• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.223-232
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• 2016

Drought is one of important environmental stress for plant. Drought has deleterious effect to plant growth including maize (Zea mays L.) such as vegetative and/or reproductive growth, root extension, photosynthesis efficiency, flowering, anthesis-silking interval (ASI), fertilization, and grain filling. In this study, we screened drought tolerant maize in 21 cultivars from different sources, sixteen NAM parent lines (B73, CML103, CML228, CML247, CML277, CML322, CML333, CML69, Ki11, Ki3, Ky21, M37W, Mo18w, NC350, Oh43 and Tx303), four Korean hybrids (Cheongdaok, Gangdaok, Kwangpyeongok and Pyeonganok) and one Southeast Asian genotype (DK9955). Drought stress (DS) index was evaluated with leaf rolling score at seedling stage and ASI at silking date. The leaf rolling scoring of CML228, DK9955 and Ki11 were determined 1.28, 1.85, 1.86, respectively. However, M37W, Kwangpyeongok, B73 and NC350 were determined over the 3. ASI analysis revealed that CML228, CML103, Cheongdaok, NC350, B73, CML322, Kwangpyeongok and Ki11 are represented less than 5 days under DS and less than 3 days of difference between DS and well-watered (WW), but CML69, Ki3, Pyeonganok, M37W, Mo18w and Gangdaok were represented more than 10 days under DS and more than 8 days of difference between DS and WW. Multi-Dimensional Scaling (MDS) analysis determined CML228, Ki11, and CML322 were regarded as drought tolerance cultivars. Eventually, Ki11 showed genetic similarity with Korean cultivars by QTL analysis and MDS analysis. Ki11 has a potential for development of drought tolerance maize with Korean cultivars.

• Korean Journal of Environmental Biology
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• v.37 no.4
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• pp.652-663
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• 2019

The purpose of this study was to conduct ecological health assessments by using the fish communities in the Manchurian trout habitat. Sampling was conducted twice in July and September 2018. In the survey, 1,119 individuals belonging to 16 species and seven families were collected. The dominant species by number was Rhynchocypris kumgangensis and the subdominant species was Zacco koreanus. Korean endemic species represented 73.33% at Geybang Stream and 76.92% at Naerin Stream. Community analysis revealed that the structure of the fish community was unstable by the dominance index 0.63 (±0.09)-0.97 (±0.01), the diversity index 1.63 (±0.23)-0.33 (±0.09), the evenness index 0.79 (±0.06)-0.24 (±0.07) and the richness index 0.67 (±0.14)-1.80(±0.16). According to the analysis of the tolerance guilds, the total individual number of sensitive species was higher than the intermediate and tolerant species. The insectivore species accounted for 65.09-98.56% of the total trophic guilds, which was the highest observed. The value of the QHEI (Qualitative habitat evaluation index) in the Brachymystax lenok tsinlingensis habitat was 119.5 (±0.5)-153.5 (±0.5), indicating an optimal-suboptimal condition. The FAI (Fish assessment index) stream health value was A to B in the study sites. Correlation analysis with FAI and various factors was conducted and a high correlation in QHEI, Brachymystax lenok tsinlingensis individuals, species, individuals, diversity, richness, sensitive species, carnivorous species, insectivorous species, and omnivorous species was found. The similarity analysis was divided into three groups from A to C, based on 56.9%.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.19-36
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• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• The Korean Journal of Pesticide Science
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• v.17 no.4
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• pp.350-358
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• 2013

Both 13-week and 1-year studies in dog were required for pesticide registration in domestic pesticide control authority. It is raising issue up whether to request 1-year dog study of pesticides using non-food crop. So at this investigation, relevant toxicity test to establish acceptable daily intake (ADI), target organs, difference of no-observed adverse-effect levels (NOAELs) in 13-week and 1-year of 166 active ingredients are analyzed. The data were evaluated to determine if the 13-week dog study and the long term studies in two rodent species (mice and rats) without 1-year dog study were sufficient for the identification of NOAELs and lowest observed adverse effect levels (LOAELs) for the derivation of ADI. Toxicity end points and dose response data from 13 week and 1-year studies were compared. The analysis showed that 68 ADIs of the 166 pesticides were established from dog studies. Major target organs of dog studies were liver in 49 cases, body weight change in 21 cases, cholinesterase inhibition in 16 cases, and alteration in hematology in 14 cases. Similarity of target organ in 13-week and 1-year was 73%. 22 of 40 pesticides had similar critical effects regardless of duration and had NOAELs within a difference of 1.5-fold of each other. For the remaining 18 pesticides, 14 items had lower NOAELs in the 1-year study than 13-week study primarily due to dose selection and spacing. In only 10% of the cases were additional toxic effects identified in the 1-year study that were not observed in the 13-week study.

