• Korean Journal of Environmental Biology
• /
• v.27 no.4
• /
• pp.341-352
• /
• 2009

This study was carried out to investigated community structure of macrobenthic assemblages around the Wolseong Nuclear Power Plant, East Sea of Korea and seasonal sampling was performed from October 2007 to July 2008. A total of 163 macrobenthic fauna were collected. The overall average macrobenthos density and biomass were 1,005 individuals $m^{-2}$ and $21.81\;gWWt\;m^{-2}$, respectively. Based on the LeBris (1988) index, there were 10 dominant species accounting for approximately 69.00% of total individuals. The major dominant species were the polychaetes Spiophanes bombyx (349 inds. $m^{-2}$), Mediomastus californiensis (82 inds. $m^{-2}$), Sigambra tentaculata (55 inds. $m^{-2}$), Magelona japonica (50 inds. $m^{-2}$), Scoletoma longifolia (33 inds. $m^{-2}$) and the Unidentified amphipod (Amphipoda spp., 72 inds. $m^{-2}$). The conventional multi-variate statistics (cluster analysis and non-metric multi-dimensional scaling) applied to assess spatial variation in macrobenthic assemblages. Cluster analysis and nMDS ordination analysis based on the Bray-Curtis similarity identified 2 major station groups. The major group 1 was associated with sand dominated stations and was characterized by high abundance of the bivalves Mactra chinensis, Siliqua pulchella and the polychaete Protodorvillea egena. On the other hand, major group 2 was connected with mud dominated stations and was numerically dominated by the polychaetes M. californiensis, M. japonica, Sternaspis scutata, S. longifolia and the bivalves Thyasira tokunagai and Theora fragilis. However, macrobenthic community structure were no significant differences between the environmental variables (sediment type and depth) and heated discharge.

• Korean Journal of Ecology and Environment
• /
• v.45 no.2
• /
• pp.218-231
• /
• 2012

The objectives of this study were to evaluate the physico-chemical water quality, trophic and tolerance guilds in the control ($C_o$) and impacted streams of the abandoned mine, along with the ecological health, using a multimetric health model and physical habitat conditions of Qualitative Habitat Evaluation Index (QHEI), during the period of three years, 2005~2007. Also, eco-toxicity ($EE_t$) enclosure tests were conducted to examine the toxic effects on the outflows from the mine wastewater, using the sentinel species of Rhynchocypris oxycephalus, and we compared the biological responses of the control ($C_o$) and treatment (T) to the effluents through a Necropybased Health Assessment Index ($N_b$-HAI). Tissue impact analysis of the spleen, kidney, gill, liver, eyes, and fins were conducted in the controlled enclosure experiments (10 individuals). According to the comparisons of the control ($C_o$) vs. the treatment (T) in physicochemical water quality, outflows from the abandoned mine resulted in low pH of 3.2, strong acid wastewater, high ionic concentrations, based on an electrical conductivity, and high total dissolved solid (TDS). Physical habitat assessments, based on Qualitative Habitat Evaluation Index (QHEI) did not show any statistical differences (p>0.05) in the sampling sites, whereas, the $M_m$-EH model values in a multimetric ecological health ($M_m$-EH) model of the Index of Biological Integrity (IBI), using fish assemblages, were 16~20 (fair condition) in the control and all zero (0, poor condition) in the impacted sites of mine wastewater. In addition, in enclosure eco-toxicity ($EE_t$) tests, the model values of $N_b$-HAI ranged between 0 and 3 in the controls during the three years, indicating an excellent~good condition (Ex~G), and were >100 (range: 100~137) in the impacted sites, which indicates a poor condition (P). Under the circumstances, organ tissues, such as the liver, kidney, and gills were largely impaired, so that efficient water quality managements are required in the outflow area of the abandoned mine watershed.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.119-138
• /
• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.