• Applied Biological Chemistry
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• v.17 no.3
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• pp.219-234
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• 1974

A series of experiments was conducted to study the behavior of the phosphorus added to the soils having the high phorphorus fixing capacity derived from volcanic ash in Cheju Island. Soil samples were taken from different depths of 0-10, 10-30, and 30-50cm in six citrus orchards where heavy application of phosphate fertilizer has been practised. Various forms of phosphorus were determined and phosphorus adsorption experiments were performed. The results obtained can be summarized as follows: 1. The content of inorganic phosphorus fractions determined by the method of Chang and Jackson was: water soluble P

• Applied Biological Chemistry
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• v.51 no.1
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• pp.70-78
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• 2008

Prunus sargentii R. of Rosaceae familiy, has been reported to have radical scavenging activity and anti-inflammatory effect. On these facts, biological activity and safety test were conducted to evaluate biological activities of the extracts of P. sargentii R. as a potential pharmaceutical ingredient. The electron donating ability of its ethanol extracts at a 500 ppm level showed 92%, which was higher than that of hot water extract (59%), the superoxide dismutase (SOD)-like activity of the water extract of P. sargentii R. was about 50%, the ethanol extract of P. sargentii R. was about 40% at 1,000 ppm concentration. Xanthine oxidase inhibition by the water extract of P. sargentii R. was about 40% and that by the ethanol extract was 60% respectively at 500 ppm concentration. From the measurement on lipid oxidation, the $Cu^{2+}$ chelating effect of the ethanol extract was higher than that of hot water extract. The $Fe^{2+}$ chelating effect was also shown to be about 80% at a 500 ppm concentration in both hot water extract and ethanol extract. The tyrosinase inhibition effect related to skin-whitening was 26% by hot water extract and 20% by ethanol extract respectively at a 1,000 ppm. Hyaluronidase inhibition activity related to the anti-inflammation effect was 96% in ethanolic extract at a 500 ppm. Clear zones formed by P. sargentii R. against the human skin-resident micro-flora such as Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli and Propionibacterium acnes indicated that antimicrobial activity of the ethanol extract was higher than that of the hot water extract.

• Journal of Applied Biological Chemistry
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• v.57 no.2
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• pp.145-152
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• 2014

To evaluate the improving effects of antioxidant activity, we observed antioxidant capacities such as electron donating ability (EDA), Ferric reducing antioxidant power (FRAP), inhibitory activity of xanthine oxidase (XO) and aldehyde oxidase (AO), and sensory characteristics on mixture of Smilax china L. root water extract added with water extract of fermented S. china L. leaf by Aspergillus oryzae (FSCL). Those contents of mixture with higher ratio of FSCL were proportionally high. And OD475 of mixture with higher ratio of FSCL was almost proportionally high ($R^2=0.9850$). Antioxidant capacities of EDA and FRAP of the mixture was higher than that of non-mixture. In addition, XO inhibitory activity ($IC_{50}$) of A (1.19) was 59.80% higher than that of F (2.96), and the activity of mixture by the higher ratio of FSCL was proportionally low ($R^2=0.9490$). Taste acceptability of A was slightly higher than that of F, whereas that of C was highest. And color acceptability of 40-80% mixture was higher than those of A, F, and B. Overall acceptability of C and D was highest than those of others. Moreover, hot water extract of S. china L. leaf fermented with A. oryzae was maroon color, which looks like Puerh tea style, and mixture of S. china L. root extract added with hot water extract of S. china L. leaf was high acceptability of beverage. These results suggest that mixture of extract of S. china L. root and hot water extract of S. china L. leaf fermented with A. oryzae could improve antioxidant activities.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Life Science
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• v.17 no.12
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• pp.1660-1668
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• 2007

