• Korean Journal of Weed Science
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• v.16 no.4
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• pp.264-281
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• 1996

As a consequence of wide use of herbicides, Eleocharis kuroguwai Ohwi became a dorminant problem weed for rice cultivation in Korea. To understand the establishment of the weed, experiments on physio-ecological characteristics were carried out sprouting and occurrence, the results could be summarized as follows: Sprouting percentage remained 68 to 73% until the time of field emergence, indicating many of the them are still dormant. The proportion of the dormant tubers were greater for the smaller than the bigger tubers. Apical dominance was apparent in sprouting, with 84% of tuber sprouted from only one of the apical buds. Tubers sprouted from 2 or 3 buds were less than 20%, and were mostly from the bigger tubers. When the shoot growth was compared, by controlling the others, ones from apical and the next 3 buds showed similar vigorous growth, but the later ones showed poorer growth. For the longevity of tubers, deep soil storage appeared to be better than storage in temperature controlled room to 2~$3^{\circ}C$. Emergence of E. kuroguwai was better in clay soil than in sand, and the possible depth for emergence in clay soil appeared to be up to 21cm, but was 15cm in sand. When tubers were exposed to salt solutions before emergence tests, E. kuroguwai appeared to be much sensitive to salts than S.planiculmis. Among the tubers formed in previous year, 12.7% remained still viable until the end of next crop season, but with relatively strong dormancy. The first emergence was about 10 days after planting at ordinary cropping seasons, and the days to the maximum shoot number stage were 60~90 from planting. The duration was extended at early transplanting, and shoot number, leaves per shoot, and tubers developed per plant were also greater at early plantings. The 6th order offshoots were developing when E. kuroguwai was planted at early season. When planted at later seasons, generation and the number of offshoots was reduced planted at early season. When planted at later seasons, generation and the number of offshoots was reduced and the number of tubers, runner and rhizome lengths was also reduced.

• Journal of the Mineralogical Society of Korea
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• v.20 no.1 s.51
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• pp.47-60
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• 2007

While sedimentological researches on Western coastal tidal flats of Korea have been much pelformed previously, mineralogical and biogeochemical studies are beginning to be studied. The objectives of this study were to investigate mineralogical characteritics of the inter-tidal flat sediments and to explore phase transformation of iron(oxyhydr)oxides and biomineralization by metal-reducing bacteria enriched from the inter-tidal flat sediments from Muan, Jeollanam-do, Korea. Inter-tidal flat sediment samples were collected in Chungkye-myun and Haeje-myun, Muan-gun, Jeollanam-do. Particle size analyses were performed using the pipette method and sedimentation method. The separates including sand, silt and clay fractions were examined by scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) analysis, transmission electron microscopy (TEM), and X-ray diffiaction (XRD). After enriching the metal-.educing bacteria from the into,-tidal flat sediments, the bacteria were used to study phase transformation of the synthesized iron (oxyhydr)oxides and iron biomineralization using lactate or glucose as the electron donors and Fe(III)-containing iron oxides as the electron accepters. Mineralogical studies showed that the sediments of tidal flats in Chung]rye-myun and Haeje-myun consist of quartz, plagioclase, microcline, biotite, kaolinite and illite. Biogeochemical researches showed that the metal-reducing bacteria enriched from the inter-tidal flat sediments reduced reddish brown akaganeite and mineralized nanometer-sized black magnetite. The bacteria also reduced the reddish brown ferrihydrite into black amorphous phases and reduced the yellowish goethite into greenish with formation of nm-sized phases. These results indicate that microbial Fe(III) reduction may play one of important roles in iron and carbon biogeochemistry as well as iron biomineralization in subsurface environments.

