• Radiation Oncology Journal
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• v.22 no.2
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• pp.145-154
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• 2004

Purpose : To examine whether a synthetic bile acid derivatives (HS-1200) sensitizes the radiation-induced apoptosis in human breast cancer cells (MCF-7) and to investigate the underlying mechanism. Materials and Methods : Human breast cancer cells (MCF-7) in exponential growth phase were treated with HS-1200 for 24 hours at 37$^{\circ}C$ with 5$\%$ CO$_{2}$ in air atmosphere. After removal of HS-1200, cells were irradiated with 2$\~$8 Gy X-ray, and then cultured Ii drug-free media for 24-96 hours. The effect of radiation on the clonogenicity of MCF-7 cells was determined with clonogenic cell survival assay with 16$\mu$M of HS-1200. The induction of apoptosis was determined using agarose gel electrophoresis and Hoechst staining. The expression level of apoptosis-related molecules, such as PARP, Bax, Bcl-2, Bak and AIF, were assayed by Western blotting analysis with 40$\mu$M of HS-1200 combined with 8 Gy irradiation. To examine the cellular location of cytochrome c, bax and AIF immunofluorescent stainings were undertaken. Results : Treatment of MCF-7 cells with 40$\mu$M of HS-1200 combined with 8 Gy irradiation showed several changes associated with enhanced apoptosis by agarose gel electrophoresis and Hoechst staining. HS-1200 combined with 8 Gy irradiation treatment also enhanced production of PARP cleavage products and increased Bax/Bcl-2 ratio by Western blotting. Loss of mitochondrial membrane potential ($\Delta$$\psi$$_{m}$) and increased cytochrome c staining indicated that cytochrome c had been released from the mitochondria in HS-1200 treated cells. Conclusion : We demonstrated that combination treatment with a synthetic chenodeoxycholic acid derivative HS-1200 and irradiation enhanced radiation-induced apoptosis of human breast cancer cells (MCF-7). We suggest that the increased Bax/Bcl-2 ratio In HS-1200 co-treatment group underlies the increased radio sensitivity of MCF-7 cells. Further futures studies are remained elusive.

• Journal of Radiation Protection and Research
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• v.40 no.4
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• pp.252-260
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• 2015

This paper describes the technical background for the Korean wildlife radiation dose assessment code, K-BIOTA, and the summary of its application. The K-BIOTA applies the graded approaches of 3 levels including the screening assessment (Level 1 & 2), and the detailed assessment based on the site specific data (Level 3). The screening level assessment is a preliminary step to determine whether the detailed assessment is needed, and calculates the dose rate for the grouped organisms, rather than an individual biota. In the Level 1 assessment, the risk quotient (RQ) is calculated by comparing the actual media concentration with the environmental media concentration limit (EMCL) derived from a bench-mark screening reference dose rate. If RQ for the Level 1 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 2 assessment, which calculates RQ using the average value of the concentration ratio (CR) and equilibrium distribution coefficient (Kd) for the grouped organisms, is carried out for the more realistic assessment. Thus, the Level 2 assessment is less conservative than the Level 1 assessment. If RQ for the Level 2 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 3 assessment is performed for the detailed assessment. In the Level 3 assessment, the radiation dose for the representative organism of a site is calculated by using the site specific data of occupancy factor, CR and Kd. In addition, the K-BIOTA allows the uncertainty analysis of the dose rate on CR, Kd and environmental medium concentration among input parameters optionally in the Level 3 assessment. The four probability density functions of normal, lognormal, uniform and exponential distribution can be applied.The applicability of the code was tested through the participation of IAEA EMRAS II (Environmental Modeling for Radiation Safety) for the comparison study of environmental models comparison, and as the result, it was proved that the K-BIOTA would be very useful to assess the radiation risk of the wildlife living in the various contaminated environment.