• The Korean Journal of Ecology
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• v.16 no.4
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• pp.515-526
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• 1993

In order to study the ecotypic variation of Rohinia pseudo-acacia L. distributed in southern area of Korean peninsula, 15 local populations(Daejin, Sokcho, Kangneung, Mt. Surak, Hongcheon, Kwangneung, Namhansanseong, Chungju, Yesan, Andong, Jeonju, Dalseong, Changweon, Mokpo and Wando), located from $34^{\circ}18'N\;to\;38^{\circ}36'N$, were selected based on the latitudes and geographical distances. Seeds of these populations were collected and protein contents of seeds and their band patterns were investigated. The seed proteins of all populations were electrophoresed on SDS-polyacrylamide gel. Total number of protein bands were 35, whose molecular weights ranged from 17, 258 daltons to 142, 232 daltons. The number of bands of seed proteins was 23 in Dalseong and Hongcheon and was 32 in Daejin and Sokcho, showing an increasing tendency in the number of bands as the latitude goes high. The local populations were classified into 3 local types based on protein analysis: the middle north east coastal type(Daejin, Sokcho. Kangneung), the central type (Mt. Surak, Hongcheon, Kwangneung, Namhansanseong, Chungju) and the southern type(Yesan, Andong, Jeonju, Dalseong, Changweon, Mokpo, Wando). According to the results of cluster analysis by UPGMA based on the similarity index(c0efficient of Jaccard) of the patterns, 3 local types were subdivided further into 6 types: the middle north east coastal type(Sokcho, Kangneung), the north central type I (Mt. Surak, Hongcheon), the north central type II (Narnhansanseong, Chungju, Daejin), the north central type III (Kwangneung), the south central type (Yesan, Dalseong, Jeonju) and the southern type(Andong, Changweon, Mokpo, Dalseong, Wando). The No. 12 band of the separated seed proteins showed the highest colored density in the preparations from all the populations. The No. 11~13 and No. 23~28 bands also showed high densities. As a whole, southern type populations (Changweon, Mokpo, Wando) showed high protein contents and high colored density. Total protein contents of the seeds in each population were variable from 9. 68mg / g (Mt. Surak) to 17.30mg/g (Jeonju), showing an increasing trends toward low latitudes.

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

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.2
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• pp.97-105
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• 2011

This study investigated the community structure and spatio-temporal variation of macrobenthic assemblages around Gijang Province, East Sea of Korea. Macrobenthos collected seasonally using a modified van Veen grab sampler from March to November 2006. A total of 157 macrobenthic fauna were collected. The overall average macrobenthos density were $552 \;ind/m^2$. The species number of macrobenthos was in the range from 62 in winter and spring to 122 in autumn. On the other hand, abundance fluctuated between 6,540 (in spring) and 17,920 (in autumn) inds./$18m^2$. Cluster analysis and non-metric multi-dimensional scaling (nMDS) were applied to assess the spatio-temporal fluctuation in the macrobenthic assemblages. Cluster analysis and nMDS ordination analysis based on the Bray-Curtis similarity identified 3 station groups. The group 1 (station 8~10, 12, 13, 17 and 18) was characterized by high abundance of the polychaete Lumbrineris longifolia, the bivalve Ennucula tenuis and the Amphipoda spp., with mean phi range from $6.2{\Phi}$ to $7.1{\Phi}$ (above 50m water depth). The group 2 (station 5~7, 11, 14~16) was numerically dominated by the po1ychaete Ampharete arctica and the bivalve Theorafragilis (mean phi: $6.0{\sim}7.0{\Phi}$; within 40 m water depth). Finally group 3 (station 1~4) was characterized by high density of the polychaetes Magelona japonica and Sternaspis scutata, with mean phi range from $3.5{\Phi}$ to $6.9{\Phi}$ (below 30 m water depth). In conclusion, the Macrobenthic community structure showed a distinct spatial and temporal trend, which seemed to be related to the water depth and sediment composition.