• The Journal of the Korean Society for Microbiology
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• v.14 no.1
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• pp.79-87
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• 1979

The immunosuppressive activity of newcastle disease virus(NDV) and some possible role of interferon(C-IF) in viral suppression of immune response were evaluated by SRBC-induced delayed-type hypersensitivity(DTH), rosette formation in spleen cells, number of lymphocytes in peripheral blood, hemagglutinin and hemolysin response to SRBC in ICR mice sensitized with SRBC. When NDV was inoculated before or after sensitization of mouse with SRBC, virus caused a marked inhibition of DTH, and its depressive effect was dependent on the time of virus inoculation in relation to SRBC sensitization or challenge. Rosette formation of spleen cells was significantly reduced by NDV infection. The degree of the depression of rosette formation was more prominent in mice inoculated before sensitization than after sensitization and could be related to the amount of serum interferon induced by the virus. Humoral response to SRBC of virus infected mouse was significantly depressed when NDV was inoculated 24 or 48 hours before sensitization. However, there was no difference in the response when the virus was inoculated 9 hour before and at the same time of sensitization or even after that. Lymphocytes in peripheral blood of mice were markedly diminished in numbers when NDV was inoculated 48 and 24 hour before sensitization with SRBC, but they were slightly augmented when the virus was inoculated 9 hour before and at the same time of sensitization. When UV-inactivated or heat-inactivated NDV was injected to the mouse at the same time of sensitization with SRBC, DTH and rosette formation of spleen cells were slightly depressed. DTH and rosette formation in mice treated with crude-IF were generally depressed as com pared with those of control mice. These studies suggest that the NDV causes a significant depression of cell-mediated immunity, whereas the humoral immune response is not inhibited markedly, and that the depression of immune response by NDV infection may be caused by interferon produced by NDV and direct viral activity.

• Korean journal of applied entomology
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• v.14 no.3 s.24
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• pp.97-109
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• 1975

In an attempt to develop an effective integrated system of controlling blast disease of rice caused by Pyricularia oryzae Cav., the possibility of minimizing the disease incidence by proper application of fertilizers has been investigated. Thus the effect of silicate, nitrogen, phosphorus and potassium fertilizers on the development of blast disease as well as the correlation between the rice varieties an4 strains of P. oryzae were studied. The experiments were made in 1971 and 1973 by artificial inoculation and under natural development of the blast disease on rice plants. The results obtained are summarized as follows. 1. Application of silicate fertilizer resulted in the increase of silicate as well as total sugar and potassium content but decrease of total nitrogen and phosphorus in tile leaf blades of rice plants. 2. The ratios of total C/total N. $ SiO_2/total$ N, and $K_2O/total$ N in leaf blades of rice plants increased by the application of silicate fertilizers. There was high level of negative correlation between the ratios mentioned above and the incidence of rice blast disease. 3. Application of silicate fertilizer reduced the incidence of rice blast disease. 4. The over dressing of nitrogen fertilizer resulted in the increase of total nitrogen and decrease of silicate and total sugar content in leaf blades, thus disposing the rice plants more susceptible to blast disease. 5. Over dressing of phosphorus fertilizer resulted in the increase of both total nitrogen and Phosphorus, and decrease of silicate content in the leaf blades inducing the rice plants to become more susceptible to blast disease. 6. Increased dressing of potash resulted in the increase of silicate content and $K_2O/total$ N ratio but decrease of total nitrogen content in leaf blades. When potassium content is low in the leaf blades of rice plants, the additional dressing of potash to rice plant contributed to the increase of resistance to blast disease. However, there was no significant correlation between additional potassium application and the resistance to blast disease when the potassium content is already high in the leaf blades. 7. When four rice varieties were artificially inoculated with three strains of P. oryzae, the incidence of blast disease was most severe on Pungok, least severe on Jinheung and moderate on Pungkwang and Paltal varieties. 8. Disease incidence was most severe on the second leaf from top and less sever on top and there leaf regardless of the fertilizer application when 5-6 leaf stage rice seedlings of four rice varieties were artificially inoculated with three strains of P. oryzae. 9. The pathogenicity of three strains of P. oryzae was in the order of $P_1,\;P_2,\;and\;P_3$ in their virulence when inoculated to Jinheung, Paltal, Pungkwang varieties but not with Pungok. The interaction between strains of P. oryzae and rice varieties was significant.

