• Journal of Wetlands Research
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• v.26 no.1
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• pp.21-31
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• 2024

Macromia daimoji Okumura, 1949 was designated as an endangered species and also categorized as Class II Endangered wildlife on the International Union for Conservation of Nature (IUCN) Red List in Korea. The spatial distribution of this species ranged within a region delimited by northern latitude from Sacheon-si(35.1°) to Yeoncheon-gun(38.0°) and eastern longitude from Yeoncheon-gun(126.8°) to Yangsan-si(128.9°). They generally prefer microhabitats such as slowly flowing littoral zones of streams, alluvial stream islands and temporarily formed puddles in the sand-based lowland streams. The objectives of this study were to analyze the similarity of benthic macroinvertebrate communities in M. daimoji habitats, to predict the current potential distribution patterns as well as the changes of distribution ranges under global climate change circumstances. Data was collected both from the Global Biodiversity Information Facility (GBIF) and by field surveys from April 2009 to September 2022. We adopted MaxEnt model to predict the current and future potential distribution for M. daimoji using downloaded 19 variables from the WorldClim database. The differences of benthic macroinvertebrate assemblages in the mainstream of Nakdonggang were smaller than those in its tributaries and the other streams, based on the surrounding environments and stream sizes. MaxEnt model presented that potential distribution displayed high inhabiting probability in Nakdonggang and its tributaries. Applying to the future scenarios by Intergovernmental Panel on Climate Change (IPCC), SSP1 scenario was predicted to expand in a wide area and SSP5 scenario in a narrow area, comparing with current potential distribution. M. daimoji is not only directly threatened by physical disturbances (e.g. river development activities) but also vulnerable to rapidly changing climate circumstances. Therefore, it is necessary to monitor the habitat environments and establish conservation strategies for preserving population of M. daimoji.

• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.189-203
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• 2024

This study conducted a survey two times at 20 points from April to June and from August to October 2022 to characterize the fish community and reveal the health of the Anyang stream. The survey collected 3,186 individuals belonging to 5 orders, 10 families and 34 species. The dominant species was Zacco platypus (43.4%), followed by Carassius auratus (10.1%), Cyprinus carpio (7.6%), Zacco koreanus (6.2%), Pungtungia herzi (5.2%), Oryzias sinensis (5.1%), Pseudorasbora parva (4.7%), Squalidus gracilis majimae (4.4%), Rhynchocypris oxycephalus (3.5%), Hemiculter eigenmanni (3.3%), Rhodeus ocellatus (1.4%), and Chelon haematocheilus (1.0%). Among the emerged species, 5 species (14.7%), including Rhodeus uyekii, Squalidus japonicus coreanus, and Odontobutis interrupta, were endemic to Korea, and 4 species (11.8%), including Cyprinus carpio (Israeli type), Lepomis macrochirus, Micropterus salmoides, and Poecilia reticulata, were exotic. The fish community analysis showed that the dominance was higher at the upper reaches of the Anyang stream and decreased downstream, and diversity, richness, and evenness were higher in tributary than mainstream. Stream health (fish assessment index, FAI) was generally poor, as it was rated as moderate (C) at seven sites, poor (D) at eight sites, and very poor (E) at five sites. Water quality was evaluated at eight sites, and it was rated as poor (V) at two sites, slightly poor (IV) at one site, moderate (III) at four sites, and slightly good (II) at one site, indicating that the overall water quality was poor and that the water quality had a significant impact on river health and fish habitat. Moreover, it was found that many weirs without fishways were constructed in the Anyang stream and became an obstacle to fish migration. Therefore, a systematic management plan to improve water quality and add fishways is necessary to improve the stream's health and conserve stable fish habitat.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.174-189
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• 2015

