• Animal Bioscience
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• v.36 no.1
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• pp.98-107
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• 2023

Objective: Current quail production is configured as an economic activity in scale. Advancements in quail nutrition have been limited to areas such as breeding and, automation of facilities and ambience. The objective of this study was to evaluate the performance responses, liver and oviduct morphometry, and liver histology of Japanese laying quails subjected to different levels of nitrogen-corrected apparent metabolizable energy (MEn). Methods: A completely random design was used that consisted of nine levels of MEn, six replicates, and five hens per cage with a total of 270 quails. The experimental period lasted for 10 weeks. The variables of performance were subjected to analysis of variance and then regression analysis using the broken-line model. The morphometric and histological variables were subjected to multivariate exploratory techniques. Results: The MEn levels influenced the responses to zootechnical performance. The broken-line model estimated the maximum responses for feed intake, egg production, egg weight, and egg mass as 3,040, 2,820, 1,802, and 2,960 kcal of MEn per kg of diet, respectively. Multivariate analysis revealed that the occurrence of hepatic steatosis and increased levels of Kupffer cells were not related to MEn levels. Conclusion: The level of 2,960 kcal/kg of MEn meets performance variable requirements without compromising hepatic physiology.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.521-528
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• 2016

The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.

• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.517-525
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• 2008

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.365-373
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• 2016

Much of the data used in the analysis of environmental ecological data is being obtained over time. If the number of time points is small, the data will not be given enough information, so repeated measurements or multiple survey points data should be used to perform a comprehensive analysis. The method used for that case is longitudinal data analysis or mixed model analysis. However, if the amount of information is sufficient due to the large number of time points, repetitive data are not needed and these data are analyzed using time series analysis technique. In particular, with a large number of data points in the current situation, when we want to predict how each variable affects each other, or what trends will be expected in the future, we should analyze the data using time series analysis techniques. In this study, we introduce univariate time series analysis, intervention time series model, transfer function model, and multivariate time series model and review research papers studied in Korea. We also introduce an error correction model, which can be used to analyze environmental ecological data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.109-118
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• 2022

The data of the missing section among the vertex surface sea temperature observation data was imputed using the Bidirectional Recurrent Neural Network(BiRNN). Among artificial intelligence techniques, Recurrent Neural Networks (RNNs), which are commonly used for time series data, only estimate in the direction of time flow or in the reverse direction to the missing estimation position, so the estimation performance is poor in the long-term missing section. On the other hand, in this study, estimation performance can be improved even for long-term missing data by estimating in both directions before and after the missing section. Also, by using all available data around the observation point (sea surface temperature, temperature, wind field, atmospheric pressure, humidity), the imputation performance was further improved by estimating the imputation data from these correlations together. For performance verification, a statistical model, Multivariate Imputation by Chained Equations (MICE), a machine learning-based Random Forest model, and an RNN model using Long Short-Term Memory (LSTM) were compared. For imputation of long-term missing for 7 days, the average accuracy of the BiRNN/statistical models is 70.8%/61.2%, respectively, and the average error is 0.28 degrees/0.44 degrees, respectively, so the BiRNN model performs better than other models. By applying a temporal decay factor representing the missing pattern, it is judged that the BiRNN technique has better imputation performance than the existing method as the missing section becomes longer.

• Journal of Environmental Health Sciences
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• v.42 no.4
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• Journal of Technology Innovation
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• v.24 no.2
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• pp.91-113
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• 2016

The purpose of this study is to examine the causal relationship among R&D spending and variables of technology trade, and to explore promoting R&D activities and revitalizing technology trade. To analyze the causal relationship, we built a multivariate model that consists of government R&D spending, private R&D spending, technical importation and export of techniques, and employed the Granger-causality test based on an error correction model. The results show that there are five Granger-causality relationship among them in the short run, as well as there are eleven Granger-causality relationship among a total of twelve causal relationship, excluding only a unidirectional causality relationship from the government R&D spending to the export of techniques, in the long run. Besides, we attempted the impulse-response analysis on them to observe the reaction of any dynamic system in response to some external change. The significance of this paper is to make sure the causal relationship between R&D investments and the technology trade by analyzing empirically, and to suggest several implications for promoting the R&D activities and revitalizing the technology trade.