• Korean Journal of Food Science and Technology
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• v.8 no.2
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• pp.95-106
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• 1976

In order to investigate chemical components and mineral of ginseng cultivated in Korea and to establish an appropriate extraction method, the present work was carried out with Raw ginseng(SC), White ginseng(SB) and Ginseng tail(SA). The results determined could be summarized as follows : 1. Among the proximate components, moisture content of SC, SB and SA were 66.37%, 12.61% and 12.20% respectively. The content of crude ash in SA was the highest value of three kinds of ginseng root: SA 6.04%, SB 3.52% and SC 1.56%. The crude protein of Dried ginseng root(SA and SB) was about 12-14%, which was more than two times compared with that of SC(6.30%) The content of pure protein seemed to be in similar tendency with that of crude protein in three kinds of ginseng root: 2.26% in SC, 5.94% in SB and 5.76% in SA. There was no significant difference in the content of fat among the kinds of ginseng root. $(1.1{\sim}2.5%)$ 2. The highest Ginseng extract was obtained by use of Continuous extractor which is a modified Soxhlet apparatus for 60 hours extraction with 60-80% ethanol. 3. Ginseng and the above-mentioned ginseng extract (Ginseng tail extract: SAE, White Ginseng extract : SBE, Raw Ginseng extract: SCE) were analyzed by volumetric method for the determination of Chlorine and Calcium, by colorimetric method for that of Iron and Phosphorus, by Atomic Absorption Spectrophotometer for that of Zinc, Copper and Manganese. The results were as follows : 1. The content of phosphorus in SA, SB and SC were 1.818%, 1.362%, 0.713% respectively and phosphorus content in three kinds of extract were in low level (SAE: 0.03%, SBE: 0.063%, SCE: 0.036%) 2. In the Calcium content, SA, SB and SC were 0.147%, 0.238%, 0.126% and the Calcium contents of Ginseng extracts were 0.023%, 0.011% and 0.016%. The extraction ratio of Calcium from SA was the highest value (15.6%), while that in the case of SB was 4.6%. 3. The Chlorine content of SA was 0.11%, this was slightly higher than others(SB: 0.07%, SC: 0.09%) and extraction ratio of SA and SB were 36.4%, 67.1% while that of SC was 84.4%. 4. The Iron content of SA, SB and SC were 125ppm, 32.5ppm and 20ppm but extraction ratio was extremely low (SAE: 1.33%, SBE: 0.83%, SCE: 1.08%), 5. The Manganese content of SA, SB and SC were 62.5ppm, 25.0ppm and 5.0ppm respectively but the Manganese content of extract could not determined, Copper content of SA, SB and SC were 15.0ppm, 20.0ppm and those of extract were 7.5ppm, 6.5ppm, 4.5ppm while those of extraction ratio were 50%, 32.5% and 90% respectively, Zinc was abundant in Ginseng compared with other herbs, (SA: 45.5ppm, SB: 27.5ppm and SC: 5.5ppm) and the extracted amount were 4.5ppm, 1.25ppm 1.50ppm respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Applied Biological Chemistry
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• v.26 no.2
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• pp.82-89
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• 1983

In order to develop a commercial storage method of onions by irradiation combined with natural low temperature, two local varieties of onions, precocious species and late ripening, were stored at natural low temperature storage room ($450{\times}650{\times}250cmH.$; year-round temperature change, $2{\sim}17^{\circ}C$; R.H., $80{\sim}85%$) on batch scale following irradiation with optimum dose level. Precocious and late varieties were all sprouted after five to seven months storage, whereas $10{\sim}15$ Krad irradiated precocious variety was $2{\sim}4%$ sprouted after nine months storage, but sprouting was completly inhibited at the same dose for late variety. The extent of loss due to rot attack after ten months storage were $23{\sim}49%$ in both control and irradiated group of precocious variety but those of late variety were only $4{\sim}10%$. The weight loss of irradiated precocious variety after ten months storage was $13{\sim}16$, while that of late variety was $5.3{\sim}5.9%$ after nine months storage. The moisture content, during whole storage period, of two varieties were $90{\sim}93$ with negligible changes. The total sugar content differed little with varieties and doses immediatly after irradiation, but decreased by the elapse of storage period. 33.6% of its content was decreased in control and 12.5% in irradiated group but $20{\sim}26$ decreased in both control and irradiated group of late variety after nine months storage. No appreciable change was observed immediately after irradiation irrespective of variety and dose, but decreased slightly with storage. Ascorbic acid content of precocious variety was increased slightly with dose immediately after irradiation, but those of late variety decreased slightly. Ascorbic acid content were generally decreased during whole storage period. An economical preservation method of onions appliable to late variety, would be to irradiate onion bulbs at dost range of $10{\sim}15$ Krad followed by storage at natural low temperature storage room.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.