• Journal of Nutrition and Health
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• v.52 no.5
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• pp.488-500
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• 2019

Purpose: This study examined the effects of a mobile app-based program to reduce the dietary sugar intake according to the stages of the behavioral change in dietary sugar reduction in female college students. Methods: The program used in this study can monitor the dietary sugar intake after recording the dietary intake and provide education message for the reduction of dietary sugar intake. In an eight-week pre-post intervention study, 68 female college students were instructed to record all the food they consumed daily and received weekly education information. At pre-post intervention, the subjects were asked to answer the questionnaire about sugar-related nutrition knowledge, sugar-intake behavior, and sugar-intake frequency. For statistical analysis, ANOVA and a paired t-test were used for comparative analysis according Precontemplation (PC), Contemplation Preparation (C P), and A M (Action Maintenance) stage. Results: Significant differences were observed in the frequency of snacking, experience of nutrition education, and preference for sweetness according to the stages of behavior change in dietary sugar reduction. After finishing an intervention, the sugar-related nutrition knowledge score was increased significantly in the stages of Precontemplation (PC) and Contemplation Preparation (C P). The score of the sugar intake behavior increased significantly in all stages. The intake frequency of chocolate, muffins or cakes, and drinking yogurt decreased significantly in the PC stage and the intake frequency of biscuits, carbonated beverages, and fruit juice decreased significantly in the C P stage. Subjects in the PC and C P stages had an undesirable propensity in nutrition knowledge, sugar-intake behavior, and sugar-intake frequency compared to the A M stage, but this intervention improved significantly their nutrition knowledge, sugar-intake behavior, and sugar-intake frequency. Conclusion: This program can be an effective educational tool in the stages of PC and C P, and is expected to further increase the usability and sustainability of mobile application if supplemented appropriately to a health platform program.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.864-881
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• 2021

Purpose: This study aims is to provide a total care solution preventing disaster based on Big Data and AI technology and to service safety considered by individual situations and various risk characteristics. The purpose is to suggest a method that customized comprehensive index services to prevent and respond to safety accidents for calculating the living safety index that quantitatively represent individual safety levels in relation to daily life safety. Method: In this study, we use method of mixing AHP(Analysis Hierarchy Process) and Likert Scale that extracted from consensus formation model of the expert group. We organize evaluation items that can evaluate life safety prevention services into risk indicators, vulnerability indicators, and prevention indicators. And We made up AHP hierarchical structure according to the AHP decision methodology and proposed a method to calculate relative weights between evaluation criteria through pairwise comparison of each level item. In addition, in consideration of the expansion of life safety prevention services in the future, the Likert scale is used instead of the AHP pair comparison and the weights between individual services are calculated. Result: We obtain result that is weights for life safety prevention services and reflected them in the individual risk index calculated through the artificial intelligence prediction model of life safety prevention services, so the comprehensive index was calculated. Conclusion: In order to apply the implemented model, a test environment consisting of a life safety prevention service app and platform was built, and the efficacy of the function was evaluated based on the user scenario. Through this, the life safety index presented in this study was confirmed to support the golden time for diagnosis, response and prevention of safety risks by comprehensively indication the user's current safety level.

• Journal of Popular Narrative
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• v.25 no.3
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• pp.303-345
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• 2019

