• Journal of Pharmacopuncture
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• v.22 no.3
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• pp.166-170
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• 2019

Objectives: Attentional and memory functions are important aspects of neural plasticity that, theoretically, should be amenable to pharmacopuncture treatments. A previous study from our laboratory suggested that quantitative electroencephalographic (qEEG) measurements of theta/beta ratio (TBR), an index of attentional control, may be indicative of academic performance in a first-semester medical school course. The present study expands our prior report by extracting and analyzing data on frontal theta and beta asymmetries. We test the hypothesis that the amount of frontal theta and beta asymmetries (fTA, fBA), are correlated with TBR and academic performance, thereby providing novel targets for pharmacopuncture treatments to improve cognitive performance. Methods: Ten healthy male volunteers were subjected to 5-10 min of qEEG measurements under eyes-closed conditions. The qEEG measurements were performed 3 days before each of first two block examinations in anatomy-physiology, separated by five weeks. Amplitudes of the theta and beta waveforms, expressed in ${\mu}V$, were used to compute TBR, fTA and fBA. Significance of changes in theta and beta EEG wave amplitude was assessed by ANOVA with post-hoc t-testing. Correlations between TBR, fTA, fBA and the raw examination scores were evaluated by Pearson's product-moment coefficients and linear regression analysis. Results: fTA and fBA were found to be negatively correlated with TBR (P<0.03, P<0.05, respectively) and were positively correlated with the second examination score (P<0.03, P=0.1, respectively). Conclusion: Smaller fTA and fBA were associated with lower academic performance in the second of two first-semester medical school anatomy-physiology block examination. Future studies should determine whether these qEEG metrics are useful for monitoring changes associated with the brain's cognitive adaptations to academic challenges, for predicting academic performance and for targeting phamacopuncture treatments to improve cognitive performance.

• Management & Information Systems Review
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• v.38 no.1
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• pp.1-21
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• 2019

This study focuses on (1) the relationship between the network intensity of top managers and the R&D investment of Korean firms, and (2) the moderating effects of the type (related-division vs. unrelated-division) and system (physical division vs. spin-offs) of corporate division on this relationship. The sample of this study was all type and/or system of corporate division implemented by Korean firms during 18-years (1999-2016) study periods. The results of multiple regression analyses as follow. First, as was expected in hypothesis 1 the network intensity of top managers has a strong positive linear relation with the R&D investment of Korean firms. Second, regarding the moderating effect of division type the results show that related-divisions significantly intensify the positive relationship of the network intensity of top managers with the R&D of Korean firms although unrelated-divisions did not. Third, in the analysis of moderating effect of corporate division system the results present the stronger positive moderating effect of spin-offs rather than physical divisions. The findings of the study implies that strong network intensity of top managers can be beneficial to long-term decision such as R&D investment of Korean firms. They accords to network theory that emphasize the importance of strong network effect among top managers based on their trust. The findings also implies that researchers and practitioners should consider organizational-level factors such as organizational structure, culture, corporate governance, etc as well as individual-level factors such as the characteristics and relationships of organizational members when making the decision for firm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.669-685
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• 1997

Previous studies, reporting the inverse relationship between osteoarthritis and osteoporosis suggest the existence of possible pathophysiologic mechanisms between them. To examinine the hypothesis that 'bone mineral densities of women with osteoarthritis are significantly higher than that of women without osteoarthritis in Korea', subjects from the health and nutritional examination survey in Kuri city were sampled. Samples were selected through multi-stage sampling frame using established clusters in Kuri city. From August 18 to September 10,1997, the survey was conducted. Among the. total number of selected sample population (1,656 people), response .ate was 52.4 percent (348 men and 519 women). 420 women who took BMD measurement, radiologic exam, and anthropometric exam were selected for the analysis. The analytic results are as follows. 1. General characteristics: Mean BMD was $0.493g/cm^2$, mean age was 43.0, mean BMI was $23.9kg/m^2$. The number of women who experienced menopause was 106, hysterectomy was 19. There were 0 case of osteoarthritis of hip, 64 cases of osteoarthritis of knee, and 2 cases of osteoarthritis of hand. 2. Univariate analysis results: Mean BMD of women with the osteoarthritis of knee was significantly lower than that of women without the osteoarthritis of knee(0.4269 vs. $0.5057g/cm^2$). But, there were too few cases of osteoarthritis of hip and hand, so comparative studies of BMD in osteoarthritis of hip and hand could not be conducted. There were significant differences of BMD among pre-menopause group(0.5204), post-menopause group(0.4206), and hysterectomy group(0.4881). Additionally, there were significant differences of BMD among diabetes group(0.4297), impaired glucose tolerance group(0.4874), and normal group(0.5057). Furthermore, age, parity, BMI, bioimpedance were significantly related with BMD. 3. Multivariate analysis results: To examinine the relationship between osteoarthritis and BMD while controlling the other variables' effects which were significant in the univariate analyses, multiple linear regression analysis was done. But, it was found that osteoarthritis of knee was not a significant variable to BMD anymore. While age and menopause had significant negative relationship with BMD. Diabetes, parity, BMI, and bioimpedance did not have significant relationships with BMD. After stratification of subjects according to menopause, multiple linear regression analyses were done to each strata. Consequently, age in post-menopause group, age and osteoarthritis of knee in hysterectomy group showed significant negative relationship with BMD. The results did not support the many results of other previous studies done with white men and women. further studies of biological plausibility to Korean women are recommended. Also it is suggested that longitudinal study to verify the relationship between osteoarthritis and BMD will be valuable.

