• Journal of Nutrition and Health
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• v.54 no.5
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• pp.459-473
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• 2021

Purpose: Although numerous systematic reviews or meta-analysis have reported the hypotensive effects of garlic, the application of these results in the area of functional food is limited. This is because the trials used various garlic preparations and patients with differing hypertensive intensities. To validate the use of garlic powder as a blood pressure lowering functional food, we performed the current meta-analysis, focusing on the study of prehypertensive subjects. Methods: Literature search was carried out using various database up to July 2020, including PubMed, Cochrane, ScienceDirect and Korean studies Information Service System, and each study was screened by pre-stated inclusion/exclusion criteria. We identified nine trials that met the eligibility, of which two studies with moderate or high risk of bias were excluded. Results: Meta-analysis of the seven studies revealed that an intake of garlic powder significantly lowered the systolic blood pressure (SBP) and diastolic blood pressure (DBP) by -6.0 mmHg (95% confidence interval [CI], -11.2, -0.8; p = 0.025) and -2.7 mmHg (95% CI, -5.3, -0.1; p = 0.046), respectively. Shapes of the funnel plot for both SBP and DBP seemed symmetrical, and the Egger's regression revealed no publication bias. Moreover, duration of the intervention period was inversely associated with the pooled effects of garlic powder on SBP (p = 0.019) and DBP (p = 0.019), and this result was supported by the subgroup-analysis. The daily dose of garlic powder, baseline value of each biomarker, and subject number, did not moderate the effects on SBP and DBP. Conclusion: Results of the present meta-analysis indicate that garlic powder supplements are superior to placebo for improving the BP in prehypertensive individuals.

• Korean Journal of Culture and Social Issue
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• v.8 no.1
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• pp.165-187
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• 2002

This study examines Korean representation of the biotechnology and psychological factors which can influence lay people's perception and attitude about biotechnology. Korean college students(N=433) and lay adults(N=90) whom had college education participated in the study. Participants of the study 1 were asked to list words which comes to mind when associate with the biotechnology in broad sense, and several specific applications in health, medicines, agriculture and research. Participants of the study 2 were asked to list possible benefits and costs of biotechnology and their specific applications. In study 3, Participants responded the questionnaires about perceptions and attitudes of biotechnology. Korean people associated the biotechnology with its costs or risks and benefits. Korean college students mainly got the informations of the biotechnology from TV, newspapers, or internet. They trusted the scientist group and NGO group on their judgements about the assessment of risk and benefit of the biotechnology. College students showed the positive attitude with the applications in medicines and negative attitude with the applications in agriculture and public using of individual's genetic information. The radicalism, sensitivity in behavioral activation system, and trust/cynicism were to be found as a significant influencing factor for interest/knowledge and behavioral intention in related with biotechnology. Finally, more extensive knowledge of biotechnology did not lead to greater acceptance of it.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.599-609
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• 2024

The government has introduced the Regional Innovation System (RIS) to establish regional innovation platforms, leading to the creation of the Bio-PRIDE shared university in the Chungbuk region, centered around the bio-health industry. The purpose of this study is to analyze the educational service quality of the Bio-PRIDE shared university through a qualitative approach. To achieve this, in-depth exploration of the experiences of students participating in the Bio-PRIDE shared university was conducted using Focus Group Interviews (FGI) and telephone interviews. The study identified three main findings: 1) Motivations for student participation included recommendations and support from professors, economic benefits and scholarships, the flexibility and convenience of remote learning, and the appeal of new learning experiences and challenges. 2) Experiences during participation revealed themes such as the effectiveness and challenges of remote classes, the value of providing up-to-date technology and content, student engagement and commitment, and communication and support from faculty. 3) The impact of participation after completing the program highlighted the importance of innovative learning experiences and expanded choices, the significance of information accessibility and learning support, the need for improved promotion and awareness of the shared university, and the necessity for administrative support and communication. In conclusion, the Bio-PRIDE shared university has successfully implemented educational innovation, offering students new learning experiences and contributing to the broadening of their academic perspectives. This study will serve as a foundational resource for improving the educational service quality and ensuring the successful operation of the Bio-PRIDE shared university in the future.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.2
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• pp.55-65
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• 2024

Background : Tuberculosis (TB) remains a significant public health issue worldwide, particularly among healthcare workers (HCWs) at high risk of exposure. Latent tuberculosis infection (LTBI) is a state where individuals are infected with Mycobacterium tuberculosis but do not show clinical symptoms. Early detection and treatment of LTBI are crucial to prevent progression to active TB. This study aimed to investigate the prevalence and risk factors of LTBI among Korean Medicine (KM) workers in Seoul, South Korea. Methods : This study analyzed 368 adults aged 19 and over working in Korean medicine institutions in Seoul by September 2023. Participants underwent a tuberculin skin test (TST) and completed a survey collecting demographic information, occupation, work duration, smoking status, BCG vaccination, TB history, and comorbidities. Data were analyzed using descriptive statistics and chi-square tests, with significance set at p < 0.05. Results : The average age of participants was 43.1 years, with an LTBI prevalence rate of 3.5%. Significant risk factors included age and history of TB, Older age and a history of TB were associated with higher LTBI positivity. Conclusion : The study identified the prevalence and risk factors of LTBI among Korean medicine workers in Seoul. The findings highlight the need for targeted LTBI screening and preventive measures, especially for older workers and those with a history of TB. While the prevalence was lower than in other healthcare settings, the results emphasize the importance of regular LTBI testing and prevention education for KM workers. Future large-scale studies are needed to confirm these findings and further understand the relationship between various risk factors and LTBI in KM settings.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.7
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• pp.1056-1063
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• 2010

