• Korean Chemical Engineering Research
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• v.43 no.1
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• pp.153-160
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• 2005

There have been large areas of soil contaminated with high levels of explosives. For this experimental work, 2,4,6-trinitrotoluene (TNT) was tested as a representative explosive contaminant of concern in both aqueous and soil samples and its removal was evaluated using three different chemical treatment methods: 1) the classical Fenton reaction which utilizes hydrogen peroxide ($H_2O_2$) and soluble iron at pH less than 3; 2) a modified Fenton reaction which utilizes chelating agents, $H_2O_2$, and soluble iron at pH 7; and 3) a Fenton-like process which utilizes iron minerals instead of soluble iron and $H_2O_2$, generating a hydroxyl radical. Using classic Fenton reaction, 93% of TNT was removed in 20 h at pH 3 (soil spiked with 300 mg/L of TNT, 3% $H_2O_2$ and 1mM Fe(III)), whereas 21% removed at pH 7. The modified Fenton reaction, using nitrilotriacetic acid (NTA), oxalate, ethylenediaminetetraacetic acid (EDTA), acetate and citrate as representative chelating agents, was tested with 3% $H_2O_2$ at pH 7 for 24 h. Results showed the TNT removal in the order of NTA, EDTA, oxalate, citrate and acetate, with the removal efficiency of 87%, 71%, 64%, 46%, and 37%, respectively, suggesting NTA as the most effective chelating agent. The Fenton-like reaction was performed with water contaminated with 100 mg/L TNT and soil contaminated with 300 mg/L TNT, respectively, using 3% $H_2O_2$ and such iron minerals as goethite, magnetite, and hematite. In the goethite-water system, 33% of TNT was removed at pH 3 whereas 28% removed at pH 7. In the magnetite-water system, 40% of TNT was removed at pH 3 whereas 36% removed at pH 7. In the hematite-water system, 40% of TNT was removed at pH 3 whereas 34% removed at pH 7. For further experiments combining the modified Fenton reaction with the Fenton-like reaction, NTA, EDTA, and oxalate were selected with the natural iron minerals, magnetite and hematite at pH 7, based on the results from the modified Fenton reaction. As results, in case magnetite was used, 79%, 59%, and 14% of TNT was removed when NTA, oxalate, and EDTA used, respectively, whereas 73%, 25%, and 19% removed in case of hematite, when NTA, oxalate, and EDTA used, respectively.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Korean Journal of Food Science and Technology
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• v.31 no.6
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• pp.1529-1535
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• 1999

This study was done for the extraction of carotenoid from Phaffia rhodozyma in combination with PEF and other methods. PEF treatment conditions were $30{\sim}80\;kV/cm,\;100{\sim}1000\;Hz\;and\;100{\sim}1000\;{\mu}s$. In order to increase permeability of yeast cell wall, various methods such as freezing-thawing, mechanical treatment, solvents, permeabilizing agents, and yeast cell wall lytic enzyme were used before PEF treatment. The combination of PEF $(50\;kV/cm,\;300\;Hz,\;1000\;{\mu}s)$ and conventional methods such as solvent and freezing-thawing pre-treatment had no effects on the extraction of carotenoid pigments. The extent of extracted carotenoid by the PEF $treatment(50\;kV/cm,\;300\;Hz,\;1000\;{\mu}s)$ combined with yeast cell wall lytic enzyme and mechanical pre-treatment increased 52% and 69.8% more than the sum of that by each treatment, respectively. Permeabilizing agents, especially Tween 20 and capric acid, enhanced the extraction efficiency of carotenoid pigments from P. rhodozyma cells. These results indicated the feasibility for the continuous extracting carotenoid pigments from P. rhodozyma by PEF combined with other permeabilizing methods.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.77-81
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• 2009

Purpose: The PET of the PET/CT (Positron Emission Tomography/Computed Tomography) quantitatively shows the biological and chemical information of the body, but has limitation of presenting the clear anatomic structure. Thus combining the PET with CT, it is not only possible to offer the higher resolution but also effectively shorten the scanning time and reduce the noises by using CT data in attenuation correction. And because, at the CT scanning, the contrast media makes it easy to determine a exact range of the lesion and distinguish the normal organs, there is a certain increase in the use of it. However, in the case of using the contrast media, it affects semi-quantitative measures of the PET/CT images. In this study, therefore, we will be to establish the reliability of the SUV (Standardized Uptake Value) with CT data correction so that it can help more accurate diagnosis. Materials and Methods: In this experiment, a total of 30 people are targeted - age range: from 27 to 72, average age : 49.6 - and DSTe (General Electric Healthcare, Milwaukee, MI, USA) is used for equipment. $^{18}F$- FDG 370~555 MBq is injected into the subjects depending on their weight and, after about 60 minutes of their stable position, a whole-body scan is taken. The CT scan is set to 140 kV and 210 mA, and the injected amount of the contrast media is 2 cc per 1 kg of the patients' weight. With the raw data from the scan, we obtain a image showing the effect of the contrast media through the attenuation correction by both of the corrected and uncorrected CT data. Then we mark out ROI (Region of Interest) in each area to measure SUV and analyze the difference. Results: According to the analysis, the SUV is decreased in the liver and heart which have more bloodstream than the others, because of the contrast media correction. On the other hand, there is no difference in the lungs. Conclusions: Whereas the CT scan images with the contrast media from the PET/CT increase the contrast of the targeted region for the test so that it can improve efficiency of diagnosis, there occurred an increase of SUV, a semi-quantitative analytical method. In this research, we measure the variation of SUV through the correction of the influence of contrast media and compare the differences. As we revise the SUV which is increasing in the image with attenuation correction by using contrast media, we can expect anatomical images of high-resolution. Furthermore, it is considered that through this trusted semi-quantitative method, it will definitely enhance the diagnostic value.