This article analyzes the multi-layered communication in the Webtoon Princess Pari, released on the Daum portal site, created (written and illustrated) by Kim Naim, through analyzing the narrative structure and comments with the qualitative / quantitative methodology. The webtoon Princess Pari is structured in an omnibus style in which unit narratives are intermittently articulated, multi-lined, and interconnected. As integrated narratives which link with unitary narratives, Pari's growth story as a shaman and a romance narrative are structured. The classical original story of the shaman was used as a prehistory corresponding to the prequel of the webtoon through a preview, and the writer restructured the narrative to overcome the contradictions of the gender asymmetry and the patriarchal ideology of the original text. The viewer then creates a conversational space by giving critical and reflective comments. According to a statistical analysis conducted through sampling, the types of comments can be classified as follows: Appreciation and criticism of the contents ≫ Emotional response ≫ Intuitive overall review ≫ Knowledge and reflection ≫ Comments on comments. In the process of creation and acceptance of the Webtoon, a multi-layered dialogue between classical and modern, content and audience, acceptance and creation has been at play. In the creation dimension, the writer used a device to fill the gap of mythical symbols of the contents. At the level of the audience, they formed a culture of sharing information, knowledge, and reflection about tradition/folk/culture through comments. This corresponds to classical and modern dialogue through the webtoon. The viewers form a sympathetic bond, attempt hermeneutical coordination, supplement the information, and search for a balanced angle through controversial conversation. In addition, by commenting on attitudes, views, and perspective, the commentators showed a behavioral pattern corresponding to meta-criticism in literature. The viewers' comments acted as feedback on the creation of the webtoons, so that the creation and acceptance itself influenced the production of the content of the webtoon. The webtoon Princess Pari, which was based on Korean classical narrative, has been reorganized onto 'moving and dynamic' content, which leads to sense, thinking, criticism and reflection through the formation of various dialogues.

• Information Systems Review
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• v.18 no.3
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• pp.209-233
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• 2016

Internet companies that utilize social network have increased in number. The introduction of diverse social media services facilitated innovative changes in e-business. Social network service (SNS), which is a domain of social media, is a web-based service designed to strengthen human relations in the Internet and build new social relations. The remarkable growth of social network services and the profit generation and perception of this service are the new growth engines of this digital age. Given this development, many global IT companies views SNS as the most powerful form of social media. Thus, they invest efforts to develop business models using SNS.2) This study verifies the impact of privacy exposure in SNS as a result of privacy invasion. This study examines the purpose of using the SNS and user's awareness of the significance of personal information, which are key factors that affect self-disclosure of personal information. This study utilizes theory of reasoned action (TRA) to provide a theoretical platform that describes the specific behavior and emotional response of individuals. This study presents a research model that considers negative attitude (negatude). In this model, self-disclosure in SNS is considered a TRA. TRA is a subjective norm, a behavioral intention, and a key variable of exposure behavior. A survey was conducted on college students at Y university in Seoul to empirically verify the research model. The students have experiences in using SNS. A total of 198 samples were collected. Path analysis was applied to analyze the relations of factors. The results of path analysis show the statistically insignificant impact of privacy invasion on negatude, subjective norm, behavioral intention, and exposure behavior. The impact of unrecognized privacy invasion was also considered insignificant. The impacts of intention to use SNS on negatude, subjective norm, behavioral intention, and exposure behavior was significant. A significant impact was also found for the significance of personal information on subjective norm, behavioral intention, and exposure behavior, whereas the impact on negatude was insignificant. The impact of subjective norm on behavioral intention was significant. Lastly, the impact of behavioral intention on exposure behavior was insignificant. These findings are significant because the study examined the process of self-disclosure by integrating psychological and social factors based on theoretical discussion.

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

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• Korean Journal of Applied Biomechanics
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• v.12 no.2
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• pp.279-293
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• 2002

In the study the subjects who 10 university golfers act, and the kinetic factors were analyzed by the ground reaction system. the conclusion are as follows. 1) In the golf putting swing, the ground reaction factors of sagital plane in aspect are showen that the left and right foot sufficient difference, in the level of p <.05. 2) In the golf putting swing, the ground reaction factors of frontal plane in aspect is showen that the left foot has no significant difference in AD BS in the level of p < .05. In success, IP, FS. It can show significant difference. In addition, the right foot is shown the success, There is significant difference. 3) In the golf putting swing, the ground reaction factors of the vertical plane in aspect are shown that the left foot has no significant difference in BS, FS in the level p < .05. In success, AD, IP. It can show significant difference. In addition, the right foot is shown the success, There is significant difference. 4) In the golf putting swing, the ground reaction factors of torque in aspect are shown that the left foot had no significant difference in BS in the level p < .05. In success, AD, IP, FS. It can show significant difference. In addition, the right foot has no significant difference in IP in the level p < .05. AD, BS, FS. There is significant difference. The summarized conclusions are as follows. The first that the power of sagital plane needs the motion which can get the good power change in the stabilized pose. The second is that the small motion can make good putting in stabilized pose. The third is that the body weight move to the direction of the ball. The fourth is that the putting which looks perfect oscillation is good motion.

