• Journal of Ginseng Research
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• v.39 no.2
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• pp.141-147
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• 2015

Background: Adipocytes, which are the main cellular component of adipose tissue, are the building blocks of obesity. The nuclear hormone receptor $PPAR{\gamma}$ is a major regulator of adipocyte differentiation and development. Obesity, which is one of the most dangerous yet silent diseases of all time, is fast becoming a critical area of research focus. Methods: In this study, we initially aimed to investigate whether the ginsenoside Rf, a compound that is only present in Panax ginseng Meyer, interacts with $PPAR{\gamma}$ by molecular docking simulations. After we performed the docking simulation the result has been analyzed with several different software programs, including Discovery Studio, Pymol, Chimera, Ligplus, and Pose View. All of the programs identified the same mechanism of interaction between $PPAR{\gamma}$ and Rf, at the same active site. To determine the drug-like and biological activities of Rf, we calculate its absorption, distribution, metabolism, excretion, and toxic (ADMET) and prediction of activity spectra for substances (PASS) properties. Considering the results obtained from the computational investigations, the focus was on the in vitro experiments. Results: Because the docking simulations predicted the formation of structural bonds between Rf and $PPAR{\gamma}$, we also investigated whether any evidence for these bonds could be observed at the cellular level. These experiments revealed that Rf treatment of 3T3-L1 adipocytes downregulated the expression levels of $PPAR{\gamma}$ and perilipin, and also decreased the amount of lipid accumulated at different doses. Conclusion: The ginsenoside Rf appears to be promising compound that could prove useful in antiobesity treatments.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.257-271
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• 2019

The use of food grade hexane (FGH) for edible oil extraction is responsible for the presence of benzene in the crude oil. Benzene is a Group 1 carcinogen and could pose a serious threat to the health of consumer. However, its detection still depends on classical methods using chromatography which requires a rapid non-destructive detection method. Hence, the aim of this study was to investigate the feasibility of using Fourier transform infrared (FTIR) spectroscopy combined with multivariate analysis to detect and quantify the benzene residue in edible oil (sesame and cottonseed oil). Oil samples were adulterated with varying quantities of benzene, and their FTIR spectra were acquired with an attenuated total reflectance (ATR) method. Optimal variables for a partial least-squares regression (PLSR) model were selected using the variable importance in projection (VIP) and the selectivity ratio (SR) methods. The developed PLS models with whole variables and the VIP- and SR-selected variables were validated against an independent data set which resulted in $R^2$ values of 0.95, 0.96, and 0.95 and standard error of prediction (SEP) values of 38.5, 33.7, and 41.7 mg/L, respectively. The proposed technique of FTIR combined with multivariate analysis and variable selection methods can detect benzene residuals in edible oils with the advantages of being fast and simple and thus, can replace the conventional methods used for the same purpose.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.1-9
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• 2022

Most factories deal with toxic or flammable chemicals in their industrial processes. These hazardous substances pose a risk of leakage due to accidents, such as fire and explosion. In the event of chemical release, massive casualties and property damage can result; hence, quantitative risk prediction and assessment are necessary. Several methods are available for evaluating chemical dispersion in the atmosphere, and most analyses are considered neutral in dispersion models and under far-field wind condition. The foregoing assumption renders a model valid only after a considerable time has elapsed from the moment chemicals are released or dispersed from a source. Hence, an initial dispersion model is required to assess risk quantitatively and predict the extent of damage because the most dangerous locations are those near a leak source. In this study, the dispersion model for initial consequence analysis was developed with three-dimensional unsteady advective diffusion equation. In this expression, instantaneous leakage is assumed as a puff, and wind velocity is considered as a coordinate transform in the solution. To minimize the buoyant force, ethane is used as leaked fuel, and two different diffusion coefficients are introduced. The calculated concentration field with a molecular diffusion coefficient shows a moving circular iso-line in the horizontal plane. The maximum concentration decreases as time progresses and distance increases. In the case of using a coefficient for turbulent diffusion, the dispersion along the wind velocity direction is enhanced, and an elliptic iso-contour line is found. The result yielded by a widely used commercial program, ALOHA, was compared with the end point of the lower explosion limit. In the future, we plan to build a more accurate and general initial risk assessment model by considering the turbulence diffusion and buoyancy effect on dispersion.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.4
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• pp.15-27
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• 2024

Ecosystem-disturbing plant species pose a significant threat to native ecosystems due to their high reproductive capacity, making it essential to monitor their distribution and develop effective mitigation strategies. Consequently, it is crucial to enhance the evaluation of the impacts of these species in environmental impact assessments by incorporating scientific evidence alongside qualitative assessments. This study introduces a dispersal model into the species distribution model to simulate the potential spread of ecosystem-disturbing plant species, reflecting their ecological characteristics. Additionally, we developed mitigation scenarios and quantitatively calculated reduction rates to propose effective mitigation strategies. The species distribution model showed a reliable AUC (Area Under the Curve) of at least 0.890. The dispersal model's results were also credible, with 31 out of 34 validation coordinates falling within the predicted spread range. Simulating the impact of the spread of ecosystem-disturbing plant species over the next five years revealed that one project site had potential habitats for Ambrosia artemisiifolia, necessitating robust mitigation measures such as seed removal. Another project site, with potential habitats for Symphyotrichum pilosum, indicated that physical removal methods within the site were effective due to the species' relatively short dispersal distance. These findings can serve as fundamental data for project executors and reviewers in evaluating the impact of the spread of ecosystem-disturbing plant species during the planning stages of projects.

