• Journal of the Korea Organic Resources Recycling Association
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• v.26 no.4
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• pp.21-29
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• 2018

The contents of total nitrogen(T-N), phosphate($T-P_2O_5$), and potash($T-K_2O$) are important factors to determine the application rate of the livestock compost to prevent nutrients accumulation and maintain their appropriate levels in arable lands. The concentrations of nutrient, organic matter, salt, water content, heavy metal in livestock compost in circulation were investigated with 659 samples from 2016 to 2017. In order to investigate the fluctuation nutrient contents of livestock composts with the same product name, 19 samples were collected and analyzed T-N, and $T-P_2O_5$, and $T-K_2O$ concentration during two years. The mean levels of T-N, $T-P_2O_5$, and $T-K_2O$ in livestock composts of from 2016 to 2017 were 1.73%, 1.88%, and 1.66%, respectively. The average contents of organic matter, water, and salt were 38.9%, 40.9%, and 1.2%, respectively. There were found that the maximum concentrations of Cr, Ni, Cu, and Zn in some livestock composts were exceeded the criteria of the official standard of commercial fertilizer. The maximum variation coefficient of T-N, $T-P_2O_5$ and $T-K_2O$ content of livestock composts was found to be 24%, 27%, and 50% on average, respectively. In order to manage the nutrients in agricultural soils, it will be reasonable that the error range of T-N and $T-P_2O_5$ content in livestock composts should be recommended to be 27% in mean as variation coefficient in case of displaying the nutrient element in liverstock compost.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.30-39
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• 2019

An analytical method was developed for the determination of sedaxane in agricultural products using liquid chromatograph-tandem mass spectrometry (LC-MS/MS). The samples were extracted with acetonitrile and partitioned with dichloromethane to remove the interference, and then purified by using silica SPE cartridges to clean up. The analytes were quantified and confirmed by using LC-MS/MS in positive ion mode using multiple reaction monitoring (MRM). The matrix-matched calibration curves were linear over the calibration ranges ($0.001-0.25{\mu}g/mL$) into a blank extract with $r^2$>0.99. For validation, recovery tests were carried out at three different concentration levels (LOQ, 10LOQ, and 50LOQ, n=5) with five replicates performed at each level. The recoveries were ranged between 74.5 to 100.8% with relative standard deviations (RSDs) of less than 12.1% for all analytes. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40, 2003) and Food Safety Evaluation Department guidelines (2016). The proposed analytical method was accurate, effective and sensitive for sedaxane determination in agricultural commodities.

• Korean Journal of Microbiology
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• v.54 no.4
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• pp.384-397
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• 2018

The aim of this study is to screen the strains of Bacillus spp. possessing safety, probiotic activity, and so on, which can be utilized as probiotic resource for using the feed and supplement food of companion animal. About 300 isolates were isolated from traditional Korean sauces, four isolates that did not have or produce the six kinds of B. cereus type vomiting and diarrhea toxin genes, ${\beta}$-hemolytic, and three kinds of carcinogenic enzymes were selected. Antibiotic gene retention, cell surface hydrophobicity, antibiotic sensitivity, and glucose utilization were analyzed for four isolates, and finally SRCM 100731 was selected. SRCM 100731 was named as Bacillus amyloliquefaciens SRCM 100731 16S rRNA sequencing analysis, and carried out optimization of cell growth for industrial applications such as pet food and feed. The effects of 14 different components on cell growth were investigated and three significant positive factors, molasses, sodium chloride, and potassium chloride were selected as the main factors based on a Plackett-Burman design. In order to find out optimal concentration on each constituent, we carried out central composite design. The predicted optimized concentrations were 7% molasses, 1.1% sodium chloride, 0.5% potassium chloride. Finally, an overall about 7-fold increase in dry cell weight yield ($12.6625{\pm}0.0658g/L$) was achieved using the optimized medium compared with the non-optimized medium ($1.8273{\pm}0.0214g/L$). This research is expected to be highly utilized in the growing pet industry by establishing optimal cultivation conditions for industrial application as well as screening Bacillus amyloliquefaciens SRCM 100731 as probiotic resource for companion animal.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.21-33
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• 2021

The purpose of this study is to determine the order of priority for the use of amendments, matching the optimal amendment to the specific site in Korea. This decision-making process must prioritize the stabilization and economic efficiency of amendment for heavy metals and metalloid based on domestic site contamination scenarios. For this study, total 5 domestic heavy metal contaminated sites were selected based on different pollution scenarios and 13 amendments, which were previously studied as the soil stabilizer. Batch extraction experiments were performed to quantify the stabilization efficiency for 8 heavy metals (including As and Hg) for 5 soil samples, representing 5 different pollution scenarios. For each amendment, the analyses using XRD and XRF to identify their properties, the toxicity characteristics leaching procedure (TCLP) test, and the synthetic precipitation leaching procedure (SPLP) test were also conducted to evaluate the leaching safety in applied site. From results of batch experiments, the amendments showing > 20% extraction lowering efficiency for each heavy metal (metalloid) was selected and the top 5 ranked amendments were determined at different amount of amendment and on different extraction time conditions. For each amendment, the total number of times ranked in the top 5 was counted, prioritizing the feasible amendment for specific domestic contaminated sites in Korea. Mine drainage treatment sludge, iron oxide, calcium oxide, calcium hydroxide, calcite, iron sulfide, biochar showed high extraction decreasing efficiency for heavy metals in descending order. When the economic efficiency for these amendments was analyzed, mine drainage treatment sludge, limestone, steel making slag, calcium oxide, calcium hydroxide were determined as the priority amendment for the Korean field application in descending order.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Atmosphere
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• v.31 no.2
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• pp.229-240
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• 2021

The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.

