• Journal of Nutrition and Health
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• v.42 no.7
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• pp.650-663
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• 2009

The purposes of this study were to investigate one portion size of menus served and to evaluate nutrient intake of lunch at three elderly care facility food services located in Seoul. A weighed plate method was employed to measure plate wastes and consumption of the menus served. Yield factors were calculated from cooking experiments based on standardized recipes, and were used to evaluate nutrient intake. One hundred elderly participated in this study for measuring plate waste and were asked to complete questionnaire. Nutrient analyses for the served and consumed meal were performed using CAN program. The yield factors of rice dishes after cooking are 2.4 regardless of rice dish types, 1.58 for thick soups, 0.60 to 0.70 for meat dishes, and 1.0 to 1.25 branched vegetable. Average consumption quantity of dishes were 235.97 g for rice, 248.53 g for soup, 72.83 g for meat dishes, 39.80 g for vegetables and 28.36 g for Kimchi. On average the food waste rate is 14.0%, indicating the second highest plate waste percentage of Kimchi (26.2%), and meat/fish dish (17.3%). The evaluation results of NAR (Nutrition Adequacy Ratio) showed that iron (0.12), calcium (0.64), riboflavin (0.80), and folic acid (0.97) were less than 1.0 in both male and female elderly groups, indicating significant differences of NAR among three facilities. Compared to the 1/3 Dietary Reference Intake (DRIs) for the elderly groups, nutrient intake analysis demonstrated that calcium (100%) and iron (100%), followed by riboflavin, vitamin A, and Vitamin B6 did not met of the 1/3 EAR (Estimated Average Requirement). For the nutritious meal management, a professional dietitian should be placed at the elderly care center to develop standardized recipes in consideration of yield factors and the elderly's health and nutrition status.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.65-88
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• 2008

