• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.11
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• pp.1401-1407
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• 2008

1-Deoxynojirimycin (DNJ) is a strong $\alpha$-glucosidase inhibitor which inhibits hyperglycemia in animals. To select the Bacillus strains highly producing DNJ, 4,000 strains were isolated from soil and grain samples. By the inhibitory activity against $\alpha$-glucosidase, nine Bacillus strains were selected and then identified by 16S rDNA sequencing. B. subtilis S10 was finally selected as the best strain for the production of DNJ. Various carbon sources and nitrogen sources in culture medium were evaluated for the highest production of DNJ. As the results, the optimized concentration of carbon source and nitrogen source was 1.0% galactose and 1.6% polypeptone and the concentration of DNJ produced was 0.75 g/L. The effect of culture supernatant of B. subtilis S10 on lowering blood glucose level was investigated in streptozotocin (STZ)-induced diabetic mice model. Mice were randomly assigned to control group (saline) and three test groups such as acarbose group, silkworm powder group and B. subtilis S10 group. After eight-week oral feeding, blood glucose levels of the B. subtilis S10 and silkworm powder groups were respectively $209.1{\pm}19.6\;mg/dL$ (59.1%) and $208.6{\pm}39.8\;mg/dL$ (59.0%) lower than $510{\pm}10\;mg/dL$ of the control group. These results indicated that the culture supernatant of B. subtilis S10 was able to reduce the blood glucose level in STZ-induced diabetic mice.

• Journal of the Korean Society of Groundwater Environment
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• v.5 no.4
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• pp.177-191
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• 1998

For various kinds of waters including surface water, shallow groundwater (＜70 m deep) and deep groundwater (500∼810 m deep) from the Pungki area, an integrated study based on hydrochemical, multivariate statistical, thermodynamic, environmental isotopic (tritium, oxygen-hydrogen, carbon and sulfur), and mass-balance approaches was attempted to elucidate the hydrogeochemical and hydrologic characteristics of the groundwater system in the gneiss area. Shallow groundwaters are typified as the 'Ca-HCO$_3$'type with higher concentrations of Ca, Mg, SO$_4$and NO$_3$, whereas deep groundwaters are the 'Na-HCO$_3$'type with elevated concentrations of Na, Ba, Li, H$_2$S, F and Cl and are supersaturated with respect to calcite. The waters in the area are largely classified into two groups: 1) surface waters and most of shallow groundwaters, and 2) deep groundwaters and one sample of shallow groundwater. Seasonal compositional variations are recognized for the former. Multivariate statistical analysis indicates that three factors may explain about 86% of the compositional variations observed in deep groundwaters. These are: 1) plagioclase dissolution and calcite precipitation, 2) sulfate reduction, and 3) acid hydrolysis of hydroxyl-bearing minerals(mainly mica). By combining with results of thermodynamic calculation, four appropriate models of water/ rock interaction, each showing the dissolution of plagioclase, kaolinite and micas and the precipitation of calcite, illite, laumontite, chlorite and smectite, are proposed by mass balance modelling in order to explain the water quality of deep groundwaters. Oxygen-hydrogen isotope data indicate that deep groundwaters were originated from a local meteoric water recharged from distant, topograpically high mountainous region and underwent larger degrees of water/rock interaction during the regional deep circulation, whereas the shallow groundwaters were recharged from nearby, topograpically low region. Tritium data show that the recharge time was the pre-thermonuclear age for deep groundwaters (＜0.2 TU) but the post-thermonuclear age for shallow groundwaters (5.66∼7.79 TU). The $\delta$$\^$34/S values of dissolved sulfate indicate that high amounts of dissolved H$_2$S (up to 3.9 mg/1), a characteristic of deep groundwaters in this area, might be derived from the reduction of sulfate. The $\delta$$\^$13/C values of dissolved carbonates are controlled by not only the dissolution of carbonate minerals by dissolved soil CO$_2$(for shallow groundwaters) but also the reprecipitation of calcite (for deep groundwaters). An integrated model of the origin, flow and chemical evolution for the groundwaters in this area is proposed in this study.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Economic and Environmental Geology
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• v.38 no.5 s.174
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• pp.547-561
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• 2005

General characteristics of groundwater contamination by As were reviewed with several recent researches, and its occurrence in groundwater of Korea was investigated based on a ffw previous studies and a groundwater quality survey in Nonsan and Geumsan areas. In Bangladesh, which has been known as the most serious arsenic calamity country, about $28\%$ of the shallow groundwaters exceeded the Bangladesh drinking water standard, $50{\mu}g/L$, and it was estimated that about 28 million people were exposed to concentrations greater than the standard. Groundwater was characterized by circum-neutral pH with a moderate to strong reducing conditions. Low concentrations of $SO_4^{2-}$ and $NO_3^-$, and high contents of dissolved organic carbon (DOC) and $NH_4^+$ were typical chemical characteristics. Total As concentrations were enriched in the Holocene alluvial aquifers with a dominance of As(III) species. It was generally agreed that reductive dissolution of Fe oxyhydroxides was the main mechanism for the release of As into groundwater coupling with the presence of organic matters and microbial activities as principal factors. A new model has also been suggested to explain how arsenic can naturally contaminate groundwaters far from the ultimate source with transport of As by active tectonic uplift and glaciatiion during Pleistocene, chemical weathering and deposition, and microbial reaction processes. In Korea, it has not been reported to be so serious As contamination, and from the national groundwater quality monitoring survey, only about $1\%$ of grounwaters have concentrations higher than $10{\mu}g/:L.$ However, it was revealed that $19.3\%$ of mineral waters, and $7\%$ of tube-well waters from Nonsan and Geumsan areas contained As concentrations above $10{\mu}g/:L.$. Also, percentages exceeding this value during detailed groundwater quality surveys were $36\%\;and\;22\%$ from Jeonnam and Ulsan areas, respectively, indicating As enrichment possibly by geological factors and local mineralization. Further systematic researches need to proceed in areas potential to As contamination such as mineralized, metasedimentary rock-based, alluvial, and acid sulfate soil areas. Prior to that, it is required to understand various geochemical and microbial processes, and groundwater flow characteristics affecting the behavior of As.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.