• Journal of Applied Biological Chemistry
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• v.64 no.2
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• pp.113-119
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• 2021

Olive (Olea europaea L.) leaves, a raw material for health functional foods and cosmetics have abundant polyphenols including oleuropein (major bioactive compound) with various biological activities: antioxidant, antibacterial, antiviral, anticancer activity, and inhibit platelet activation. Oleuropein has been reported as skin protectant, antioxidant, anti-ageing, anti-cancer, anti-inflammation, anti-atherogenic, anti-viral, and anti-microbial activity. Despite oleuropein is the important compound in olive leaves, there is still no quantitative approach to reveal oleuropein content in commercial products. Therefore, a validated method of analysis has to develop for oleuropein. In this study, the components and oleuropein content in 10 types of products were analyzed using a developed method with ultra-performance liquid chromatography to quadrupole time-of-flight mass spectrometry, charge of aerosol detector, and photodiode array. The total of 18 compounds including iridoids (1, 3, 4, 14, and 16-18), coumarin (2), phenylethanoids (5, 9, and 11), flavonoids (6-8, 10, 12, and 13), lignan (15), were tentatively identified in the leaves extract based high resolution mass spectrometry data, and the content of oleuropein in each product was almost identical between two detection methods. The oleuropein in three commercial product (A, G, H) was contained more over the suggested content, and it of five products (B, E, H, I, J) were analyzed within 5-10% error range. However, the two products (C, D) were found far lower than suggested contents. This study provides that analytical results of oleuropein could be a potential information for the quality control of leaf extract for a manufactured functional food.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.1
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• pp.41-50
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• 2022

To study the properties of Sr²⁺ exchange into zeolite A with increasing the molar concentration of Na⁺ in given exchange solution, four single crystals of fully dehydrated Sr²⁺- and Na⁺- exchanged zeolite A were prepared by the bath method using mixed ion-exchange solutions. The Sr(NO₃)₂:NaNO₃ molar rations of the ion exchange solution were 1:1(crystal 1), 1:100(crystal 2), 1:250(crystal 3), and 1:500 (crystal 4), respectively, with a total concentration of 0.05 M. The single-crystals were then vacuum dehydration at 623 K and 1×10^-4 Pa for 2 days. Their single-crystal structures were determined by single-crystal synchrotron X-ray diffraction techniques in the cubic space group Pm3-m, at 100(1) K, and were then refined to the final error indices of R₁/wR₂=0.047/0.146, 0.048/0.142, 0.036/0.128, and 0.040/0.156 for crystals 1, 2, 3, and 4, respectively. In crystals 1 and 2, the 6 Sr²⁺ ions are found at three different crystallographic sites. In crystal 3, 1 Sr²⁺ and 10 Na⁺ ions are found in large cavity and sodalite unit. In crystal 4, only 12 Na⁺ ions occupy three equipoints. The degree of Sr²⁺ ion-exchange decreased sharply from 100 to 16.7 to 0% as the initial Na⁺ concentration increase and the Sr²⁺ concentration decrease. In addition, the unit cell constant of the zeolite framework decreased with this lower level of Sr²⁺ exchange.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.622-629
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• 2022

The study aim was to derive a stem taper equation for Phyllostachys pubescens, a type of bamboo in South Korea, and to develop a stem volume table. To derive the stem taper equation, three stem taper models (Max & Burkhart, Kozak, and Lee) were used. Since bamboo stalks are hollow because of its woody characteristics, the outer and inner diameters of the tree were calculated, and connecting them enabled estimating the tree curves. The results of the three equations for estimating the outer and inner diameters led to selection of the Kozak model for determining the optimal stem taper because it had the highest fitness index and lowest error and bias. We used the Kozak model to estimate the diameter of Phyllostachys pubescens by stem height, which proved optimal, and drew the stem curve. After checking the residual degree in the stem taper equation, all residuals were distributed around "0", which proved the suitability of the equation. To calculate the stem volume of Phyllostachys pubescens, a rotating cube was created by rotating the stem curve with the outer diameter at 360°, and the volume was calculated by applying Smalian's method. The volume of Phyllostachys pubescens was calculated by deducting the inner diameter calculated volume from the outer diameter calculated volume. The volume of Phyllostachys pubescens was only 20~30% of the volume of Larix kaempferi, which is a general species. However, considering the current trees/ha of Phyllostachys pubescens and the amount of bamboo shoots generated every year, the individual tree volume was predicted to be small, but the volume/ha was not very different or perhaps more. The significance of this study is the stem taper equation and stem volume table for Phyllostachys pubescens developed for the first time in South Korea. The results are expected to be used as basic data for bamboo trading that is in increasing public and industrial demand and carbon absorption estimation.