The purpose of this study is to investigate current nutrition labeling status, levels of recognition and patterns of consumption of domestically processed meat products. The survey results show that 47.4% of products (81 out of 171) were labeled with nutrition information. Where general product labeling and nutrition labeling were provided, it was read by 84.9% and 66.8% of the survey subjects, respectively. The most common reasons for not reading product labeling were 'hard to understand it' (46.2%) and 'not concerned' (30.8%). This was attributed to respondents finding it 'useless' (39.3%) and 'hard to understand the nutrition contents' (32.8%). As for the positive effect of enforcing a nutrition labeling system, 62% of respondents affirmed 'ease of selecting products which are good for health'. The reading of general product labeling showed a significant positive correlation (p<0.01) with the reading of nutrition labeling. The amount the nutrition labeling was read showed a negative correlation (p<0.05) with comprehension of the information on the nutrition labeling contained. Therefore, providing more information on the nutrition labeling for the consumers of processed meat products and also educating them more comprehensively about the nutrition, which would ultimately help them improve their dietary life, is needed.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• The Journal of Korean Academy of Sensory Integration
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• v.19 no.1
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• pp.69-82
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• 2021

Objective : This recent work intended to provide basic information for researchers and practitioners related to occupational therapy about Developmental Coordination Disorder (DCD) in South Korea. The previous research of screening DCD and the effects of intervention programs were reviewed. Methods : Peer-reviewed papers relating to DCD and published in Korea from January 1990 to December 2020 were systematically reviewed. The search terms "developmental coordination disorder," "development coordination," and "developmental coordination" were used to identify previous Korean research in this area from three representation database, the Research Information Sharing Service, Korean Studies Information Service System, and Google Scholar. We found a total of 4,878 articles identified through the three search engines and selected seventeen articles for analysis after removing those that corresponded to the overlapping or exclusion criteria. We adopted "the conceptual model" to analyze the selected articles about DCD assessment and intervention. Results : We found that twelve of the 17 studies showed the qualitative level of Level 2 using non-randomized approach between the two groups. The Movement Assessment Battery for Children and its second edition were the most frequently used tools in assessing children for DCD. Among the intervention studies, the eight articles (47%) were adopted a dynamic systems approach; a normative functional skill framework and cognitive neuroscience were each used in 18% of the pieces; and 11% of the articles were applied neurodevelopmental theory. Only one article was used a combination approach of normative functional skill and general abilities. These papers were mainly focused on the movement characteristics of children with DCD and the intervention effect of exercise or sports programs. Conclusion : Most of the reviewed studies investigated the movement characteristics of DCD or explore the effectiveness of particular intervention programs. In the future, it would be useful to investigate the feasibility of different assessment tools and to establish the effectiveness of various interventions used in rehabilitation for better motor performance in children with DCD.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.287-316
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• 2023

As global energy crisis and the COVID-19 pandemic have emerged as social issues, there is a growing demand for companies to move away from profit-centric business models and embrace sustainable management that balances environmental, social, and governance (ESG) factors. ESG activities of companies vary across industries, and industry-specific weights are applied in ESG evaluations. Therefore, it is important to develop strategic management approaches that reflect the characteristics of each industry and the importance of each ESG factor. Additionally, with the stance of strengthened focus on ESG disclosures, specific guidelines are needed to identify and report on sustainable management activities of domestic companies. To understand corporate sustainability strategies, analyzing ESG reports and news articles by industry can help identify strategic characteristics in specific industries. However, each company has its own unique strategies and report structures, making it difficult to grasp detailed trends or action items. In our study, we analyzed ESG reports (2019-2021) and news articles (2019-2022) of six companies in the 'Finance,' 'Manufacturing,' and 'IT' sectors to examine the sustainability strategies of leading domestic ESG companies. Text mining techniques such as keyword frequency analysis and topic modeling were applied to identify industry-specific, ESG element-specific management strategies and issues. The analysis revealed that in the 'Finance' sector, customer-centric management strategies and efforts to promote an inclusive culture within and outside the company were prominent. Strategies addressing climate change, such as carbon neutrality and expanding green finance, were also emphasized. In the 'Manufacturing' sector, the focus was on creating sustainable communities through occupational health and safety issues, sustainable supply chain management, low-carbon technology development, and eco-friendly investments to achieve carbon neutrality. In the 'IT' sector, there was a tendency to focus on technological innovation and digital responsibility to enhance social value through technology. Furthermore, the key issues identified in the ESG factors were as follows: under the 'Environmental' element, issues such as greenhouse gas and carbon emission management, industry-specific eco-friendly activities, and green partnerships were identified. Under the 'Social' element, key issues included social contribution activities through stakeholder engagement, supporting the growth and coexistence of members and partner companies, and enhancing customer value through stable service provision. Under the 'Governance' element, key issues were identified as strengthening board independence through the appointment of outside directors, risk management and communication for sustainable growth, and establishing transparent governance structures. The exploration of the relationship between ESG disclosures in reports and ESG issues in news articles revealed that the sustainability strategies disclosed in reports were aligned with the issues related to ESG disclosed in news articles. However, there was a tendency to strengthen ESG activities for prevention and improvement after negative media coverage that could have a negative impact on corporate image. Additionally, environmental issues were mentioned more frequently in news articles compared to ESG reports, with environmental-related keywords being emphasized in the 'Finance' sector in the reports. Thus, ESG reports and news articles shared some similarities in content due to the sharing of information sources. However, the impact of media coverage influenced the emphasis on specific sustainability strategies, and the extent of mentioning environmental issues varied across documents. Based on our study, the following contributions were derived. From a practical perspective, companies need to consider their characteristics and establish sustainability strategies that align with their capabilities and situations. From an academic perspective, unlike previous studies on ESG strategies, we present a subdivided methodology through analysis considering the industry-specific characteristics of companies.