• Tuberculosis and Respiratory Diseases
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• v.48 no.6
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• pp.909-921
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• 2000

Background : Defects in apoptotic signaling pathways play important role in tumor initiation, progression, metastasis and resistance to treatment. Several proteins which may promote tumorigenesis by inhibiting apoptosis were identified. The survivin protein is the member of inhibitor of apoptosis protein(IAPs) family which inhibits apoptosis. Unlike other IAPs, it is expressed in during the fetal period but not in adult differentiated tissues. Many reports have stated that survivin is selectively expressed in many cancer cell lines and cancer tissues. We performed immunohistochemical analysis for survivin expression in non-mall cell lung cancer to get evaluate its clinical implication. Methods : Twenty nine surgically resected lung cancers were examined. Immunohistochemical staining were performed by immuno-peroxidase technique using avidin-biotinylated horseradish pemxidase complex in the formalin-fixed, paraffin-embedded tissue $4{\mu}m$ section. Anti-survivin polyclonal antibody was used for primary antibody and anti-p53 monoclonal antibody was also used to analyze the correlation between survivin and p53 expression. The survivin expression scores were determined by as the sum of the stained area and intensity. Results : Immunohistochemical analysis showed cancer specific expression of survivin in 20 of 29 cases (69.0%). Western blot analysis also showed the selective survivin expression in tumor tissue. There was no correlation between survivin expression and clinicopathological parameters and prognosis. We analyzed the ∞π'elation between survivin expression and p53 expression, but found none. Conclusion: We confirmed the tumor specific expression of survival in non-small cell lung canær. But this expression was not correlated with clinical parameters as well as histology, tumor stage, recurrence, and survival rate. Also it was not statistically correlated with the expression of p53.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.551-560
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• 2016

The aim of this study was to verify the effect of the parameters in relation to college students' awareness of sexual assault and sexual violence experience. The subjects were 408 male and female college students attending four universities located in Jeju island. Sexual assault awareness measures, sexual violence experience scale, and the scale of sexual violence allowance scale were used in the questionnaires. First, correlation analysis showed a significant negative correlation between sexual violence awareness and sexual violence. Also, sexual violence awareness and sexual violence allowance scale showed a considerable negative correlation. This suggests that the higher the sexual assault awareness, the lower the sexual damage and sexual violence allowance scale. Second, after analyzing the division of the subject into research model and competition model to verify the mediation effect in relationship of the sexual violence awareness and sexual violence experience, it was clear that sexual awareness does not have a direct effect on the experience of sexual damage. It rather mediated the sexual violence allowance scale. Therefore, the research model, which is the full mediation model, was selected. In other words, sexual assault awareness appeared to fully mediate the relationship between sexual assault awareness and sexual assault experience. This means that sexual assault perception has an indirect influence through sexual assault allowance rather than a direct effect on the sexual assault experience. Thus, the more well established that sexual assault awareness is, the lower the sexual assault allowance scale. Finally, the lowered sexual assault allowance scale could reduce the sexual assault harm. To improve the sexual assault recognition, a systematic sexual violence prevention education should be conducted. Sexual assault prevention education would allow students in dating relationships to become mature in their human rights and maintain the proper emotional relationship and enable a reduction in the harm sexual assault as a result of giving them the consideration. Finally, this study had a limitation in selecting the subjects as the college students living in Jeju. Therefore, in order to compensate for this limitation, follow-up studies should be carried out on with a sample of generalized and various research subjects.

• Journal of the Korea Organic Resources Recycling Association
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• v.2 no.2
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• pp.3-17
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• 1994

The inhibitory effect of sodium ion on the anaerobic degradation of food waste was studied by an anaerobic batch toxicity assay and inhibition model. The anaerobic degradation activity of food waste spiked with over $2g\;Na^+/L$ of sodium ion was severely inhibited at the initial stage of the exposure. The inhibition response of anaerobic microorganisms on the sodium ion estimated from the methane production was differed according to the concentration of sodium ion. The relative acclimation time(RAT) and methanation rate(RMR), defined as the ratios of initial lag time and maximum methane production rate of the sample spiked with sodium ion to the control. respectively, were used to evaluate the acclimation and inhibitory effects quantitatively on the anaerobic microorganisms. When sodium ion was increased from $2g\;Na^+/L$ to $20g\;Na^+/L$, the RAT was exponentially increased from 18.9 to 90. but the RMR was linearly decreased from 0.97 to 0.02. The effects of sodium ion for the maximum methanation rate, first order kinetic constant and ultimate methane production were well evaluated by a generalized nonlinear expression model. it could be described by the uncompetitive inhibition mode. The sodium ion concentration causing 50% inhibition of methanation activity was about $11g\;Na^+/L$, and the critical sodium ion beyond to compelete inhibition was 20 to $21g\;Na^+/L$. The presented results could be used to obtain the design or operation parameters of the anaerobic process treating food waste of high salt.

