• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Radiation Oncology Journal
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• v.29 no.2
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• pp.63-70
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• 2011

Purpose: To evaluate the extent of pain response and objective response to palliative radiotherapy (RT) for bone metastases from hepatocellular carcinoma according to RT dose. Materials and Methods: From January 2007 to June 2010, palliative RT was conducted for 103 patients (223 sites) with bone metastases from hepatocellular carcinoma. Treatment sites were divided into the high RT dose and low RT dose groups by biologically effective dose (BED) of 39 $Gy_{10}$. Pain responses were evaluated using the numeric rating scale. Pain scores before and after RT were compared and categorized into 'Decreased', 'No change' and 'Increased'. Radiological objective responses were categorized into complete response, partial response, stable disease and progression using modified RECIST (Response Evaluation Criteria In Solid Tumors) criteria; the factors predicting patients' survival were analyzed. Results: The median follow-up period was 6 months (range, 0 to 46 months), and the radiologic responses existed in 67 RT sites (66.3%) and 44 sites (89.8%) in the high and low RT dose group, respectively. A dose-response relationship was found in relation to RT dose (p=0.02). Pain responses were 75% and 65% in the high and low RT dose groups, respectively. However, no statistical difference in pain response was found between the two groups (p=0.24). There were no differences in the toxicity profiles between the high and low RT dose groups. Median survival from the time of bone metastases diagnosis was 11 months (range, 0 to 46 months). The Child-Pugh classification at the time of palliative RT was the only significant predictive factor for patient survival after RT. Median survival time was 14 months under Child-Pugh A and 2 months under Child-Pugh B and C. Conclusion: The rate of radiologic objective response was higher in the high RT dose group. Palliative AT with a high dose would provide an improvement in patient quality of life through enhanced tumor response, especially in patients with proper liver function.

• Journal of dental hygiene science
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• v.10 no.5
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• pp.403-409
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• 2010

The purpose of this study was to examine the teaching efficiency of clinical training for dental hygiene students in Gyeonggi Province. The subjects in this study were 371 dental hygiene juniors in seven different colleges in Gyeonggi Province, on whom a self-administered survey was conducted. The collected data were analyzed with a SPSS WIN 12.0 program, and the findings of the study were as follows: 1. The teaching efficiency of clinical training that the dental hygiene students undergone was on the average. As for evaluation of the factors of teaching efficiency, they gave the highest marks to the role model factor(3.40). 2. The size of the institutions where they received clinical training made no statistically significant differences to the teaching efficiency of their clinical training. The university hospitals ranked first in professional knowledge, one of the sub-directory of teaching efficiency, and the gap between them and the others was statistically significant(p=.005). 3. Concerning links between satisfaction level with the major and view of teaching efficiency, stronger satisfaction with the major led to better perception of teaching efficiency(p=.001). Among the subdirectory of teaching efficiency, that made statistically significant differences to view of interpersonal skills, performance as a supporter, fair evaluation, academic organization skills(p=.005), encouragement and support, teaching methods, professional academic knowledge(p=.001), communicative competency, performance as a role model and cooperation with the staff of dental clinics(p=.000). 4. There were no statistically significant gaps in teaching efficiency according to teaching styles. Among the sub-directory of teaching efficiency, statistically significant differences were found only in encouragement and support(p=.005). The above-mentioned findings suggest that the teaching efficacy of the clinical training was approximately on the average, and that a better satisfaction with the major led to a higher teaching efficacy. Therefore a wide variety of teaching methods and systematic training programs should be developed to boost the quality of clinical training to improve its teaching efficacy.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.12 s.138
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• pp.1596-1604
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• 2004

Quality Function Deployment(QFD) is a product development tool which ensures that the voice of the customer needs is heard and translated into products. To develop a sensible brassiere for middle-aged women QFD was adopted. In this study the applicability and usefulness of QFD was examined through the engineering design process for a sensible brassiere for middle-aged women. The customer needs for the wear comfort of brassiere was made by one-on-one survey of 100 women who aged 30-40. The customer competitive assessment was generated by wearing tests of 10 commercial brassieres. The subjective assessment was conducted in the enviornmental chamber that was controlled at $28{\pm}1^{\circ}C,\;65{\pm}3\%RH.$ As a results, we developed twenty-one customer needs and corresponding HOWs for the wear comfort of brassiere. The Customer Competitive Assessment was generated by wearing tests of commercial brassiere. The subjective measurement scale and dimension for the evaluation of sensible brassiere were extracted from factor analysis. Four factors were fitting, aesthetic property, pressure sensation, displacement of brassiere due to movement. The most critical design parameter was wire-related property and second one was stretchability of main material of brassiere. Also, wearing comfort of brassiere was affected by the interaction of initial stretchability of wing and support of strap. Engineering design process, QFD was applicable to the development of technical and aesthetic brassieres.

