5/ masses also showed temporal variations both yearly and seasonally. The ratios of PM$\_$2.5/PM$\_$10/ was 0.54, which similar to the value of 0.60 in North America. Soil-related chemical components (such as Al, Ca, Fe, Si, and Mn) were abundant in PM$\_$10/, while anthropogenic components (such as As, Cd, Cr, V, Zn and Pb) were abundant in PM2s. Total water soluble ions constituted 30∼50 % of PM$\_$2.5/ mass, and sulfate, nitrate and ammonium were main components in water soluble ions. Reactive farms of NH$_4$$\^$+/were considered as NH$_4$NO$_3$ and (NH$_4$)$_2$SO$_4$ during the sampling periods. In the results of PCA for PM$\_$2.5/, we identified three principal components. Major contribution to PM$\_$2.5/ seemed to be soil, oil combustion, unidentified source. Further study, the detailed interpretation of these data will need efforts in order to identify emission sources.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.124-138
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• 2018

This research was conducted to examine the availability of spatial data for assessing absorption rates of $CO_2$ in the forest of Ansan-si and evaluate the validity of methods that analyze $CO_2$ absorption. To statistically assess the $CO_2$ absorption rates per year, the 1:5,000 Digital Forest-Map (Lim5000) and Standard Carbon Removal of Major Forest Species (SCRMF) methods were employed. Furthermore, Land Cover Map (LCM) was also used to verify $CO_2$ absorption rate availability per year. Great variations in $CO_2$ absorption rates occurred before and after the year 2010. This was due to improvement in precision and accuracy of the Forest Basic Statistics (FBS) in 2010, which resulted in rapid increase in growing stock. Thus, calibration of data prior to 2010 is necessary, based on recent FBS standards. Previous studies that employed Lim5000 and FBS (2015, 2010) did not take into account the $CO_2$ absorption rates of different tree species, and the combination of SCRMF and Lim5000 resulted in $CO_2$ absorption of 42,369 ton. In contrast to the combination of SCRMF and Lim5000, LCM and SCRMF resulted in $CO_2$ absorption of 40,696 ton. Homoscedasticity tests for Lim5000 and LCM resulted in p-value <0.01, with a difference in $CO_2$ absorption of 1,673 ton. Given that $CO_2$ absorption in forests is an important factor that reduces greenhouse gas emissions, the findings of this study should provide fundamental information for supporting a wide range of decision-making processes for land use and management.

• Journal of Animal Science and Technology
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• v.51 no.3
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• pp.185-192
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• 2009

Fatty acid synthase (FASN) is a multi-functional enzyme with a central role in the synthesis of long-chain fatty acid and has been considered as a positional candidate gene for BTA 19 quantitative trait loci (QTL) affecting milk-fat content and fatty acid composition. In this study, we sequenced the FASN gene in several cattle breeds including Hanwoo and imported beef cattle, and identified novel DNA polymorphisms and their linkage relationship in Hanwoo. We found a significant frequency difference of the FASN (AF285607) g.17924 A$\rightarrow$G polymorphism between Hanwoo (70%) and other breeds and this polymorphism has been known for an association with fatty acid composition in Angus. Furthermore, by direct DNA sequencing in 18 unrelated Hanwoo, we identified 27 SNPs including nine novel variations in the FASN gene. Among 27 SNPs identified in the FASN gene, four SNPs were further genotyped in 100 Hanwoo and 96 imported beef cattle, and analyzed for haplotype construction and association with beef quality traits. We performed haplotype block and linkage disequilibrium studies using four selected SNPs. Two different haplotype blocks (block A: g.10568 C$\rightarrow$T and g.11280 G$\rightarrow$ A; block B: g.13125 C$\rightarrow$T and g.17924 G$\rightarrow$A) were constructed and the block A in particular had a very high r2 (0.936), which indicated a nearly complete linkage disequilibrium existed between the g.10568 C$\rightarrow$T and g.11280 G$\rightarrow$A polymorphisms. A total of four major haplotypes (frequency > 0.05) were identified with the four polymorphisms including TATG (0.36), CGCG (0.31), CGTA (0.19) and TACG (0.06). Statistical association analysis revealed that the g.10568 C$\rightarrow$T and g.11280 G$\rightarrow$A polymorphisms in the FASN were significantly associated with meat color (P=0.004) and texture (P=0.0114). The g.13125 C$\rightarrow$T and g.17924 G$\rightarrow$A polymorphisms in the FASN were also significantly associated with back-fat thickness and quantity index (P=0.0179 and 0.0495, respectively). Our findings suggested that the FASN gene polymorphisms may be used for determining the (unsaturated) fatty acid contents and carcass trait in the Hanwoo beef.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.8 no.3
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• pp.251-261
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• 2003