The non-ionic surfactant (NIS) Tween 80 was evaluated for its ability to influence invitro cumulative gas production, dry matter digestibility, cellulolytic enzyme activities, anaerobic microbial growth rates, and adhesion to substrates by mixed rumen microorganisms on rice straw, alfalfa hay, cellulose filter paper and tall fescue hay. The addition of NIS Tween 80 at a level of 0.05% increased significantly (P<0.05) in vitro DM digestibility, cumulative gas production, microbial growth rate and cellulolytic enzyme activity from all of substrates used in this study. In vitro cumulative gas production from the NIS-treated substrates; rice straw, alfalfa hay, filter paper and tall fescue hay was significantly (P<0.05) improved by 274.8, 235.2, 231.1 and 719.5% compared with the control, when substrates were incubated for 48 hr in vitro. The addition of 0.05% NIS Tween 80 to cultures growing on alfalfa hay resulted in a significant increase in CMCase (38.1%), xylanase (121.4%), Avicelase (not changed) and amylase (38.2%) activities after 36 h incubation. These results indicated that the addition of 0.05% Tween 80 could greatly stimulate the release of some kinds of cellulolytic enzymes without decreasing cell growth rate in contrast to trends reported with aerobic microorganism. Our SEM observation showed that NIS Tween. 80 did not influence the microbial adhesion to substrates used in the study. Present data clearly show that improved gas production, DM digestibility and cellulolytic enzyme activity by Tween 80 is not due to increased bacterial adhesion on the substrates.

• Journal of Life Science
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• v.18 no.4
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• pp.515-521
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• 2008

This experiment was conducted to investigate effects of non-ionic or zwitterionic (+/-) surfactants on digestibility of rice straw, and changes of growth of rumen mixed microbes, pH, and gas production during in vitro fermentation. Also, during in vitro ruminal fermentation, microbial attachment on rice straw was investigated using scanning electron microscopy (SEM). Tween 80 or SOLFA-850 for non-ionic surfactant (NIS), and 3-(Dodecyldimethylammonio) propanesulfanate (DDAP) for zwitterionic surfactant (ZIS) was supplemented by 0.05% and 0.1% in Dehority's artificial medium containing Holtein rumen fluid, respectively, and the substrate for fermentation was rice straw passed through 1 mm screen. The experiment was composed of 7 treatments (two levels of two NISs, two levels of a ZIS) including the control, and 6, 12, 24, 48 and 72 hr of fermentation time with 3 replications per treatment. Treatment of Tween 80 increased in vitro DM digestibilities during 48 hr and 72 h post fermentations compared to the other treatments, whereas treatment of DDAP as a ZIS resulted in decreased DM digestibility than that of the control from 24 hr post fermentation (P<0.05). Gas production in vitro was greater (P<0.05) with addition of NIS than the control or ZIS, and increased as fermentation time elapsed. Rumen mixed microbial growth was greatest with addition of Tween 80 as NIS, and lowest when DDAP as ZIS was supplemented to the fermentation tube (P<0.05). In SEM observation, rumen microbial population attached on rice straw particle was greater with addition of NIS, but was less with addition ZIS compared with the control. In conclusion we could not found any positive effects of ZIS surfactants on rumunal fermentation characteristics and rumen microbial growth rates.

• Applied Chemistry for Engineering
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• v.19 no.3
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• pp.299-303
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• 2008

A thermally cross-linkable polymer, poly[(2,5-dimethoxy-1,4-phenylenevinylene)-alt-(1,4-phenylenevinylene)] (Cross-PPV), was synthesized by the Heck coupling reaction. In order for the polymer to be cross-linkable, 20 mol% excess divinylbenzene was added. The chemical structure of Cross-PPV and thermally crosslinked Cross-PPV were confirmed by FT-IR spectroscopy. From the FT-IR, UV-Vis, and PL spectral data, thermally crosslinked Cross-PPV was insoluble in common organic solvents. The HOMO and LUMO energy level of thermally cross-linked Cross-PPV were estimated -5.11 and -2.56 eV, respectively, which were determined by the cyclic voltammetry and UV-Vis spectroscopy. From the energy level data, one can easily notice that thermally crosslinked Cross-PPV can be used for hole injection layer effectively. Bilayer structured device (ITO/crosslinked Cross-PPV/PM-PPV/Al) was fabricated using poly(1,4-phenylenevinylene-(4-dicyanomethylene-4H-pyran)-2,6-vinylene-1,4-phenylenevinylene-2,5-bis(dodecyloxy)-1,4-phenylenevinylene (PM-PPV) as the emitting layer, which have HOMO and LUMO energy levels of -5.44 eV and -3.48 eV, respectively. The bilayered device had much enhanced the maximum efficiency (0.024 cd/A) and luminescence ($45cd/m^2$) than those of a single layer device (ITO/PM-PPV/Al, 0.003 cd/A, $3cd/m^2$). The enhanced performance originated from that fact that cross-linked Cross-PPV facilitatse the hole injection to the emissive layer and the injected hole and electron from ITO and Al are recombined in emitting layer (PM-PPV) effectively.

