Development of protein resources as food has been a big issue especially in Southeast Asia region, and intake of protein is also insufficient in Korea. To cope with this shortage of protein resources and its improvement together with increased production of high nutritive animal protein, studies were carried out on feeding of Korean native goats. In the experiments were made absorption of carbohydrate and volatile fatty acid in miniature rumen, and absorption of amino acid in rumen as in vivo were conducted as part of studies on nutritional absorption in rumen. Those nutritional for improvement of feeding and management as described above are summarized as following. 1. According to the result of test on the nutritional absorption of native goat by means of miniature rumen method, absorption ratio of VFA measured at 0.5, 1 and 2 hours after injection of nutrition showed propionic acid 70-86%, acetic acid 74-87%, and lactic acid 76-89%. In the absorption of organic substances, ethyl alcohol of 0.5% showed 29-87% and lactic acid of 0.1M showed 12-27% of absorption ratio. 2. Result of absorption measurement in rumen from L-type free amino acid injection in the content of rumen vein showed lower rate at menthionine-free group compared to whole-egg amino acid injection in the content of rumen vein showed lower rate at methioine-free group compared to whole-egg amino acid group, and high absorption ratio was observed at methionine 3 times group and urea added group. Deficiency of methionine caused no change of the content in mucous membranes. 3. Absorption of amino acid in rumen muscular layer showed equal tendency as in the mucous membrane without exerting any influence of methionine deficiency. At the methionine3-times group, content of methionine and glutamine were increased by 14.7 and 4.4 times as compared to whole-egg amino acid group, an absorption ratio of glutamine, proline and valine were increased at urea added group. 4. In general, concentration of amino acid in rumen vein plasma was lower than in rumen mucous membrane and muscular layer. Absorption ratio of amino acid is decreased due to methionine deficiency, and tripling of methionine or urea adding caused increment of amino acid. Absorption pattern is thus varied depending on the composition of amino acid. 5. At the urea added group, content of ammonia-N, amino-N and urea were increased in rumen muscular layer. As the inside of goat's rumen was unable to clean thoroughly, investigation was made on remaining bacteria, however, variation of ammonia-N was affected by these bacterial content. 6. Variation in rumen structure by differential absorption of amino acid was observed by general microscope and fluorescent microscope. According to the result of observation in the methionine 3 times group, single cylinder epithelium of mucous membrane showed rather thin, and it was thick at urea added group though no significant differences existed among test groups in submucous membrane and muscular layer.
The purpose of this study was to investigate the nutrient intake status of university students in Chuncheon area (175 males and 131 females). This study was conducted by employing a self-administered questionnaire. Dietary assessment was measured by a 24-h recall method. The average height and weight of male students were
Purpose : Although influenza virus is one of the most important causes of acute respiratory tract infections(ARTIs) in children, virus isolation is not popular and there are only a few clinical studies on influenza in Korea. We evaluated the epidemiologic and clinical features of ARTIs by influenza virus in children. Methods : From February 1995 to August 2001, nasopharyngeal aspirations were obtained and cultured for the isolation of influenza virus in children admitted with ARTIs. The medical records of patients with influenza virus infection were reviewed retrospectively. Results : Respiratory viruses were isolated in 997(22.0%) out of 4,533 patients examined, and influenza virus was isolated in 164 cases(3.6%). Influenza virus was isolated year after year mainly from December to April of next year. The ratio of male and female was 1.9 : 1 with a median age of 15 months. The most common clinical diagnosis of influenza virus infection was pneumonia, and fever and cough developed in most patients. There was no difference between influenza A and B infection in clinical diagnoses and symptoms. All patients recovered without receiving antiviral treatment except for one patient diagnosed with pneumonia who had underlying disease of Down syndrome with ventricular septal defect. Conclusion : ARTIs caused by influenza virus developed every winter and spring during the period of study. Because fatal complication can develop in the high risk group, prevention, early diagnosis and proper management of influenza should be emphasized.
Recently, with the rapid expansion of the franchise restaurants, ensuring food safety has become essential for restaurant growth. Consequently, the need for food safety training and related material is in increasing demand. In this study, we identified potentially hazardous risk factors for ensuring food safety in restaurants through a food safety monitoring tool, and developed training materials for restaurant employees based on the results. The surveyed restaurants, consisting of 6 Korean restaurants and 1 Japanese restaurant were located in Seoul. Their average check was 15,500 won, ranging from 9,000 to 23,000 won. The range of their total space was 297.5 to
In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used