• Food Engineering Progress
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• v.15 no.4
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• pp.404-412
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• 2011

The purpose of this study is to prepare extruded rice flours suitable for baking rice cookies. The extruded rice flours were prepared at 100 and 130$^{\circ}C$ temperature and 25 and 27% moisture content in a co-rotating twin screw extruder. The rice extrudates were dried at 100$^{\circ}C$ for 18 hr and subsequently ground into the fine flour. Characteristics of the extruded rice flours were examined by rapid visco analysis, hydration property analysis, differential scanning calorimetry (DSC), and in vitro digestion test. Water absorption, solubility, and swelling power of all extruded rice flours were higher than those of native rice flour. DSC analysis showed that native rice flour had a peak at about 65$^{\circ}C$ while all extruded rice flours did not show any peaks since they were already gelatinized during the extrusion proess. Viscosity of the extruded rice flours decreased with increasing temperature and lowering moisture content in the extrusion proess. The extruded rice flours prepared at 130$^{\circ}C$ exhibited lower viscosity than those prepared at 100$^{\circ}C$. The operating temperature of the extrusion proess was critical for the starch digestion in vitro. The extruded rice flours prepared at 130$^{\circ}C$ showed a rapid decrease in digestible starch content while an increased level of slowly digestible starch content was observed compared to those treated at 100$^{\circ}C$ in the extruder. Cookies were prepared with a mixture of wheat flour and extruded rice flours at the ratio of 7 to 3. The cookies made with the extruded rice flours had lower spread factor and darker yellow color than those prepared with wheat flour only. Hardness of the extruded rice flour-added cookies was similar to that of the wheat flour cookie whereas their overall acceptance was better. Therefore the rice cookies partially supplemented with extruded rice flours may have a potential as early childhood foods which require soft texture and allergy reduction.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.302-311
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• 2018

BACKGROUND: Dithianon (75%) formulation were mixed and sprayed as closely as possible by normal practice on the ten farms located in the Mungeong of South Korea. Patches, cotton gloves, socks, masks, and XAD-2 resin were used for measurement of the potential exposure of dithianon on the applicators wearing standardized whole-body outer and inner dosimeter (WBD). This study has been carried out to determine the dermal and inhalation exposure to dithianon during preparation of spray suspension and application with a power sprayer on a apple orchard. METHODS AND RESULTS: A personal air monitor equipped with an air pump, IOM sampler and cassette, and glass fiber filter was used for inhalation exposure. The field studies were carried out in a apple orchard. The temperature and relative humidity were monitored with a thermometer and a hygrometer. Wind speed was measured using a pocket weather meter. All mean field fortification recoveries were between 85.1% and 99.1% in the level of 100 LOQ (limit of quantification), while the LOQ for dithianon was $0.05{\mu}g/mL$ using HPLC-DAD. The exposure to dithianon on arms of the mixer/loader (0.0794 mg) was higher than other body parts (head, hands, upper body, or legs). The exposure to dithianon on the applicator's legs (3.78 mg) was highest in the body parts. The dermal exposures for mixer/loader and applicator were 10 and 8.10 mg, respectively, from a grape orchard. The inhalation exposure during application was estimated as 0.151 mg, and the ratio of inhalation exposure was 11.2% of the dermal exposure (inner clothes). CONCLUSION: The dermal and inhalation exposure on the applicator appeared to be 4.203 mg - 25.064 mg and $0.529{\mu}g-116.241{\mu}g$, respectively. The total exposures on the agricultural applicators were at the level of 2.596 mg - 25.069 mg to dithianon during treatment for apple orchard. The TER showed 3.421 (>1) when AOEL of dithianon was used as a reference dose for the purpose of risk assessment of the mixing/loading and application.

