• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.2
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• pp.1-19
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• 2013

This study was carried out to analyze distribution situation of alien plants and to propose management plan in the 3 Folk village in Gyeongsangbuk-do which is Cultural property designated by the State; Hahoe, Yangdong and Hangae. This research is for improve of sincerity of historical site and provide basic information which use about administration of preservation. The results are as follows. 1. Overall flora and alien plants appearance The total flora in the 3 folk villages were listed total 752 taxa including 127 families, 430 genera, 614 species, 5 subspecies, 100 varieties and 33 forms. Among them, woody plants take 263 taxa(35.0%) and herbaceous plants take 489 taxa (65.0%). Flora in the Hahoe, Yangdong and Hangae village were total 534, 479 taxa and 408 taxa and exotic plant index was 30.1%, 38.2% and 37.0% respectively. In types of exotic plants, ornamental exotic plants were 135 taxa, deciduous exotic plants were 21 taxa, cultivating exotic plants were 64 taxa, and naturalized exotic plants were 80 taxa and those result lead that the ornamental exotic plants is the highest ratio. According to the villages, Hahoe village had 161 taxa(30.1%), Yangdong Village had 183 taxa(38.2%), and Hangae village had 151 taxa(37.0%) that Yangdong village showed the most number of exotic plants. 2. Planting of landscape exotic plants in the unit cultural assets Meanwhile, Ornamental exotic plants in old house's gardens in Andong Hahoe village which is designated as a unit assets, those are total 30 taxa; followed by the Okyeon house(8 taxa) is highest and the Yangjindang(7), the Hadong house(6) and the Chunghyodang(5). Magnolia denudata appears the most as for 4 times and Campsis grandiflora etc. each took 2 times. Based on the Yangdong village, Gyeongju, that are found total 51 taxa; followed by the Dugok house(16 taxa) the Sujoldang(14), the Mucheondang(13), and the Sangchunheon (12). High appearance rate of ornamental exotic plants were Viburnum opulus for. hydrangeoides, Lycoris squamigera, Caragagna sinica and Magnolia denudata etc. Based on the Hangae village, Seongju, that are designated total 62 taxa; followed by the Jinsa house(35 taxa), the Gyori house(25), the Hanju head family house(20), and the Hahoe house(16). Taxa with high appearance rates were Caragana sinica, Juniperus chinensis var. horizontalis, Magnolia denudata, Viburnum opulus for. hydrangeoides, Chaenomeles speciosa etc. 3. Problems of exotic plant landscapes in the outer spaces of the folk villages Problems of exotic plant landscapes in the outer spaces of the Hahoe village are as follows. In lower of the Mansongjeong forest, Ambrosia artemisifolia, which are ecosystem disturbance plants designated by the Ministry of Environment, live with high dominance value. This should be have a remove with Sicyos angulatus immediately. In the Nakdong river bed around the Mansongjeong forest is covered with a riparian vegetation forest belt of Robinia pseudoacacia L. forest, Populus nigra var. italic community, and Populus x tomentiglandulosa community colony. Based on the Yangdong village, the planted or naturally distributed Ailanthus altissima colony, sporadically distributed Robinia pseudoacacia as well as Amorpha fruticosa are detected all over the village and ecotones. Based on the Hangae village, Ailanthus altissima and Robinia pseudoacacia are sporadically distributed around the village and there is a sign of spreading. similarity of exotic plantsis 47.0% to 48.6% and a reason why this happened is all of research site in Gyeongsanbuk-do and that is why growth norm of plant is similar, exotic plant which is sales for ornamental and it infer to require related countermeasure of each villages and joint related countermeasure.

• Journal of Preventive Medicine and Public Health
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• v.27 no.2 s.46
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• pp.258-273
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• 1994

To compare the physical complaints of vinylhouse farmers with those of non-vinylhouse farmers, a personal interviews on 250 vinylhouse and 142 non-vinylhouse farmers were conducted in Sungjoo county in Kyungpook province selected by a random sampling from July 5 to July 10, 1993. Blood pressure of the subjects was also measured. Vinylhouse farmers had a higher average age, larger family size, shorter experience of farming, more working hours per day and working days per year and higher annual income than the non-vinylhouse farmers. The frequency of pesticide spray of the vinylhouse farmers was 3.4 times on the average in June 1993 as compared with 2.0 times of non-vinylhouse farmers, and 16.7 times for the vinylhouse farmers during the last one year while it was 8.3 times for the non-vinylhouse farmers in the same period. While 39.6% of vinylhouse farmers experienced pesticide intoxication symptoms such as headache, nausea, vomiting, dizziness, itching, and skin irritation, etc. during the month of June, 25.4% of non-vinylhouse farmers experienced such symptoms. The most frequent symptoms among eight symptoms that constitute the farmers' syndrome were lumbago, numbness of hand or foot, shoulder pain and dizziness regardless of sex and type of farming. Prevalence of the farmers' syndrome in male and female among vinylhouse farmers were 22.1%, 43.4%, respectively, and the prevalence in non-vinylhouse farmers was 23.2% for male and 50.7% for female. There was no statistically significant difference in the prevalence of farmers' syndrome between vinylhouse and non-vinylhouse farmers. However, the prevalence in female was about 2 times higher than that of male. When the effects of other factors were adjusted by multiple logistic regression for farmers' syndrome, the prevalence in female was 3.0 times higher than that of male. The prevalence of farmers' syndrome was increased as the age of farmers increased in both vinylhouse and non-vinylhouse farmers, and adjusted odds ratio of farmers' syndrome increased by 3% as the age increased by 1 year. Adjusted odds ratio for Farmers' syndrome in farmers who experienced pesticide intoxication during the month of June was 3.1 times higher than that of farmers who did not have such experience. While the prevalence of hypertension in male and female non-vinylhouse farmers were 22.4%, 13.7%, respectively, the prevalence in vinylhouse farmers were 13.5% for male and 12.0% for female. However, there was no association between farmers' syndrome and hypertension. It was found in this study that the vinylhouse farmers are at a high risk of pesticide intoxication, which is associated with tile common physical complaints. To reduce such risk it is necessary to develop farming methods which do not require the pesticide or may use less pesticide, a safer method of pesticide spraying, and the protective equipments which can be worn at a high temperature and have a better protective effect. Also education of farmers for the correct methods of ventilation after pesticide spraying in the vinylhouse and wearing the protective equipments may be considered as a supportive method. Since inappropriate posture at work and intensive labor may cause farmers' syndrome, it is recommended to develop farming tools which reduce physical burden and take a rest and exercise periodically during work. It is necessary to strengthen the hypertension management program of the Kyungpook province, because the prevalence of hypertension was as high as about 15%.

• 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.