• Economic and Environmental Geology
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• v.55 no.6
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• pp.737-760
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• 2022

For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations. Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment. To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries. As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., ³⁴S and ¹⁸O of SO₄^2-, ¹³C and ¹⁴C of DIC, ²H and ¹⁸O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe²⁺/Fe³⁺, H₂S/SO₄^2-, NH₄⁺/NO₃^-). A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface. In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling). The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling. However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data. Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.31-52
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• 2024

Considering the significant relationship between a tree's branch structure and physiology, understanding the detailed branch structure is crucial for fields such as species classification, and 3D tree modelling. Recently, terrestrial laser scanning (TLS) and quantitative structure model (QSM) have enhanced the understanding of branch structures by capturing the radius, length, and branching angle of branches. Previous studies examining branch structure with TL S and QSM often relied on mean or median of branch structure parameters, such as the radius ratio and length ratio in parent-child relationships, as representative values. Additionally, these studies have typically focused on the relationship between trunk and the first order branches. This study aims to explore the distribution of branch structure parameters up to the third order in Aesculus hippocastanum, Ginkgo biloba, and Prunus yedoensis. The gamma distribution best represented the distributions of branch structure parameters, as evidenced by the average of Kolmogorov-Smirnov statistics (radius = 0.048; length = 0.061; angle = 0.050). Comparisons of the mode, mean, and median were conducted to determine the most representative measure indicating the central tendency of branch structure parameters. The estimated distributions showed differences between the mode and mean (average of normalized differences for radius ratio = 11.2%; length ratio = 17.0%; branching angle = 8.2%), and between the mode and median (radius ratio = 7.5%; length ratio = 11.5%; branching angle = 5.5%). Comparisons of the estimated distributions across branch orders and species were conducted, showing variations across branch orders and species. This study suggests that examining the estimated distribution of the branch structure parameter offers a more detailed description of branch structure, capturing the central tendencies of branch structure parameters. We also emphasize the importance of examining higher branch orders to gain a comprehensive understanding of branch structure, highlighting the differences across branch orders.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.416-423
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• 2023

Figs has known to have antioxidant, whitening, anti-inflammatory, and antibacterial effects in their leaves, roots, stems, latex, and fruits. In order to develop cosmetic materials based on natural products, we have studied on the skin activity of the ficin in latex as well as the whitening function of the fruit extract with 70% ethanol, and used it as a raw material for released cosmetic product. However, there is little research on the demand for the development of new eutectic solvent extraction methods and its ability to control skin inflammation and psoriasis regulation. Thus, in this study, we evaluated the effectiveness of fig fruit extracts and fractions using eutectic solvent extraction for skin inflammation control and psoriasis. First, fig fruits were extracted under optimal eutectic solvent conditions and fractionated with n-hexane, dichloromethane, ethyl acetate, and butanol. First, the antioxidant activity and inhibition of nitric oxide (NO) production were confirmed in mouse macrophage RAW264.7 cells. In addition, as a result of observing the mRNA expression through RT-PCR, pro-inflammatory cytokines such as TNF-α, IL1α, and IL-1β were suppressed significantly in the hexane, dichloromethane, and ethyl acetate fractions. In addition, it was confirmed in TNF-α stimulated HaCaT keratinocyte model. Finally, chemokine CC motif ligand 20 (CCL20), marker gene of human psoriasis skin disease, was significantly suppressed in the hexane, dichloromethane, and ethyl acetate fractions. These results suggested its anti-inflammatory and skin soothing effect and the possibility of development as an excellent skin soothing natural cosmetic material in the future through future clinical trials.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.10
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• pp.1346-1355
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• 2012

