Studies on the Breeding of Cold Hardiness and Technique of Overwintering Cultivation in Citrus (감귤(柑橘)의 내한성(耐寒性) 품종육성(品種育成) 및 내한(耐寒) 재배기술(栽培技術)에 관(關)한 연구(硏究))
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- Korean Journal of Agricultural Science
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- v.4 no.2
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- pp.126-140
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- 1977
Present studies were carried out for breeding cold resistant clones of Citrus, improving overwintering techniques of Citrus in Jeju island as well as other southern region, Result obtained were as follows 1. In the vinyl house covered with two sheets of straw mat, 12 indivuals were found as non-injury and 15 were slightly injured by leaf freeging test at
Background: Positive end, expiratory pressure (PEEP) has become one of the standard therapies for adult respiratory distress syndrome (ARDS). Total static compliance has been proposed as a guide to determine the size of PEEP ('best PEEP') which is of unproven clinical benefit and remains controversial. Besides increasing functional residual capacity and thus improving oxygenation, PEEP stimulate prostacyclin secretion and was proposed for the treatment of acute pulmonary embolism. But little is known about the effect of PEEP on hemodynamic and gas exchange disturbances in acute pulmonary embolism. Methods: To study the validity of total static compliance as a predictor of 'best PEEP' in ARDS and acute pulmonary embolism, experimental ARDS was induced in mongrel dog with oleic acid and acute pulmonary embolism with autologous blood clot. Then hemodynamic and gas exchange parameters were measured with serial increment of PEEP. Results:In ARDS group, total static compliance and oxygen transport were maximal at 5 cm
Purpose: This study analyzed the foodservice satisfaction and menu preference of 506 high school boarding students in Jeju surveyed from July 2-30, 2012 with the aim of providing basic data for improving the quality of boarding food-service management. Methods: The data were analyzed using descriptive analysis, t-test, and Pearson's correlation coefficients, using the SPSS Win program (version 12.0). Results: Regarding satisfaction with dormitory foodservice, the satisfaction scores for service and hygiene were 3.46 (out of 5 scales), whereas the score for menu quality was 3.26 points. In terms of satisfaction by meal, dinner showed the highest score, at 3.70 (out of 5 scales). The satisfaction scores for breakfast were significantly higher in girls (3.36) than boys (2.93). Regarding intake of meals provided, dinner showed the highest score, at 3.96 (out of 5 scales), whereas breakfast showed the lowest score, at 3.63 points. Intake of lunch and dinner was significantly higher in boys (4.12, 4.17, respectively) than girls (3.72, 3.76, respectively). Regarding the requirements of subjects for dormitory foodservice, 43.4% of subjects selected improvement of food taste and 36.6% of girls chose menu diversity. In terms of menu preferences for main dishes, the students preferred noodles (4.06) and one-dish cooked rice (3.92) to cooked rice (3.66). The subjects preferred beef rib soup (4.10) and Kimchi stew (3.99) in soups and stews. With regard to the menu preferences for side dishes, steamed foods showed the highest score, at 3.95 (out of 5 scales), whereas seasoned foods showed the lowest score, at 2.89 points. The students preferred beef, pork, and chicken to fish and vegetables. The students preferred dessert the most with fruit juices (4.52). Bread and rice cake were more favored by girls, showing significant differences between boys and girls (p < 0.05, p < 0.01, respectively). Conclusion: Development of a systematic nutrition education program that can encourage practice of proper eating habits is needed. In addition improvement of the quality of boarding school meals through the service of various menus is needed.
Purpose : Granulocyle-colony stimulating factor (G-CSF) has been widely used to treat neutropenia caused by chemotherapy or radiotherapy. The efficacy of recombinant human hematopoietic growth factors in improving oral mucositis after chemotherapy or radiotherapy has been recently demonstrated in some clinical studies. This study was designed to determine whether G-CSF can modify the radiation injury of the intestinal mucosa in mice. Materials and Methods : One hundred and five BALB/c mice weighing 20 grams were divided into nine subgroups including G-CSF alone group
Background : Nonspecific interstitial pneumonitis (NSIP) is most likely to be confused with usual interstitial pneumonitis (UIP). Unlike patients witþ UIP, the majority of patients with NSIP have a good prognosis, with most patients improving after treatment with corticosteroids. Therefore it is clinically important to differentiate NSIP from UIP. Up to now, the only means of differentiating these two diseases was by means of surgical lung biopsy. American Thoracic Society (ATS) proposed a clinical diagnostic criteria for UIP to provide assistance to clinicians in its diagnosis without surgical lung biopsy. This study is aimed to investigate whether there were clinical and radiological differences between NSIP and UIP, and the usefulness of ATS clinical diagnostic criteria for UIP in Korea. Methods : We studied 60 patients with UIP and NSIP confirmed by surgical lung biopsy. Clinical manifestations, pulmonary function test, arterial blood gas analysis, bronchoalveolar lavage (BAL), and high resolution computed tomography (HRCT) were evaluated and analyzed by Chi-square test or t-test. The clinical criteria for UIP proposed by ATS were applied to all patients with idiopathic interstitial pneumonia. Results : Forty-two patients with UIP and 18 with NSIP were pathologically identified. Among the 18 patients with NSIP (M : F=1 : 17), the mean age was 55.2
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