• Journal of Ginseng Research
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• v.47 no.4
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• pp.552-560
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• 2023

Background: Ginseng Radix (Panax ginseng Meyer, Araliaceae) has been used medicinally to treat the brain and nervous system problems worldwide. Recent studies have revealed physiological effects that could potentially benefit cognitive performance or mood. The present study aimed to investigate the antidepressant effects of Korean red ginseng water extract (KGE) and its active component in an unpredictable chronic mild stress (UCMS)-induced animal model and elucidate the underlying mechanisms. Methods: The antidepressant potential of the UCMS model was evaluated using the sucrose preference test and open field tests. The behavioral findings were further corroborated by the assessment of neurotransmitters and their metabolites from the prefrontal cortex and hippocampus of rats. Three doses of KGE (50, 100, and 200 mg/kg) were orally administered during the experiment. Furthermore, the mechanism underlying the antidepressant-like action of KGE was examined by measuring the levels of brain-derived neurotrophic factor (BDNF)/CREB, nuclear factor erythroid 2-related factor 2 (Nrf2), and Kelch-like ECH-associated protein 1 (Keap1) proteins in the prefrontal cortex of UCMS-exposed rats. Results: KGE treatment normalized UCMS-induced depression-related behaviors. Neurotransmitter studies conducted after completing behavioral experiments demonstrated that KGE caused a reduction in the ratio of serotonin and dopamine, indicating a decrease in serotonin and dopamine turnover. Moreover, the expression of BDNF, Nrf2, Keap1 and AKT were markedly increased by KGE in the prefrontal cortex of depressed rats. Conclusion: Our results provide evidence that KGE and its constituents exert antidepressant effects that mediate the dopaminergic and serotonergic systems and expression of BDNF protein in an animal model.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.

• East Asian mathematical journal
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• v.33 no.1
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• pp.23-36
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• 2017

We study an optimization problem for HARA utility function under a multi-scale Heston's stochastic volatility model. We investigate a practical strategy that do not depend on the incorporated factor which is unobservable in the market.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.193-201
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• 2020

This study is to evaluate the effectiveness of problem-solving ability by applying a team-based learning model to the classes of humanities and social science students, and to conduct a structural model analysis on the relationship between sub-factors. Team-based learning was conducted six times in six teams with 30 students in the second and third grades of the humanities and social sciences. The problem solving ability score of the target students was significantly higher after team-based learning and was statistically significant. There was no problem in normality with the latent variables, which are the sub-factors of problem solving ability, and the factor load value was statistically significant at the .001 level in the confirmatory factor analysis of the observed variables for the latent variables, which was a valid model. A good level of fitness was also shown in the verification of the fitness of the research model. As a result, it was analyzed that latent variables of cause analysis, problem clarification, planning execution, performance evaluation, and alternative development had an indirect or direct influence on each other.

• Korean Security Journal
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• no.48
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• pp.177-206
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• 2016

The purpose of this study is to verify the factors that affect the fear of crime by applying the risk interpretation model. Especially, whereas previous studies have not proven micro individual factor that the risk interpretation model had presented, This study includes micro individual elements such as neighborhood factor, perceived risk of crime, fears of crime as main variables. This study utilized secondary data of the National Crime Victimization Survey 2012, conducted by the Korean Institute of Criminology. In this study, multiple regression analysis of two stages and Sobel Test were conducted for verifying the individual influence of each independent variables and identifying the causal relationship between the variables set out in the risk analysis model. As the result, it appeared that the higher level of perceived risk of crime, neighborhood factor, crime experience, education, income cause the higher degree of the fear of crime. On the other hand, the lower degree of age was found to induce the higher level of the fear of crime. In addition, female showed the higher degree of the fear of crime than man. The causal relationship between the variables set out in the risk interpretation model was presented significantly in all variables, except for education.

