• Korean Journal of Environmental Biology
• /
• v.26 no.3
• /
• pp.138-144
• /
• 2008

The effects of salinity on oxygen consumption, operculum movement number and ammonia nitrogen excretion were examined in cultured tilapia, Oreochromis mossambicus (total length 10$\pm$1.0 cm, total weight 17.3$\pm$4.2 g). The fish were exposured to certain salinities (0, 3, 9, 15, 21, 27 and 32 psu) step by step with 3 psu$\cdot$d$^{-1}$ and changed rapidly from certain salinity to another salinity. The oxygen consumption of the fish which was transferred step by step showed increasing tendency in the range of 3 $\sim$ 27 psu, but markedly appeared very low value at 32 psu. The tolerance limit in tilapia by salinity change without acclimation was shown 23.3 psu from 96-h TL$_{50}$. The oxygen consumption, operculum movement and ammonia nitrogen excretion of fish which was transferred rapidly from certain salinity to each salinity (0, 9, 15, 21 and 32 psu) showed a changing point at 15 psu; they showed increasing and/or decreasing pattern before 15 psu, and showed decreasing pattern after 15 psu. From these results, it was concluded that the appropriate salinity without physiological change for Java tilapia was below 15 psu.

• Journal of Preventive Medicine and Public Health
• /
• v.55 no.3
• /
• pp.280-288
• /
• 2022

Objectives: One of the most widely used pesticides today is chlorpyrifos (CPF). Cytochrome P450 (CYP)2B6, the most prominent catalyst in CPF bioactivation, is highly polymorphic. The objective of our study was to evaluate the role of CYP2B6^*6, which contains both 516G>T and 785A>G polymorphisms, in CPF toxicity, as represented by the concentration of 3,5,6-trichloro-2-pyridinol (TCPy), among vegetable farmers in Central Java, Indonesia, where CPF has been commonly used. Methods: A cross-sectional study was conducted among 132 vegetable farmers. Individual socio-demographic and occupational characteristics, as determinants of TCPy levels, were obtained using a structured interviewer-administered questionnaire and subsequently used to estimate the cumulative exposure level (CEL). TCPy levels were detected with liquid chromatography-mass spectrometry. CYP2B6^*6 gene polymorphisms were analyzed using a TaqMan^® SNP Genotyping Assay and Sanger sequencing. Linear regression analysis was performed to analyze the association between TCPy, as a biomarker of CPF exposure, and its determinants. Results: The prevalence of CYP2B6^*6 polymorphisms was 31% for ^*1/^*1, 51% for ^*1/^*6, and 18% for ^*6/^*6. TCPy concentrations were higher among participants with CYP2B6^*1/^*1 than among those with ^*1/^*6 or ^*6/^*6 genotypes. CYP2B6^*6 gene polymorphisms, smoking, CEL, body mass index, and spraying time were retained in the final linear regression model as determinants of TCPy. Conclusions: The results suggest that CYP2B6^*6 gene polymorphisms may play an important role in influencing susceptibility to CPF exposure. CYP2B6^*6 gene polymorphisms together with CEL, smoking habits, body mass index, and spraying time were the determinants of urinary TCPy concentrations, as a biomarker of CPF toxicity.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.53-77
• /
• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Economic and Environmental Geology
• /
• v.48 no.1
• /
• pp.15-24
• /
• 2015

Extensive base-metal and/or gold bearing ore mineralizations occur in the Pacitan mineralized district of the south western portions in the East Java, Indonesia. Metallic ore bodies in the Pacitan mineralized district are classified into two major types: 1) skarn type replacement ore bodies, 2) fissure filling hydrothermal ore bodies. Skarn type replacement ore bodies are developed typically along bedding planes of limestone as wall rock around the quartz porphyry and are composed mineralogically of skarn minerals, magnetite, and base metal sulfides. Hydrothermal ore bodies differ mineralogically in relation to distance from the quartz porphyry as source igneous rock. Hydrothermal ore bodies in the district are porphyry style Cu-Zn-bearing stockworks as proximal ore mineralization and Pb-Zn(-Au)-bearing fissure filling hydrothermal veins as distal ore mineralization. Sulfur isotope compositions in the sulfides from skarn and hydrothermal ore bodies range from 6.7 to 8.2‰ and from 0.1 to 7.9‰, respectively. The calculated ${\delta}^{34}S$ values of $H_2S$ in skarn-forming and hydrothermal fluids are 0.9 to 7.1‰ (5.6-7.1‰ for skarn-hosted sulfides and 0.9-6.8‰ for sulfides from hydrothermal deposits). The change from skarn to hydrothermal mineralization would have resulted in increased $SO_4/H_2S$ ratios and corresponding decreases in ${\delta}^{34}S$ values of $H_2S$. The calculated ${\delta}^{18}O$ water values are: skarn magnetite, 9.6 and 9.7‰; skarn quartz, 6.3-9.6‰; skarn calcite, 4.7 and 5.8‰; stockwork quartz, 3.0-7.7‰; stockwork calcite, 1.2 and 2.0‰; vein quartz, -3.9 - 6.7‰. The calculated ${\delta}^{18}O_{water}$ values decrease progressively with variety of deposit types (from skarn through stockwork to vein), increasing paragenetic time and decreasing temperature. This indicates the progressively increasing involvement of isotopically less-evolved meteoric waters in the Pacitan hydrothermal system. The ranges of ${\delta}D_{water}$ values are from -65 to -88‰: skarn, -67 to -84‰; stockwork, -65 and -76‰; vein, -66 to -88‰. The isotopic compositions of fluids in the Pacitan hydrothermal system show a progressive shift from magmatic hydrothermal dominance in the skarn and early hydrothermal ore mineralization periods toward meteoric hydrothermal dominance in the late ore mineralization periods.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.109-122
• /
• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.