• Korean Security Journal
• /
• no.13
• /
• pp.471-485
• /
• 2007

This study is performed to find out what type instructions are produced to players by coaches and what effects are resulted in cooperative spirit of the concerned group. Furthermore the study has its aims at advancing instructors' skills by using finding of it. The study used 'SPSS 11.0 FOR WINDOW - Statistical Package' to analyze the collected samples and dealt with data of 174 individuals. Statistical analysis of the research for hypothesis verification was about frequency, trust level, mutual relationship, variables, and T-verification. The meaningful level for any result was ranged within 95%(p< .05), 99%(p<.01). The finding are as follows. Effects on pleasure, one of elements of team spirits taken by instructor's training style are analyzed as follows. It was proved to be meaningful in relation with a series of activities like training, democratic, social, compensatory aspects and showed also considerable relation with power based behaviors. That says, players are found to enjoy high pleasure when social and bureaucratic behaviors of instructors are very energetic. In addition to that, training, democratic, and compensatory activities didn't show any meaningful effect. Team spirit was found to play a main role between instructor's behaviors and training, democratic, social rewarding activities. Democratic and social acts influence on team spirit. Looking into the detailed aspects, team spirit was resulted very high in the individuals with low democratic mind and was shown high group spirit by groups with high sociable activities. Teamworks was found to be affected by relation between instructor's acts and training, democratic, social and compensatory aspects and it showed meaningful relations with training, social, bureaucratic behaviors. Low degree of training and bureaucratic activities are found to prefer for power team spirit, and high social activities led a strong teamworks. Group binding spirit was influenced by training, democratic, social compensatory, bureaucratic behaviors and it showed to give effects on democratic, social, and bureaucratic activities of instructors. Low degree of democratic and bureaucratic behaviors are found to produce strong team spirit. In contrast with that, strong social activities was found to be motive of powerful team spirit. Value of team spirit was found to play a main role between instructor's behaviors and training, democratic, social, rewarding activities. It didn't show any meaningful effect on behavior of instructors.

• Korean Journal of Food Science and Technology
• /
• v.37 no.3
• /
• pp.484-491
• /
• 2005

Quantitative microbial risk assessment (QMRA) analyzes potential hazard of microorganisms on public health and offers structured approach to assess risks associated with microorganisms in foods. This paper addresses specific risk management questions associated with Staphylococcus aureus in kimbab and improvement and dissemination of QMRA methodology, QMRA model was developed by constructing four nodes from retail to table pathway. Predictive microbial growth model and survey data were combined with probabilistic modeling to simulate levels of S. aureus in kimbab at time of consumption, Due to lack of dose-response models, final level of S. aureus in kimbeb was used as proxy for potential hazard level, based on which possibility of contamination over this level and consumption level of S. aureus through kimbab were estimated as 30.7% and 3.67 log cfu/g, respectively. Regression sensitivity results showed time-temperature during storage at selling was the most significant factor. These results suggested temperature control under $10^{\circ}C$ was critical control point for kimbab production to prevent growth of S. aureus and showed QMRA was useful for evaluation of factors influencing potential risk and could be applied directly to risk management.

• The Korean Society of Law and Medicine
• /
• v.23 no.4
• /
• pp.29-74
• /
• 2022

As social disasters occur under the Disaster Management Act, which can damage the people's "life, body, and property" due to the rapid spread and spread of unexpected COVID-19 infectious diseases in 2020, information collected through inspection and reporting of infectious disease pathogens (Article 11), epidemiological investigation (Article 18), epidemiological investigation for vaccination (Article 29), artificial technology, and prevention policy Decision), (3) It was used as an important basis for decision-making in the context of an infectious disease crisis, such as promoting vaccination and understanding the current status of damage. In addition, medical policy decisions using infectious disease data contribute to quarantine policy decisions, information provision, drug development, and research technology development, and interest in the legal scope and limitations of using infectious disease data has increased worldwide. The use of infectious disease data can be classified for the purpose of spreading and blocking infectious diseases, prevention, management, and treatment of infectious diseases, and the use of information will be more widely made in the context of an infectious disease crisis. In particular, as the serious stage of the Disaster Management Act continues, the processing of personal identification information and sensitive information becomes an important issue. Information on "medical records, vaccination drugs, vaccination, underlying diseases, health rankings, long-term care recognition grades, pregnancy, etc." needs to be interpreted. In the case of "prevention, management, and treatment of infectious diseases", it is difficult to clearly define the concept of medical practicesThe types of actions are judged based on "legislative purposes, academic principles, expertise, and social norms," but the balance of legal interests should be based on the need for data use in quarantine policies and urgent judgment in public health crises. Specifically, the speed and degree of transmission of infectious diseases in a crisis, whether the purpose can be achieved without processing sensitive information, whether it unfairly violates the interests of third parties or information subjects, and the effectiveness of introducing quarantine policies through processing sensitive information can be used as major evaluation factors. On the other hand, the collection, provision, and use of infectious disease data for research purposes will be used through pseudonym processing under the Personal Information Protection Act, consent under the Bioethics Act and deliberation by the Institutional Bioethics Committee, and data provision deliberation committee. Therefore, the use of research purposes is recognized as long as procedural validity is secured as it is reviewed by the pseudonym processing and data review committee, the consent of the information subject, and the institutional bioethics review committee. However, the burden on research managers should be reduced by clarifying the pseudonymization or anonymization procedures, the introduction or consent procedures of the comprehensive consent system and the opt-out system should be clearly prepared, and the procedure for re-identifying or securing security that may arise from technological development should be clearly defined.

• 대한공업교육학회지
• /
• v.34 no.2
• /
• pp.103-127
• /
• 2009

The purpose of this study is to offer the basic data regarding the problems of the employment training activities and their solutions by way of the research and analysis of the awareness of career development of university students and their preparation for employment opportunities. The results of the study are as follows. First, it is necessary that the students themselves make plans for future jobs and their preparation for them, from the start of their university work. This includes taking employment preparation courses as liberal arts requirements. It also needs to have a systematic association with some organizations such as employment preparation centers. Second, it is necessary that the career portfolios of university students be accepted as materials for objective evaluation so that the companies use them at the time of hiring new employees. If those materials are stored and managed in a database even after their graduation, they will be the strong foundation for the competitive power of the university.Third, it is necessary that university students establish the orientation of employment training in advance, according to their personal and disciplinary possibilities by diagnosing the level of basic employment ability they possess and that they find out the appropriate programs, both personal and disciplinary, to enforce the abilities they need to develop further. Accordingly, it is necessary to have an evaluation system in order to assess student's basic employment abilities, so as to increase the degree of their employment preparation and its support strategy based on the evaluation. Fourth, in the higher education level, university students' lower awareness (M=2.86) of their discipline satisfaction, their major selection, and the university's employment opportunity service shows that it is necessary that there be close connection between learning and work. For short-term purpose, the quantitative and qualitative evaluation must be preceded about the various employment training programs and self-development programs offered by the university. From the long-term perspective, it is urgently necessary that the university ensure the human resources development experts for the purpose of diagnosing employment services within the university.

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