• Journal of dental hygiene science
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• v.17 no.6
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• pp.463-471
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• 2017

The purpose of this study was to examine the causes of career interruptions among dental hygienists, institutional measures required for their long service and ways of creating a stable employment culture for them in determine how to resolve labor shortage, create stable jobs, and step up the reemployment of idle manpower. In addition, the following suggestions are made for the establishment of a stable employment culture for dental hygienists by analyzing related literature, research materials, and information such as forums for establishing appropriate jobs for female dental workers. First, a system should be set up to prevent career interruption among dental hygienists. The work environment should be improved to prevent career breaks, and the wages, working hours, and working style should be efficiently structured to maintain the tenure of employees. Second, a plan should be devised to make use of idle manpower, and a variety of necessary programs should be developed. With respect to regular working hours, the time conversion system should be used, which reduces the amount of time one would want to work while receiving a national subsidy. Third, dental hygienists working in different occupations for marriage, childbirth, childcare, school and personal hygiene should make a way to return to the dental system immediately when they want. Fourth, the government should take institutional measures and offer down-to-earth support and benefits for women consideration their social characteristics to guarantee a balance between work and childcare.

• Journal of dental hygiene science
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• v.15 no.2
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• pp.106-112
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• 2015

This study was conducted to provide with baseline data with the purpose of increasing the values of medical services. Self-administered questionnaire survey was conducted on 236 patients at a dental clinic follow-up visit in dentist clinic Gyeongnam area from June 2013 to August 2013. All statistical analyses were performed using SPSS. The motivation visiting the dental clinic is that the first one is introduction from the family and friends, the second is accessibility, the third is conspicuity and the last one is awareness of the dentist. The main variables in the process of treatment are service system, kindness, satisfaction of service, efficient of re-call system. The relief of discomfort at revisit show the highest score in the process of implant treatment and intention of revisit hereafter do in the prostheses process. In the correlation between main variables, service system and relief of discomfort at revisit (r=0.440, p<0.001), kindness and satisfaction of medical service (r=0.675, p<0.001), revisit hereafter and satisfaction of service (r=0.387, p<0.001) and efficiency of re-call system and revisit showed the highest correlation. The influence on satisfaction of dental service show meaningful level in kindness (p<0.001) and efficiency of re-call system (p<0.05). The intention of revisit is affected meaningfully by relief of uncomfort (p<0.05), service system (p<0.05), kindness (p<0.01) and efficiency of re-call system (p<0.01). In summary, the personal network of patients is most important variable at intention for revisit of dental clinic. As satisfaction of kindness and efficiency of re-call system is higher, satisfaction of medical service and intention for revisit are shown higher. Therefore further research for improvement of satisfaction for medical service and of intention of revisit at the dental clinic should be carried out.

• Journal of Life Science
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• v.30 no.11
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• pp.947-955
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• 2020

The foot-and-mouth disease virus (FMDV), a member of the Aphthovirus genus in the Picornaviridae family, affects wild and domesticated ruminants and pigs. During replication of the FMDV RNA (ribonucleic acid) genome, FMDV-encoding RNA polymerase 3D acts in a highly location-specific manner. This suggests that specific RNA structures recognized by 3D polymerase within non-coding regions of the FMDV genome assist with binding during replication. One such region is the cis-acting replication element (CRE), which functions as a template for RNA replication. The FMDV CRE adopts a stem-loop conformation with an extended duplex stem, supporting a novel 15-17 nucleotide loop that derives stability from base-stacking interactions, with the exact RNA nucleotide sequence of the CRE producing different RNA secondary structures. Here, we show that CRE sequences of FMDVs isolated in Korea from 2010 to 2017 exhibit A and O genotypes. Interestingly, variations in the RNA secondary structure of the Korean FMDVs are consistent with the phylogenetic relationships between these viruses and reveal the specificity of FMDV infections for particular host species. Therefore, we conclude that each genetic clade of Korean FMDV is characterized by a unique functional CRE and that the evolutionary success of new genetic lineages may be associated with the invention of a novel CRE motif. Therefore, we propose that the specific RNA structure of a CRE is an additional criterion for FMDV classification dependent on the host species. These findings will help correctly analyze CRE sequences and indicate the specificity of host species for future FMDV epidemics.