• Journal of radiological science and technology
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• v.37 no.1
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• pp.49-56
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• 2014

Recently with CT developed, various studies for reduction of exposure dose is underway. Study of bismuth shields in these studies is actively underway, and has already been applied in the clinical. However, the application of the PET/CT examination was not activated. Therefore, through this study, depending on the application of bismuth shields in the PET/CT examination, we identify the quality of the image and the impact on the Standard Uptake Value (SUV). In this study, to apply to the shielding of the breast, by using the bismuth shields that contains 0.06 mm Pb ingredients, was applied to the PET/CT GEMINI TF 64 (Philips Healthcare, Cleveland, USA). Phantom experiments using the NEMA IEC Body Phantom, images were acquired according to the presence or absence of bismuth shields apply. Also, When applying, images were obtained by varying the spacing 0, 1, 2 cm each image set to the interest range in the depth of the phantom by using EBW-NM ver.1.0. When image of the PET Emission acquires, the SUV was in increased depending on the use of bismuth shields, difference in the depth to the surface from deep in the phantom increasingly SUV increased (P<0.005). Also, when using shields, as the more gab decreased, SUV is more increased (P<0.005). Through this study, PET/CT examination by using of bismuth shields which is used as purpose of reduction dose. When using shields, the difference of SUV resulting from the application of bismuth shields exist and that difference when gab is decrease and surface is wider. Therefore, setting spacing of shield should be considered, if considering the reduction of the variation of SUV and image quality, disease of deep organs should be a priority rather than superficial organ disease. Use of bismuth shielding factor considering the standard clinical examination, decrease unnecessary exposure can be expected to be considered.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.4
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• pp.1-34
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• 2010

Recently, failure rates of Kosdaq IPO firms are increasing and their survival rates tend to be very low, and when these firms do fail, often times backed by a number of governmental financial supports, they may inflict severe financial damage to investors, let alone economy as a whole. To ensure investors' confidence in Kosdaq and foster promising and healthy businesses, it is necessary to precisely assess their intrinsic values and survivability. This study investigates what contributed to the failure of IPO firms and analyzed how these elements are factored into corresponding firms' stock returns. Failure risks are assessed at the time of IPO. This paper considers factors reflecting IPO characteristics, a firm's underwriter prestige, auditor's quality, IPO offer price, firm's age, and IPO proceeds. The study further went on to examine how, if at all, these failure risks involved during IPO led to post-IPO stock prices. Sample firms used in this study include 98 Kosdaq firms that have failed and 569 healthy firms that are classified into the same business categories, and Logit models are used in estimate the probability of failure. Empirical results indicate that auditor's quality, IPO offer price, firm's age, and IPO proceeds shown significant relevance to failure risks at the time of IPO. Of other variables, firm's size and ROA, previously deemed significantly related to failure risks, in fact do not show significant relevance to those risks, whereas financial leverage does. This illustrates the efficacy of a model that appropriately reflects the attributes of IPO firms. Also, even though R&D expenditures were believed to be value relevant by previous studies, this study reveals that R&D is not a significant factor related to failure risks. In examing the relation between failure risks and stock prices, this study finds that failure risks are negatively related to 1 or 2 year size-adjusted abnormal returns after IPO. The results of this study may provide useful knowledge for government regulatory officials in contemplating pertinent policy and for credit analysts in their proper evaluation of a firm's credit standing.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.8-9
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• 2017