The objective of this study was to reconstruct spatial information using the results of the investigation and evaluation of the health of the living organisms, habitat, and water quality at the investigation points for the aquatic ecosystem health of the Nakdong River basin, to support the rational decision making of the aquatic ecosystem preservation and restoration policies of the Nakdong River basin using spatial analysis techniques, and to present efficient management methods. To analyze the aquatic ecosystem health of the Nakdong River basin, punctiform data were constructed based on the position information of each point with the aquatic ecosystem health investigation and evaluation results of 250 investigation sections. To apply the spatial analysis technique, the data need to be reconstructed into areal data. For this purpose, spatial influence and trends were analyzed using the Kriging interpolation(ArcGIS 10.1, Geostatistical Analysis), and were reconstructed into areal data. To analyze the spatial distribution characteristics of the Nakdong River basin health based on these analytical results, hotspot(Getis-Ord Gi, $G^*_i$), LISA(Local Indicator of Spatial Association), and standard deviational ellipse analyses were used. The hotspot analysis results showed that the hotspot basins of the biotic indices(TDI, BMI, FAI) were the Andong Dam upstream, Wangpicheon, and the Imha Dam basin, and that the health grades of their biotic indices were good. The coldspot basins were Nakdong River Namhae, the Nakdong River mouth, and the Suyeong River basin. The LISA analysis results showed that the exceptional areas were Gahwacheon, the Hapcheon Dam, and the Yeong River upstream basin. These areas had high bio-health indices, but their surrounding basins were low and required management for aquatic ecosystem health. The hotspot basins of the physicochemical factor(BOD) were the Nakdong River downstream basin, Suyeong River, Hoeya River, and the Nakdong River Namhae basin, whereas the coldspot basins were the upstream basins of the Nakdong River tributaries, including Andong Dam, Imha Dam, and Yeong River. The hotspots of the habitat and riverside environment factor(HRI) were different from the hotspots and coldspots of each factor in the LISA analysis results. In general, the habitat and riverside environment of the Nakdong River mainstream and tributaries, including the Nakdong river upstream, Andong Dam, Imha Dam, and the Hapcheon Dam basin, had good health. The coldspot basins of the habitat and riverside environment also showed low health indices of the biotic indices and physicochemical factors, thus requiring management of the habitat and riverside environment. As a result of the time-series analysis with a standard deviation ellipsoid, the areas with good aquatic ecosystem health of the organisms, habitat, and riverside environment showed a tendency to move northward, and the BOD results showed different directions and concentrations by the year of investigation. These aquatic ecosystem health analysis results can provide not only the health management information for each investigation spot but also information for managing the aquatic ecosystem in the catchment unit for the working research staff as well as for the water environment researchers in the future, based on spatial information.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.179-187
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• 2010

