• The Journal of Korean Society for Radiation Therapy
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• v.29 no.2
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• pp.19-26
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• 2017

Objectives: In the Lung, the VMAT rotates continuously and examines radiation. That increases the low doses to normal lung. Due to that, the incidence of radiation pneumonia among radiation side effects may increase. The cause of radiation pneumonia is the lower dose area of the lungs. The H-VMAT was applied to patients who applied to reduce radiation in the lower doses of the lungs. We wanted to assess the usefulness of the H-VMAT by comparing the radiation doses to the low dose areas of the lungs and the normal organs. Materials and Methods: A total of 26 patients who applied for a H-VMAT procedure were applied to the patient. The prescription dose applied to total dose 44 Gy from 22 divisions. For each patient, a plan was implemented with Conventional RT, VMAT and H-VMAT. Conventional RT was carried out in four to five fields each, considering the size, location, shape, and location of the PTV. In the case of a VMAT plan, the two Half ARC, three Half ARC method and the two Full ARC were planned. The H-VMAT was planned by adding two Static fields in the VMAT, taking into account the dose of the lung and the tolerance dose of the organs. Results: In the NSCLC, the lung doses $V_5$ and $V_{10}$ of the lungs except for the treatment plan volume were the lowest with $55.40{\pm}13.39%$ and $32.05{\pm}11.37%$ of H-VMAT. And, in the SCLC, the lung doses of V5 and V10 were the lowest at $64.32{\pm}16.15%$ and $35.50{\pm}9.91%$, respectively. The spinal dose of VMAT in NSCLC was $21.15{\pm}4.02Gy$, which was 7.94 Gy lower than other treatment methods. The lowest spinal dose was delivered at $19.72{\pm}1.82Gy$ for SCLC. The mean dose delivered to the esophagus was also $17.44{\pm}2.04Gy$ and $17.84{\pm}9.20Gy$ in SCLC and NSCLC, respectively. Conclusion: When comparing the value of the surrounding normal organ dose, the VMAT showed that less doses were transmitted from the heart, esophagus and spinal cord than the rest of the treatment plan. However, it was similar to VMAT in normal organs except for the spinal cord. VMAT has increased doses of some normal organs but did not exceed the tolerance dose. It showed a low value in $V_5$, $V_{10}$. When comparing Conventional RT, VMAT, and H-VMAT, If the dose to the heart, esophagus and spinal cord is lower than the tolerance dose, it is thought to reduce the incidence of radiation pneumonia by applying H-VMAT that show the benefits of low doses of the lungs.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.185-192
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• 2006

This experiment was conducted to assess genetic diversity of waxy corn inbred lines and to identify SSR markers related to major characteristics affected kernel quality for improving waxy corn $F_1$ hybrid with good quality. Diversity of 64 waxy com inbred lines was evaluated using 30 microsatellite markers. The 30 microsatellite markers representing 30 loci in the maize genome detected polymorphisms among the 64 inbred lines and revealed 225 alleles with a mean of 7.5 alleles per primer. The polymorphism Information content (PIC) value ranged from 0.14 to 0.87, with an average of 0.69. Based on Nei's genetic distances, the 64 inbred lines were classified into 9 groups by the cluster analysis. The group I included 26 inbred lines (41%), other groups included 3 to 9 inbred lines. One-way analysis of variance was conducted to identify significant relationship between individual markers and major characteristics that affect kernel quality. The analysis showed that umc1019 was related to amylopectin and crude protein content, me 1020 to amylopectin content and peak viscosity, and bnlg1537 to 100-kernel weight, kernel length, and kernel width.