• International Journal of Vascular Biomedical Engineering
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• v.2 no.1
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• pp.17-24
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• 2004

Tumor angiogenesis was simulated using a two-dimensional computational model. The equation that governed angiogenesis comprised a tumor angiogenesis factor (TAF) conservation equation in time and space, which was solved numerically using the Galerkin finite element method. The time derivative in the equation was approximated by a forward Euler scheme. A stochastic process model was used to simulate vessel formation and vessel elongation towards a paracrine site, i.e., tumor-secreted basic fibroblast growth factor (bFGF). In this study, we assumed a two-dimensional model that represented a thin (1.0 mm) slice of the tumor. The growth of the tumor over time was modeled according to the dynamic value of bFGF secreted within the tumor. The data used for the model were based on a previously reported model of a brain tumor in which four distinct stages (namely multicellular spherical, first detectable lesion, diagnosis, and death of the virtual patient) were modeled. In our study, computation was not continued beyond the 'diagnosis' time point to avoid the computational complexity of analyzing numerous vascular branches. The numerical solutions revealed that no bFGF remained within the region in which vessels developed, owing to the uptake of bFGF by endothelial cells. Consequently, a sharp, declining gradient of bFGF existed near the surface of the tumor. The vascular architecture developed numerous branches close to the tumor surface (the brush-border effect). Asymmetrical tumor growth was associated with a greater degree of branching at the tumor surface.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.19-30
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• 2016

This study analyzed relative importance among business model factors for improving business performance of Knowledge Service Enterprises using the Business Model methodology. It also compares and analyzes the relative importance of manufacturing enterprises by using the previous research conclusion. This study finds Product & Service factor(0.361) is the most important among Marketing(0.251), Financial aspects(0.234), and Infrastructure(0.154) are follows. For the sub factors, Value Proposition(0.254) is the most importance factors and Revenue Streams(0.154), and Key Activities(0.107), and Key Resources(0.100), and Channels(0.086) are follows. Also, The Marketing has higher relative importance for Manufacturing enterprises, whereas the Product&Service has higher relative importance for Knowledge Service Enterprises. It proves that there is a difference in the relative importance between Manufacturing Enterprises and Knowledge Service Enterprises. This study concludes the importance of business model factor is different for each respective industry. Therefore, it suggests to consider different industrial aspects when build the business model for each industry.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.67-81
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• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• The Korean Journal of Food And Nutrition
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• v.29 no.5
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• pp.746-752
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• 2016

In this study, we analyzed the dietary style of Chinese singles; in addition, the effect of convenience food consumption on the quality of life of singles was evaluated through construct model development on the relationship between the frequency of consumption and satisfaction with convenience food and quality of life. A statistical analysis of 153 surveys from Tianj was conducted using SPSS 12.0 for Windows and SEM using AMOS 5.0 statistics package. The reliability of the data was confirmed by an exploratory factor analysis and Cronbach's alpha coefficient. The measurement model was confirmed as appropriate by a confirmatory factor analysis of the measurement model in conjunction with AMOS. The results of a factor analysis were as follows. Dietary style was categorized into four factors. The level of satisfaction with convenience food was categorized into seven factors and quality of life was categorized into four factors. The reliability of these findings was supported by a Cronbach's alpha coefficient of 0.6 and higher for all the factors. For the level of satisfaction with convenience food based on dietary style and the quality of life of singles, a structural equation model was constructed and analyzed. The results of all tests indicated that the model satisfied the recommended level of goodness of fit index and thus, the overall research model was appropriate. The current study highlights the increased interest in eating habits of singles and is necessary for further improvement in nutrition education.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.335-356
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• 2011

In this paper, we attempted to suggest a way to evaluate appropriateness and efficiency for the energy consumption structure. For this, based on Markowitz (1952)' mean-variance portfolio model, we constructed an optimal fossil fuel mix. In constructing the optimal mix, we first defined returns on fossil fuels (oil, coal and natural gas) as TOE (Ton of Oil Equivalent) per $1. Then, by using the dynamic latent common factor model, we decomposed the growth rates of the returns on each fossil fuel into two parts : the common part and the idiosyncratic part. Finally, based on the results from the dynamic latent common factor model, we constructed the optimal fossil fuel mix implied by the mean-variance portfolio model. Our results indicate that for the fossil fuel mix to be on the efficient frontier, it is crucial to reduce oil consumption as low as possible. Moreover, our results imply that it is more efficient to increase natural gas consumption rather than coal consumption in reducing oil consumption. These results are in line with the strategies for the future energy consumption structure pursued by Korea and indicate that reduction in oil use can improve overall efficiency in energy consumption.