• Korean Journal of Environmental Agriculture
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• v.40 no.2
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• pp.134-141
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• 2021

BACKGROUND: The analytical method was established for determination of fungicide chinomethionat in several animal commodities using gas chromatography (GC) coupled with electron capture detector (ECD). METHODS AND RESULTS: In order to verify the applicability, the method was optimized for determining chinomethonat in various livestock products including beef, pork, chicken, milk and egg. Chinomethionat residual was extracted using acetone/dichloromethane(9/1, v/v) with magnesium sulfate and sodium chloride (salting outassociated liquid-liquid extraction). The extract was diluted by direct partitioning into dichloromethane to remove polar co-extractives in the aqueous phase. The extract was finally purified with optimized silica gel 10 g. CONCLUSION: The method limit of quantitation (MLOQ) was 0.02 mg/kg, which was in accordance with the maximum residue level (MRL) of chinomathionate as 0.05 mg/kg in livestock product. Recovery tests were carried out at two levels of concentration (MLOQ, 10 MLOQ) and resulted in good recoveries (84.8~103.0%). Reproducibilities were obtained (Coefficient of variation <5.2%), and the linearity of calibration curves were reasonable (r²>0.995) in the range of 0.01-0.2 ㎍/mL. This established analytical method was fully validated and could be useful for quantification of chinomathionat in animal commodities as official analytical method.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Korean Chemical Engineering Research
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• v.54 no.3
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• pp.394-403
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• 2016

$H_2$ production by chemical looping is an efficient method to convert hydrocarbon fuel into hydrogen with the simultaneous capture of concentrated $CO_2$. This process involves the use of an iron based oxygen carrier that transfers pure oxygen from oxidizing gases to fuels by alternating reduction and oxidation (redox) reactions. The enhanced reactivities of copper oxide doped iron-based oxygen carrier were reported, however, the fundamental understandings on the interaction between $Fe_2O_3$ and CuO are still lacking. In this study, we studied the effect of dopant of CuO to $Fe_2O_3/ZrO_2$ particle on the morphological changes and the associated reactivity using various methods such as SEM/EDX, XRD, BET, TPR, XPS, and TGA. It was found that copper oxide acted as a chemical promoter that change chemical environment in the iron based oxygen carrier as well as a structural promoter which inhibit the agglomeration. The enhanced reduction reactivity was mainly ascribed to the increase in concentration of $Fe^{2+}$ on the surface, resulting in formation of charge imbalance and oxygen vacancies. The CuO doped $Fe_2O_3/ZrO_2$ particle also showed the improved reactivity in the steam oxidation compared to $Fe_2O_3/ZrO_2$ particle probably due to acting as a structural promoter inhibiting the agglomeration of iron species.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.141-156
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• 2023

Due to the unprecedented worldwide pandemic of the new Covid-19 infection, business trends of companies have changed significantly. Therefore, it is strongly required to monitor the rapid changes of innovation trends to design and plan future businesses. Since the pandemic, many studies have attempted to analyze business changes, but they are limited to specific industries and are insufficient in terms of data objectivity. In response, this study aims to analyze business trends after Covid-19 using Crunchbase, a global startup data. The data is collected and preprocessed every two years from 2018 to 2021 to compare the business trends. To capture the major trends, a network analysis is conducted for the industry groups and industry information based on the co-occurrence. To analyze the minor trends, LDA-based topic modelling and word2vec-based clustering is used. As a result, e-commerce, education, delivery, game and entertainment industries are promising based on their technological advances, showing extension and diversification of industry boundaries as well as digitalization and servitization of business contents. This study is expected to help venture capitalists and entrepreneurs to understand the rapid changes under the impact of Covid-19 and to make right decisions for the future.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.647-660
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• 2020

The objectives of this study are to develop a framework for selecting multi-GCMs considering Asia monsoon characteristics and assess it's applicability. 12 climate variables related to monsoon climates are selected for GCM selection. The framework for selecting multi-GCMs includes the evaluation matrix of GCM performance based on their capability to simulate historical climate features. The climatological patterns of 12 variables derived from individual GCM over the summer monsoon season during the past period (1976-2005) and they are compared against observations to evaluate GCM performance. For objective evaluation, a rigorous scoring rule is implemented by comparing the GCM performance based on the results of statistics between historical simulation derived from individual GCM and observations. Finally, appropriate 5 GCMs (NorESM1-M, bcc-csm1-m, CNRM-CM5, CMCC-CMS, and CanESM2) are selected in consideration of the ranking of GCM and precipitation performance of each GCM. The selected 5 GCMs are compared with the historical observations in terms of monsoon season and monthly mean to validate their applicability. The 5 GCMs well capture the observational climate characteristics of Asia for the 12 climate variables also they reduce the bias between the entire GCM simulations and the observational data. This study demonstrates that it is necessary to consider various climate variables for GCM selection and, the method introduced in this study can be used to select more reliable climate change scenarios for climate change assessment in the Asia region.

