• Nutrition Research and Practice
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• v.11 no.4
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• pp.340-346
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• 2017

BACKGROUND/OBJECTIVES: Despite the importance of consuming sufficient amounts of vegetables, daily vegetable intake among adolescents in Korea is lower than the current dietary recommendation. The objective of this study was to examine determinants affecting vegetable preference in order to suggest a stage-tailored education strategy that can promote vegetable consumption in adolescents. SUBJECTS/METHODS: Adolescents (n = 400, aged 16-17 years) from two high schools participated in a cross-sectional study. Survey variables were vegetable preference, the social cognitive theory (SCT) and stages of change (SOC) constructs. Based on vegetable preference, subjects were classified into two groups: a low-preference group (LPG) and a high-preference group (HPG). SOC was subdivided into pre-action and action/maintenance stages. To compare SCT components and SOC related to vegetable preference, chi-squared and t-tests, along with stepwise multiple-regression analysis, were applied. RESULTS: In the LPG, a similar number of subjects were classified into each stage. Significant differences in self-efficacy, affective attitudes, and vegetable accessibility at home and school were detected among the stages. Subjects in the HPG were mainly at the maintenance stage (81%), and there were significant differences among the stages regarding self-efficacy, affective attitudes, and parenting practice. In the predictions of vegetable preference, self-efficacy and parenting practice had a significant effect in the "pre-action" stage. In the action/maintenance stage, outcome expectation, affective attitudes, and vegetable accessibility at school had significant predictive value. In predicting the vegetable preference for all subjects, 42.8% of the predictive variance was accounted for by affective attitudes, self-efficacy, and vegetable accessibility at school. CONCLUSION: The study revealed that different determinants affect adolescent vegetable preference in each stage. Self-efficacy and affective attitudes are important determinants affecting vegetable preference. Additionally, school-based nutrition intervention that focuses on enhancing affective attitudes, self-efficacy, and vegetable exposure may constitute an effective education strategy for promoting vegetable consumption among adolescents.

• The Journal of Internal Korean Medicine
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• v.40 no.3
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• pp.343-355
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• 2019

Objectives: Traditional oriental medicine is used in treating breast cancer-related lymphedema to alleviate symptoms. Upper limb lymphedema is a symptom that is frequently observed in patients with breast cancer, and it impairs their quality of life. This systematic review aimed to summarize the current available evidence to evaluate the effect of traditional oriental medicine on upper limb lymphedema in breast cancer patients. Methods: The review evaluated randomized controlled trials (RCTs) measuring the effect of herbal medicine, acupuncture, and moxibustion on upper limb lymphedema in breast cancer patients within four electronic databases. The Cochrane risk of bias (ROB) tool was used to assess the quality of the RCTs. Results: In total, 23 RCTs met the inclusion criteria. Among them, 22 studies reported that the rate of severity of lymphedema improved after treatment in the traditional treatment group using herbal medicine, acupuncture, or moxibustion better than in the conventional medicine group. The methodological quality of the RCTs was insufficient with an unclear and high ROB. Conclusions: Traditional oriental medicine may have a potential to improve lymphedema in patients with breast cancer. To confirm the clinical recommendation, further research with a rigorous study design is required to support the effects of traditional oriental medicine.

• Nutrition Research and Practice
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• v.15 no.1
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• pp.38-53
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• 2021

BACKGROUND/OBJECTIVES: Abdominal obesity is associated with metabolic disorders, and, in recent years, its prevalence in Korea has continuously increased. The change of lifestyle, particularly diet, is critical for the reduction of abdominal obesity. This study evaluated the effectiveness of an intervention focused on dietary self-efficacy and behaviors on the improvement of abdominal obesity. SUBJECTS/METHODS: Abdominally obese adults with additional cardiovascular risk factors were recruited through 16 medical facilities in South Korea from the year 2013 to 2014. The participants were randomly divided into 2 groups: an intensive intervention group (IG) that received a multi-component intervention to reduce abdominal obesity, by mainly focusing on dietary attitude and dietary behavior change, and a minimal information intervention group (MG) that received a brief explanation of health status and a simple recommendation for a lifestyle change. The interventions were provided for 6 mon, and health examinations were conducted at baseline, 3-, 6-, and 12-mon follow-ups. A path analysis was conducted to identify the process governing the changes in abdominal obesity. RESULTS: The IG showed an improvement in self-efficacy for eating control and diet quality at 6-mon follow-up. Abdominal obesity improved in both groups. Waist circumference was observed to be decreased through the path of "improved self-efficacy for eating control in food availability-eating restriction-improved dietary quality" in IG. Most changes in follow-ups were not significantly different between two groups. CONCLUSIONS: The intensive program targeting the modification of dietary behavior influenced management of abdominal obesity, and the effect occurred through a step-by-step process of change in attitude and behavior. Generally, improvements were also seen in the MG, which supports the necessity of regular health check-ups and brief consultation. The results can be used for further development and implementation of more successful interventions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.1-14
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• 2023