• Safety and Health at Work
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• v.15 no.3
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• pp.317-326
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• 2024

Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion: This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

• Journal of Distribution Science
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• v.13 no.8
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• pp.61-71
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• 2015

Purpose - Before the year 2000, the housing prices in Korea were increasing every decade. After 2000, for the first time, Korea experienced a decrease in housing prices, and the repetitive cycle of price fluctuation started. Such a "boom and bust cycle" is a worldwide phenomenon. The current study proposes a mathematical model to explain price fluctuation cycles based on the theory of consumer psychology. Specifically, the model incorporates the effects of buyer expectations of future prices on actual price changes. Based on the model, this study investigates various independent variables affecting the amplitude of price fluctuations in housing markets. Research design, data, and methodology - The study provides theoretical analyses based on a mathematical model. The proposed model uses the following assumptions of the pricing mechanism in housing markets. First, the price of a house at a certain time is affected not only by its current price but also by its expected future price. Second, house investors or buyers cannot predict the exact future price but make a subjective prediction based on observed price changes up to the present. Third, the price is determined by demand changes made in previous time periods. The current study tries to explain the boom-bust cycle in housing markets with a mathematical model and several numerical examples. The model illustrates the effects of consumer price elasticity, consumer sensitivity to price changes, and the sensitivity of prices to demand changes on price fluctuation. Results - The analytical results imply that even without external effects, the boom-bust cycle can occur endogenously due to buyer psychological factors. The model supports the expectation of future price direction as the most important variable causing price fluctuation in housing market. Consumer tendency for making choices based on both the current and expected future price causes repetitive boom-bust cycles in housing markets. Such consumers who respond more sensitively to price changes are shown to make the market more volatile. Consumer price elasticity is shown to be irrelevant to price fluctuations. Conclusions - The mechanism of price fluctuation in the proposed model can be summarized as follows. If a certain external shock causes an initial price increase, consumers perceive it as an ongoing increasing price trend. If the demand increases due to the higher expected price, the price goes up further. However, too high a price cannot be sustained for long, thus the increasing price trend ceases at some point. Once the market loses the momentum of a price increase, the price starts to drop. A price decrease signals a further decrease in a future price, thus the demand decreases further. When the price is perceived as low enough, the direction of the price change is reversed again. Policy makers should be cognizant that the current increase in housing prices due to increased liquidity can pose a serious threat of a sudden price decrease in housing markets.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.385-388
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• 2023

Gypsy moth (Lymantria dispar), a polyphagous insect pest belonging to the family Lymantriidae, is widely distributed in Korea, Japan, Siberia, Europe, and North America. They pose a threat to various host plants including pear trees, apple trees, and blueberries. Traditionally considered a forest pest, the increasing incursion of gypsy moths into agricultural land near forested areas has intensified damage to crops lacking effective control methods. This study aimed to investigate the temperature-dependent development of gypsy moths to enhance outbreak prediction and advance technology development. The effects of temperature on development of each life stage were investigated under constant temperature conditions of 18, 21, 24, 27, 30, and 33℃ (14L:10D, RH 60±5%) utilizing egg masses collected in Jeollanam-do Jangheung-gun in 2021. The results revealed that higher temperatures accelerated the development rate of the gypsy moth larvae with optimal development occurring at 30℃. However, the survival rate was lowest at 33℃. At the favorable temperature of 30℃, the total development period was 43.8 days for females and 42.5 days for males. The developmental threshold temperature were 13.1℃ for females and 12.5℃ for males, with effective accumulated temperature of 641.1 DD and 657.8 DD, respectively.

• The korean journal of orthodontics
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• v.26 no.4
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• pp.414-420
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• 1996

Cephalometric measureements have disadvantage of representing cranio-facial structures in two dimension only and therefore they pose limitations in describing three-dimentional structures of cranio-facial region. More interests have been put on the correlation between the two planes. This study evaluated correlations between facial type score, which allows effects on malocclusion, growth change prediction and establishment of treatment method and prognosis, and measurements from submentovertex view. Cephalometric view and submentovertex view were taken of skeletal Class I adults with optimal profile and correlations between them have been observed. Following results were obtained: 1. To learn about factors that influence average condylar angulation, FACE, INT-CO-ANG, MN-CORPUS, CON-RATIO, GON-RATIO, MN-RATIO were used as variables and underwent multiple regression analysis. As a result, the following equation was obtained : CON-AVE=.l73(FACE)-.322(INT-CO-ANG)+36.34(GON-RATIO) +.420(MN-CORPUS) (($R^2=.85451$) 2. The following equation was obtained concerning facial type score. FACE= .050(CON-ANG)+.023(INT-CO-ANG)-.075(MN-CORPUS)($R^2=.31547$) 3. Among the submentovertex measurements, MN-CORPUS, CON-RATIO, GON-RATIO, MN-RATIO showed close correlations. (P<0.05) 4. Average condylar angualtions were $23.37^{\circ}$ on the right and $20.71^{\circ}$ on left. There was a difference between the two. FACE : facial type soore. CON-ANG: mean value of condylar angulation. CON-AVE: mean value of Rt. Lt condylar angulation. INT-CO-ANG : angle between Rt. Lt condylar axis. MN-CORPUS : angle formed between RT. Lt gonion & pogonion. CON-RATIO: lntercondylar distance/mandibular body length. GON-RATIO : intergonion distanoe/mandibular body length. MN-RATIO: lntermylohyoid distance/mandibular body length. MX-RATIO: intermaxillary tuberosity distance/ANS-PNS distance.