Understanding consumers' preferences and use of media types is imperative for marketing and advertising managers, especially in today's fragmented market. A clear understanding assists managers in making more effective selections of appropriate media outlets, yet individuals' choices of type and use of media are based on a variety of characteristics. This paper examines one personality trait, sensation seeking, which has not appeared in the literature examining "new" media preferences and use. Sensation seeking is a personality trait defined as "the need for varied, novel, and complex sensations and experiences and the willingness to take physical and social risks for the sake of such experiences" (Zuckerman 1979). Six hypotheses were developed from a review of the literature. Particular attention was given to the Uses and Gratification theory (Katz 1959), which explains various reasons why people choose media types and their motivations for using the different types of media. Current theory suggests that High Sensation Seekers (HSS), due to their needs for novelty, arousal and unconventional content and imagery, would exhibit higher frequency of use of new media. Specifically, we hypothesize that HSS will use the internet more than broadcast (H1a) or print media (H1b) and more than low (LSS) (H2a) or medium sensation seekers (MSS) (H2b). In addition, HSS have been found to be more social and have higher numbers of friends therefore are expected to use social networking websites such as Facebook/MySpace (H3) and chat rooms (H4) more than LSS (a) and MSS (b). Sensation seekers can manifest into a range of behaviors including disinhibition,. It is expected that alternative social networks such as Facebook/MySpace (H5) and chat rooms (H6) will be used more often for those who have higher levels of disinhibition than low (a) or medium (b) levels. Data were collected using an online survey of participants in extreme sports. In order to reach this group, an improved version of a snowball sampling technique, chain-referral method, was used to select respondents for this study. This method was chosen as it is regarded as being effective to reach otherwise hidden population groups (Heckathorn, 1997). A final usable sample of 1108 respondents, which was mainly young (56.36% under 34), male (86.1%) and middle class (58.7% with household incomes over USD 50,000) was consistent with previous studies on sensation seeking. Sensation seeking was captured using an existing measure, the Brief Sensation Seeking Scale (Hoyle et al., 2002). Media usage was captured by measuring the self reported usage of various media types. Results did not support H1a and b. HSS did not show higher levels of usage of alternative media such as the internet showing in fact lower mean levels of usage than all the other types of media. The highest media type used by HSS was print media, suggesting that there is a revolt against the mainstream. Results support H2a and b that HSS are more frequent users of the internet than LSS or MSS. Further analysis revealed that there are significant differences in the use of print media between HSS and LSS, suggesting that HSS may seek out more specialized print publications in their respective extreme sport activity. Hypothesis 3a and b showed that HSS use Facebook/MySpace more frequently than either LSS or MSS. There were no significant differences in the use of chat rooms between LSS and HSS, so as a consequence no support for H4a, although significant for MSS H4b. Respondents with varying levels of disinhibition were expected to have different levels of use of Facebook/MySpace and chat-rooms. There was support for the higher levels of use of Facebook/MySpace for those with high levels of disinhibition than low or medium levels, supporting H5a and b. Similarly there was support for H6b, Those with high levels of disinhibition use chat-rooms significantly more than those with medium levels but not for low levels (H6a). The findings are counterintuitive and give some interesting insights for managers. First, although HSS use online media more frequently than LSS or MSS, this groups use of online media is less than either print or broadcast media. The advertising executive should not place too much emphasis on online media for this important market segment. Second, social media, such as facebook/Myspace and chatrooms should be examined by managers as potential ways to reach this group. Finally, there is some implication for public policy by the higher levels of use of social media by those who are disinhibited. These individuals are more inclined to engage in more socially risky behavior which may have some dire implications, e.g. by internet predators or future employers. There is a limitation in the study in that only those who engage in extreme sports are included. This is by nature a HSS activity. A broader population is therefore needed to test if these results hold.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.125-148
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• 2009

