• Horticultural Science & Technology
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• v.33 no.1
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• pp.93-105
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• 2015

This study was performed to analyze genetic relationships of the four major cucurbitaceous crops including watermelon, melon, cucumber, and squash/pumpkin. Among 120 EST-SSR primer sets selected from the International Cucurbit Genomics Initiative (ICuGI) database, PCR was successful for 51 (49.17%) primer sets and 49 (40.8%) primer sets showed polymorphisms among eight Cucurbitaceae accessions. A total of 382 allele-specific PCR bands were produced by 49 EST-SSR primers from 24 Cucurbitaceae accessions and used for analysis of pairwise similarity and dendrogram construction. Assessment of the genetic relationships resulted in similarity indexes ranging from 0.01 to 0.85. In the dendrogram, 24 Cucurbitaceae accessions were classified into two major groups (Clade I and II) and 8 subgroups. Clade I comprised two subgroups, Clade I-1 for watermelon accessions [I-1a and I-1b-2: three wild-type watermelons (Citrullus lanatus var. citroides Mats. & Nakai), I-1b-1: six watermelon cultivars (Citrullus lanatus var. vulgaris S chrad.)] a nd C lade I -2 for melon and cucumber accessions [I-2a-1 : 4 melon cultivars(Cucumis melo var. cantalupensis Naudin.), I-2a-2: oriental melon cultivars (Cucumis melo var. conomon Makino.), and I-2b: five cucumber cultivars (Cucumis sativus L.)]. Squash and pumpkin accessions composed Clade II {II-1: two squash/ pumpkin cultivars [Cucurbita moschata (Duch. ex Lam.)/Duch. & Poir. and Cucurbita maxima Duch.] and II-2: two squash/pumpkin cultivars, Cucurbita pepo L./Cucurbita ficifolia Bouche.}. These results were in accordance with previously reported classification of Cucurbitaceae species, indicating that watermelon EST-SSRs show a high level of marker transferability and should be useful for genetic study in other cucurbit crops.

• Korean Journal of Psychosomatic Medicine
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• v.23 no.1
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• pp.26-35
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• 2015

Objectives:The purpose of this study was to evaluate reliability and validity of the Korean version of the Postconcussional Syndrome Questionnaire(KPCSQ) which was originally developed in 1992 by Lees-Haley. Methods:Patients with traumatic brain injury were recruited from April 2009 to December 2011 from the Korean University Ansan Hospital. We selected patients that met the ICD-10 diagnostic criteria of postconcussional syndrome and organic mental disorder including organic mood disorder, organic emotionally labile disorder, organic anxiety disorder and organic personality disorder. The KPCSQ, Trait and State Anxiety Inventory(STAI-I, II), and Center for Epidemiologic Studies Depression Scale(CESD) were administered to all subjects. Factor analysis of the items were performed and test-retest correlation were evaluated. Internal consistency of the KPCSQ and its subscales was assessed with Cronbach's alpha. External validity of the KPCSQ were examined by correlation coefficient with the STAI-I, II, and CESD. Results:The Cronbach's alpha coefficient of the total PCSQ was 0.956. The test-retest reliability coefficient was 0.845. The PCSQ showed significant correlation with STAI-I, II and CESD. The factor analysis of the PCSQ yielded 4 factors model. Factor 1 represented 'affective and cognitive symptoms', factor 2 represented 'somatic symptoms', factor 3 represented 'infrequent symptoms' and factor 4 represented 'exaggeration or inattentive response'. There was no significant difference between the PCS group and the organic mental disorder group in the score on each measure. The scores on KPCSQ and its subscales in the subjects that had scored 5 or more in 'exaggeration or inattentive response' are significantly higher than those in the subjects had scored 4 in 'exaggeration or inattentive response'. Conclusions:This study suggests that the Korean version of PCSQ is a valid and reliable tool for assessing psychiatric symptomatology of patients with traumatic brain injury. Further investigations with greater numbers of subjects are necessary to assess the clinical usefulness of the KPCSQ.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.113-137
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• 2021

