• Korean Journal of Environmental Agriculture
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• v.1 no.1
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• pp.22-30
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• 1982

Water quality of downstream of the Nagdong river, using for agricultural irrigation of the Gimhae plain, were observed. Water temperature, turbidity, residue, pH, BOD, COD, DO, hardness, chloride, sulphate, phosphate, inorganic nitrogenous compounds, sodium, general bacteria, E. coli and heavy metals of the water were investigated at Daejeo, Sikman, Bongrim, Noksan, Machal and Jangyou pumping stations in the Gimhae plain in May, July and October, 1981. The results obtained were as follows; 1) Average value of analyzed components of the water at all sampling sites were 7.8 pH, 6.3 ppm BOD, 6.5 ppm COD, 6.4 ppm DO, 231 ppm hardness, 582 ppm Cl-, 412 ppm $SO_4--$, 2.32 ppm $PO_4---$, 3.8 ppm $NH_4+,\;478\;ppm\;Na+$, 2964 No. /100 ml total coliform, 0.0040 ppm Cd, 0.0066 ppm Pb, respectively. 2) The most heavily polluted site of all investigated ones was Sikman. It seemed to be caused by the vast quantity of wastewater discharged from industrial district in Gimhae city. The next polluted sites were Bongrim, Daejeo and Noksan, and comparatively less polluted sites were Machal and Jangyou, judging from both appearance and physicochemical observation. 3) At Sikman, the most heavily polluted site, average value of components were 8.0 pH, 8.1 ppm BOD, 8.2 ppm COD. These values were close to the limit point of agricultural water quality standard of 8.0 ppm BOD (COD). 4) Any apparent variation was not observed by the sampling season in most components except DO and $NH_4+$. DO of October was higher than that of May or July but $NH_4+$ was low. 5) $NH_4+$ content was comparatively high in downstream of the Nagdong river of which water is used as the agricultural irrigation in the Gimhae plain. Therefore, fertilizer application on the farming land must make account of nitrogen content of the irrigation water 6) It was considered that chloride and sodium contents would not influence the crop cultivation in common season, but in dry season irrigation must be done carefully.

• Microbiology and Biotechnology Letters
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• v.48 no.3
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• pp.328-336
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• 2020

Parachlorella sp. is an efficient fatty acid producer that can be used in the production of biofuels, feeds, and fertilizers. Microalgae show varying responses to culture conditions, even those within the same species. In this study, growth and fatty acid composition of a newly isolated Parachlorella sp. from the Nakdong river of Korea in different culture media were investigated. The microalga was cultivated in 400 ml bubble column photobioreactors using BG-11, BBM, TAP, and modified TAP (MTAP) media. It was shown that using BBM led to greater fatty acid accumulation (34%), while using TAP medium led to greater biomass productivity (0.34 g/l/day). Composition of the TAP medium was modified to have the N:P ratio of BBM while also varying concentrations of N and P to improve fatty acid productivity. One of the modified TAP media, MTAP-1 (104.8 mgN/l, 135.2 mgP/l, N:P ratio = 0.77), showed the highest fatty acid concentration of 0.69 ± 0.04 g/l, while those from TAP and BBM were 0.48 ± 0.06 g/l and 0.40 ± 0.02 g/l, respectively. The results showed that microalgal fatty acid productivity could be enhanced by changing the N:P ratio and concentrations.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.434-451
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• 2017

For this study, groundwater samples for 3 years from 2011 through 2013 were collected at 106 groundwater monitoring site in Korea. These groundwater samples were analyzed for 13 pesticides such as cabofuran, pentachlorobenzene, hexachlorobenzene, simazine, atrazine, lindane (gamma-HCH), alachlor, heptachlor, chlordane (total), endosulfan (1, 2), dieldrin, endrin, 4,4-DDT. The objectives of this study were to determine the detection frequency and their concentrations of 13 pesticides and evaluate the health risk level considering ingestion, inhalation, and skin contact using concentrations of 13 pesticides in groundwater samples. An analysis was used for the simultaneous determination for 13 pesticides using GC-MS. GC-MS was performed on HP-5ms, using helium ($1ml\;min^{-1}$) as carrier gas. The average recoveries of the pesticides were from 92.8% to 120.8%. The limits of detection (LODs) were between $0.004{\mu}g\;L^{-1}$ and $0.118{\mu}g\;L^{-1}$ and the limits of quantification (LOQs) were between $0.012{\mu}g\;L^{-1}$ and $0.354{\mu}g\;L^{-1}$. 106 groundwater wells were selected. 54 wells were from well to monitor background groundwater quality and 52 wells were from well to monitor groundwater quality in industrial or contamination source area. Eight pesticides including pentachlorobenzene, lindane (Gamma-HCH), heptachlor, chlordane (total), Endosulfan (1, 2), dieldrin, endrin, and 4,4-DDT were not detected in groundwater samples. The detection frequency for hexachlorobenzene, alachlor, carbofuran and simazine was 23.4%, 11.4%, 7.3%, and 1.0%, respectively. Atrazine was detected once in 2011. The average concentrations were $0.00423{\mu}g\;L^{-1}$ for carbofuran, $0.000243{\mu}g\;L^{-1}$ for alachlor, $0.00015{\mu}g\;L^{-1}$ for simazine, and $0.00001{\mu}g\;L^{-1}$ for hexachlorobenzene. The detection frequency of hexachlorobenzene was high, but the average concentration was low. In the contaminated groundwater, the detection frequency for hexachlorobenzene, alachlor, carbofuran, simazine and atrazine was 26.1%, 21.3%, 7.1%, 1.9% and 0.3%, respectively. In the uncontaminated groundwater, detection frequency for hexachlorobenzene, carbofuran and alachlor were 20.2%, 7.5%, and 1.9% respectively. Simazine and atrazine were not detected at uncontaminated groundwater wells. According to the purpose of groundwater use, atrazine was detected for agricultural groundwater use. Hexachlorobenzene showed high detection frequency at agricultural groundwater use area where the animal feeding area and golf course area were located. Alachlor showed more than 50% detection frequency at cropping area, pollution concern river area, and golf course area. Atrazine was detected in agricultural water use area. By land use, the maximum detection frequency of alachlor was found near an orchard. For human risk assessment, the cancer risk for the 5 pesticides was between $10^{-7}$ and $10^{-10}$, while the non-cancer risk (HQ value) was between $10^{-4}$ and $10^{-6}$. For conclusion, these monitoring study needs to continue because of the possibility of groundwater contamination based on various purpose of groundwater use.