• Economic and Environmental Geology
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• v.54 no.4
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• pp.409-425
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• 2021

Carbon dioxide has been known to be a typical greenhouse gas causing global warming, and a number of efforts have been proposed to reduce its concentration in the atmosphere. Among them, carbon dioxide capture and storage (CCS) has been taken into great account to accomplish the target reduction of carbon dioxide. In order to commercialize the CCS, its safety should be secured. In particular, if the stored carbon dioxide is leaked in the arable land, serious problems could come up in terms of crop growth. This study was conducted to investigate the effect of carbon dioxide leaked from storage sites on soil fertility. The leakage of carbon dioxide was simulated using the facility of its artificial injection into soils in the laboratory. Several soil chemical properties, such as pH, cation exchange capacity, electrical conductivity, the concentrations of exchangeable cations, nitrogen (N) (total-N, nitrate-N, and ammonia-N), phosphorus (P) (total-P and available-P), sulfur (S) (total-S and available-S), available-boron (B), and the contents of soil organic matter, were monitored as indicators of soil fertility during the period of artificial injection of carbon dioxide. Two kinds of soils, such as non-cultivated and cultivated soils, were compared in the artificial injection tests, and the latter included maize- and soybean-cultivated soils. The non-cultivated soil (NCS) was sandy soil of 42.6% porosity, the maize-cultivated soil (MCS) and soybean-cultivated soil (SCS) were loamy sand having 46.8% and 48.0% of porosities, respectively. The artificial injection facility had six columns: one was for the control without carbon dioxide injection, and the other five columns were used for the injections tests. Total injection periods for NCS and MCS/SCS were 60 and 70 days, respectively, and artificial rainfall events were simulated using one pore volume after the 12-day injection for the NCS and the 14-day injection for the MCS/SCS. After each rainfall event, the soil fertility indicators were measured for soil and leachate solution, and they were compared before and after the injection of carbon dioxide. The results indicate that the residual concentrations of exchangeable cations, total-N, total-P, the content of soil organic matter, and electrical conductivity were not likely to be affected by the injection of carbon dioxide. However, the residual concentrations of nitrate-N, ammonia-N, available-P, available-S, and available-B tended to decrease after the carbon dioxide injection, indicating that soil fertility might be reduced. Meanwhile, soil pH did not seem to be influenced due to the buffering capacity of soils, but it is speculated that a long-term leakage of carbon dioxide might bring about soil acidification.

• Journal of Periodontal and Implant Science
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• v.46 no.5
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• pp.320-328
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• 2016

Purpose: Human saliva, as a vital part of the immune defense system, contains a number of distinct proteins and peptides. Recently human common salivary protein 1 (CSP1) has been identified as an abundant salivary protein and may play a role in promoting the binding of cariogenic bacteria to salivary pellicles. However, nothing else is known regarding the role of CSP1 in periodontology. The aim of this study was to quantify and compare CSP1 levels between healthy subjects and periodontal patients. Methods: This controlled clinical study was conducted in periodontally healthy individuals and patients with chronic periodontitis Chonbuk National University Hospital, with Institutional Review Board approval. Whole saliva samples were collected from 36 healthy subjects and 33 chronic periodontitis patients and analyzed. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis and immune blotting were conducted to ensure that anti-CSP1 monoclonal antibody (mAb) binds to CSP1 in human saliva. A sandwich enzyme-linked immunosorbent assay (ELISA) system was house-fabricated using mAb-hCSP1#14 and mAb-hCSP1#4 as a capture and a detector mAb, respectively. The CSP1 concentrations in saliva from 36 healthy subjects and 33 periodontal patients were quantified using the CSP1 sandwich ELISA system, and the results were analyzed using the Student's t-test. Results: Immunoblot analysis using mAb-hCSP1 as a probe confirmed that CSP1 in human saliva existed as a single band with a molecular weight of approximately 27-kDa. The quantification of CSP1 concentrations by CSP1 ELISA showed that the median values (25th to 75th percentiles) of periodontal patients and healthy subjects were 9,474 ng/mL (range, 8,434.10,139 ng/mL) and 8,598 ng/mL (range, 7,421.9,877 ng/mL), respectively. The Student's t-test indicated the presence of a statistically significant difference between the 2 groups (P=0.024). Conclusions: The presence of a significant difference in CSP1 levels between healthy subjects and periodontal patients suggests that CSP1 may be a potential biomarker for the detection or screening of periodontitis patients.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.4
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• pp.329-334
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• 2017

The structures, which are made up with the huge number of degrees-of-freedom and the assembly of substructures, have a great complexity. In order to increase the computational efficiency, the analysis models have to be simplified. Many substructuring techniques have been developed to simplify large-scale engineering problems. The techniques are very powerful for solving nonlinear problems which require many iterative calculations. In this paper, a modal derivatives-based model order reduction method, which is able to capture the stretching-bending coupling behavior in geometrically nonlinear systems, is adopted and investigated for its performance evaluation. The quadratic terms in nonlinear beam theory, such as Green-Lagrange strains, can be explained by the modal derivatives. They can be obtained by taking the modal directional derivatives of eigenmodes and form the second order terms of modal reduction basis. The method proposed is then applied to a co-rotational finite element formulation that is well-suited for geometrically nonlinear problems. Numerical results reveal that the end-shortening effect is very important, in which a conventional modal reduction method does not work unless the full model is used. It is demonstrated that the modal derivative approach yields the best compromised result and is very promising for substructuring large-scale geometrically nonlinear problems.

• Membrane Journal
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• v.27 no.6
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• pp.487-498
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• 2017

In this study, pervaporation performances of water/methanol and water/butanol mixture were evaluated using zeolite 4A membranes manufacutred by FINETECH by experimental works and numerical modeling. Permeation and separation characteristics, such as flux and separation factor, were analyzed by gas chromatography (TCD) and liquid nitrogen traps. Experiments have shown that water is selectively separated from a mixture of water and methanol (separation factor up to approximately 250) and water and butanol (separation factor up to approximately 1,500). Generalized Maxwell Stefan (GMS) theory was implemented to predict pervaporation behaviors of water/alcohol mixtures and diffusional coefficients of zeolite layer were obtained through parameter estimation using $MATLAB^{(R)}$ optimization toolbox. Since the pore size of zeolite 4A are much larger than kinetic diameter of water molecules and smaller than those of methanol and butanol, zeolite 4A membranes can be applied to in situ water removal process such as membrane reactors or hybrid reaction-dehydration process.