UGC(User Generated Contents) is emerging as the center of e-business in the web 2.0 era. The trend reflects changing roles of users in production and consumption of contents on websites and helps us to understand new strategies of websites such as web portals and social network websites. Nowadays, we consume contents created by other non-professional users for both utilitarian (e.g., knowledge) and hedonic values (e.g., fun). Also, contents produced by ourselves (e.g., photo, video) are posted on websites so that our friends, family, and even the public can consume those contents. This means that non-professionals, who used to be passive audience in the past, are now creating contents and share their UGCs with others in the Web. Accessible media, tools, and applications have also reduced difficulty and complexity in the process of creating contents. Realizing that users create plenty of materials which are very interesting to other people, media companies (i.e., web portals and social networking websites) are adjusting their strategies and business models accordingly. Increased demand of UGC may lead to website visits which are the source of benefits from advertising. Therefore, they put more efforts into making their websites open platforms where UGCs can be created and shared among users without technical and methodological difficulties. Many websites have increasingly adopted new technologies such as RSS and openAPI. Some have even changed the structure of web pages so that UGC can be seen several times to more visitors. This mainstream of UGCs on websites indicates that acquiring more UGCs and supporting participating users have become important things to media companies. Although those companies need to understand why general users have shown increasing interest in creating and posting contents and what is important to them in the process of productions, few research results exist in this area to address these issues. Also, behavioral process in creating video UGCs has not been explored enough for the public to fully understand it. With a solid theoretical background (i.e., theory of implementation intentions), parts of our proposed research model mirror the process of user behaviors in creating video contents, which consist of intention to upload, intention to edit, edit, and upload. In addition, in order to explain how those behavioral intentions are developed, we investigated influences of antecedents from three motivational perspectives (i.e., intrinsic, editing software-oriented, and website's network effect-oriented). First, from the intrinsic motivation perspective, we studied the roles of self-expression, enjoyment, and social attention in forming intention to edit with preferred editing software or in forming intention to upload video contents to preferred websites. Second, we explored the roles of editing software for non-professionals to edit video contents, in terms of how it makes production process easier and how it is useful in the process. Finally, from the website characteristic-oriented perspective, we investigated the role of a website's network externality as an antecedent of users' intention to upload to preferred websites. The rationale is that posting UGCs on websites are basically social-oriented behaviors; thus, users prefer a website with the high level of network externality for contents uploading. This study adopted a longitudinal research design; we emailed recipients twice with different questionnaires. Guided by invitation email including a link to web survey page, respondents answered most of questions except edit and upload at the first survey. They were asked to provide information about UGC editing software they mainly used and preferred website to upload edited contents, and then asked to answer related questions. For example, before answering questions regarding network externality, they individually had to declare the name of the website to which they would be willing to upload. At the end of the first survey, we asked if they agreed to participate in the corresponding survey in a month. During twenty days, 333 complete responses were gathered in the first survey. One month later, we emailed those recipients to ask for participation in the second survey. 185 of the 333 recipients (about 56 percentages) answered in the second survey. Personalized questionnaires were provided for them to remind the names of editing software and website that they reported in the first survey. They answered the degree of editing with the software and the degree of uploading video contents to the website for the past one month. To all recipients of the two surveys, exchange tickets for books (about 5,000~10,000 Korean Won) were provided according to the frequency of participations. PLS analysis shows that user behaviors in creating video contents are well explained by the theory of implementation intentions. In fact, intention to upload significantly influences intention to edit in the process of accomplishing the goal behavior, upload. These relationships show the behavioral process that has been unclear in users' creating video contents for uploading and also highlight important roles of editing in the process. Regarding the intrinsic motivations, the results illustrated that users are likely to edit their own video contents in order to express their own intrinsic traits such as thoughts and feelings. Also, their intention to upload contents in preferred website is formed because they want to attract much attention from others through contents reflecting themselves. This result well corresponds to the roles of the website characteristic, namely, network externality. Based on the PLS results, the network effect of a website has significant influence on users' intention to upload to the preferred website. This indicates that users with social attention motivations are likely to upload their video UGCs to a website whose network size is big enough to realize their motivations easily. Finally, regarding editing software characteristic-oriented motivations, making exclusively-provided editing software more user-friendly (i.e., easy of use, usefulness) plays an important role in leading to users' intention to edit. Our research contributes to both academic scholars and professionals. For researchers, our results show that the theory of implementation intentions is well applied to the video UGC context and very useful to explain the relationship between implementation intentions and goal behaviors. With the theory, this study theoretically and empirically confirmed that editing is a different and important behavior from uploading behavior, and we tested the behavioral process of ordinary users in creating video UGCs, focusing on significant motivational factors in each step. In addition, parts of our research model are also rooted in the solid theoretical background such as the technology acceptance model and the theory of network externality to explain the effects of UGC-related motivations. For practitioners, our results suggest that media companies need to restructure their websites so that users' needs for social interaction through UGC (e.g., self-expression, social attention) are well met. Also, we emphasize strategic importance of the network size of websites in leading non-professionals to upload video contents to the websites. Those websites need to find a way to utilize the network effects for acquiring more UGCs. Finally, we suggest that some ways to improve editing software be considered as a way to increase edit behavior which is a very important process leading to UGC uploading.

• 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.