• Journal of Astronomy and Space Sciences
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• v.36 no.1
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• pp.21-33
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• 2019

This paper focuses on the interpretation of radiation fluxes from active galactic nuclei. The advantage of positron annihilation spectroscopy over other methods of spectral diagnostics of active galactic nuclei (therefore AGN) is demonstrated. A relationship between regular and random components in both bolometric and spectral composition of fluxes of quanta and particles generated in AGN is found. We consider their diffuse component separately and also detect radiative feedback after the passage of high-velocity cosmic rays and hard quanta through gas-and-dust aggregates surrounding massive black holes in AGN. The motion of relativistic positrons and electrons in such complex systems produces secondary radiation throughout the whole investigated region of active galactic nuclei in form of cylinder with radius R= 400-1000 pc and height H=200-400 pc, thus causing their visible luminescence across all spectral bands. We obtain radiation and electron energy distribution functions depending on the spatial distribution of the investigated bulk of matter in AGN. Radiation luminescence of the non-central part of AGN is a response to the effects of particles and quanta falling from its center created by atoms, molecules and dust of its diffuse component. The cross-sections for the single-photon annihilation of positrons of different energies with atoms in these active galactic nuclei are determined. For the first time we use the data on the change in chemical composition due to spallation reactions induced by high-energy particles. We establish or define more accurately how the energies of the incident positron, emitted ${\gamma}-quantum$ and recoiling nucleus correlate with the atomic number and weight of the target nucleus. For light elements, we provide detailed tables of all indicated parameters. A new criterion is proposed, based on the use of the ratio of the fluxes of ${\gamma}-quanta$ formed in one- and two-photon annihilation of positrons in a diffuse medium. It is concluded that, as is the case in young supernova remnants, the two-photon annihilation tends to occur in solid-state grains as a result of active loss of kinetic energy of positrons due to ionisation down to thermal energy of free electrons. The single-photon annihilation of positrons manifests itself in the gas component of active galactic nuclei. Such annihilation occurs as interaction between positrons and K-shell electrons; hence, it is suitable for identification of the chemical state of substances comprising the gas component of the investigated media. Specific physical media producing high fluxes of positrons are discussed; it allowed a significant reduction in the number of reaction channels generating positrons. We estimate the brightness distribution in the ${\gamma}-ray$ spectra of the gas-and-dust media through which positron fluxes travel with the energy range similar to that recorded by the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) research module. Based on the results of our calculations, we analyse the reasons for such a high power of positrons to penetrate through gas-and-dust aggregates. The energy loss of positrons by ionisation is compared to the production of secondary positrons by high-energy cosmic rays in order to determine the depth of their penetration into gas-and-dust aggregations clustered in active galactic nuclei. The relationship between the energy of ${\gamma}-quanta$ emitted upon the single-photon annihilation and the energy of incident electrons is established. The obtained cross sections for positron interactions with bound electrons of the diffuse component of the non-central, peripheral AGN regions allowed us to obtain new spectroscopic characteristics of the atoms involved in single-photon annihilation.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.485-494
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• 2018

This study aims to identify the characteristics of oxidation and chemical composition of PM in winter season, 2017 at Incheon area. The mean concentration of air pollutants were $46{\pm}22{\mu}g/m^3-PM_{10}$, $29{\pm}18{\mu}g/m^3/-PM_{2.5}$, $5{\pm}3ppb-SO_2$, $0.56{\pm}0.24ppm-CO$, $21{\pm}13ppb-O_3$ and $28{\pm}17ppb-NO_2$, respectively. The dominant ion of the $PM_{1.0}$ chemical component were organic with $3.2{\mu}g/m^3$ and nitrate with $1.9{\mu}g/m^3$. The day and night variation of the $PM_{1.0}$ chemical components was higher in nighttime than those of daytime. The averaged nitrate oxidation rate (SOR) was 0.06 and sulfate oxidation rate was 0.11 during the field campaign. In the high mass loading period, nitrate oxidation rate (NOR) was up to 0.6 and also the nitrate in $PM_{1.0}$ was increased. The averaged ratio of $NO_x/SO_2$ was 8.7 and nitrate/sulfate was 3.1, respectively. In this results, the nitrate component in $PM_{1.0}$ was influenced by NOx from the stationary source as power plant and the mobile source around the measurement site.