• Journal of the Korean Wood Science and Technology
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• v.44 no.6
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• pp.852-863
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• 2016

Wood-plastic composite (WPC) is a promising and sustainable material, and refers to a combination of wood and plastic along with some binding (adhesive) materials. In comparison to pure wood material, WPCs are in general have advantages of being cost effective, high durability, moisture resistance, and microbial resistance. The properties of WPCs come directly from the concentration of different components in composite; such as wood flour concentration directly affect mechanical and physical properties of WPCs. In this study, wood powder concentration in WPC was determined by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra from WPC in both powdered and tableted form with five different concentrations of wood powder were collected and preprocessed to remove noise caused by several factors. To correlate the collected spectra with wood powder concentration, multivariate calibration method of partial least squares (PLS) was applied. During validation with an independent set of samples, good correlations with reference values were demonstrated for both FT-NIR and FT-IR data sets. In addition, high coefficient of determination (${R^2}_p$) and lower standard error of prediction (SEP) was yielded for tableted WPC than powdered WPC. The combination of FT-NIR and FT-IR spectral region was also studied. The results presented here showed that the use of both zones improved the determination accuracy for powdered WPC; however, no improvement in prediction result was achieved for tableted WPCs. The results obtained suggest that these spectroscopic techniques are a useful tool for fast and nondestructive determination of wood concentration in WPCs and have potential to replace conventional methods.

• Journal of Korean Society of Rural Planning
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• v.11 no.4 s.29
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• pp.47-57
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• 2005

The goal of this research is to evaluate landscape-ecological characteristics of watersheds in the Nakdong River Basin by using Geogaphic Information System (GIS) and landscape indices for integation of spatio-temporal informations and multivariate statistical techniques for quantitative analysis of forest landscape. Fragmentation index and change matrix techniques using factor analysis and grid overlay method were used to efficiently analyze and manage huge amount of information for ecological-environmental assessment (land-cover and forest landscape patterns). According to the results based on the pattern analysis of land-cover changes using the change detection matrix between 1980s and 1990s, addition on 750km$^2$ became urbanized areas. The altered 442.04km$^2$ was agricultural areas which is relatively easy for shifting of land-use, and 205.1km$^2$ of forests became urbanized areas, and average elevation and slope of the whole altered areas were 75m and 4$^{\circ}$. On the other hand, 120km$^2$ of urban areas were changed into other areas (i.e., agricultural areas and green space), and fortunately, certain amount of naturalness had been recovered. But still those agricultural areas and fallow areas, which were previously urban areas, had high potential of re-development for urbanization due to their local conditions. According to the structural analysis of forest landscape using the landscape indices, the forest fragmentation of watersheds along the main stream of the Nakdong River was more severe than my other watersheds. Furthermore, the Nakdong-sangju and Nakdong-miryang watersheds had unstable forest structures as well as least amount of forest quantity. Thus, these areas need significant amount of forest through a new forest management policy considering local environmental conditions.

• Archives of Plastic Surgery
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• v.47 no.6
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• pp.551-558
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• 2020

Background Giant congenital melanocytic nevus (GCMN) is a rare disease, for which complete surgical resection is recommended. However, the size of the lesions presents problems for the management of the condition. The most popular approach is to use a tissue expander; however, single-stage expansion in reconstructive surgery for GCMN cannot always address the entire defect. Few reports have compared tissue expansion techniques. The present study compared single and serial expansion to analyze the risk factors for complications and the surgical outcomes of the two techniques. Methods We retrospectively reviewed the medical charts of patients who underwent tissue expander reconstruction between March 2011 and July 2019. Serial expansion was indicated in cases of anatomically obvious defects after the first expansion, limited skin expansion with two more expander insertions, or capsular contracture after removal of the first expander. Results Fifty-five patients (88 cases) were analyzed, of whom 31 underwent serial expansion. The number of expanders inserted was higher in the serial-expansion group (P<0.001). The back and lower extremities were the most common locations for single and serial expansion, respectively (P =0.043). Multivariate analysis showed that sex (odds ratio [OR], 0.257; P=0.015), expander size (OR, 1.016; P=0.015), and inflation volume (OR, 0.987; P=0.015) were risk factors for complications. Conclusions Serial expansion is a good option for GCMN management. We demonstrated that large-sized expanders and large inflation volumes can lead to complications, and therefore require risk-reducing strategies. Nonetheless, serial expansion with proper management is appropriate for certain patients and can provide aesthetically satisfactory outcomes.