• Tuberculosis and Respiratory Diseases
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• v.44 no.6
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• pp.1285-1295
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• 1997

Background : EGFR is one of the initial step in signal transduction pathway about multistep carcinogenesis. It is homologous to oncogene erbB-2 and is the receptor for EGF and TGF alpha. EGFR has important role in the growth and differentiation of tumor cells. So, EGFR in non-small cell lung cancer was examined to search for possible evidence as clinical prognostic factor. Methods : To investigate the role of EGFR in lung cancer, the author performed immunohistochemical stain of EGFR on 57 resected primary non-small cell lung cancer specimens. And the author analyzed the correlation between EGFR expression, clinical parameters, Sand $G_1$ phase fraction and survival. Results : 1) EGFR were detected in 56% of total 57 patients (according to histologic type, squamous cancer 50%, adenocarcinoma 63%, large cell cancer 75%) (according to TNM stage, stage I 64%, stage II 38%, stage III 55%) (according to cellular differentiation, well 50%, moderately 52%, poorly 65%). All differences were insignificant 2) Using the flow cytometric analysis, mean S-phase fraction of EGFR (+) and (-) group were 22.3(${\pm}10.5$)%. 18.0(${\pm}10.9$)% (p>0.05), mean $G_1$-phase fraction of EGFR (+) and (-) group were 68.4(${\pm}11.6$)%, 71.1(${\pm}12.8$)%, (p>0.05) 3) Two-year survival rate of EGFR (+) and (-) group were 53%, 84%, median survival time of EGFR (+) and (-) group were 26, 53 months. (p<0.05, Kaplan-Meier, generalized Wilcox) Conclusion : EGFR immunostaining may be a simple and useful method for survival prediction in non-small cell lung cancer.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.7-17
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• 2005

The determination of seismic velocities in refractors for near-surface seismic refraction investigations is an ill-posed problem. Small variations in the computed time parameters can result in quite large lateral variations in the derived velocities, which are often artefacts of the inversion algorithms. Such artefacts are usually not recognized or corrected with forward modelling. Therefore, if detailed refractor models are sought with model based inversion, then detailed starting models are required. The usual source of artefacts in seismic velocities is irregular refractors. Under most circumstances, the variable migration of the generalized reciprocal method (GRM) is able to accommodate irregular interfaces and generate detailed starting models of the refractor. However, where the very-near-surface environment of the Earth is also irregular, the efficacy of the GRM is reduced, and weathering corrections can be necessary. Standard methods for correcting for surface irregularities are usually not practical where the very-near-surface irregularities are of limited lateral extent. In such circumstances, the GRM smoothing statics method (SSM) is a simple and robust approach, which can facilitate more-accurate estimates of refractor velocities. The GRM SSM generates a smoothing 'statics' correction by subtracting an average of the time-depths computed with a range of XY values from the time-depths computed with a zero XY value (where the XY value is the separation between the receivers used to compute the time-depth). The time-depths to the deeper target refractors do not vary greatly with varying XY values, and therefore an average is much the same as the optimum value. However, the time-depths for the very-near-surface irregularities migrate laterally with increasing XY values and they are substantially reduced with the averaging process. As a result, the time-depth profile averaged over a range of XY values is effectively corrected for the near-surface irregularities. In addition, the time-depths computed with a Bero XY value are the sum of both the near-surface effects and the time-depths to the target refractor. Therefore, their subtraction generates an approximate 'statics' correction, which in turn, is subtracted from the traveltimes The GRM SSM is essentially a smoothing procedure, rather than a deterministic weathering correction approach, and it is most effective with near-surface irregularities of quite limited lateral extent. Model and case studies demonstrate that the GRM SSM substantially improves the reliability in determining detailed seismic velocities in irregular refractors.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.