• Journal of the Korean Ceramic Society
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• v.39 no.12
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• pp.1183-1189
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• 2002

In order to develope the porous alumina ceramics with high strength, the pore characteristics and compressive strength were investigated in terms of relation to the conditions of spark-plasma sintering and the contents of graphite as a pore precursor. Porous alumina bodies were successfully prepared by spark-plasma sintering and burning out graphite in air. High porous bodies were fabricated by sintering at 1000${\circ}C$ for 3 min under a pressure of 30 MPa, heating rate of 80${\circ}C$/min and on-off pulse type of 12:2. For example, alumina bodies prepared by the addition of 10∼30 vol% graphite showed high porosity of 50∼57%. Also, the open porosity increased with graphite content. The relationship between pore characteristics and graphite contents could be explained by percolation model depending on cluster number and size. Porous alumina bodies prepared by the addition of 10∼30 vol% graphite showed the high compressive strength of 55∼200 MPa. This great improvement in strength was considered to be mainly due to the spark-plasma discharges and the self-heating action between particles.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1203-1210
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• 2012

As the area of upland crops increase, it is become more important for farmers to understand status of soil water at their own fields due to key role of proper irrigation. In order to estimate daily water balance and soil water content with simple weather data and irrigation records, we have developed the model for estimating water balance and soil water content, called AFKAE0.5, and verified its simulated results comparing with daily change of soil water content observed by soil profile moisture sensors. AFKAE0.5 has two hypothesis before establishing its system. The first is the soil in the model has 300 mm in depth with soil texture. And the second is to simplify water movement between the subjected soil and beneath soil dividing 3 categories which is defined by soil water potential. AFKAE0.5 characterized with determining the amount of upward and downward water between the subjected soil and beneath soil. As a result of simulation of AFKAE0.5 at Gongju region with red pepper cultivation in 2005, the water balance with input minus output is recorded as - 88 mm. the amount of input water as precipitation, irrigation, and upward water is annually 1,043, 0, and 207 mm, on the other, output as evapotranspiration, run-off, and percolation is 831, 309, and 161 mm, respectively.

• Applied Biological Chemistry
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• v.44 no.2
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• pp.97-102
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• 2001

Cheju island depends on a hydrogeologically vulnerable aquifer system as its principle source of drinking water. Most of golf courses are located in the area which is important for the ground water recharge, and pesticides are applied to golf courses often at relatively high rates. Therefore, turf pesticides in golf course should be applied without adversely impacting ground water. In this experiment, downward movement of pesticides was monitored in model greens of golf course, where different adsorbents were layered in 3-cm thickness at 35-cm depth, and effect of the adsorption layer on the leaching loss of pesticides was investigated. Major leachings were observed in the periods of heavy rain and very limited leaching was observed under artificial irrigation. Fenitrothion and triadimefon, which have relatively short persistence and high adsorption coefficient, were found in the leachate in low concentrations only at the first rainfall event, around 20 days after the pesticide application. However, diniconazole, which has a relatively long half-life (97 days), was detected in the leachate during the whole period of experiment and concentration was much higher than those of the other pesticides. Maximum leachate concentrations were 1.9, 10.3, and 84.5 ${\mu}l^{-1}$ for fenitrothion, triadimefon, and diniconazole, respectively. Therefore, in golf course green which allows rapid water percolation and has extremely low adsorption capacity, persistence in soil could be more important factor in determination of leaching potential of pesticides. Total quantity of pesticides leached from the model green was <0.2% for fenitrothion and triadimefon and 1.8% for diniconazole. Adsorption layers significantly reduced pesticide leaching, and active carbon and Orpar were more effective than zeolite. In the model green having adsorption layer of active carbon or Orpar, leaching loss of pesticides was reduced below 0.01% of the initial application.

• Journal of Wetlands Research
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• v.25 no.2
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• pp.99-110
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• 2023

Non-point source (NP) pollutants in an agricultural landuse are discharged from a large area compared to those in other land uses, and thus effective source control measures are needed. To develop appropriate control measures, it is necessary to quantify discharge load of each source and evaluate the degree of water quality improvement by implementing different options of the control measures. This study used Hydrological Simulation Program-FORTRAN (HSPF) to quantify pollutant discharge loads from different sources and effects of different control measures on water quality improvements, thereby supporting decision making in developing appropirate pollutant control strategies. The study area is the Gyeseong river watershed in Changnyeong county, Gyeongsangnam-do, with agricultural areas occupying the largest proportion (26.13%) of the total area except for the forest area. The main pollutant sources include chemical and liquid fertilizers for agricultural activities, and manure produced from small scale livestock facilities and applied to agriculture lands or stacked near the facilities. Source loads of chemical fertilizers, liquid fertilizers and livestock manure of small scale livestock facilities, and point sources such as municipal wastewater treatment plants (WWTPs), community WWTPs, private sewage treament plants were considered in the HSPF model setup. Especially, NITR and PHOS modules were used to simulate detailed fate and transport processes including vegitation uptake, nutrient deposition, adsorption/desorption, and loss by deep percolation. The HSPF model was calibrated and validated based on the observed data from 2015 to 2020 at the outlet of the watershed. The calibrated model showed reasonably good performance in simulating the flow and water quality. Five Pollutants control scenarios were established from three sectors: agriculture pollution management (drainge outlet control, and replacement of controlled release fertilizers), livestock pollution management (liquid fertilizer reduction, and 'manure management of small scale livestock facilities) and private STP management. Each pollutant control measure was further divided into short-term, mid-term, and long-term scenarios based on the potential achievement period. The simulation results showed that the most effective control measure is the replacement of controlled release fertilizers followed by the drainge outlet control and the manure management of small scale livestock facilities. Furthermore, the simulation showed that application of all the control measures in the entire watershed can decrease the annual TN and TP loads at the outlet by 40.6% and 41.1%, respectively, and the annual average concentrations of TN and TP at the outlet by 35.1% and 29.2%, respectively. This study supports decision makers in priotizing different pollutant control measures based on their predicted performance on the water quality improvements in an agriculturally dominated watershed.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.