• Korean Journal of Environment and Ecology
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• v.28 no.6
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• pp.762-768
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• 2014

The purpose of this study is to analyze the soil $CO_2$ efflux and micro-climate of a preserved forest area located in a Mt. bulam urban nature park Quercus acutissima stand from June 2013 to May 2014. The research showed that the soil and heterotrophic $CO_2$ efflux were $28.14{\pm}7.99$ to $582.47{\pm}318.51$ and $12.32{\pm}8.04$ to $415.71{\pm}159.92mg\;CO_2{\cdot}m^{-2}{\cdot}h^{-1}$, respectively. In addition the seasonal soil $CO_2$ efflux of summer, autumn, winter, spring were 1169.1, 454.81, 72.08 and $494.23g\;CO_2{\cdot}m^{-2}{\cdot}month^{-1}$, respectively. On the other hand, the seasonal heterotrophic $CO_2$ efflux were 526.20, 340.09, 45.13 and $374.9g\;CO_2{\cdot}m^{-2}{\cdot}month^{-1}$, respectively. Moreover, the annual soil and heterotrophic $CO_2$ efflux was found to be 2190.22 and $1286.33g\;CO_2{\cdot}m^{-2}{\cdot}yr^{-1}$, respectively. The exponential function was also utilized for the regression analysis in order to correlate the environmental factors with the soil and heterotrophic $CO_2$ efflux. It was found out that both air and soil temperatures were positively correlated with the soil and heterotrophic $CO_2$ efflux. However, the amount of solar radiation and soil moisture has showed low correlation for both types of $CO_2$ efflux. Contribution of root $CO_2$ efflux to total soil $CO_2$ efflux in this Quercus acutissima stand was 33.60%.

• Journal of Environmental Policy
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• v.7 no.4
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• pp.35-55
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• 2008

Assessing vulnerability to climate change is the first step to take when setting up appropriate adaptation strategies. Adaptive capacity to climate change is the important factor comprising vulnerability. An adaptive capacity index in agricultural water management system was developed considering agricultural water supply and demand for rice production in Jeolla-do, Korea. The agricultural water supply was assumed to be equal to the amount of water stored in the major agricultural reservoirs, while data on the agricultural water demand was obtained from the dynamic simulation results by Korea Agriculture Corporation(KAC). The spatial unit for analysis was conducted at the county(Si, Gun, Gu) level and temporal scale was based on every month from 1991-2003. Adaptive capacity for drought stress index(ACDS index) was calculated as the percentage of data points where the irrigated water supply was greater than the crop water demand. The ACDS index was compared with SWSCI(Standard Water Storage Capacity Index) and the relationship showed high degree of fit($R^2$=0.84) using the exponential function, indicating that the developed ACDS index is useful for evaluating the status of the balance between agricultural water supply and demand, especially for the small sized agricultural reservoirs. This study provided the methodological basis for developing climate change vulnerability index in agricultural water system which is projected to be more frequently exposed to drought condition in the future due to climate change. Further research should be extended to the study on the water demand of the crops other than rice and to the projection of the change in ACDS index in the future.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Radiation Oncology Journal
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• v.19 no.3
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• pp.245-251
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• 2001

Prupose : The genes involved on the suppression or radiation-induced apoptosis by genistein in K562 leukemia cell line was investigated. Materials and methods : K562 cells in exponential growth phase were irradiated with a linear accelerator at room temperature. For X-ray irradiation and drug treatment, cultures were prepared at $2\times10^5\;cells/mL$. The cells were irradiated with 10 Gy (Clinac 1800C, Varian, USA), Stock solutions of herbimycin A (HMA, Calbiochem, UK) and genistein (Calbiochem, UK) were prepared in dimethylsulfoxide (DMSO, Sigma, UK). After incubation at $37^{\circ}C$ for 24 h, PCR-select cDNA subtractive hybridization, dot hybridization, DNA sequencing and Northern hybridization were examined. Results : Smad6 gene was identified from the differentially expressed genes in K562 cells incubated with genistein which had been selected by PCR-select cDNA subtractive hybridization. The mRNA expression of Smad6 in K562 cells incubated with genistein was also higher than control group by Northern hybridization analysis. Conclusion : We have shown that Smad6 involved on the suppression of radiation-induced apoptosis by genistein in K562 leukemia cell line. It is plausible that the relationship between Smad6 and the suppression of radiation-induced apoptosis is essential for treatment development based on molecular targeting designed to modify radiation-induced apoptosis.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1143-1154
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• 2021

