• Journal of fish pathology
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• v.26 no.2
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• pp.65-75
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• 2013

Population growth equation of scuticociliate Miamiensis avidus was obtained from the experimental results of in vitro culture condition to estimate the growth rate and carrying capacity from the growth equation. In addition, intraperitoneal infections into olive flounder Paralichthys olivaceus were carried out into 2 different conditions: different concentrations of M. avidus in same water temperature and same concentration of M. avidus in different water temperatures. Olive flounder mortality was threshold dependent with both the temperature and M. avidus density parameters. In this paper, we propose a mathematical model to study M. avidus growth in olive flounder based upon the interactions between parasite and host. The mathematical model was logistic growth differential equation (1.2). The parameters were found with Matlab program through the Levenberge-Marquardt method. In theorem, equilibrium values between the infected fish population and dead population could found. Our equilibrium points were a stable equilibrium and an unstable equilibrium. From the equation (1.6), it was possible to predict the amount of cumulative mortality of olive flounder along with the time after M. avidus infection.

• The Korean Journal of Emergency Medical Services
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• v.26 no.2
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• pp.97-111
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• 2022

Purpose: Automated External Defibrillator (AED) usage in out-of-hospital cardiac arrests (OHCAs) improves the survival of patients. In Singapore, public AEDs are protected by locked boxes with a 'break glass' mechanism to deter theft. Community responders have sustained injuries while breaking glass to retrieve AEDs. This unprecedented study aimed to elucidate the factors influencing successful retrieval of an AED and to document the prevalence of injuries. Methods: A survey was created and distributed. Participants were required to have responded to an OHCA in the past 12 months. Comparison tests were performed with the Fischer-Freeman-Halton Exact test or Pearson chi square test at 5% significance levels, and with multiple logistic regression with a logit link function. Results: Eighty-eight participants were eligible. The success of retrieving an AED was found not to be impacted by occupation, age, gender or time. Participants who responded to an OHCA because of activation by the myResponder App were more likely to retrieve an AED successfully. (AOR 11.111, 95% CI: 2.141-58.824) Conclusion: Use of the myResponder mobile application is associated with the greater success of retrieving an AED. Successful retrieval of an AED is not impacted by time, gender, age, or the occupation of the responder. Community responders in Singapore remain motivated to respond to Cardiac Arrests despite risk of injury.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.397-407
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• 2017

This research aims to analyze and compare the drought resistance of 7 species of landscape trees commonly grown in Korea. The 7 species are: Camellia japonica, Rhaphiolepis indica, Quercus glauca, Machilus thunbergii, Daphniphyllum macropodum, Dendropanax morbifera and Cinnamomum camphora. In order to analyze their drought resistance, the samples were left without irrigation for 30 days (05/09/2016 ~ 05/10/2016), during which period their respective drought resistor, relative water content, electrolyte elution figures and proline content were measured. As the non-irrigation proceeded, C. camphora was the first to wither, followed by D. morbifera, then D. macropodum, then M. thunbergii, then Q. glauca, then R. indica then finally C. japonica. Of the 7 species, Q. glauca, C. japonica and R. indica can be considered highly drought resistant, since they survived for longer than 3 weeks without irrigation. Relative water content (RWC) plummeted dramatically after the first 15 days of non-irrigation. Whereas RWC readings of C. camphora, D. morbifera, D. macropodum and M. tunbergii dropped by 40% or more, the other 4 species reported a relatively low rate of decrease at 20% or lower. The Camellia japonica, the R. indica and Q. glauca, which were the species with relatively high drought resistance, showed low proline content and electrolyte elution figures, whereas those of C. camphora, D. macropodum, D. morbifera and M. tunbergii were higher. Analysis through the nonlinear regression analysis logistic model showed that non-irrigation proved fatal for the 7 sample species in a range of 22.7 to 37.6 days. The C. japonica, R. indica, Q. glauca and M. tunbergii demonstrated a high drought resistance of 30 days or longer, whereas C. camphora, D. morbifera and D. macropodum had a low resistance of 25 days or less to drought from lack of water. In conclusion, out of the 7 species of broad-leaved evergreen trees tested, C. japonica, R. indica and Q. glauca seem to be suitable for use as landscape trees, owing to their high drought resistance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.306-316
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• 2015

An experiment in a controlled environment was conducted to evaluate the genotypic differences of grain yield and yield-related elements of rice under elevated air temperature. Eight rice genotypes included in three maturing group (early, medium, and medium-late maturing group) were grown with 1/5,000 a Wagner pots at four plastic houses that were controlled to the temperature regimes of ambient temperature (AT), $AT+1.5^{\circ}C$, $AT+3.0^{\circ}C$, and $AT+5.0^{\circ}C$ throughout the rice growing season in 2011. Ripened grain ratio and 1000 grain weight showed the most susceptible and tolerant responses to elevated air temperature, respectively. The grain yield reduction was attributable to the sharp decrease of ripened grain ratio. Grain yield was significantly decreased above the treatment of $AT+1.5^{\circ}C$ and $AT+3.0^{\circ}C$ in early maturing group and the others, respectively. Highly correlation to average temperature from heading to 20 days was revealed in yield (r = -0.69), ripened grain ratio (r = -82), fully-filled grain (r = -70), and 1000 grain weight (r = -0.31). The responses of yield and yield-related elements except number of spikelets and panicle to elevated air temperature were fitted to a logistic function. The parameters of logistic function for each elements except grain yield could not be applied to the other varieties. In conclusion, yield and yield-related elements responded differentially to elevated air temperature according to maturity groups and rice varieties. Ongoing global warming is expected to decrease the grain yield not only by decreasing the grain weight but also decreasing the ripened grain ratio in the future. However, the yield reduction would be mitigated by adopting and/or breeding the less sensitive varieties to high temperature.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.6
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• pp.329-333
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• 2006

Most of volcanic ash soils in South Korea are distributed in Jeju province which is an island placed on southern part of Korea and has steep slope mountain area. There are many soils containing high contents of organic matter (OM) derived from volcanic ash in Jejudo, also. Therefore, irrigation and drainage in volcanic ash soil different with general soil which has low OM content have to be applied with another management way, but studies searching appropriate methods for them are set on insufficient situation because the area of volcanic ash soil in South Korea is only 1.3% (130,000ha). This study was conducted for analysis of soil water content and irrigation quantity appropriate for crops cultivated in volcanic ash soil with high OM content. Although soils with different soil color have the same soil texture, soil water characteristics curve by soil color showed the difference of water retention capability by OM content. But, this characteristics classified with soil color could be unified by scaling technique with similitude analysis method which get dimensionless water content using a present water content, a residual water content and saturated water content (or water content at 10kPa). A relation of gravimetric soil water content (GSWC) and dimensionless water content by the results showed a form of power function. The dimensionless water content (DWC) express a relative saturation degree of present water content. This was also expressed by van Genuchten model which describe the relation between relative saturation degrees and matric potentials. These results on soil water characteristics curve (SWCC) of volcanic ash soil will be the basic of irrigation plan in area having high organic contents into soil.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.