• Korean Journal of Environmental Biology
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• v.29 no.4
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• pp.296-304
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• 2011

This study was carried out to investigate the relation between water quality and structure of the aquatic ecosystem in the Lake Gachang from February to December in 2010. The annual mean COD (Chemical Oxygen Demand) in Lake Gachang was 3.5 mg $L^{-1}$, indicating, level II of environmental standards and the trophic state was mesotrophic. The seasonal succession of phytoplankton showed that Bacillariophyceae was mostly dominant species throughout the year except August. In case of zooplankton, rotifers dominate in the most seasons, but copepod (Nauplii) in August. The macrophyte plants showed diverse species composition consisted of 9 varieties, 77 species, 64 genera, 34 families and 24 orders. Surveyed species of macroinvertebrates were classified into 1 phyla, 2 classes, 4 orders, 7 families, 9 species. The macroinvertebrates showed FFG (Functional Feeding Groups) such as GC (Gathering-Collector) and SH (Shedder). A total of 42 species of fish was collected including $Zacco$ $koreanus$ and $Coreoperca$ $herzi$. In this study, we investigated environmental factors including pollutant source, load, water quality and distribution characteristics of biota such as phytoplankton, zooplankton, macrophyte plants, macroinvertebrates, fish.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.764-778
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• 1997

To find the medical insurance utilization of workers when suffering from low back pain, an analysis was made toward the data of medical insurance benefits matched with the general characteristics of 10,183 workers, who were registered continuously from 1993 to 1995 at a medical insurance cooperation for industrial workers. The results were as follows; 1. The period prevalence of the medical insurance utilization for low back pain for 3 years from 1993 to 1995 was calculated as 17.1% for male workers and 19.4% for female workers. Most common cause of utilization was other dorsopathies including the herniation of lumbar discs. 2. The utilization rate increased significantly as the present age and the age joining the company got older(p<0.001). As the duration of employment got longer, the utilization rate of the male showed the tendency to increase and that of the female increased significantly(p<0.05). Among male workers employed at cement and concrete manufacturing companies showed higher utilization rate and among female laborers showed significantly higher utilization rate than clerical workers(p<0.01). 3. Annual utilization rate for low back pain didn't show any difference, but the portion of other dorsopathies among cause of utilization showed the tendency to increase from 1993 to 1995. 4. The mean number of claims for outpatient medical care for low back pain differed significantly by age, working duration, type of industries, income level(p<0.05), and the mean of total visiting days for care of low back pain differed significantly by working duration. In conclusion, considering the fact that the medical insurance utilization for low back pain increased annually and other dorsopathies including the herniation of dorsopathies were increasing, an effective preventive or management program for low back pain toward worker employed at industries were required.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.