This study was carried out to investigate the characteristics of black jujube and Zizyphus jujube extracts during lactic acid fermentation. Both extracts were fermented using Lactobacillus fermentum YL-3. As a result, viable cell number rapidly increased until 24 hours, after which it gradually decreased. Before lactic acid fermentation, the $IC_{50}$ of black jujube, which was 0.014 mg/mL, was lower than that of Zizyphus jujube. Further, black jujube showed stronger antioxidant activity (374.21 mg AA eq/g) than Zizyphus jujube. Contents of total polyphenolics in both extracts were 15.46 mg/g and 13.61 mg/g, respectively, whereas contents of total flavonoids were 374.21 ${\mu}g/g$ and 64.25 ${\mu}g/g$. After lactic acid fermentation, there was no significant increase in DPPH or ABTS free radical scavenging activity. Total polyphenolic content of Zizyphus jujube decreased to 12.39 mg/g upon fermentation, whereas flavonoid content significantly increased to 291.58 ${\mu}g/g$. Further, polyphenolic and flavonoid contents of black jujube increased from 15.46 mg/g to 17.46 mg/g and from 374.21 ${\mu}g/g$ to 1,135.29 ${\mu}g/g$, respectively. These results demonstrate that 9-Times Steamed and Dried increased functional components. Especially, lactic acid fermented black jujube showed remarkably high antioxidant activity. These results confirm the potential use of lactic acid fermented black jujube as a valuable resource for the development of functional foods.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of the Korean GEO-environmental Society
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• v.15 no.1
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• pp.63-68
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• 2014

In this study, it was found that determined the photocatalytic degradation of antibiotics (lincomycin, LM) with various catalyst composite of titanium dioxide ($TiO_2$), hydroxyapatite (HAP) and germanium (Ge) under UV-A irradiation. At first, various type of complex catalysts were investigated to compare the enhanced photocatalytic potential. It was observed that in order to obtain the removal efficiencies were $TiO_2/HAP/Ge$ > $TiO_2/Ge$ > $TiO_2/HAP$. The composition of $TiO_2/HAP/Ge$ using a statistical approach based on mixture analysis design, one of response surface method was investigated. The independent variables of $TiO_2$ ($X_1$), HAP ($X_2$) and Ge ($X_3$) which consisted of 6 condition in each variables was set up to determine the effects on LM ($Y_1$) and TOC ($Y_2$) degradation. Regression analysis on analysis of variance (ANOVA) showed significant p-value (p < 0.05) and high coefficients for determination value ($R^2$ of $Y_1=99.28%$ and $R^2$ of $Y_2=98.91%$). Contour plot and response curve showed that the effects of $TiO_2/HAP/Ge$ composition for LM degradation under UV-A irradiation. And the estimated optimal composition for TOC removal ($Y_2$) were $X_1=0.6913$, $X_2=0.2313$ and $X_3=0.0756$ by coded value. By comparison with actual applications, the experimental results were found to be in good agreement with the model's predictions, with mean results for LM and TOC removal of 99.2% and 49.3%, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.300-307
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• 2015

In order to develop optimum paddy-upland rotation system, we evaluated the 1st and the 2nd upland growth and yield characteristics of foxtail millet, proso millet, sorghum rotated from paddy field and rice rotated from upland in paddy-upland rotation. Average number of ears per hill was 3.3 in the 2nd upland cultivation. The value was greater by 1 ear as compared to 1st upland cultivation (2.2 ears per hill). In average yield per 10a, the 2nd upland cultivation showed 220.3 kg, 23% increased yield compared to the 1st upland cultivation (179 kg per 10a). In average number of ears per hill, the 2nd upland cultivation showed 8.3 ears, increased 4 ears compared to the 1st upland cultivation (4.2 ears per hill). In average yield per 10a, the 2nd upland cultivation showed 152.8 kg, 16.8% increased yield compared to the 1st upland cultivation (130.8 kg per 10a). In average days from seeding to heading of 5 sorghum varieties, there were no significant difference between the 1st (68.6 days) and the 2nd (67.4 days) upland cultivation rotated from paddy field. In the average number of grains per ears, the 2nd upland cultivation showed 2,931.6 grains per ear, 12% increased compared to the 1st upland cultivation (2,619.6 grains per ears). Average yield per 10a of sorghum in the 2nd upland cultivation showed 242.3 kg, 4.6% increased compared to the 1st upland cultivation (231.7 kg per 10a). In growth and yield characteristics of rice in paddy-upland rotation, culm length in paddy-upland-paddy plot showed 82.9 cm, 7.3 cm longer compared to the continuous rice paddy field (75.6 cm). Ear length was also 1 cm longer than that of the continuous rice paddy field. In average number of ears per hill, paddy-upland-paddy plot showed 25.0 ears, 4.3 ears more than that of the continuous rice paddy field (20.7 ears per hill). In average yield of rice per 10a, the paddy-upland-paddy rotation plot showed 526.8 kg, 9.8% higher yield compared to the continuous rice paddy field (479.9 kg per 10a).