• Atmosphere
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• v.12 no.4
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• pp.20-42
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• 2002

Based on the "Mid to Long Term Plan for Space Development", a project to launch COMeS (Communication, Oceanography, and Meteorological Satellite) into the geostationary orbit is undergoing. Accordingly, KMA (Korea Meteorological Administration) has defined the meteorological missions and prepared the user requirements to fulfill the missions. To make a realistic user requirements, we prepared a first draft based on the ideal meteorological products derivable from a geostationary platform and sent the RFI (request for information) to the sensor manufacturers. Based on the responses to the RFI and other considerations, we revised the user requirement to be a realistic plan for the 2008 launch of the satellite. This manuscript introduces the revised user requirements briefly. The major mission defined in the revised user requirement is the augmentation of the detection and prediction ability of the severe weather phenomena, especially around the Korean Peninsula. The required payload is an enhanced Imager, which includes the major observation channels of the current geostationary sounder. To derive the required meteorological products from the Imager, at least 12 channels are required with the optimum of 16 channels. The minimum 12 channels are 6 wavelength bands used for current geostationary satellite, and additional channels in two visible bands, a near infrared band, two water vapor bands and one ozone absorption band. From these enhanced channel observation, we are going to derive and utilize the information of water vapor, stability index, wind field, and analysis of special weather phenomena such as the yellow sand event in addition to the standard derived products from the current geostationary Imager data. For a better temporal coverage, the Imager is required to acquire the full disk data within 15 minutes and to have the rapid scan mode for the limited area coverage. The required thresholds of spatial resolutions are 1 km and 2 km for visible and infrared channels, respectively, while the target resolutions are 0.5 km and 1 km.

• Microbiology and Biotechnology Letters
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• v.46 no.3
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• pp.253-260
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• 2018

Beneficial microorganisms are widely used in the forestry, livestock, and, in particular, agricultural sectors to control soilborne diseases and promote plant growth. However, the industrial utilization of these microorganisms is very limited, mainly due to uncertainty concerning their ability to colonize and persist in soil. In this study, the survival of beneficial microorganisms in field soil microcosms was investigated for 13 days using quantitative PCR with B. subtilis group-specific primers. Bacterial community dynamics of the treated soils were analyzed using 16S ribosomal RNA (rRNA) gene amplicon sequencing on the Illumina MiSeq platform. The average 16S rRNA gene copy number per g dry soil of Bacillus spp. was $4.37{\times}10^6$ after treatment, which was 1,000 times higher than that of the control. The gene copy number was generally maintained for a week and was reduced thereafter, but remained 100 times higher than that of the control. Bacterial community analysis indicated that Acidobacteria ($26.3{\pm}0.9%$), Proteobacteria ($24.2{\pm}0.5%$), Chloroflexi ($11.1{\pm}0.4%$), and Actinobacteria ($9.7{\pm}2.5%$) were abundant phyla in both treated and non-treated soils. In the treated soils, the relative abundance of Actinobacteria was lower, whereas those of Bacteroidetes and Firmicutes were higher compared to the control. Differences in total relative abundances of operational taxonomic units belonging to several genera were observed between the treated and non-treated soils, suggesting that inoculation of soil with the Bacillus strains influenced the relative abundances of certain groups of bacteria and, therefore, the dynamics of resident bacterial communities. These changes in resident soil bacterial communities in response to inoculation of soil with beneficial Bacillus spp. provide important information for the use of beneficial microorganisms in soil for sustainable agriculture.