Soybean yield has been low and unstable in Japan and other areas in East Asia, despite long history of cultivation. This is contrasting with consistent increase of yield in North and South America. This presentation tries to describe perspective of breaking stagnation of soybean yield in East Asia, considering the factors of the different yields between regions. Large amount of rainfall with occasional dry-spell in the summer is a nature of monsoon climate and as frequently stated excess water is the factor of low and unstable soybean yield. For example, there exists a great deal of field-to-field variation in yield of 'Tanbaguro' soybean, which is reputed for high market value and thus cultivated intensively and this results in low average yield. According to our field survey, a major portion of yield variation occurs in early growth period. Soybean production on drained paddy fields is also vulnerable to drought stress after flowering. An analysis at the above study site demonstrated a substantial field-to-field variation of canopy transpiration activity in the mid-summer, but the variation of pod-set was not as large as that of early growth. As frequently mentioned by the contest winners of good practice farming, avoidance of excess water problem in the early growth period is of greatest importance. A series of technological development took place in Japan in crop management for stable crop establishment and growth, that includes seed-bed preparation with ridge and/or chisel ploughing, adjustment of seed moisture content, seed treatment with mancozeb+metalaxyl and the water table control system, FOEAS. A unique success is seen in the tidal swamp area in South Sumatra with the Saturated Soil Culture (SSC), which is for managing acidity problem of pyrite soils. In 2016, an average yield of $2.4tha^{-1}$ was recorded for a 450 ha area with SSC (Ghulamahdi 2017, personal communication). This is a sort of raised bed culture and thus the moisture condition is kept markedly stable during growth period. For genetic control, too, many attempts are on-going for better emergence and plant growth after emergence under excess water. There seems to exist two aspects of excess water resistance, one related to phytophthora resistance and the other with better growth under excess water. The improvement for the latter is particularly challenging and genomic approach is expected to be effectively utilized. The crop model simulation would estimate/evaluate the impact of environmental and genetic factors. But comprehensive crop models for soybean are mainly for cultivations on upland fields and crop response to excess water is not fully accounted for. A soybean model for production on drained paddy fields under monsoon climate is demanded to coordinate technological development under changing climate. We recently recognized that the yield potential of recent US cultivars is greater than that of Japanese cultivars and this also may be responsible for different yield trends. Cultivar comparisons proved that higher yields are associated with greater biomass production specifically during early seed filling, in which high and well sustained activity of leaf gas exchange is related. In fact, the leaf stomatal conductance is considered to have been improved during last a couple of decades in the USA through selections for high yield in several crop species. It is suspected that priority to product quality of soybean as food crop, especially large seed size in Japan, did not allow efficient improvement of productivity. We also recently found a substantial variation of yielding performance under an environment of Indonesia among divergent cultivars from tropical and temperate regions through in a part biomass productivity. Gas exchange activity again seems to be involved. Unlike in North America where transpiration adjustment is considered necessary to avoid terminal drought, under the monsoon climate with wet summer plants with higher activity of gas exchange than current level might be advantageous. In order to explore higher or better-adjusted canopy function, the methodological development is demanded for canopy-level evaluation of transpiration activity. The stagnation of soybean yield would be broken through controlling variable water environment and breeding efforts to improve the quality-oriented cultivars for stable and high yield.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.83-92
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• 2022

Purpose: To evaluate usefulness of Tomotherapy and Halcyon VMAT treatment in radiation therapy for bi-breast cancer Materials and Methods: For 10 patients with bi-breast cancer, volumetric modulated arc therapy(VMAT) treatment plan was established using helical Tomotherapy(Accuray. USA) with field width of 5.0-cm and pitch of 0.287, and Halcyon(Varian Medical System, Palo Alto, CA, Version 3.0 USA) of 6arc and 8arc. Prescribed dose was 42.4 Gy/16, and V _40.3Gy of Planning Target Volume(PTV) was 90%. The quality of plan was evaluated by comparing the dose to tumor and normal organs, and the efficiency was evaluated by comparing total MU and beam on time. Results: About three treatment plans(Tomotherapy, Halcyon 6Arc VMAT, Halcyon 8Arc VMAT) , the mean homogeneity index(H.I) of PTV were 1.07, 1.10, 1.11, and the mean conformity index(C.I) of PTV were 1.21, 1.16, 1.17, respectively. The average value of the dose assessment item for a normal organ is V _5Gy(%) of both lung was 36.3, 31.2, 29.7, and V _15Gy(%) were 18.6, 15.5, 14.6, respectively. The mean heart dose(Gy) were 4.17, 2.69, 2.51. Total MU were (7498.6, 2494.2, 2471.5), and beam on time(sec) were 462.5, 195.4, 198.0. Conclusion: Halcyon VMAT showed similar quality of treatment plan compared to helical Tomotherapy, while also protecting normal organs. In addition, the efficiency of radiotherapy increased due to a decrease in Beam On Time and MU.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.