Since the implementation of economic reforms in 1978, the Chinese economy grows rapidly at an average annul growth rate of 9% over the post two decades. Franchising has been widely recognized as an important source of entrepreneurial activity. Trust is important in that it facilitates relational exchanges by permits partners to transcend short-run inequities or risks to concentrate on long-term profits or gains. In the relationship between the franchisors and franchisees, trust has been described as an important source of competitive advantage. However, little research has been done on the factors affecting trust in Chinese franchisor-franchisee relationships. The purpose of this study is to investigate what factors affect the trust in the franchise system in China, and to provide guidelines and insights to franchisors which enter Chinese market. In this study, according to Morgan and Hunt (1994), trust is defined as the extending when one party has confidence in an exchange partner's reliability and integrity. We offered a conceptual model of the empirical study. The model shows that the factors affecting the trust include franchisor's supports, communication, satisfaction with previous outcome and conflict. We also suggested the franchisor's supports and communication like to enhance the franchisee's satisfaction with previous outcome, and the franchisor's supports, communication and he franchisee's satisfaction with previous outcome tend to decrease conflict. Before the formal study, a pretest involving exploratory interviews with owners from three franchisees was conducted to make sure the questionnaire was relevant and clear to the respondents. The data were collected using trained interviewers to carry out personal interviews with the aid of an unidentified, muti-page, structured questionnaire. The respondents comprised of owners, managers, and owner managers of franchisee-owned food service franchises located in Beijing, China. Even though a total of 256 potential franchises were initially contacted, the finally usable sample consisted of 125 respondents. As expected, the sampling method was successful in soliciting respondents with waried personal and firm characteristics. Self-administrated questionnaires were used for all measures. And established scales were used to measure the latent constructs in this study. The measures tapped the franchisees' perceptions of the relationship with the referent franchisor. Five-point Likert-type scales ranging from "strongly disagree" (=1) to "strongly agree" (=7) were used throughout the constructs (trust, eight items; support, five items; communication, four items; satisfaction, six items; conflict, three items). The reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.80. The proposed measurement model was estimated using SPSS 12.0 and AMOS 5.0 analysis package. We conducted A series of exploratory factor analyses and confirmatory factor analyses to assess the convergent validity, discriminant validity, and reliability. The results indicate reasonable overall fits between the model and the observed data. The overall fit of measurement model were $X^2$= 159.699, p=0.004, d.f. = 116, GFI =.879, NFI =.898, CFI =.969, IFI =.970, TLI =.959, RMR =.058. The results demonstrated that the data reasonably fitted the model. We also examined construct reliability and reliability and average variance extracted (AVE). The construct reliability of each construct was greater than.80 and the AVE of each construct was greater than.50. According to the analysis of Structure Equation Modeling (SEM), the results of path model indicated an adequate fit of the model: $X^2$= 142.126, p = 0.044, d.f. = 115, GFI =.892, NFI =.909, CFI =.981, IFI =.981, TLI =.974, RMR =.057. As hypothesized, the results showed that it is strategically important to establish trust in a franchise system, and the franchisor's supports, communication and satisfaction with previous outcome tend to reinforce franchisee's trust. The results also showed trust seems to decrease as the experience of conflict episodes increases. And we also noticed that franchisor's supports and communication tend to enhance the franchisee's satisfaction with previous outcome, and communication tend to decrease conflict. If the trust between the franchisor and franchisee can be established in a franchise system, franchising offers many benefits and reduces many costs. To manage a mutual trust of relationship with their franchisees, franchisor's should provide support effectively to their franchisees. Effective assistant services have direct effect on franchisees' satisfaction with previous outcome and trust in franchisor. Especially, franchise sales process, orientation, and training in the start-up period are key elements for success of the franchise system. Franchisor's support is an accumulated separate satisfaction evaluation with different kind of service provided by the franchisor. And providing support definitely can improve the trustworthy image of the franchisor. In the franchise system, conflicts of interests and exertions of different power sources are very common. The experience of conflict episodes seems to negatively relate to trust. Therefore, it is important to reduce the negative side of the relationship conflicts. Communication actually plays a broader role in reducing conflict and establish mutual trust in franchisor-franchisee relationship. And effective communication between franchisors and franchisees can improve franchisees' satisfaction toward the franchise system. As the diversification of Chinese markets, both franchisors and franchisees must keep the relevant, timely, and reliable communication. And it is very important to improve the quality of communication. Satisfaction with precious outcomes seems to positively relate to trust. Franchisors and franchisees that are highly satisfied with the previous outcomes that flow from their relationship will perceive their partner as advancing their goal achievement. Therefore, it is necessary for both franchisor and their franchisees to make the welfare of partner with effort. Little literature has focused on what factors affect the trust between franchisors and their franchisees in China. This study developed the hypotheses regarding the factors affecting trust in the transaction relationship. The results of data analysis supported the hypotheses strongly. There are certain limitations in this study. First, we may point out that some other factors missed in this study could be significantly important. Second, the context of this study, food service industry, limits its potential generalizability for all franchise systems. More studies in different categories of franchise system are needed to broaden its generalizability. Third, the model was tested empirically in a sample in Beijing, more empirical tests of the proposed model in other Chinese areas are needed. Finally, the analysis in this study was solely based on the perception of franchisees and the opinions of franchisors were not included.

• Journal of Chest Surgery
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• v.36 no.8
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• pp.545-558
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• 2003

