• The Plant Pathology Journal
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• v.40 no.2
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• pp.171-191
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• 2024

Identification of Helicotylenchus species is very challenging due to phenotypic plasticity and existence of cryptic species complexes. Recently, the use of rDNA barcodes has proven to be useful for identification of Helicotylenchus. Molecular markers are a quick diagnostic tool and are crucial for discriminating related species and resolving cryptic species complexes within this speciose genus. However, DNA barcoding is not an error-free approach. The public databases appear to be marred by incorrect sequences, arising from sequencing errors, mislabeling, and misidentifications. Herein, we provide a comprehensive analysis of the newly obtained, and published DNA sequences of Helicotylenchus, revealing the potential faults in the available DNA barcodes. A total of 97 sequences (25 nearly full-length 18S-rRNA, 12 partial 28S-rRNA, 16 partial internal transcribed spacer [ITS]-rRNA, and 44 partial cytochrome c oxidase subunit I [COI] gene sequences) were newly obtained in the present study. Phylogenetic relationships between species are given as inferred from the analyses of 103 sequences of 18S-rRNA, 469 sequences of 28S-rRNA, 183 sequences of ITS-rRNA, and 63 sequences of COI. Remarks on suggested corrections of published accessions in GenBank database are given. Additionally, COI gene sequences of H. dihystera, H. asiaticus and the contentious H. microlobus are provided herein for the first time. Similar to rDNA gene analyses, the COI sequences support the genetic distinctness and validity of H. microlobus. DNA barcodes from type material are needed for resolving the taxonomic status of the unresolved taxonomic groups within the genus.

• 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.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.385-398
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• 2020

This study aims to examine genetics problem-solving processes of high school students with different learning approaches. Two second graders in high school participated in a task that required solving the complicated pedigree problem. The participants had similar academic achievements in life science but one had a deep learning approach while the other had a surface learning approach. In order to analyze in depth the students' problem-solving processes, each student's problem-solving process was video-recorded, and each student conducted a think-aloud interview after solving the problem. Although students showed similar errors at the first trial in solving the problem, they showed different problem-solving process at the last trial. Student A who had a deep learning approach voluntarily solved the problem three times and demonstrated correct conceptual framing to the three constraints using rule-based reasoning in the last trial. Student A monitored the consistency between the data and her own pedigree, and reflected the problem-solving process in the check phase of the last trial in solving the problem. Student A's problem-solving process in the third trial resembled a successful problem-solving algorithm. However, student B who had a surface learning approach, involuntarily repeated solving the problem twice, and focused and used only part of the data due to her goal-oriented attitude to solve the problem in seeking for answers. Student B showed incorrect conceptual framing by memory-bank or arbitrary reasoning, and maintained her incorrect conceptual framing to the constraints in two problem-solving processes. These findings can help in understanding the problem-solving processes of students who have different learning approaches, allowing teachers to better support students with difficulties in accessing genetics problems.

• Journal of the Korean Geotechnical Society
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• v.15 no.4
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• pp.113-132
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• 1999

Recently in Korea, application of the soil nailing is gradually extended to the sites of excavations and slopes having various ground conditions and field characteristics. Design of the soil nailing is generally carried out in two steps, The First step is to examine the minimum safety factor against a sliding of the reinforced nailed-soil mass based on the limit equilibrium approach, and the second step is to check the maximum displacement expected to occur at facing using the numerical analysis technique. However, design parameters related to the soil nailing system are so various that a reliable design method considering interrelationships between these design parameters is continuously necessary. Additionally, taking into account the anisotropic characteristics of in-situ grounds, disturbances in collecting the soil samples and errors in measurements, a systematic analysis of the field measurement data as well as a rational technique of the optimum design is required to improve with respect to economical efficiency. As a part of these purposes, in the present study, a procedure for the optimum design of a soil nailing excavation wall system is proposed. Focusing on a minimization of the expenses in construction, the optimum design procedure is formulated based on the genetic algorithm. Neural network theory is further adopted in predicting the maximum horizontal displacement at a shotcrete facing. Using the proposed procedure, various effects of relevant design parameters are also analyzed. Finally, an optimized design section is compared with the existing design section at the excavation site being constructed, in order to verify a validity of the proposed procedure.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Journal of Food Hygiene and Safety
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• v.33 no.2
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• pp.140-145
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• 2018