• Information Systems Review
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• v.6 no.2
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• pp.137-159
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• 2004

The next generation information appliances are those that can be connected with other appliances through a wired or wireless network in order to make it possible for them to transmit and receive data between them and to be remotely controlled from inside or outside of the home. Many electronic companies have aggressively invested in developing new information appliances to take the initiative in upcoming home networking era. They require systematic methods for developing new information appliances and sharing the knowledge acquired from the methods. This paper stored the knowledge acquired from developing the information appliances and developed a knowledge management system that supports the companies to use the knowledge and develop their own information appliances. In order to acquire the knowledge, this paper applied two methods for User-Centered Design in stead of using the general ones for knowledge acquisition. This paper suggested new product ideas by analyzing and observing user actions and stored the knowledge in knowledge bases, which included Knowledge from Analyzing User Actions and Knowledge from Observing User Actions. Seven new product ideas, suggested from the User-Centered Design, were made into design mockups and their videos were produced to show the real situations where they would be used in home of the future, which were stored in the knowledge base of Knowledge from Producing New Emotive Life Videos. Finally, data on present development states of future homes in Europe and Japan and newspapers articles from domestic newspapers were collected and stored in the knowledge base of Knowledge from Surveying Technology Developments. This paper developed a web-based knowledge management system that supports the companies to use the acquired knowledge. Knowledge users can get the knowledge required for developing new information appliances and suggest their own product ideas by using the knowledge management system. This will make the results from this research not confined to a case study of product development but extended to playing a role of facilitating the development of the next generation information appliances.

• Journal of the Society of Disaster Information
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• v.11 no.4
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• pp.556-569
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• 2015

With about 75% of the population of Korea criticizing the government's disaster policy and a failure to respond to large-scale emergency like the Sewol ferry sinking means that there is a deep distrust in the government. In order to prevent dreadful disasters such as the Sewol ferry sinking, it is important to secure a prime time with respect to disaster safety. Improving crisis management skills and managerial role of police officers who are in close proximity to the people is necessary for the success of disaster management. With disaster management as one of the most essential missions of the police, as a part of a national crisis management, a step by step strengthening of the disaster safety management system of the police is necessary, as below. First, at the prevention phase, law enforcement officers were not injected into for profit large-scale assemblies or events, but in the future the involvement, injection should be based on the level of potential risk, rather than profitability. In the past and now, the priortiy was the priority was on traffic flow, traffic communication, however, the paradigm of traffic policy should be changed to a safety-centered policy. To prevent large-scale accidents, police investigators should root out improper routines and illegal construction subcontracting. The police (intelligence) should strengthen efforts to collect intelligence under the subject of "safety". Second, with respect to the preparatory phase, on a survey of police officers, the result showed that 72% of police officers responded that safety management was not related to the job descriptions of the police. This, along with other results, shows that the awareness of disaster safety must be adopted by, or rather changed in the police urgently. The training in disaster safety education should be strengthened. A network of experts (private, administrative, and police) in safety management should be established to take advantage of private resources with regard to crisis situtions. Third, with respect to the response phase, for rapid first responses to occur, a unified communication network should be established, and a real-time video information network should be adopted by the police and installed in the police situation room. Fourth, during the recovery phase, recovery teams should be injected, added and operated to minimize secondary damage.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• KOMUNHWA
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• no.70
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• pp.163-178
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• 2007