Adult respiratory distress syndrome (ARDS) is of particular interest because of its severity of the associated lung injury and its high mortality. However, the pathophysiologies of ARDS in infant and childhood groups are still not well clarified inspite of many previous investigations. To investigate the time course of pathophysiology of ARDS in infant and childhood groups, this study was designed with experimental endotoxin-induced ARDS model using young rabbits (8 week-old). Material and Method: Rabbits were divided into the control group (n=8) and the endotoxin-treated group (n=32). The endotoxin group was subdivided into 4 groups by the sampling times as 3, 6, 12 and 24 hr-groups (G- $E_{3,6,12,24,}$ each n=8). The experimental ARDS was made by a bolus injection of endotoxin (Escherichia coli serotype 055 : B5, 0.50 mg/kg) via rabbit ear vein. For evaluation of the hematologic and inflammatory markers, and superoxide dismutase (SOD) concentrations, the blood samples were taken from the heart. The bronchoalveolar lavage fluid (BALF) were obtained for analysis of the leukocytes and protein concentration. With biopsy of the lung, histopathologic changes of the lung were also evaluated. Result: In the endotoxin groups, significant leukopenia (owing to pancytopenia) occurred in 3 and 6-hr groups, which was followed by significant leukocytosis (owing to neutrophilia) in the 12 and 24-hr groups (p<0.05). Serum levels of tumor necrosis factor-$\alpha$ (TNF-$\alpha$) and interleukin-1 $\beta$ (IL-1 $\beta$) in the endotoxin groups were higher than those of control group (p<0.05). Serum levels of superoxide dismutase (SOD) of G- $E_{3}$ and G- $E_{6}$ were higher than those of control group, whereas those of G- $E_{12}$ were lower than those of control groups (p<0.05). Total leukocyte counts and protein con-centrations in BALF were significantly elevated in the endotoxin groups compared to the control group (p < 0.05). The hemorrhagic pattern of BALF showed occurred in the endotoxin groups. The endotoxin groups (in G- $E_{6}$) had severe infiltration of inflammatory cells (lymphocyte and monocyte) in the pulmonary interstitium and parenchyma, migrations of neutrophil and eosinophil into alveolar spaces and interstitial widening, which are the evidences of acute lung injury. In the endotoxin groups, there were significant positive correlations between the BALF findings and the immunologic markers (TNF-$\alpha$, IL-1$\beta$, SOD) (p<0.05). Conclusion: Severe acute lung injury occurred in all the endotoxin-treated rabbits. The pathophysiologic findings were so progressive until 6-hr by time dependant pattern, and then recovered slowly, Variable hematologic, immuno-logic, and pathologic factors were well correlated in the development and progression of endoxin-induced lung injury. The pathophysiologic responses were sensitive and rapid in young rabbit Young rabbit seemed to be a useful experimental animal model for infant and childhood groups.roups.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.20-31
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• 2022

Purpose Broad Quantification Calibration(B.Q.C) is the procedure for Quantitative Analysis to measure Standard Uptake Value(SUV) in SPECT/CT scanner. B.Q.C was performed with Tc-99m, I-123, I-131, Lu-177 respectively and then we acquired the phantom images whether the SUVs were measured accurately. Because there is no standard for SUV test in SPECT, we used ACR Esser PET phantom alternatively. The purpose of this study was to lay the groundwork for Quantitative Analysis with various isotopes in SPECT/CT scanner. Materials and Methods Siemens SPECT/CT Symbia Intevo 16 and Intevo Bold were used for this study. The procedure of B.Q.C has two steps; first is point source Sensitivity Cal. and second is Volume Sensitivity Cal. to calculate Volume Sensitivity Factor(VSF) using cylinder phantom. To verify SUV, we acquired the images with ACR Esser PET phantom and then we measured SUV_mean on background and SUV_max on hot vials(25, 16, 12, 8 mm). SPSS was used to analyze the difference in the SUV between Intevo 16 and Intevo Bold by Mann-Whitney test. Results The results of Sensitivity(CPS/MBq) and VSF were in Detector 1, 2 of four isotopes (Intevo 16 D1 sensitivity/D2 sensitivity/VSF and Intevo Bold) 87.7/88.6/1.08, 91.9/91.2/1.07 on Tc-99m, 79.9/81.9/0.98, 89.4/89.4/0.98 on I-123, 124.8/128.9/0.69, 130.9, 126.8/0.71, on I-131, 8.7/8.9/1.02, 9.1/8.9/1.00 on Lu-177 respectively. The results of SUV test with ACR Esser PET phantom were (Intevo 16 BKG SUV_mean/25mm SUV_max/16mm/12mm/8mm and Intevo Bold) 1.03/2.95/2.41/1.96/1.84, 1.03/2.91/2.38/1.87/1.82 on Tc-99m, 0.97/2.91/2.33/1.68/1.45, 1.00/2.80/2.23/1.57/1.32 on I-123, 0.96/1.61/1.13/1.02/0.69, 0.94/1.54/1.08/0.98/ 0.66 on I-131, 1.00/6.34/4.67/2.96/2.28, 1.01/6.21/4.49/2.86/2.21 on Lu-177. And there was no statistically significant difference of SUV between Intevo 16 and Intevo Bold(p>0.05). Conclusion Only Qualitative Analysis was possible with gamma camera in the past. On the other hand, it's possible to acquire not only anatomic localization, 3D tomography but also Quantitative Analysis with SUV measurements in SPECT/CT scanner. We could lay the groundwork for Quantitative Analysis with various isotopes; Tc-99m, I-123, I-131, Lu-177 by carrying out B.Q.C and could verify the SUV measurement with ACR phantom. It needs periodic calibration to maintain for precision of Quantitative evaluation. As a result, we can provide Quantitative Analysis on follow up scan with the SPECT/CT exams and evaluate the therapeutic response in theranosis.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

• 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.

