• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.2
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• pp.183-193
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• 1995

Assuming that fishing nets are porous structures to suck water into their mouth and then filtrate water out of them, the flow resistance N of nets with wall area S under the velicity v was taken by $R=kSv^2$, and the coefficient k was derived as $$k=c\;Re^{-m}(\frac{S_n}{S_m})n(\frac{S_n}{S})$$ where $R_e$ is the Reynolds' number, $S_m$ the area of net mouth, $S_n$ the total area of net projected to the plane perpendicular to the water flow. Then, the propriety of the above equation and the values of c, m and n were investigated by the experimental results on plane nettings carried out hitherto. The value of c and m were fixed respectively by $240(kg\cdot sec^2/m^4)$ and 0.1 when the representative size on $R_e$ was taken by the ratio k of the volume of bars to the area of meshes, i. e., $$\lambda={\frac{\pi\;d^2}{21\;sin\;2\varphi}$$ where d is the diameter of bars, 21 the mesh size, and 2n the angle between two adjacent bars. The value of n was larger than 1.0 as 1.2 because the wakes occurring at the knots and bars increased the resistance by obstructing the filtration of water through the meshes. In case in which the influence of $R_e$ was negligible, the value of $cR_e\;^{-m}$ became a constant distinguished by the regions of the attack angle $ \theta$ of nettings to the water flow, i. e., 100$(kg\cdot sec^2/m^4)\;in\;45^{\circ}<\theta \leq90^{\circ}\;and\;100(S_m/S)^{0.6}\;(kg\cdot sec^2/m^4)\;in\;0^{\circ}<\theta \leq45^{\circ}$. Thus, the coefficient $k(kg\cdot sec^2/m^4)$ of plane nettings could be obtained by utilizing the above values with $S_m\;and\;S_n$ given respectively by $$S_m=S\;sin\theta$$ and $$S_n=\frac{d}{I}\;\cdot\;\frac{\sqrt{1-cos^2\varphi cos^2\theta}} {sin\varphi\;cos\varphi} \cdot S$$ But, on the occasion of $\theta=0^{\circ}$ k was decided by the roughness of netting surface and so expressed as $$k=9(\frac{d}{I\;cos\varphi})^{0.8}$$ In these results, however, the values of c and m were regarded to be not sufficiently exact because they were obtained from insufficient data and the actual nets had no use for k at $\theta=0^{\circ}$. Therefore, the exact expression of $k(kg\cdotsec^2/m^4)$, for actual nets could De made in the case of no influence of $R_e$ as follows; $$k=100(\frac{S_n}{S_m})^{1.2}\;(\frac{S_m}{S})\;.\;for\;45^{\circ}<\theta \leq90^{\circ}$$, $$k=100(\frac{S_n}{S_m})^{1.2}\;(\frac{S_m}{S})^{1.6}\;.\;for\;0^{\circ}<\theta \leq45^{\circ}$$

• Economic and Environmental Geology
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• v.56 no.5
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• pp.603-618
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• 2023

This study focused on evaluating the suitability of the WRK (waste repository Korea) bentonite as a buffer material in the SNF (spent nuclear fuel) repository. The U (uranium) adsorption/desorption characteristics and the adsorption mechanisms of the WRK bentonite were presented through various analyses, adsorption/desorption experiments, and kinetic adsorption modeling at various pH conditions. Mineralogical and structural analyses supported that the major mineral of the WRK bentonite is the Ca-montmorillonite having the great possibility for the U adsorption. From results of the U adsorption/desorption experiments (intial U concentration: 1 mg/L) for the WRK bentonite, despite the low ratio of the WRK bentonite/U (2 g/L), high U adsorption efficiency (>74%) and low U desorption rate (<14%) were acquired at pH 5, 6, 10, and 11 in solution, supporting that the WRK bentonite can be used as the buffer material preventing the U migration in the SNF repository. Relatively low U adsorption efficiency (<45%) for the WRK bentonite was acquired at pH 3 and 7 because the U exists as various species in solution depending on pH and thus its U adsorption mechanisms are different due to the U speciation. Based on experimental results and previous studies, the main U adsorption mechanisms of the WRK bentonite were understood in viewpoint of the chemical adsorption. At the acid conditions (＜pH 3), the U is apt to adsorb as forms of UO₂²⁺, mainly due to the ionic bond with Si-O or Al-O(OH) present on the WRK bentonite rather than the ion exchange with Ca²⁺ among layers of the WRK bentonite, showing the relatively low U adsorption efficiency. At the alkaline conditions (>pH 7), the U could be adsorbed in the form of anionic U-hydroxy complexes (UO₂(OH)₃^-, UO₂(OH)₄^2-, (UO₂)₃(OH)₇^-, etc.), mainly by bonding with oxygen (O^-) from Si-O or Al-O(OH) on the WRK bentonite or by co-precipitation in the form of hydroxide, showing the high U adsorption. At pH 7, the relatively low U adsorption efficiency (42%) was acquired in this study and it was due to the existence of the U-carbonates in solution, having relatively high solubility than other U species. The U adsorption efficiency of the WRK bentonite can be increased by maintaining a neutral or highly alkaline condition because of the formation of U-hydroxyl complexes rather than the uranyl ion (UO₂²⁺) in solution,and by restraining the formation of U-carbonate complexes in solution.