As a part of an on-going project investigating flux of materials in Gomso Tidal Flat, we have monitored temporal and spatial distribution of nitrogen components(TN, PON, DON, DIN) and have sought the relationships with the freshwater input(tidal range, salinity), the biological activities(chlorophyll-${\alpha}$, TP, DIP, silicate) and the resuspended bottom sediment in seawater(SPM) from 1999 to 2000. TN in seawater was 39.05 $\mu\textrm{m}$ol 1$\^$-1/ (31.03∼42.93 $\mu\textrm{m}$ol 1$\^$-1/) without any statistical difference(p<0.05) between the studied periods. Organic nitrogen (DON and PON) occupied 75％, 95％, 73％, and 75％ in April, August, September and November, respectively. DON and PON have been found within the narrow concentration ranges of 11.30∼16.38 $\mu\textrm{m}$ol 1$\^$-1/ and 13.16∼20.04 $\mu\textrm{m}$ol 1$\^$-1/ in spite of severe environmental differences through the studied periods. Dissolved fractions of nitrogen(DON and DIN) occupied 53∼65％ of TN. Only DIN varied with an evident temporal variability: low concentrations(1.325∼1.616 $\mu\textrm{m}$ol 1$\^$-1/) in August and high enrichment(8.377∼14.65 $\mu\textrm{m}$ol 1$\^$-1/) in September. High consumption rate of DIN by phytoplankton and a long-lasted drought probably induced such low concentration of DIN in August. Eventually heavy precipitation probably introduced plenty of new nitrogen sources into Gomso Bay in September. The portion of PON, DON and DIN in the total nitrogen was 40％, 38％ and 22％, respectively. Their contents were in the order of DON>PON>DIN for the year round except PON>DON>DIN only in September. The highest DON portion in August probably due to the active microbial decomposition of organic material in summer. Only in April, some evident negative correlations have been found between chlorophyll-${\alpha}$ and DIN mostly nitrate(-0.64, p<0.01), phosphate(-0.46, p<0.01) and silicate(-0.55, p<0.01). The Si(OH)$_4$/DIN/DIP ratios in the water column suggests the limitation of DIN for the growth of phytoplankton during the dry summer in Gomso Bay, which was the case of August in this work. Even with some difference between the studied periods, the primary factors on the distribution of nitrogen components in seawater overlying the Gomso Tidal Flat have been the tidal range and the freshwater input, but the additional variations were due to the biological activities.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Journal of Korean Neurosurgical Society
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• v.50 no.3
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• pp.166-172
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• 2011

Objective : The intralaminar screw (ILS) fixation technique offers an alternative to pedicle screw (PS) and lateral mass screw (LMS) fixation in the C7 spine. Although cadaveric studies have described the anatomy of the pedicles, laminae, and lateral masses at C7, 3-dimensional computed tomography (CT) imaging is the modality of choice for pre-surgical planning. In this study, the goal was to determine the anatomical parameter and optimal screw trajectory for ILS placement at C7, and to compare this information to PS and LMS placement in the C7 spine as determined by CT evaluation. Methods : A total of 120 patients (60 men and 60 women) with an average age of $51.7{\pm}13.6$ years were selected by retrospective review of a trauma registry database over a 2-year period. Patients were included in the study if they were older than 15 years of age, had standardized axial bone-window CT imaging at C7, and had no evidence of spinal trauma. For each lamina and pedicle, width (outer cortical and inner cancellous), maximal screw length, and optimal screw trajectory were measured, and the maximal screw length of the lateral mass were measured using m-view 5.4 software. Statistical analysis was performed using Student's t-test. Results : At C7, the maximal PS length was significantly greater than the ILS and LMS length (PS, $33.9{\pm}3.1$ mm; ILS, $30.8{\pm}3.1$ mm; LMS, $10.6{\pm}1.3$; p<0.01). When the outer cortical and inner cancellous width was compared between the pedicle and lamina, the mean pedicle outer cortical width at C7 was wider than the lamina by an average of 0.6 mm (pedicle, $6.8{\pm}1.2$ mm; lamina, $6.2{\pm}1.2$ mm; p<0.01). At C7, 95.8% of the laminae measured accepted a 4.0-mm screw with a 1.0 mm of clearance, compared with 99.2% of pedicle. Of the laminae measured, 99.2% accepted a 3.5-mm screw with a 1.0 mm clearance, compared with 100% of the pedicle. When the outer cortical and inner cancellous height was compared between pedicle and lamina, the mean lamina outer cortical height at C7 was wider than the pedicle by an average of 9.9 mm (lamina, $18.6{\pm}2.0$ mm; pedicle, $8.7{\pm}1.3$ mm; p<0.01). The ideal screw trajectory at C7 was also measured ($47.8{\pm}4.8^{\circ}$ for ILS and $35.1{\pm}8.1^{\circ}$ for PS). Conclusion : Although pedicle screw fixation is the most ideal instrumentation method for C7 fixation with respect to length and cortical diameter, anatomical aspect of C7 lamina is affordable to place screw. Therefore, the C7 intralaminar screw could be an alternative fixation technique with few anatomic limitations in the cases when C7 pedicle screw fixation is not favorable. However, anatomical variations in the length and width must be considered when placing an intralaminar or pedicle screw at C7.