• 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 Yeungnam Medical Science
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• v.14 no.1
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• pp.155-167
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• 1997

The identification of serum HBV DNA is very important for the assessment of the disease activity in persistent infection, for the evaluation of the infectivity of an individuals blood. The dot blot, however, has limited sensitivity and sometimes inconsistent with other serological markers and clinical settings. Using the most important recent advance in molecular biology, the polymerase chain reaction(PCR), specific DNA sequences can be amplified more than a million-fold in a few hours and with this technique the detection of the extreme low level of DNA is possible. This study was to determine sensitivity of the PCR for the detection of serum HBV DNA in comparison with dot blot analysis and to investigate the serum HBV DNA status and clinical significance of PCR in patients with chronic HBsAg positive liver disease. The subjects of this study were 17 patients with asymptomatic HBsAg carriers(9 HBeAg positive patients, 8 anti-HBe positive patients), 91 chronic hepatitis B(50 HBeAg positive patients, 41 anti-HBe positive patients), 57 liver cirrhosis(21 HBeAg positive patients, 36 anti-HBe positive patients), 27 hepatocellular carcinoma(10 HBeAg positive patients, 17 anti-HBe positive patients). The results were summerized as following; The detection rates of HBV DNA by dot blot, PCR were 58.9%, 72.2% in HBeAg positive patients, 34.3%, 53.9% in anti-HBe positive patients. The detection rates of HBV DNA by PCR in HBeAg negative patients were 25.0% in asymptomatic HBsAg carriers, 61.0% in chronic hepatitis B, 52.8% in liver cirrhosis, 52.9% in hepatocellular carcinoma. The positive rate for HBV DNA is a significant difference between HBeAg positive and negative asymptomatic HBsAg carriers, but not significantly difference in other groups. In conclusions, this study confirmed that the PCR is much more sensitive than the dot blot analysis in detecting the HBV DNA in the sera of patients with chronic liver disease. The presence of HBV DNA in the serum was detected by PCR with higher sensitivity and it suggested that active viral replication is still going on in most patients with chronic HBsAg positive liver disease irrespective of HBeAg/anti-HBe status, and PCR may be used as a prognostic factor in asymptomatic HBsAg carriers.

• Horticultural Science & Technology
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• v.33 no.3
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• pp.409-419
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• 2015

This study was conducted to establish an efficient screening method for watermelon plants resistant to Fusarium wilt (FW), which is caused by Fusarium oxysporum f. sp. niveum (Fon). An HA isolate was prepared from a wilted watermelon plant in Haman-gun and identified as F. oxysporum f. sp. niveum based on morphological characteristics, molecular analyses of ITS (internal transcribed spacer) and TEF (translation elongation factor $1{\alpha}$) sequences, and host specificity on cucurbits including watermelon, melon, oriental melon, and cucumber. The assay for disease response of watermelon differentials indicated that the HA isolate was race 0. Among seven liquid media tested, the highest amount of Fon spores was produced from V8-juice broth, which was selected as a medium for mass production of Fon. The disease assay for 21 watermelon and 11 watermelon-rootstock cultivars demonstrated that 20 watermelon cultivars except for 'Soknoranggul' were susceptible; 'Soknoranggul' was moderately resistant. All the tested rootstock cultivars were highly resistant to the HA isolate. The evaluation of disease development depending on various conditions suggested that an efficient screening method for FW resistance in watermelon plants is to dip the roots of 10-day-old seedlings in spore suspension of $1.0{\times}10^5-1.0{\times}10^6conidia{\cdot}mL^{-1}$ for 30 min., to transplant the seedlings to plastic pots with a fertilized soil, and then to cultivate the plants at $25^{\circ}C$ for 3 weeks.