Among many functions of the museum, the educational function is being emphasized. Museums should actively expand their scope of action through special exhibits or various educational activities. It is through exhibits that museums prove their reason for existence and raise calls for support. Through such activities, university museums should demonstrate the role and reason of existence of a museum inside a university to the members of the university. They should carry out various activities to raise the presence of the museum in the university. That may be the way for university museums, which face many difficulties in reality, to find their breakthrough. Especially in a situation of a low-budget, holding overseas exhibition may be a good opportunity to display the excellence of the Korean culture and, at the same time, publicize the school and the museum. Also, through such overseas exhibition, benchmarking of facilities and activities of advanced museums can be made and networks can be set up with museums around the world. This paper introduces the planning and progressing procedures of exhibitions abroad through the experiences of the Korea University Museum. I sincerely hope that it will contribute to the hereafter development of university museums. Aside from such overseas exhibition, the Korea Foundation has been supporting prestigious foreign museums to establish a Korean exhibition room in order to form a perpetual space to systematically introduce Korean culture and art to foreign audience. Thus far, the National Museum of Korea has stood in the forefront in lending relics to Korean exhibition rooms abroad. I believe it would be a worthwhile activity for the Korea Association of University Museums, which has approximately 100 institutions as its members, to actively participate in the establishment of Korean exhibitions rooms of foreign museums for the development of university museums. Participating in the establishment of Korean exhibition rooms is attractive as it will lead to a constant exchange with foreign museums instead of a one-time exchange. Localization and globalization, which became a big issue about ten years ago, is reality from which university museums cannot be free. In such reality, it is time for university museums to look back on whether they are ready to make their way forward in this era called the century of culture.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.81-96
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• 2013

Video information retrieval has become a very important issue because of the explosive increase in video data from Web content development. Meanwhile, content-based video analysis using visual features has been the main source for video information retrieval and browsing. Content in video can be represented with content-based analysis techniques, which can extract various features from audio-visual data such as frames, shots, colors, texture, or shape. Moreover, similarity between videos can be measured through content-based analysis. However, a movie that is one of typical types of video data is organized by story as well as audio-visual data. This causes a semantic gap between significant information recognized by people and information resulting from content-based analysis, when content-based video analysis using only audio-visual data of low level is applied to information retrieval of movie. The reason for this semantic gap is that the story line for a movie is high level information, with relationships in the content that changes as the movie progresses. Information retrieval related to the story line of a movie cannot be executed by only content-based analysis techniques. A formal model is needed, which can determine relationships among movie contents, or track meaning changes, in order to accurately retrieve the story information. Recently, story-based video analysis techniques have emerged using a social network concept for story information retrieval. These approaches represent a story by using the relationships between characters in a movie, but these approaches have problems. First, they do not express dynamic changes in relationships between characters according to story development. Second, they miss profound information, such as emotions indicating the identities and psychological states of the characters. Emotion is essential to understanding a character's motivation, conflict, and resolution. Third, they do not take account of events and background that contribute to the story. As a result, this paper reviews the importance and weaknesses of previous video analysis methods ranging from content-based approaches to story analysis based on social network. Also, we suggest necessary elements, such as character, background, and events, based on narrative structures introduced in the literature. We extract characters' emotional words from the script of the movie Pretty Woman by using the hierarchical attribute of WordNet, which is an extensive English thesaurus. WordNet offers relationships between words (e.g., synonyms, hypernyms, hyponyms, antonyms). We present a method to visualize the emotional pattern of a character over time. Second, a character's inner nature must be predetermined in order to model a character arc that can depict the character's growth and development. To this end, we analyze the amount of the character's dialogue in the script and track the character's inner nature using social network concepts, such as in-degree (incoming links) and out-degree (outgoing links). Additionally, we propose a method that can track a character's inner nature by tracing indices such as degree, in-degree, and out-degree of the character network in a movie through its progression. Finally, the spatial background where characters meet and where events take place is an important element in the story. We take advantage of the movie script to extracting significant spatial background and suggest a scene map describing spatial arrangements and distances in the movie. Important places where main characters first meet or where they stay during long periods of time can be extracted through this scene map. In view of the aforementioned three elements (character, event, background), we extract a variety of information related to the story and evaluate the performance of the proposed method. We can track story information extracted over time and detect a change in the character's emotion or inner nature, spatial movement, and conflicts and resolutions in the story.