• Korean Journal of Ecology and Environment
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• v.48 no.2
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• pp.129-138
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• 2015

Forest vegetation of Geumsusan (1,016.0 m) and Doraksan (964.4 m) in Woraksan National Park is classified into mountain forest vegetation. Mountain forest vegetation is subdivided into deciduous broad-leaved forest, mountain valley forest, coniferous forest, riparian forest, afforestation and other vegetation. Including 77 communities of mountain forest vegetation and 5 communities of other vegetation, the total of 82 communities were researched; mountain forest vegetation classified by physiognomy classification are 37 communities deciduous broad-leaved forest, 16 communities of mountain valley forest, 8 communities of coniferous forests, 1 community of riparian forest, 15 afforestation and 5 other vegetation. As for the distribution rate for surveyed main communities, Quercus variabilis and Quercus mongolica communities account for 33.031 percent of deciduous broadleaved forest, Cornus controversa community takes up 29.142 percent of mountain valley forest, Pinus densiflora community holds 64.477 percent of mountain coniferous forest holds. In conclusion, minority species consisting of Quercus variabilis, Quercus mongolica, Pinus densiflora, Quercus serrata and Cornus controversa are distributed as dominant species of the uppermost part in a forest vegetation region in Woraksan National Park. In addition, because of vegetation succession and climate factors, numerous colonies formed by the two species are expected to be replaced by Quercus variabilis, Quercus mongolica, Cornus controversa and Fraxinus mandshurica which are climax species in the area.

• Journal of Bio-Environment Control
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• v.13 no.1
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• pp.44-50
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• 2004

This experiment was conducted to investigate the effects of different levels of light intensity (100, 200, 400 ${\mu}mol\;{\codt}\;m^{-2}\;{\cdot}\;s^{-1}$, and natural light) on the growth and the fruit quality of cucumber(Cucumis sativus cv. Hyakunari-3). The results of this experiment indicated that plant height and length of lateral shoots were decreased under low light condition, but it was not significantly different among treatments. Leaf area and root weight were lowest under low light intensity(100 ${\mu}mol\;{\codt}\;m^{-2}\;{\cdot}\;s^{-1}$), but no significant differences were noted between 200 and 400 ${\mu}mol\;{\codt}\;m^{-2}\;{\cdot}\;s^{-1}$. Photosynthesis rate was decreased with reduced light intensity and total chlorophyll contents, root activity and xylem sap were also decreased under low light intensity, but there was no significant difference between 200 and 400 ${\mu}mol\;{\codt}\;m^{-2}\;{\cdot}\;s^{-1}$. From the SEM observation the erosion of the guard cells and closed stomata in low light treatment were shown and the size of stoma were small also the stomatal aperture were decreased with reducing the light intensity. Chlorosis in leaves and aborted-liked fruits were appeared under low light condition and Ca and Mg uptake in leaves were decreased by shading in proportion to the decrease of light intensity. Fruit yields were decreased by 65% under 400 ${\mu}mol\;{\codt}\;m^{-2}\;{\cdot}\;s^{-1}$, and by 80${\sim}$90% under 200 and 100 ${\mu}mol\;{\codt}\;m^{-2}\;{\cdot}\;s^{-1}$, compared to those under the natural light. This low intensity of light caused the sharp decrease in the early harvested yields within two weeks and the fruit yields of lateral shoots were greatly decreased.