• The Korean Journal of Nuclear Medicine
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• v.34 no.1
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• pp.10-21
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• 2000

Purpose: Episodic memory is described as an 'autobiographical' memory responsible for storing a record of the events in our lives. We performed functional brain activation study using ${H_2}^{15}O$ PET to reveal the neural basis of the encoding and the retrieval of episodic memory in human normal volunteers. Materials and Methods: Four repeated ${H_2}^{15}O$ PET scans with two reference and two activation tasks were performed on 6 normal volunteers to activate brain areas engaged in encoding and retrieval with verbal materials. Images from the same subject were spatially registered and normalized using linear and nonlinear transformation. Using the means and variances for every condition which were adjusted with analysis of covariance, t-statistic analysis were performed voxel-wise. Results: Encoding of episodic memory activated the opercular and triangular parts of left inferior frontal gyrus, right prefrontal cortex, medial frontal area, cingulate gyrus, posterior middle and inferior temporal gyri, and cerebellum, and both primary visual and visual association areas. Retrieval of episodic memory activated the triangular part of left inferior frontal gyrus and inferior temporal gyrus, right prefrontal cortex and medial temporal area, and both cerebellum and primary visual and visual association areas. The activations in the opercular part of left inferior frontal gyrus and the right prefrontal cortex meant the essential role of these areas in the encoding and retrieval of episodic memory. Conclusion: We could localize the neural basis of the encoding and retrieval of episodic memory using ${H_2}^{15}O$ PET, which was partly consistent with the hypothesis of hemispheric encoding/retrieval asymmetry.

• Radiation Oncology Journal
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• v.17 no.1
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• pp.70-77
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• 1999

Purpose : The expression of p53, P211WAF/CIP, Bcl-2, and Bax underlying the radiation-induced apoptosis in different pH environments using SCK mammary adenocarcinoma cell line was investigated. Materials and Methods Mammary adenocarcinoma cells of hi) mice (SCK cells) in exponential growth phase were irradiated with a linear accelerator at room temperature. The cells were irradiated with 12 Gy and one hour later, the media was replaced with fresh media at a different pHs. After Incubation at 37Microbioiogy, College of Medicine Dong A University for 0$\~$48 h, the extort of apoptosis was determined using agarose gel electrophoresis and flow cytometry. The progression of cells through the cell cycle after irradiation in different pHs was also determined with flow cytometry. Western blot analysis was used to monitor p53, p211WAFfCIP, Bcl-2, and Bu protein levels. Results : The induction of apoptosis by irradiation in pH 6.6 medium was markedly less than that in pH 7.5 medium. The radiation-induced G2IM arrest in pH 6.6 medium lasted markedly longer than that in pH 7.5 medium. Considerable amounts of p53 and p21 proteins already existed at pH 7.5 and increased the level of p53 and p21 significantly after 12 Gy X-irradiation. An incubation at pH 6.6 after 12 Gy X-irradiation did not change the level of p53 and p21 protein levels significantly. Bcl-2 proteins were not significantly affected by radiation and showed no correlation with cell susceptibility to radiation-induced apoptosis in different pHs. An exposure to 12 Gy of X-rays increased the level of Bax protein at pH 7.5 but at pH 6.6, it was slight. Conclusions : The molecular mechanism underlying radiation-induced apoptosis in dinerent pH environments using SCK mammary adenocarcinoma cell line was dependent of the expression p53 and P211YVAF/CIP proteins. We may propose following hypothesis that an acidic stress augments the radiation-induced G2iM arrest, which inhibiting the irradiated cells undergo post-mitotic apoptosis. The effects of environmental acidity on anti-apoptotic and pro-apoptotic function of Bcl-2 family was unclear in SCK mammary adenocarcinoma cell line.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.59-72
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• 2018

The purpose of this study is to investigate the factors affecting the intention to use healing farming by setting aged readiness, rural healing supply condition, and rural healing service as independent variables. This study has been started based on the idea that it is necessary to provide healing service through healing agriculture to the rapidly increasing elderly people and urban people who need mental rest. Therefore, the purpose of this study is to find out the various factors influencing intention to use healing agriculture through empirical analysis. Second, we want to examine whether the variables influenced the intention to use more. Third, based on the results of these tests, we suggest the policy for activation of healing agriculture by examining the factors necessary for the promotion of physical and mental health of the elderly in the aging society and the revitalization of healing agriculture for the mental healing of the urban people. For this study, a questionnaire survey was conducted for men and women over 30 years old, and the final 356 copies were analyzed. The validity of the research hypothesis was verified by linear regression analysis. The results of the analysis are as follows. First, the physical preparation, emotional preparation, and economic preparation of aged care preparations were found to have a significant effect on intention to use. Second, natural landscape, accessibility, and stability of rural healing supply conditions were found to have a significant effect on intention to use. But economics and expertise were dismissed. Third, crop cultivation, animal medication and healing facilities of rural healing service were proved to have significant influence on intention to use. In order to increase the utilization of healing agriculture, it is necessary for the consumer to be well prepared for aging. In rural healing supply conditions, accessibility and safety should be provided for healing facilities in rural healing services.And to increase the intention to use it.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.