• Land and Housing Review
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• v.13 no.4
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• pp.55-71
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• 2022

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

• Clean Technology
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• v.26 no.4
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• pp.329-335
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• 2020

Methanol synthesized from renewable hydrogen and captured CO2 has recently attracted great interest as a sustainable energy carrier for large-scale renewable energy storage. In this study, molten carbonate fuel cell's performance was investigated with the direct conversion of methanol into syngas inside the anode chamber of the cell. The internal reforming of methanol may significantly improve system efficiency since the heat generated from the electrochemical reaction can be used directly for the endothermic reforming reaction. The porous Ni-10 wt%Cr anode was sufficient for the methanol steam reforming reaction under the fuel cell operating condition. The direct supply of methanol into the anode chamber resulted in somewhat lower cell performance, especially at high current density. Recycling of the product gas into the anode gas inlet significantly improved the cell performance. The analysis based on material balance revealed that, with increasing current density and gas recycling ratio, the methanol steam reforming reaction rate likewise increased. A methanol conversion more significant than 90% was achieved with gas recycling. The results showed the feasibility of electricity and syngas co-production using the molten carbonate fuel cell. Further research is needed to optimize the fuel cell operating conditions for simultaneous production of electricity and syngas, considering both material and energy balances in the fuel cell.

• Journal of the Korean Institute of Gas
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• v.26 no.3
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• pp.45-53
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• 2022

In order to improve the emission of diesel engines, natural gas-diesel dual fuel combustion compression ignition engines are in the spotlight. In particular, a reactivity controlled compression ignition (RCCI) combustion strategy is investigated comprehensively due to its possibility to improve both efficiency and emissions. With advanced diesel direct injection timing earlier than TDC, it achieves spontaneous reaction with overall lean mixture from a homogeneous mixture in the entire cylinder area, reducing nitrogen oxides (NOx) and particulate matter (PM) and improving braking heat efficiency at the same time. However, there is a disadvantage in that the amount of incomplete combustion increases in a low load region with a relatively small amount of fuel-air. To solve this, sensitive control according to the diesel injection timing and fuel ratio is required. In this study, experiments were conducted to improve efficiency and exhaust emissions of the natural gas-diesel dual fuel engine at low load, and evaluate combustion stability according to the diesel injection timing at the operation point for power generation. A 6 L-class commercial diesel engine was used for the experiment which was conducted under a 50% load range (~50 kW) at 1,800 rpm. Two injectors with different spray patterns were applied to the experiment, and the fraction of natural gas and diesel injection timing were selected as main parameters. Based on the experimental results, it was confirmed that the brake thermal efficiency increased by up to 1.3%p in the modified injector with the narrow-angle injection added. In addition, the spray pattern of the modified injector was suitable for premixed combustion, increasing operable range in consideration of combustion instability, torque reduction, and emissions level under Tier-V level (0.4 g/kWh for NOx).

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.201-223
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• 2022

Currently, species extinctions are increasing due to climate change and continued anthropogenic impact. We selected 300 species for conservation with emphasis on plants co-occurring in the Baekdudaegan area, which is a large ecological axis of Korea. We aimed to investigate the vegetation community and environmental characteristics of Zabelia tyaihyonii in the limestone habitat among the target plant species in the Baekdudaegan region to derive effective conservation strategies. In Danyang-gun, Yeongwol-gun, and Jecheon-si, we selected 36 investigation sites where Z. tyaihyonii was present. We investigated the vegetation, flora, soil and physical environment. We also found notable plants such as Thalictrum petaloideum, Sillaphyton podagraria, and Neillia uekii at the investigation sites. We classified forest vegetation community types into 4 vegetation units and 7 species group types. With canonical correspondence analysis (CCA) of the vegetation community and habitat factors, we determined the overall explanatory power to be 75.2%, and we classified the environmental characteristics of the habitat of Z. tyaihyonii into a grouping of three. Among these, we detected a relationship between the environmental factors elevation, slope, organic matter, rock ratio, pH, potassium, and sodium. We identified numerous rare and endemic plants, including Thalictrum petaloideum, in the investigation site, and determined that these groups needed to be preserved at the habitat level. In the classification of the vegetation units analyzed based on the emerging plants and the CCA, we reaffirmed the uniqueness and specificity of the vegetation community in the habitat of Z. tyaihyonii. We anticipate that our results will be used as scientific evidence for the empirical conservation of the native habitats of Z. tyaihyonii.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.