According to the clear potential of mobile banking growth, many studies related to this are being conducted, but in Korea, it is concentrated on the analysis of technical factors or consumers' intentions, behaviors, and satisfaction. In addition, even though it has a strong customer base of 20s, there are few studies that have been conducted specifically for this customer group. In order for mobile banking to take a leap forward, a strategy to secure various perspectives is needed not only through research on itself but also through research on external factors affecting mobile banking. Therefore, this study analyzes impulsiveness, credit card use, and SNS addiction among various external factors that can significantly affect mobile banking in their 20s. This study examines whether the relationship between impulsiveness and mobile banking usage depends on whether or not a credit card is used, and checks whether a customer's impulsiveness is possible by examining whether a credit card is used. Based on this, it is possible to establish new standards for classification of marketing target groups of mobile banking. After finding out the static or unsuitable relationship between whether to use a credit card and impulsiveness, we want to indirectly predict the customer's impulsiveness through whether to use a credit card or not to use a credit card. It also verifies the mediating effect of SNS addiction in the relationship between impulsiveness and mobile banking usage. For this analysis, the collected data were conducted according to research problems using the SPSS Statistics 25 program. The findings are as follows. First, positive urgency has been shown to have a significant static effect on mobile banking usage. Second, whether to use credit cards has shown moderating effects in the relationship between fraudulent urgency and mobile banking usage. Third, it has been shown that all subfactors of impulsiveness have significant static relationships with subfactors of SNS addiction. Fourth, it has been confirmed that the relationship between positive urgency, SNS addiction, and mobile banking usage has total effect and direct effect. The first result means that mobile banking usage may be high if positive urgency is measured relatively high, even if the multi-dimensional impulsiveness scale is low. The second result indicates that mobile banking usage rates were not affected by the independent variable, negative urgency, but were found to have a significant static relationship with negative urgency when using credit cards. The third result means that SNS is likely to become addictive if lack of premeditation or lack of perseverance is high because it provides instant enjoyment and satisfaction as a mobile-based service. This also means that SNS can be used as an avoidance space for those with negative urgency, and as an emotional expression space for those with high positive urgency.

• Korean Journal of Heritage: History & Science
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• v.52 no.1
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• pp.4-21
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• 2019

In this paper, 23 Silla gold earrings from the sixth and seventhand centuries, excavated from the Yeongnam region, were analyzed. Based on the silver content of the gold plate, they were classified into three types. The classifications included type I(20-50wt%), type II(10-20wt%) and type III (less than 10wt%). In the analysis process, the composition and morphological differences were identified on the surface of the gold plate. In the case of type I and II earrings, it was observed that the fine holes were concentrated in a relatively higher part of the gold content. The causes of the difference in the surface composition of the gold plate were divided into four categories: 1) surface treatment, 2) thermal diffusivity in the manufacturing process, 3) differences in composition of alluvial gold, and 4) the refining method of gold. It is possible that depletion gilding was attempted to increase the gold content while intentionally removing the other metals from the surface of the gold alloy in the portion where the gold deposit is relatively concentrated on the surface of the gold plating. The highest copper content was detected in the earring with the highest gold content of the analyzed earrings, and it was assumed that thermal diffusion had occurred between the gold plate and the metal rod during the manufacturing process rather than intentional addition. Copper was detected only in the thin ring earring type, and copper was not detected in the thick ring earring type or pendant type. It also proves that this earring has a high degree of tightness at higher temperatures, as there was an invisible edge finish on other earrings and horizontal wrinkles on the gold plate surface. In terms of the material of the gold plate, we examined whether the silver content of the gold plate was natural gold or added by alloy through analyzing the alluvial gold collected in the region. As a result of the analysis, it was found that on average about 13wt% of silver is included. This suggests that type II is natural gold, type III is refined gold, and type I seems to have been alloyed with natural gold. Here, we investigated the refining method introduced in the ancient literature, both at home and abroad, about the possibility of alloying silver after the refining process of type III earrings and then making pure gold. It was found that from ancient refining methods, silver which had been present in the natural gold was removed by reacting and combining with silver chloride or silver sulfide, and long-term efforts and techniques were required to obtain pure gold through this method. Therefore, it was concluded that the possibility of adding a small amount of silver in order to increase strength after making pure gold through a refining process is low.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.227-237
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• 2020

Microclimatic data were measured, and the human thermal sensation was analyzed at 10 local climate zones based on the major land cover classification to investigate the thermal environment of urban areas during summer nighttime. From the results, the green infrastructure areas (GNIAs) showed an average air temperature of 1.6℃ and up to 2.4℃ lower air temperature than the gray infrastructure areas (GYIAs), and the GNIAs showed an average relative humidity of 9.0% and up to 15.0% higher relative humidity. The wind speed of the GNIAs and GYIAs had minimal difference and showed no significance at all locations, except for the forest location, which had the lowest wind speed owing to the influence of trees. The local winds and the surface roughness, which was determined based on the heights of buildings and trees, appeared to be the main factors that influenced wind speed. At the mean radiant temperature, the forest location showed the maximum value, owing to the influence of trees. Except at the forest location, the GNIAs showed an average decrease of 5.5℃ compared to GYIAs. The main factor that influenced the mean radiant temperature was the sky view factor. In the analysis of the human thermal sensation, the GNIAs showed a "neutral" thermal perception level that was neither hot nor cold, and the GYIAs showed a "slightly warm" level, which was a level higher than those of the GNIAs. The GNIAs showed a 3.2℃ decrease compared to the GYIAs, except at the highest forest location, which indicated a half-level improvement in the human thermal environment.