The selection of activation function has a great influence on the groundwater level prediction performance of artificial neural network (ANN) model. In this study, five activation functions were applied to ANN model for two groundwater level observation wells in the middle mountainous area of the Pyoseon watershed in Jeju Island. The results of the prediction of the groundwater level were compared and analyzed, and the optimal activation function was derived. In addition, the results of LSTM model, which is a widely used recurrent neural network model, were compared and analyzed with the results of the ANN models with each activation function. As a result, ELU and Leaky ReLU functions were derived as the optimal activation functions for the prediction of the groundwater level for observation well with relatively large fluctuations in groundwater level and for observation well with relatively small fluctuations, respectively. On the other hand, sigmoid function had the lowest predictive performance among the five activation functions for training period, and produced inappropriate results in peak and lowest groundwater level prediction. The ANN-ELU and ANN-Leaky ReLU models showed groundwater level prediction performance comparable to that of the LSTM model, and thus had sufficient potential for application. The methods and results of this study can be usefully used in other studies.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.241-247
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• 2001

Boundary line method was adopted to analyze the relationships between rice yield and meteorological conditions during rice growing period. Boundary lines of yield responses to mean temperature($T_a$) and sunshine hour( $S_{h}$) and diurnal temperature range($T_r$) were well-fitted to hyperbolic functions of f($T_a$) =$$\beta$_{0t}$(1-EXP(-$$\beta$_{1t}$ $\times$ ($T_a$) ) and f( $S_{h}$)=$$\beta$_{0t}$((1-EXP($$\beta$_{1t}$$\times$ $S_{h}$)), to quadratic function of f($T_r$) =$\beta$$_{0r}$(1-($T_r$ 1r)$^2$), respectively. to take into account to, the sterility caused by low temperature during reproductive stage, cooling degree days [$T_c$ =$\Sigma$(20-$T_a$] for 30 days before heading were calculated. Boundary lines of yield responses to $T_c$ were fitted well to exponential function of f($T_c$) )=$\beta$$_{0c}$exp(-$$\beta$_{1c}$$\times$$T_c$ ). Excluding the constants of $\beta$$_{0s}$ from the boundary line functions, formed are the relative function values in the range of 0 to 1. And these were used as yield indices of the meteorological elements which indicate the degree of influence on rice yield. Assuming that the meteorological elements act multiplicatively and independently from each other, meteorological yield index (MIY) was calculated by the geometric mean of indices for each meteorological elements. MIY in each growth period showed good linear relationship with rice yield. The MIY's during 31 to 45 days after transplanting(DAT) in vegetative stage, during 30 to 16 days before heading (DBH) in reproductive stage and during 20 days after heading (DAH) in ripening stage showed greater explainablity for yield variation in each growth stage. MIY for the whole growth period was calculated by the following three methods of geometric mean of the indices for vegetative stage (MIVG), reproductive stage (HIRG) and ripening stage (HIRS). MI $Y_{I}$ was calculated by the geometric mean of meteorological indices showing the highest determination coefficient n each growth stage of rice. That is, (equation omitted) was calculated by the geometric mean of all the MIY's for all the growth periods devided into 15 to 20 days intervals from transplanting to 40 DAH. MI $Y_{III}$ was calculated by the geometric mean of MIY's for 45 days of vegetative stage (MIV $G_{0-45}$ ), 30 days of reproductive stage (MIR $G_{30-0}$) and 40 days of ripening stage (MIR $S_{0-40}$). MI $Y_{I}$, MI $Y_{II}$ and MI $Y_{III}$ showed good linear relationships with grain yield, the coefficients of determination being 0.651, 0.670 and 0.613, respectively.and 0.613, respectively.