• Journal of Radiation Protection and Research
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• v.28 no.3
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• pp.199-206
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• 2003

Because the MIRD phantom, the representative mathematical phantom was developed for the calculation of internal radiation dose, and simulated by the simplified mathematical equations for rapid computation, the appropriateness of application to external dose calculation and the closeness to real human body should be justified. This study was intended to modify the MIRD phantom according to the comparison of the organ absorbed doses in the two phantoms exposed to monoenergetic broad parallel photon beams of the energy between 0.05 MeV and 10 MeV. The organ absorbed doses of the MIRD phantom and the Zubal yokel phantom were calculated for AP and PA geometries by MCNP4C, general-purpose Monte Carlo code. The MIRD phantom received higher doses than the Zubal phantom for both AP and PA geometries. Effective dose in PA geometry for 0.05 MeV photon beams showed the difference up to 50%. Anatomical axial views of the two phantoms revealed the thinner trunk thickness of the MIRD phantom than that of the Zubal phantom. To find out the optimal thickness of trunk, the difference of effective doses for 0.5 MeV photon beams for various trunk thickness of the MIRD phantom from 20 cm to 36 cm were compared. The optimal thunk thickness, 24 cm and 28 cm for AP and PA geometries, respectively, showed the minimum difference of effective doses between the two phantoms. The trunk model of the MIRD phantom was modified and the organ doses were recalculated using the modified MIRD phantom. The differences of effective dose for AP and PA geometries reduced to 7.3% and the overestimation of organ doses decreased, too. Because MIRD-type phantoms are easier to be adopted in Monte Carlo calculations and to standardize, the modifications of the MIRD phantom allow us to hold the advantage of MIRD-type phantoms over a voxel phantom and alleviate the anatomical difference and consequent disagreement in dose calculation.

• Journal of Life Science
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• v.28 no.6
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• pp.656-662
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• 2018

Flavin-containing monooxygenases from Corynebacterium (cFMOs) were mutagenized based on homology modeling to develop variants with an enhanced indigoid production capability. The four mutants, F170Y, A210G, A210S, and T326S, which fused to a maltose-binding protein (MBP), were constructed, and their biochemical properties were characterized. Of these, purified MBP-T326S required a higher concentration of exogenous FAD (100 mM) than the wild-type MBP-cFMO for optimal activity and showed a 3.8-fold increase in the $k_{cat}/K_m$ value at $100{\mu}M$ FAD compared to that of MBP-cFMO at $2{\mu}M$ FAD. The indole oxygenase activities of MBP-T326S decreased to 63-77% compared to that of the MBP-cFMO In addition, MBP-T326S displayed a very low level of futile NADPH oxidase activities (21-24%) in the absence of a substrate. Mutant proteins except for T326S displayed similar $K_m$ and increased $k_{cat}/K_m$ values compared to the wild-type. MBP-F170Y and -A210S mutants showed elevated indole oxygenase activity higher than 3.1- and 2.9-fold, respectively, in comparison with MBP-cFMO. When indigoid production was carried out in LB broth with 2.5 g/l of tryptophan, Escherichia coli expressing cFMO produced 684 mg/l of indigo and 104 mg/l of indirubin, while cells harboring T326S produced 1,040 mg/l of indigo and 112 mg/l of indirubin. The results indicate that the production of indigo was 13% higher when compared to a previous report in which an E. coli expressing FMO from Methylophaga produced 920 mg/l of indigo. The protein engineering of cFMO based on homology modeling provided a more rational strategy for developing indigoid-producing strains.