• Journal of Korea Foundry Society
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• v.41 no.2
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• pp.144-152
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• 2021

Based on the analysis of the current state of the world's foundry industry, we looked at the international competitiveness of Korea's foundry industry for the past 20 years. Korea's total foundry production is 2.52 million tons, and the production per company (so-called productivity) is 2,831 tons, which is the eighth largest in the world and down one position for the case of total foundry production, while productivity remains its position compared to three years ago. Korea is the only one of the top 10 foundry to see a decline in production. Similar to the global situation, Korean products consist of 38% of grey csat iron, 31% of ductile cast iron, 15% of aluminum, and 9% of cast steel. In order to obtain statistics on Korea's foundry industry, the survey conducted a service project for approximately nine months from April 2020. Various statistical surveys and sample in-depth surveys by the Korean standard industry class were evaluated for various contents of the domestic casting industry. We also looked at the number of companies, the distribution by region, the number of workers and the percentage of foreigners, and the distribution of each job, as well as the R&D investment status according to the size of the enterprise. Together, sales, exports, sales and various profit ratios were analyzed to measure the earning power of foundry industry. In addition, the classification by grouping the foundry industry according to the process utilized by focusing on each company, and to determine the sales, exports, and yield status for each process was also investigated on the basis. Based on these data, the domestic foundry industry has presented a variety of offers for the following issues for sustainable growth; global ranking, marginal corporate restructuring, training of domestic technical people, differentiated support policies by company size and process.

• Journal of The Korean Association For Science Education
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• v.41 no.1
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• pp.33-46
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• 2021

Inquiry occupies an important place in science education, and research related to inquiry is widely conducted. However, due to the inclusiveness of the concept of "exploration," each researcher perceives its meaning differently, and approaches may vary. In addition, criticisms have been raised that the results of classes using inquiry in science education do not guarantee meaningful changes to students. Therefore, this study attempts to identify the trend of SSCI-level research papers dealing with inquiry in science education over the past three years to confirm the current status and effectiveness of the inquiry. Researches used in the analysis are International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education, and limited to those that directly suggest "inquiry (enquiry)" as a keyword. Based on extracted 75 papers, the classification process was conducted, and an analysis frame was derived inductively by reflecting the subject and characteristics. Specific cases for each category were presented by dividing into three aspects: perception and perspective on inquiry, support and strategy for inquiry, and teacher professional development for inquiry. The results of examining the implications for scientific inquiry are as follows: First, rather than defining inquiry as an implicit proposition or presenting it as a step-by-step procedure, it was induced to grasp the meaning of inquiry more comprehensively and holistically. Second, as to whether the inquiry-based instruction is effective in all aspects of the cognitive, functional, and affective domains of science, the limitations are clearly presented, and the context-dependent and subject-specific properties and limitations of inquiry are emphasized. Third, uncertainty in science inquiry-based instruction can help learners to begin their inquiry and develop interest, but in the process of recognizing data and restructuring knowledge, explicit and specific guidance and scaffolding should be provided at an appropriate timing.

• Korean Journal of Environment and Ecology
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• v.35 no.3
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• pp.256-264
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• 2021

Rapid urbanization around the world has negatively affected wildlife habitats, including birds. Wild birds settled in the city are adapting to the changed surroundings, and are typically known to make nests using materials that are easy to find around the city. This study was conducted for the purpose of analyzing the nesting materials on the Paridae using artificial bird nests installed in cities. In this study, the researchers established a total of 33 artificial bird nests in urban parks (22) and forests (11) in Cheonan-si, Chungcheongnam-do. Then we collected 4 artificial bird nests in urban parks (18.19%) and 5 in urban forests (45.46%) to compare the characteristics of bird nest materials by the nest, species, and urban green area types. Eight nests, excluding a nest abandoned by a pair of Paridae, were used for the material analysis. The collected nests were dried, and classified into natural materials (vegetable materials, animal materials, moss, and soil) and artificial materials (cotton, paper pieces, plastics, vinyl, and synthetic fibers), and then each nest was weighed. The classification result shows that the portion of moss (50.65%) was the highest in all nests, followed by soil (21.43%), artificial material (13.95%), vegetable material (5.78%), animal material (4.57%), and others (3.59%) in that order. Artificial materials were used in all nests in urban green areas. Moreover, although the Paridae used about 5.16% more vegetable material than the Parus varius, it was not significant (t=2.17, p=0.07). Plant materials and soil were most preferred in urban forests, and moss, animal, and artificial materials were widely used in that order in urban parks. There was a significant difference in the use of vegetable materials between urban parks and urban forests (t=3.07, p<0.05^*). In the habitats like urbanized and dry areas, where artificial materials are highly accessible, artificial materials replaced some roles of natural materials. This study is a basic study for the analysis of the types of materials used in artificial bird nests to understand the habitat system of urban ecosystems. It can be used as the basic data for ecological studies and conservation of the Paridae species.

• 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.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.