A Study on Sickness and the Status of Medical Care in a Rural Area (일부(一部) 농촌주민(農村住民)의 상병(傷病) 및 의료실태(醫療實態)에 관(關)한 조사연구(調査硏究))
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- Journal of Preventive Medicine and Public Health
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- v.14 no.1
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- pp.65-74
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- 1981
This survey was made to determine the overall health situation on (1) the status of sickness; (2) the medical care utilization; (3) the medical cost in Mi-Kum Myun, Nam Yang Ju Gun, Kyung-Gi Do. The survey with questionnaire was carried out with 2,840 peoples in 560 households from August 9th to 16th, 1979. The findings from the survey were as follows; 1. Annual morbidity rate of the prolonged ill cases was 97.2 per 1,000 population (male 94.7, female 99.6), The highest age specific morbidity rate was 274.5 of the 45-to 64-year group and the lowest was 21.9 of the 5-to 14-year group. 2. Annual morbidity rate of the new patients was 777.5 per 1,000 population(male 644.5, female 909.5). 3. The chief complaints distribution of the prolonged ill cases was: local pain 36.6%, indigestion 22.4%, and coughing 7.3%, respectively, In terms of age and sex distribution, a large number of female of the 45-to 64-year group complained of local pain or general pain and a large number of both sexes of the 25-to 44-year group complaned of indigestion. 4. The major diseases of the new patients which classified with International Classfication of Diseases (I.C.D.) were disease of the respiratory system, disease of the digestive system, and disease of the musculo-skeletal system and connective tissue for male, disease of the respiratory system, disease of the digestive system, and accident, poisoning, violence for female. 5. Total ill days of the 92 new patients were 536 days and average ill days per case were
Plants of Vitex negundo are known to develop numerous trichomes throughout their body, where certain trichome types have been believed to be one of the plausible structures for the unique scents. In the current study. structural aspects of the trichomes have been examined in leaves and stems of Vitex negundo using TEM and SEM. Trichome types as well as structural changes that occurred in certain trichomes during secretion have been mainly focused. Three type of glandular trichomes and two types of non-glandular trichomes were developed in the epidermis of young and mature Vitex negundo plants. The glandular trichomes included the peltate type (Type 1), the capitate type (Type 2), and degraded capitate type (Type 3), whereas the non-glandular warty trichomes contained the multicellular (Types 4) and unicellular type (Type 5). Type 1 and 2 consisted of head and stalk cells, but their number and size were different. One secretory cavity was formed from the four head cells in the former, but only two head cells were involved in the latter. The cytoplasmic density in the head cell was quite high and in particular, sER and Golgi bodies were well developed. At initiation of their development, the cuticle layer of the head cells separated from the outer tangential wall to form a secretory cavity. Subsequently the cavity expanded acropetally and a large number of secretory vesicles continuously produced from the head cells until they filled the entire cavity. The cavity contained materials that would be soon discharged into intercellular spaces and/or into the air. The cavity began to decrease the volume by contracting at initial secretion but degrade rapidly within short time. It has been suggested that the mode of secretion in V. negundo is probably the eccrine secretion, since no break or rupture of the cavity has been observed during examination. Contrastingly Type 3 exhibited deterioration of the head cell at early stage. Type 4 was about
In temperate zone planting rice at different date subjects the Crop to different climatic condition. The present study aimed at comparison of the change in source-sink relationship of the Japonica(J) and that of IndicaxJaponica(I
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