• Journal of Bio-Environment Control
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• v.19 no.4
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• pp.251-256
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• 2010

Objective of this research was to investigate the influence of $NH_4^+$ and $NO_3^-$ ratios in liquid feeding on the growth of snapdragon 'Potomac Red' and changes in medium chemical properties. The seeds were sown into 200 plug trays and fertigated once a week with nutrient solution containing various ratios of $NH_4^+$ and $NO_3^-$ such as 0 : 100, 27 : 73, 50 : 50, 73 : 27, and 100 : 0. The total N concentrations were adjusted to 50, 100 and $150\;mg{\cdot}L^{-1}$ in plug stages of 2, 3, and 4, respectively. Determination of seedling growth and analysis of plant tissue and root medum were conducted at 56 days after sowing. The treatment of 27 : 73 ($NH_4^+:NO_3^-$) had the greatest plant height, fresh weight, and dry weight. The N and P contents in 27 : 73 ($NH_4^+:NO_3^-$) treatment based on the above ground plant tissues were 2.39 and 0.39%, respectively, which were the greatest among treatments. The elevation of $NH_4^+$ ratio in fertigation solution decreased tissue Ca and Mg contents, but that did not influence tissue K content. The variations in $NH_4^+:NO_3^-$ ratios impacted the soil solution pH and the difference among treatments had been severe since three weeks after sowing. Elevation of $NH_4^+$ ratios in fertigation solution increased electrical conductivity and concentrations of K, Ca, and Mg in soil solution of root medum. The $NH_4^+$ and $NO_3^-$ concentrations in the soil solution were high in weeks 2, 3, and 4, then decreased gradually as the biomass of seedlings increased. Medium P concentration decreased gradually as seedlings grew, but statistical differences were not observed among treatments.

• Korean Journal of Ecology and Environment
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• v.36 no.3 s.104
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• pp.336-343
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• 2003

Two types of statistical models, simple and multivariate log linear models, were studied for continuity simulation and trend analysis of water qualities in incoming flows to Lake Paldang. Water quality is a function of one independent variable (flow) in the simple log linear model, and of three different variables (flow, time, and seasonal cycle) in multivariate model. The independent variables act as surrogate variables of water quality in both models. The model coefficients were determined by the monthly data. The water qualities included 5-day Biochemical Oxygen Demand ($BOD_5$), Total Nitrogen (TN), and Total Phosphorus (TP) measured from 1995 to 2000 in the South and the North branches of Han River and the Kyoungan Stream. The results indicated that the multivariate model provided better agreements with field measurements than the simple one in a31 attempted cases. Flow dependency, seasonality, and temporal trends of water quality were tested on the determined coefficients of the multivariate model. The test of flow dependency indicated that BOD concentrations decreased as the water flow increased. In TN and TP concentrations, however, there were no discernible flow effects. From the temporal trend analyses, the following results were obtained: 1) no trends on BOD at all three upstreams, 2) increase on TN at the South Branch and the Kyoungan Stream, 3)decrease on TN at the North Branch,4) no trends on TP at the North and the South Branches and 5) increase on TP at the Kyoungan Stream by 3 to 8% per years. The seasonality test showed that there were significant seasonal variations in all three water qualities at three incoming flows.