• Korean Journal of Ecology and Environment
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• v.39 no.3 s.117
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• pp.340-351
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• 2006

To reveal physicochemical factors contributing to variation of phytoplankton communities, the study was carried out biweekly at 6 stations from Feb. 2004 to Feb. 2005 in the lower part of the Han River, Korea. As results, water temperature was changed from $0.3^{\circ}C$ to $26.6^{\circ}C$, pH: 6.6${\sim}$9.1, DO: 1.89${\sim}$22.23 mg $L^{-1}$, BOD: 0.38${\sim}$9.20 mg $L^{-1}$, COD: 1.4${\sim}$15.2 mg $L^{-1}$, Conductivity: $62.5{\sim}500.0\;{\mu}s\;cm^{-1}$, SS: 3.00${\sim}$159.3 mg $L^{-1}$, and Chl a $1.7{\sim}71.3\;{\mu}g\;L^{-1}$. Phytoplankton standing crops ranged from min. $3.6{\times}10^2\;cells\;mL^{-1}$ (July 2004, St. 3) to max. $2.3{\times}10^4\;cells\;mL^{-1}$ (Feb. 2005, St. 6), and mean of those varied from $5.9{\times}10^3\;cells\;mL^{-1}$in spring, $2.1{\times}10^3\;cells\;mL^{-1}$ in summer, $4.1{\times}10^3\;cells\;mL^{-1}$ in autumn and $8.5{\times}10^3\;cells\;mL^{-1}$ in winter, respectively. In order to investigate factors influencing the total phytoplankton standing crops a multiple regression analysis was adopted for the correlation between standing crops and environmental factors. The coefficient of determination ($R^2$) value of the regression was 0.465, it showed that environmental factors which predominantly influenced phytoplankton standing crops were water temperature, COD, $NO_2-N$, $PO_4-N$, Discharge and pH. six stations could be divided into 3 groups based on similarity index in terms of environmental factors. In ANOVA analysis for physicochemical and biological factors, water temperature, chlorophyll a, silicate, phytoplankton standing crops were the same group differed little from stations. However, Station 1and 2 were grouped followed in dissolved oxygen, conductivity, COD, nitrite, nitrate, ammonia and phosphate, and Station 3, 4 and 5 were followed in dissolved oxygen, conductivity, pH and phosphate.

• Korean Journal of Environmental Biology
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• v.28 no.4
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• pp.179-187
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• 2010

The Theory of Island Biogeography describes that the number of species on an island affected by island area and distance from the mainland. This study was performed to compare and analyze the community structure of the macro-invertebrates in three isolated islands, around Korean waters in terms of the Theory of Island Biogeography. Macrobenthic animals were collected using a modified underwater quadrat in August 2009. A total of 104 macrobenthic species were sampled with a mean density of 399 individuals $m^{-2}$ and biomass of 1,506.70 g $m^{-2}$. Based on the abundance and biomass data, there were 10 dominant species accounting for approximately 67.17% of total individuals. The highest densities were found in the amphipoda Amphipoda spp., the bivalvia Modiolus agripetus and Mytilus coruscus, the Sipunculida Phascolosoma scolops and the polychaeta Syllidae unid.. On the contrary, the top ten species made up 95.66% of the total biomass while the three most abundant, the bivalves M. coruscus, Streostria circumpicta and M. agripetus. 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 station groups. The group 1 was consisted with Gageodo (except for lower station at Transect 2) and Dokdo station and was numerically dominated by the polychaetes Eunice antennata and Syllidae unid., the cirripedia Megabalanus rosa and the bivalvia M. coruscus. However, group 2 was associated with Sohwado station and was characterized by high abundance of the anomura Petrolisthes japonicus, the gastropoda Lirularia pygmaea and the brachiopoda Coptothyris grayi. In conclusion, these results suggested that the species diversity and community structure of macrobenthos in three isolated island seemed slightly related to island area and distance from the mainland.