• Nuclear Medicine and Molecular Imaging
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• v.43 no.4
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• pp.323-329
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• 2009

Purpose: Radioimmunoassay (RIA) was usually performed by the batch assay. To improve the efficiency of RIA without increase of the cost and time, random assay could be a choice. We investigated the possibility of the random assay using automatic dispenser by assessing the agreement between batch assay and random assay. Materials and Methods: The experiments were performed with four items; Triiodothyronine (T3), free thyroxine (fT4), Prostate specific antigen (PSA), Carcinoembryonic antigen (CEA). In each item, the sera of twenty patients, the standard, and the control samples were used. The measurements were done 4 times with 3 hour time intervals by random assay and batch assay. The coefficient of variation (CV) of the standard samples and patients' data in T3, fT4, PSA, and CEA were assessed. ICC (Intraclass correlation coefficient) and coefficient of correlation were measured to assessing the agreement between two methods. Results: The CVs (%) of T3, fT4, PSA, and CEA measured by batch assay were 3.2$\pm$1.7%, 3.9$\pm$2.1%, 7.1$\pm$6.2%, 11.2$\pm$7.2%. The CVs by random assay were 2.1$\pm$1.7%, 4.8$\pm$3.1%, 3.6$\pm$4.8%, and 7.4$\pm$6.2%. The ICC between the batch assay and random assay were 0.9968 (T3), 0.9973 (fT4), 0.9996 (PSA), and 0.9901 (CEA). The coefficient of correlation between the batch assay and random assay were 0.9924(T3), 0.9974 (fT4), 0.9994 (PSA), and 0.9989 (CEA) (p<0.05). Conclusion: The results of random assay showed strong agreement with the batch assay in a day. These results suggest that random assay using automatic dispenser could be used in radioimmunoassay.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.10
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• pp.1293-1299
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• 2007

This study was performed to assess the nutritional status of the elderly residing in a silver town. A survey was conducted in April, 2005 for fifty two healthy persons (men 18, women 34) aged over 65 years living in a private silver town of Busan Metropolitan City, Korea. General data were collected using questionnaires at homes of the subjects and food consumption data for 3 days were obtained by investigating the individual leftovers from the meals served at the foodservice facilities by well-trained graduate students of the Department of Food & Nutrition. Nutrient intake was calculated from food consumption data using Can Pro (version 3.0, Korea). Average daily intake of energy was 1077.6 kcal in men and 974.2 kcal in women. Average protein intake of the men and women amounted to 85.5 g and 67.7 g, respectively. The consumption of cereals contributed 45.3% of energy intake for all subjects. Only 1.1% of calcium intake was from milk and milk products. Almost 100% of the elderly did not reach 75% of estimated energy requirement (EER) in energy intake and 75% of estimated average requirement (EAR) in folic acid intake. Percentages of the subjects who consumed protein above 125% of EAR were 100% and 91% in men and women, respectively. All subjects consumed above EAR in dietary intake of protein and iron. These data suggest that nutrient intake of the elderly living in the silver town would be inadequate, and thus the efficient management of foodservice is necessary to improve the nutritional status of the elderly living in the silver town.

• Journal of the Korean Society of Groundwater Environment
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• v.7 no.3
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• pp.103-115
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• 2000

The formation of brown-colored precipitates is one of the serious problems frequently encountered in the development and supply of groundwater in Korea, because by it the water exceeds the drinking water standard in terms of color. taste. turbidity and dissolved iron concentration and of often results in scaling problem within the water supplying system. In groundwaters from the Pajoo area, brown precipitates are typically formed in a few hours after pumping-out. In this paper we examine the process of the brown precipitates' formation using the equilibrium thermodynamic and kinetic approaches, in order to understand the origin and geochemical pathway of the generation of turbidity in groundwater. The results of this study are used to suggest not only the proper pumping technique to minimize the formation of precipitates but also the optimal design of water treatment methods to improve the water quality. The bed-rock groundwater in the Pajoo area belongs to the Ca-$HCO_3$type that was evolved through water/rock (gneiss) interaction. Based on SEM-EDS and XRD analyses, the precipitates are identified as an amorphous, Fe-bearing oxides or hydroxides. By the use of multi-step filtration with pore sizes of 6, 4, 1, 0.45 and 0.2 $\mu\textrm{m}$, the precipitates mostly fall in the colloidal size (1 to 0.45 $\mu\textrm{m}$) but are concentrated (about 81%) in the range of 1 to 6 $\mu\textrm{m}$in teams of mass (weight) distribution. Large amounts of dissolved iron were possibly originated from dissolution of clinochlore in cataclasite which contains high amounts of Fe (up to 3 wt.%). The calculation of saturation index (using a computer code PHREEQC), as well as the examination of pH-Eh stability relations, also indicate that the final precipitates are Fe-oxy-hydroxide that is formed by the change of water chemistry (mainly, oxidation) due to the exposure to oxygen during the pumping-out of Fe(II)-bearing, reduced groundwater. After pumping-out, the groundwater shows the progressive decreases of pH, DO and alkalinity with elapsed time. However, turbidity increases and then decreases with time. The decrease of dissolved Fe concentration as a function of elapsed time after pumping-out is expressed as a regression equation Fe(II)=10.l exp(-0.0009t). The oxidation reaction due to the influx of free oxygen during the pumping and storage of groundwater results in the formation of brown precipitates, which is dependent on time, $Po_2$and pH. In order to obtain drinkable water quality, therefore, the precipitates should be removed by filtering after the stepwise storage and aeration in tanks with sufficient volume for sufficient time. Particle size distribution data also suggest that step-wise filtration would be cost-effective. To minimize the scaling within wells, the continued (if possible) pumping within the optimum pumping rate is recommended because this technique will be most effective for minimizing the mixing between deep Fe(II)-rich water and shallow $O_2$-rich water. The simultaneous pumping of shallow $O_2$-rich water in different wells is also recommended.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Korean Society of Forest Science
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• v.33 no.1
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• pp.33-58
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• 1977