The skin of the human body occupies the largest surface area of the body and acts as a protection for the person's internal organs. As such, the skin is a major target of oxidative stressors, and these oxidative stressors are known to contribute to skin aging over the course of time. For the most part, an antioxidant is an effective approach to utilize to prevent symptoms related to the reactive oxygen species (ROS)-induced aging of the skin. Therefore, we investigated the antioxidant and anti-aging activity of the leaves of the Quercusaliena Blume extract (QBE). In our study, we confirmed that the cell viability tested with XTT {2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide innersalt} assay was not affected up to a concentration of $100{\mu}g/mL$. In addition, the cell viability of HDF cells induced by hydrogen peroxide was recovered from 81% to 104% after treatment with QBE, which showed the greater protective effect than that of ascorbic acid. Treatments of QBE dose-dependently inhibited reactive oxygen species (ROS) production in HDF cells induced by hydrogen peroxide, which correlated with their protective effects on cell viability. Since QBE treatment exhibited the suppression effect of skin aging by decreasing the ROS production, QBE could be used as a not only natural anti-aging but also antioxidant resource.

• Korean Journal of Ichthyology
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• v.35 no.4
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• pp.224-235
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• 2023

South Korea is the only divided nation globally, marked by a military demarcation line establishing demilitarized and civilian control zones, ensuring national security. Consequently, these areas exhibit relatively minimal ecological disruption compared to other regions. However, the threat to safety persists due to the presence of unexploded ordnances and landmines, imposing significant constraints on ecological research. To address this, we conducted a comparative study utilizing eDNA analysis as a supplementary and alternative approach within three points of the "Road of Peace" - Inje, Yanggu, and Hwacheon courses, located within the civilian control zone. Direct surveys and indirect eDNA sampling were carried out in May, July, and September of 2022. Genetic material obtained from the samples underwent amplification, library preparation, MiSeq sequencing, and subsequent ASV generation for indirect analysis. These results were then compared with the findings of direct surveys. Our findings revealed the detection of eDNA for both observed species at the Yanggu-1 point, and for two out of four species at Yanggu-2. Hwacheon-1 displayed the detection of eDNA for one out of one observed species, whereas Hwacheon-2 yielded seven out of twelve, Hwacheon-3 showed four out of six, and all one observed species at Hwacheon-4 exhibited eDNA detection. Consequently, approximately 69% of the fish species identified through direct surveys were confirmed by indirect eDNA analysis. It is necessary to verify if certain fish species, such as the continental trout and catfish, have genetic information registered in the NCBI database. Additionally, it is believed that further marker development research utilizing different genetic sequences is essential. Given the limitations imposed by the hazardous nature of the surveyed civilian control zone, eDNA analysis proves to be a suitable supplement for fish research in the area.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Life Science
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• v.26 no.4
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• pp.419-430
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• 2016

Powdery mildew disease caused by Podosphaera xanthii is a major concern for Cucumis melo production worldwide. Knowledge on genetic behavior of the related genes and their modulating phytohormones often offer the most efficient approach to develop resistance against different diseases. Mildew Resistance Locus O (MLO) genes encode proteins with seven transmembrane domains that have significant function in plant resistance to powdery mildew fungus. We collected 14 MLO genes from ‘Melonomics’ database. Multiple sequence analysis of MLO proteins revealed the existence of both evolutionary conserved cysteine and proline residues. Moreover, natural genetic variation in conserved amino acids and their replacement by other amino acids are also observed. Real-time quantitative PCR expression analysis was conducted for the leaf samples of P. xanthii infected and phyto-hormones (methyl jasmonate and salicylic acid) treated plants in melon ‘SCNU1154’ line. Upon P. xanthii infection using 7 different races, the melon line showed variable disease reactions with respect to spread of infection symptoms and disease severity. Three out of 14 CmMLO genes were up-regulated and 7 were down-regulated in leaf samples in response to all races. The up- or down-regulation of the other 4 CmMLO genes was race-specific. The expression of 14 CmMLO genes under methyl jasmonate and salicylic acid application was also variable. Eleven CmMLO genes were up-regulated under salicylic acid treatment, and 7 were up-regulated under methyl jasmonate treatments in C. melo L. Taken together, these stress-responsive CmMLO genes might be useful resources for the development of powdery mildew disease resistant C. melo L.