As markets and industries continue to evolve rapidly, technology opportunity discovery (TOD) has become critical to a firm's survival. From a common consensus that TOD based on a firm's capabilities is a valuable method for small and medium-sized enterprises (SMEs) and reduces the risk of failure in technology development, studies for TOD based on a firm's capabilities have been actively conducted. However, previous studies mainly focused on a firm's technological capabilities and rarely on business capabilities. Since discovered technologies can create market value when utilized in a firm's business, a firm's current business capabilities should be considered in discovering technology opportunities. In this context, this study proposes a TOD method that considers both a firm's business and technological capabilities. To this end, this study uses patent data, which represents the firm's technological capabilities, and trademark data, which represents the firm's business capabilities. The proposed method comprises four steps: 1) Constructing firm technology and business capability matrices using patent classification codes and trademark similarity group codes; 2) Transforming the capability matrices to preference matrices using the fuzzy function; 3) Identifying a target firm's candidate technology opportunities using the collaborative filtering algorithm; 4) Recommending technology opportunities using a portfolio map constructed based on technology similarity and applicability indices. A case study is conducted on a security firm to determine the validity of the proposed method. The proposed method can assist SMEs that face resource constraints in identifying technology opportunities. Further, it can be used by firms that do not possess patents since the proposed method uncovers technology opportunities based on business capabilities.

• Journal of Trauma and Injury
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• v.19 no.1
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• pp.47-53
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• 2006

Purpose: Recently, the American Heart Association recommended that routine cervical spine protection in submerged patients was not necessary, except in high-energy injury situations. However, until now, this recommendation has few supportive studies and literatures. This retrospective study was performed to demonstrate the risk of cervical spine injury in patients who had been submerged in a river. Methods: Seventy-nine submerged patients who visited St. Mary's Hospital between January 2000 and December 2005 were included in this retrospective study. We investigated and analyzed the victim's age, sex, activity on submersion, mental status and level of severity at admission, prognosis at discharge, associated injuries, and risk group by using the medical records and cervical spine lateral images. According to the activity on submersion, victims were classified into three groups: high risk, low risk, and unknown risk. The reports of radiologic studies were classified into unstable fracture, stable fracture, sprain, degenerative change, and normal. Results: The patients' mean age was 36.8 yrs, and 54% were males. Of the 79 patients, adult and adolescent populations (80%) were dominant. Jumping from a high bridge (48%) was the most common activity on submersion and accounted for 52% of the high-risk group. The Glasgow coma scale at admission and the cerebral performance scale at discharge showed bimodal patterns. The results of the radiologic studies showed one stable fracture, one suspicious stable fracture, and 18 sprains. The incidence of cervical spine fracture in submerged patients was 2.5% in our study. The incidence of cervical spine injury was higher in the high-risk group than it was in the low-risk group, especially in the jumping-from-high-bridge subgroup; however this observation was not statistically significant. No other factors had any significant effect on the incidence of cervical spine injury. Conclusion: Our study showed that even submerged patients in the high risk group had a low incidence of cervical spine fracture and that the prognosis of a patient did not seem to be influenced by the cervical spine fracture itself.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1703-1710
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• 2011

Objectives: Using a PHR(Personal Health Record) is very important for the management of chronic disease and health. The young age group in this study was asked to pretend that they were members in the old age group (adult group here after) in order to investigate the recognition level differences in such conditions. Methods: We conducted face to face surveys with two age groups. 131 Adults and 398 University students, from May 11 to 22 in 2009. The questionnaires were composed of 18 items. Results & Conclusion: Adults replied more favorably than University students in using PHR willingness(University group 3.3score, Adults 3.7score) and recommendation(university 3.1score, Adults 3.8score). Adults liked paper PHR (63.2%), whereas, University students favored electronic Personal Health Record (71.1%). University students were highly concerned about disclosing the information(4.5score) of their PHR. They thought the appropriate time of education for PHR is high school or University degree. Therefore, in the future, it is vital that a PHR for young age group should be begun in early education and that an ePHR should be developed.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of Gifted/Talented Education
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• v.23 no.2
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• pp.257-273
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• 2013

The purpose of this study was to explore a more useful identification method by comparing diverse selection approaches for the gifted education programs at the each elementary school. Diverse selection methods examined in the study include 'written examinations', 'mixed evaluation', 'achievement test scores', and 'self-recommendation'. For the study, each identification group's gifted students' characteristics, such as intelligence, creativity, motivation and self-regulated learning strategies, were compared. The subjects of the study were a total of 594 gifted and normal students. The results of this study were as follows: First, there were no statistically significant differences between students in each gifted education class and gifted students who belong to the regional gifted education programs which are considered higher level of gifted education programs. While, there were statistically significant differences between two groups of gifted students and general students in all aspects examined, such as intelligence, creativity, motivation and learning strategies. In addition and most importantly, diverse identification method utilized in each school showed differences in gifted students' characteristics. Especially, students who were selected through the self-recommendation showed significantly lower intelligence, creativity, motivation and learning strategies. The implications of the study related to the identification and education for the gifted at each elementary school were discussed in depth.