A bark comprises about 10 to 20 percents of a typical log by volume, and is generally considered as an unwanted residue rather than a potentially valuable resourses. As the world has been confronted with decreasing forest resources, natural resources pressure dictate that a bark should be a raw material instead of a waste. The utilization of the largely wasted bark of genus Pinus, Quercus, and Populus grown in Korea can be enhanced by learning its physical and mechanical properties. However, the study of tree bark grown in Korea have never been undertaken. In the present paper, an investigative study is carried out on the bark of three genus, eleven species representing not only the major bark trees but major species currently grown in Korea. For each species 20 trees were selected, at Suweon and Kwang-neung areas, on the same basis of the diameter class at the proper harvesting age. One $200cm^2$ segment of bark was obtained from each tree at brest height. Physical properties of bark studied are: bark density, moisture content of green bark (inner-, outer-, and total-bark), fiber saturation point, hysteresis loop, shrinkage, water absorption, specific heat, heat of wetting, thermal conductivity, thermal diffusivity, heat of combustion, and differential thermal analysis. The mechanical properties are studied on bending and compression strength (radial, longitudinal, and tangential). The results may be summarized as follows: 1. The oven-dry specific gravities differ between wood and bark, further more even for a given bark sample, the difference is obersved between inner and outer bark. 2. The oven-dry specific gravity of bark is higher than that of wood. This fact is attributed to the anatomical structure whose characters are manifested by higher content of sieve fiber and sclereids. 3. Except Pinus koraiensis, the oven-dry specific gravity of inner bark is higher than that of outer bark, which results from higher shrinkage of inner bark. 4. The moisture content of bark increases with direct proportion to the composition ratio of sieve components and decreases with higher percent of sclerenchyma and periderm tissues. 5. The possibility of determining fiber saturation point is suggested by the measuring the heat of wetting. With the proposed method, the fiber saturation point of Pinus densiflora lies between 26 and 28%, that of Quercus accutissima ranges from 24 to 28%. These results need be further examined by other methods. 6. Contrary to the behavior of wood, the bark shrinkage is the highest in radial direction and the lowest in longitudinal direction. Quercus serrata and Q. variabilis do not fall in this category. 7. Bark shows the same specific heat as wood, but the heat of wetting of bark is higher than that of wood. In heat conductivity, bark is lower than wood. From the measures of oven-dry specific gravity (${\rho}d$) and moisture fraction specific gravity (${\rho}m$) is devised the following regression equation upon which heat conductivity can be calculated. The calculated heat conductivity of bark is between $0.8{\times}10^{-4}$ and $1.6{\times}10^{-4}cal/cm-sec-deg$. $$K=4.631+11.408{\rho}d+7.628{\rho}m$$ 8. The bark heat diffusivity varies from $8.03{\times}10^{-4}$ to $4.46{\times}10^{-4}cm^2/sec$. From differential thermal analysis, wood shows a higher thermogram than bark under ignition point, but the tendency is reversed above ignition point. 9. The modulus of rupture for static bending strength of bark is proportional to the density of bark which in turn gives the following regression equation. M=243.78X-12.02 The compressive strength of bark is the highest in radial direction, contrary to the behavior of wood, and the compressive strength of longitudinal direction follows the tangential one in decreasing order.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.